From 0b19a2e1dd2b3c79f74381457c1579b41dee8c75 Mon Sep 17 00:00:00 2001 From: Aryan Bhusari Date: Fri, 18 Jul 2025 14:26:52 -0500 Subject: [PATCH 01/42] Adding the distributed inference learning path --- assets/contributors.csv | 193 ++++++++-------- .../_index.md | 54 +++++ .../_next-steps.md | 8 + .../example-picture.png | Bin 0 -> 63167 bytes .../how-to-1.md | 64 ++++++ .../how-to-2.md | 212 ++++++++++++++++++ 6 files changed, 435 insertions(+), 96 deletions(-) create mode 100644 content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md create mode 100644 content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_next-steps.md create mode 100644 content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/example-picture.png create mode 100644 content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-1.md create mode 100644 content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md diff --git a/assets/contributors.csv b/assets/contributors.csv index 6df4525ab1..1317f49b31 100644 --- a/assets/contributors.csv +++ b/assets/contributors.csv @@ -1,96 +1,97 @@ -author,company,github,linkedin,twitter,website -Jason Andrews,Arm,jasonrandrews,jason-andrews-7b05a8,, -Pareena Verma,Arm,pareenaverma,pareena-verma-7853607,, -Ronan Synnott,Arm,,ronansynnott,, -Florent Lebeau,Arm,,,, -Brenda Strech,Remote.It,bstrech,bstrech,@remote_it,www.remote.it -Liliya Wu,Arm,Liliyaw,liliya-wu-8b6227216,, -Julio Suarez,Arm,jsrz,juliosuarez,, -Gabriel Peterson,Arm,gabrieldpeterson,gabrieldpeterson,@gabedpeterson,https://corteximplant.com/@gabe -Christopher Seidl,Arm,,,, -Michael Hall,Arm,,,, -Kasper Mecklenburg,Arm,,,, -Mathias Brossard,Arm,,,, -Julie Gaskin,Arm,,,, -Pranay Bakre,Arm,,,, -Elham Harirpoush,Arm,,,, -Frédéric -lefred- Descamps,OCI,,,,lefred.be -Fr�d�ric -lefred- Descamps,OCI,,,,lefred.be -Kristof Beyls,Arm,,,, -David Spickett,Arm,,,, -Uma Ramalingam,Arm,uma-ramalingam,,, -Konstantinos Margaritis,VectorCamp,markos,konstantinosmargaritis,@freevec1,https://vectorcamp.gr/ -Diego Russo,Arm,diegorusso,diegor,diegor,https://www.diegor.it -Jonathan Davies,Arm,,,, -Zhengjun Xing,Arm,,,, -Leandro Nunes,Arm,,,, -Dawid Borycki,,dawidborycki,,, -Ying Yu,Arm,,,, -Bolt Liu,Arm,,,, -Roberto Lopez Mendez,Arm,,,, -Arnaud de Grandmaison,Arm,Arnaud-de-Grandmaison-ARM,arnauddegrandmaison,, -Jose-Emilio Munoz-Lopez,Arm,,,, -James Whitaker,Arm,,,, -Johanna Skinnider,Arm,,,, -Varun Chari,Arm,,,, -Adnan AlSinan,Arm,,,, -Graham Woodward,Arm,,,, -Basma El Gaabouri,Arm,,,, -Gayathri Narayana Yegna Narayanan,Arm,,,, -Alexandros Lamprineas,Arm,,,, -Annie Tallund,Arm,annietllnd,annietallund,, -Cyril Rohr,RunsOn,crohr,cyrilrohr,, -Rin Dobrescu,Arm,,,, -Przemyslaw Wirkus,Arm,PrzemekWirkus,przemyslaw-wirkus-78b73352,, -Nader Zouaoui,Day Devs,nader-zouaoui,nader-zouaoui,@zouaoui_nader,https://daydevs.com/ -Alaaeddine Chakroun,Day Devs,Alaaeddine-Chakroun,alaaeddine-chakroun,,https://daydevs.com/ -Koki Mitsunami,Arm,,kmitsunami,, -Chen Zhang,Zilliz,,,, -Tianyu Li,Arm,,,, -Georgios Mermigkis,VectorCamp,gMerm,georgios-mermigkis,,https://vectorcamp.gr/ -Ben Clark,Arm,,,, -Han Yin,Arm,hanyin-arm,nacosiren,, -Willen Yang,Arm,,,, -Daniel Gubay,,,,, -Paul Howard,,,,, -Iago Calvo Lista,Arm,,,, -Stephen Theobald,Arm,,,, -ThirdAI,,,,, -Preema Merlin Dsouza,,,,, -Dominica Abena O. Amanfo,,,,, -Arm,,,,, -Albin Bernhardsson,,,,, -Przemyslaw Wirkus,,,,, -Zach Lasiuk,,,,, -Daniel Nguyen,,,,, -Joe Stech,Arm,JoeStech,joestech,, -visualSilicon,,,,, -Konstantinos Margaritis,VectorCamp,,,, -Kieran Hejmadi,,,,, -Alex Su,,,,, -Chaodong Gong,,,,, -Owen Wu,Arm,,,, -Koki Mitsunami,,,,, -Nikhil Gupta,,,,, -Nobel Chowdary Mandepudi,Arm,,,, -Ravi Malhotra,Arm,,,, -Masoud Koleini,,,,, -Na Li,Arm,,,, -Tom Pilar,,,,, -Cyril Rohr,,,,, -Odin Shen,Arm,odincodeshen,odin-shen-lmshen,, -Avin Zarlez,Arm,AvinZarlez,avinzarlez,,https://www.avinzarlez.com/ -Shuheng Deng,Arm,,,, -Yiyang Fan,Arm,,,, -Julien Jayat,Arm,JulienJayat-Arm,julien-jayat-a980a397,, -Geremy Cohen,Arm,geremyCohen,geremyinanutshell,, -Barbara Corriero,Arm,,,, -Nina Drozd,Arm,NinaARM,ninadrozd,, -Jun He,Arm,JunHe77,jun-he-91969822,, -Gian Marco Iodice,Arm,,,, -Aude Vuilliomenet,Arm,,,, -Andrew Kilroy,Arm,,,, -Peter Harris,Arm,,,, -Chenying Kuo,Adlink,evshary,evshary,, -William Liang,,wyliang,,, -Waheed Brown,Arm,https://github.com/armwaheed,https://www.linkedin.com/in/waheedbrown/,, +author,company,github,linkedin,twitter,website +Jason Andrews,Arm,jasonrandrews,jason-andrews-7b05a8,, +Pareena Verma,Arm,pareenaverma,pareena-verma-7853607,, +Ronan Synnott,Arm,,ronansynnott,, +Florent Lebeau,Arm,,,, +Brenda Strech,Remote.It,bstrech,bstrech,@remote_it,www.remote.it +Liliya Wu,Arm,Liliyaw,liliya-wu-8b6227216,, +Julio Suarez,Arm,jsrz,juliosuarez,, +Gabriel Peterson,Arm,gabrieldpeterson,gabrieldpeterson,@gabedpeterson,https://corteximplant.com/@gabe +Christopher Seidl,Arm,,,, +Michael Hall,Arm,,,, +Kasper Mecklenburg,Arm,,,, +Mathias Brossard,Arm,,,, +Julie Gaskin,Arm,,,, +Pranay Bakre,Arm,,,, +Elham Harirpoush,Arm,,,, +Frédéric -lefred- Descamps,OCI,,,,lefred.be +Fr�d�ric -lefred- Descamps,OCI,,,,lefred.be +Kristof Beyls,Arm,,,, +David Spickett,Arm,,,, +Uma Ramalingam,Arm,uma-ramalingam,,, +Konstantinos Margaritis,VectorCamp,markos,konstantinosmargaritis,@freevec1,https://vectorcamp.gr/ +Diego Russo,Arm,diegorusso,diegor,diegor,https://www.diegor.it +Jonathan Davies,Arm,,,, +Zhengjun Xing,Arm,,,, +Leandro Nunes,Arm,,,, +Dawid Borycki,,dawidborycki,,, +Ying Yu,Arm,,,, +Bolt Liu,Arm,,,, +Roberto Lopez Mendez,Arm,,,, +Arnaud de Grandmaison,Arm,Arnaud-de-Grandmaison-ARM,arnauddegrandmaison,, +Jose-Emilio Munoz-Lopez,Arm,,,, +James Whitaker,Arm,,,, +Johanna Skinnider,Arm,,,, +Varun Chari,Arm,,,, +Adnan AlSinan,Arm,,,, +Graham Woodward,Arm,,,, +Basma El Gaabouri,Arm,,,, +Gayathri Narayana Yegna Narayanan,Arm,,,, +Alexandros Lamprineas,Arm,,,, +Annie Tallund,Arm,annietllnd,annietallund,, +Cyril Rohr,RunsOn,crohr,cyrilrohr,, +Rin Dobrescu,Arm,,,, +Przemyslaw Wirkus,Arm,PrzemekWirkus,przemyslaw-wirkus-78b73352,, +Nader Zouaoui,Day Devs,nader-zouaoui,nader-zouaoui,@zouaoui_nader,https://daydevs.com/ +Alaaeddine Chakroun,Day Devs,Alaaeddine-Chakroun,alaaeddine-chakroun,,https://daydevs.com/ +Koki Mitsunami,Arm,,kmitsunami,, +Chen Zhang,Zilliz,,,, +Tianyu Li,Arm,,,, +Georgios Mermigkis,VectorCamp,gMerm,georgios-mermigkis,,https://vectorcamp.gr/ +Ben Clark,Arm,,,, +Han Yin,Arm,hanyin-arm,nacosiren,, +Willen Yang,Arm,,,, +Daniel Gubay,,,,, +Paul Howard,,,,, +Iago Calvo Lista,Arm,,,, +Stephen Theobald,Arm,,,, +ThirdAI,,,,, +Preema Merlin Dsouza,,,,, +Dominica Abena O. Amanfo,,,,, +Arm,,,,, +Albin Bernhardsson,,,,, +Przemyslaw Wirkus,,,,, +Zach Lasiuk,,,,, +Daniel Nguyen,,,,, +Joe Stech,Arm,JoeStech,joestech,, +visualSilicon,,,,, +Konstantinos Margaritis,VectorCamp,,,, +Kieran Hejmadi,,,,, +Alex Su,,,,, +Chaodong Gong,,,,, +Owen Wu,Arm,,,, +Koki Mitsunami,,,,, +Nikhil Gupta,,,,, +Nobel Chowdary Mandepudi,Arm,,,, +Ravi Malhotra,Arm,,,, +Masoud Koleini,,,,, +Na Li,Arm,,,, +Tom Pilar,,,,, +Cyril Rohr,,,,, +Odin Shen,Arm,odincodeshen,odin-shen-lmshen,, +Avin Zarlez,Arm,AvinZarlez,avinzarlez,,https://www.avinzarlez.com/ +Shuheng Deng,Arm,,,, +Yiyang Fan,Arm,,,, +Julien Jayat,Arm,JulienJayat-Arm,julien-jayat-a980a397,, +Geremy Cohen,Arm,geremyCohen,geremyinanutshell,, +Barbara Corriero,Arm,,,, +Nina Drozd,Arm,NinaARM,ninadrozd,, +Jun He,Arm,JunHe77,jun-he-91969822,, +Gian Marco Iodice,Arm,,,, +Aude Vuilliomenet,Arm,,,, +Andrew Kilroy,Arm,,,, +Peter Harris,Arm,,,, +Chenying Kuo,Adlink,evshary,evshary,, +William Liang,,wyliang,,, +Waheed Brown,Arm,https://github.com/armwaheed,https://www.linkedin.com/in/waheedbrown/,, +Aryan Bhusari,Arm,,https://www.linkedin.com/in/aryanbhusari,, \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md new file mode 100644 index 0000000000..a82cdd35f1 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md @@ -0,0 +1,54 @@ +--- +title: Distributed inference using llama.cpp + +minutes_to_complete: 30 + +who_is_this_for: This learning path is for developers with some experience using llama.cpp who want to learn about distributed inference. + +learning_objectives: + - Set up the main host and worker nodes using llama.cpp + - Run a large quantized model (e.g., Llama 3.1 405B) on CPUs in a distributed manner on Arm machines + +prerequisites: + - An AWS Graviton4 c8g.16xlarge instance to test Arm performance optimizations, or any [Arm based instance](/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider or an on-premise Arm server. + - Familiarity with -> [Deploy a Large Language Model (LLM) chatbot with llama.cpp using KleidiAI on Arm servers](/learning-paths/servers-and-cloud-computing/llama-cpu) + - Familiarity with AWS + +author: Aryan Bhusari + +### Tags +skilllevels: Introductory +subjects: ML +armips: + - Neoverse +tools_software_languages: + - LLM + - GenAI + - AWS +operatingsystems: + - Linux + + + +further_reading: + - resource: + title: Llama.cpp rpc-server code + link: https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc + type: Code + - resource: + title: PLACEHOLDER BLOG + link: PLACEHOLDER BLOG LINK + type: blog + - resource: + title: PLACEHOLDER GENERAL WEBSITE + link: PLACEHOLDER GENERAL WEBSITE LINK + type: website + + + +### FIXED, DO NOT MODIFY +# ================================================================================ +weight: 1 # _index.md always has weight of 1 to order correctly +layout: "learningpathall" # All files under learning paths have this same wrapper +learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. +--- diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_next-steps.md new file mode 100644 index 0000000000..c3db0de5a2 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_next-steps.md @@ -0,0 +1,8 @@ +--- +# ================================================================================ +# FIXED, DO NOT MODIFY THIS FILE +# ================================================================================ +weight: 21 # Set to always be larger than the content in this path to be at the end of the navigation. +title: "Next Steps" # Always the same, html page title. +layout: "learningpathall" # All files under learning paths have this same wrapper for Hugo processing. +--- diff --git 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b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-1.md @@ -0,0 +1,64 @@ +--- +title: Overview and Worker Node Configuration +weight: 2 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +## Before you begin +The instructions in this Learning Path are for any Arm server running Ubuntu 24.04.2 LTS. You will need at least three Arm server instances with at least 64 cores and 128GB of RAM to run this example. The instructions have been tested on an AWS Graviton4 c8g.16xlarge instance + +## Overview +llama.cpp is a C++ library that enables efficient inference of LLaMA and similar large language models on CPUs, optimized for local and embedded environments. Just over a year ago from its publication date, rgerganov’s RPC code was merged into llama.cpp, enabling distributed inference of large LLMs across multiple CPU-based machines—even when the models don’t fit into the memory of a single machine. In this learning path, we’ll explore how to run a 405B parameter model on Arm-based CPUs. + +For the purposes of this demonstration, the following experimental setup will be used: +- Total number of instances: 3 +- Instance type: c8g.16xlarge +- Model: Llama-3.1-405B_Q4_0.gguf + +One of the three nodes will serve as the master node, which physically hosts the model file. The other two nodes will act as worker nodes. In llama.cpp, remote procedure calls (RPC) are used to offload both the model and the computation over TCP connections between nodes. The master node forwards inference requests to the worker nodes, where all the actual computation is performed. + +## Implementation + +1. To get started, follow [this learning path](/learning-paths/servers-and-cloud-computing/llama-cpu) up to the step where you clone the llama.cpp repository. Since this setup involves multiple instances (or devices), you will need to replicate the initial setup on each device. Specifically, after executing the command below on all devices, continue with this learning path starting from Step 2. + +```bash +git clone https://github.com/ggerganov/llama.cpp +``` +2. Now we can build the llama.cpp library with the RPC feature enabled by compiling it with the -DLLAMA_RPC=ON flag +```bash +cd llama.cpp +mkdir -p build-rpc +cd build-rpc +cmake .. -DGGML_RPC=ON -DLLAMA_BUILD_SERVER=ON +cmake --build . --config Release +``` + +`llama.cpp` is now built in the `build-rpc/bin` directory. +Check that `llama.cpp` has built correctly by running the help command: +```bash +cd build-rpc +bin/llama-cli -h +``` +If everything was built correctly, you should see a list of all the available flags that can be used with llama-cli. +3. Now, choose two of the three devices to act as backend workers. If the devices had varying compute capacities, the ones with the highest compute should be selected—especially for a 405B model. However, since all three devices have identical compute capabilities in this case, you can select any two to serve as backend workers. + +Communication between the master node and the worker nodes occurs through a socket created on each worker. This socket listens for incoming data from the master—such as model parameters, tokens, hidden states, and other inference-related information. +{{% notice Note %}}The RPC feature in llama.cpp is not secure by default, so you should never expose it to the open internet. To mitigate this risk, ensure that the security groups for all your EC2 instances are properly configured—restricting access to only trusted IPs or internal VPC traffic. This helps prevent unauthorized access to the RPC endpoints.{{% /notice %}} +Use the following command to start the listeneing on the worker nodes: +```bash +bin/rpc-server -p 50052 -H 0.0.0.0 -t 64 +``` +Below are the available flag options that can be used with the rpc-server functionality: + +```output +-h, --help show this help message and exit +-t, --threads number of threads for the CPU backend (default: 6) +-d DEV, --device device to use +-H HOST, --host HOST host to bind to (default: 127.0.0.1) +-p PORT, --port PORT port to bind to (default: 50052) +-m MEM, --mem MEM backend memory size (in MB) +-c, --cache enable local file cache +``` +Setting the host to 0.0.0.0 might seem counterintuitive given the earlier security warning, but it’s acceptable in this case because the security groups have been properly configured to block any unintended or unauthorized access. \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md new file mode 100644 index 0000000000..57d19ce8e3 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md @@ -0,0 +1,212 @@ +--- +title: Configuring Master Node +weight: 3 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +## PLACEHOLDER HEADER OF SECOND STEP +4. In this learning path, we will use the following three IP addresses for the nodes. + +```bash +172.31.110.10 (Master) +172.31.110.11, 172.31.110.12 (Workers) +``` +Note that these IPs may be different in your setup. You can find the IP address of your AWS instance using the command provided below. +```bash +curl http://169.254.169.254/latest/meta-data/local-ipv4 +``` + +Now, on the master node, you can verify communication with the worker nodes using the following command: +```bash +telnet 172.31.110.11 50052 +``` +If the backend server is set up correctly, the output of the `telnet` command should look like the following: +```bash +Trying 172.31.110.11... +Connected to localhost. +Escape character is '^]'. +``` +Finally, you can execute the following command, to execute distributed inference: +```bash +bin/llama-cli -m /home/ubuntu/model.gguf -p "Tell me a joke" -n 128 --rpc 172.31.110.11:50052,172.31.110.12:50052 -ngl 99 +``` +The model file for this experiment is hosted on Arm’s private AWS S3 bucket. If you don’t have access to it, you can find a publicly available version of the model on Hugging Face. +The output: +```output +build: 5935 (2adf8d83) with cc (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0 for aarch64-linux-gnu +main: llama backend init +main: load the model and apply lora adapter, if any +llama_model_load_from_file_impl: using device RPC[172.31.110.11:50052] (RPC[172.31.110.11:50052]) - 126497 MiB free +llama_model_load_from_file_impl: using device RPC[172.31.110.12:50052] (RPC[172.31.110.12:50052]) - 126497 MiB free +llama_model_loader: loaded meta data with 30 key-value pairs and 1138 tensors from /home/ubuntu/Llama-3.1-405B_Q4_0.gguf (version GGUF V3 (latest)) +llama_model_loader: Dumping metadata keys/values. Note: KV overrides do not apply in this output. +llama_model_loader: - kv 0: general.architecture str = llama +llama_model_loader: - kv 1: general.type str = model +llama_model_loader: - kv 2: general.name str = Llama Hf +llama_model_loader: - kv 3: general.size_label str = 406B +llama_model_loader: - kv 4: general.license str = llama3.1 +llama_model_loader: - kv 5: general.tags arr[str,6] = ["facebook", "meta", "pytorch", "llam... +llama_model_loader: - kv 6: general.languages arr[str,8] = ["en", "de", "fr", "it", "pt", "hi", ... +llama_model_loader: - kv 7: llama.block_count u32 = 126 +llama_model_loader: - kv 8: llama.context_length u32 = 131072 +llama_model_loader: - kv 9: llama.embedding_length u32 = 16384 +llama_model_loader: - kv 10: llama.feed_forward_length u32 = 53248 +llama_model_loader: - kv 11: llama.attention.head_count u32 = 128 +llama_model_loader: - kv 12: llama.attention.head_count_kv u32 = 8 +llama_model_loader: - kv 13: llama.rope.freq_base f32 = 500000.000000 +llama_model_loader: - kv 14: llama.attention.layer_norm_rms_epsilon f32 = 0.000010 +llama_model_loader: - kv 15: llama.attention.key_length u32 = 128 +llama_model_loader: - kv 16: llama.attention.value_length u32 = 128 +llama_model_loader: - kv 17: llama.vocab_size u32 = 128256 +llama_model_loader: - kv 18: llama.rope.dimension_count u32 = 128 +llama_model_loader: - kv 19: tokenizer.ggml.model str = gpt2 +llama_model_loader: - kv 20: tokenizer.ggml.pre str = llama-bpe +llama_model_loader: - kv 21: tokenizer.ggml.tokens arr[str,128256] = ["!", "\"", "#", "$", "%", "&", "'", ... +llama_model_loader: - kv 22: tokenizer.ggml.token_type arr[i32,128256] = [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, ... +llama_model_loader: - kv 23: tokenizer.ggml.merges arr[str,280147] = ["Ġ Ġ", "Ġ ĠĠĠ", "ĠĠ ĠĠ", "... +llama_model_loader: - kv 24: tokenizer.ggml.bos_token_id u32 = 128000 +llama_model_loader: - kv 25: tokenizer.ggml.eos_token_id u32 = 128001 +llama_model_loader: - kv 26: tokenizer.ggml.add_bos_token bool = true +llama_model_loader: - kv 27: tokenizer.ggml.add_sep_token bool = false +llama_model_loader: - kv 28: general.quantization_version u32 = 2 +llama_model_loader: - kv 29: general.file_type u32 = 2 +llama_model_loader: - type f32: 254 tensors +llama_model_loader: - type q4_0: 883 tensors +llama_model_loader: - type q6_K: 1 tensors +print_info: file format = GGUF V3 (latest) +print_info: file type = Q4_0 +print_info: file size = 213.13 GiB (4.51 BPW) +load: special tokens cache size = 256 +load: token to piece cache size = 0.7999 MB +print_info: arch = llama +print_info: vocab_only = 0 +print_info: n_ctx_train = 131072 +print_info: n_embd = 16384 +print_info: n_layer = 126 +print_info: n_head = 128 +print_info: n_head_kv = 8 +print_info: n_rot = 128 +print_info: n_swa = 0 +print_info: is_swa_any = 0 +print_info: n_embd_head_k = 128 +print_info: n_embd_head_v = 128 +print_info: n_gqa = 16 +print_info: n_embd_k_gqa = 1024 +print_info: n_embd_v_gqa = 1024 +print_info: f_norm_eps = 0.0e+00 +print_info: f_norm_rms_eps = 1.0e-05 +print_info: f_clamp_kqv = 0.0e+00 +print_info: f_max_alibi_bias = 0.0e+00 +print_info: f_logit_scale = 0.0e+00 +print_info: f_attn_scale = 0.0e+00 +print_info: n_ff = 53248 +print_info: n_expert = 0 +print_info: n_expert_used = 0 +print_info: causal attn = 1 +print_info: pooling type = 0 +print_info: rope type = 0 +print_info: rope scaling = linear +print_info: freq_base_train = 500000.0 +print_info: freq_scale_train = 1 +print_info: n_ctx_orig_yarn = 131072 +print_info: rope_finetuned = unknown +print_info: model type = ?B +print_info: model params = 405.85 B +print_info: general.name = Llama Hf +print_info: vocab type = BPE +print_info: n_vocab = 128256 +print_info: n_merges = 280147 +print_info: BOS token = 128000 '<|begin_of_text|>' +print_info: EOS token = 128001 '<|end_of_text|>' +print_info: EOT token = 128009 '<|eot_id|>' +print_info: EOM token = 128008 '<|eom_id|>' +print_info: LF token = 198 'Ċ' +print_info: EOG token = 128001 '<|end_of_text|>' +print_info: EOG token = 128008 '<|eom_id|>' +print_info: EOG token = 128009 '<|eot_id|>' +print_info: max token length = 256 +load_tensors: loading model tensors, this can take a while... (mmap = true) +.................................................................................................... +llama_context: constructing llama_context +llama_context: non-unified KV cache requires ggml_set_rows() - forcing unified KV cache +llama_context: n_seq_max = 1 +llama_context: n_ctx = 4096 +llama_context: n_ctx_per_seq = 4096 +llama_context: n_batch = 2048 +llama_context: n_ubatch = 512 +llama_context: causal_attn = 1 +llama_context: flash_attn = 0 +llama_context: kv_unified = true +llama_context: freq_base = 500000.0 +llama_context: freq_scale = 1 +llama_context: n_ctx_per_seq (4096) < n_ctx_train (131072) -- the full capacity of the model will not be utilized +llama_context: CPU output buffer size = 0.49 MiB +llama_kv_cache_unified: RPC[172.31.110.11:50052] KV buffer size = 800.00 MiB +llama_kv_cache_unified: RPC[172.31.110.12:50052] KV buffer size = 784.00 MiB +llama_kv_cache_unified: CPU KV buffer size = 432.00 MiB +llama_kv_cache_unified: size = 2016.00 MiB ( 4096 cells, 126 layers, 1/ 1 seqs), K (f16): 1008.00 MiB, V (f16): 1008.00 MiB +llama_kv_cache_unified: LLAMA_SET_ROWS=0, using old ggml_cpy() method for backwards compatibility +llama_context: RPC[172.31.110.11:50052] compute buffer size = 1160.00 MiB +llama_context: RPC[172.31.110.12:50052] compute buffer size = 1160.00 MiB +llama_context: CPU compute buffer size = 1160.01 MiB +llama_context: graph nodes = 4668 +llama_context: graph splits = 4 +common_init_from_params: added <|end_of_text|> logit bias = -inf +common_init_from_params: added <|eom_id|> logit bias = -inf +common_init_from_params: added <|eot_id|> logit bias = -inf +common_init_from_params: setting dry_penalty_last_n to ctx_size = 4096 +common_init_from_params: warming up the model with an empty run - please wait ... (--no-warmup to disable) +main: llama threadpool init, n_threads = 64 + +system_info: n_threads = 64 (n_threads_batch = 64) / 64 | CPU : NEON = 1 | ARM_FMA = 1 | FP16_VA = 1 | MATMUL_INT8 = 1 | SVE = 1 | DOTPROD = 1 | SVE_CNT = 16 | OPENMP = 1 | REPACK = 1 | + +sampler seed: 4077122424 +sampler params: + repeat_last_n = 64, repeat_penalty = 1.000, frequency_penalty = 0.000, presence_penalty = 0.000 + dry_multiplier = 0.000, dry_base = 1.750, dry_allowed_length = 2, dry_penalty_last_n = 4096 + top_k = 40, top_p = 0.950, min_p = 0.050, xtc_probability = 0.000, xtc_threshold = 0.100, typical_p = 1.000, top_n_sigma = -1.000, temp = 0.800 + mirostat = 0, mirostat_lr = 0.100, mirostat_ent = 5.000 +sampler chain: logits -> logit-bias -> penalties -> dry -> top-n-sigma -> top-k -> typical -> top-p -> min-p -> xtc -> temp-ext -> dist +generate: n_ctx = 4096, n_batch = 2048, n_predict = 128, n_keep = 1 + +Tell me a joke! (or a funny story) +Thread starter Fiver +This thread is for any jokes you may want to share with other members. Please keep them clean! +Reactions: Fiver +A duck walks into a bar, and asks the bartender, "Have you got any bread?" +The bartender says, "No, we don't have any bread." +The duck leaves. +A few minutes later, the duck returns, and asks the bartender, "Have you got any bread?" +The bartender says, "No, I told you, we don't have any bread." +A few minutes later, the duck returns, and asks the bartender, + +llama_perf_sampler_print: sampling time = 9.48 ms / 133 runs ( 0.07 ms per token, 14032.50 tokens per second) +llama_perf_context_print: load time = 1796754.73 ms +llama_perf_context_print: prompt eval time = 1925.98 ms / 5 tokens ( 385.20 ms per token, 2.60 tokens per second) +llama_perf_context_print: eval time = 77429.95 ms / 127 runs ( 609.68 ms per token, 1.64 tokens per second) +llama_perf_context_print: total time = 79394.06 ms / 132 tokens +llama_perf_context_print: graphs reused = 0 +``` +That's it! You have sucessfully run the llama-3.1-8B model on CPUs with the power of llama.cpp RPC functionality. The following table provides brief description of the metrics from `llama_perf`:

+ +| Log Line | Description | +|-------------------|-----------------------------------------------------------------------------| +| sampling time | Time spent choosing next tokens using sampling strategy (e.g., top-k, top-p). | +| load time | Time to load the model into memory and initialize weights/buffers. | +| prompt eval time | Time to process the input prompt tokens before generation (fills KV cache). | +| eval time | Time to generate output tokens by forward-passing through the model. | +| total time | Total time for both prompt processing and token generation (excludes model load). | + +Lastly to set up OpenAI compatible API, you can use the `llama-server` functionality. The process of implementing this is described [here](/learning-paths/servers-and-cloud-computing/llama-cpu) under the "Access the chatbot using the OpenAI-compatible API" section. Here is a snippet, for how to set up llama-server for disributed inference: +```bash +bin/llama-server -m /home/ubuntu/model.gguf --port 8080 --rpc 172.31.110.11:50052,172.31.110.12:50052 -ngl 99 +``` +At the very end of the output to the above command, you will see somethin like the following: +```output +main: server is listening on http://127.0.0.1:8080 - starting the main loop +srv update_slots: all slots are idle +``` + From 5cd127069a14f8e162104401128ddfb65622179a Mon Sep 17 00:00:00 2001 From: Aryan Bhusari Date: Fri, 18 Jul 2025 16:36:21 -0500 Subject: [PATCH 02/42] Fixing some minor issues --- .../_index.md | 8 -------- .../how-to-2.md | 15 ++++++++------- 2 files changed, 8 insertions(+), 15 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md index a82cdd35f1..b7e478b5fe 100644 --- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md @@ -35,14 +35,6 @@ further_reading: title: Llama.cpp rpc-server code link: https://github.com/ggml-org/llama.cpp/tree/master/tools/rpc type: Code - - resource: - title: PLACEHOLDER BLOG - link: PLACEHOLDER BLOG LINK - type: blog - - resource: - title: PLACEHOLDER GENERAL WEBSITE - link: PLACEHOLDER GENERAL WEBSITE LINK - type: website diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md index 57d19ce8e3..9a41ca72da 100644 --- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md +++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md @@ -6,32 +6,33 @@ weight: 3 layout: learningpathall --- -## PLACEHOLDER HEADER OF SECOND STEP +(continued)
4. In this learning path, we will use the following three IP addresses for the nodes. ```bash -172.31.110.10 (Master) -172.31.110.11, 172.31.110.12 (Workers) +master_ip =" 172.31.110.10" +worker_ips = "172.31.110.11,172.31.110.12" ``` Note that these IPs may be different in your setup. You can find the IP address of your AWS instance using the command provided below. ```bash curl http://169.254.169.254/latest/meta-data/local-ipv4 ``` -Now, on the master node, you can verify communication with the worker nodes using the following command: +Now, on the master node, you can verify communication with the worker nodes using the following command on master node: ```bash telnet 172.31.110.11 50052 ``` If the backend server is set up correctly, the output of the `telnet` command should look like the following: ```bash Trying 172.31.110.11... -Connected to localhost. +Connected to 172.31.110.11. Escape character is '^]'. ``` Finally, you can execute the following command, to execute distributed inference: ```bash -bin/llama-cli -m /home/ubuntu/model.gguf -p "Tell me a joke" -n 128 --rpc 172.31.110.11:50052,172.31.110.12:50052 -ngl 99 +bin/llama-cli -m /home/ubuntu/model.gguf -p "Tell me a joke" -n 128 --rpc "$worker_ips" -ngl 99 ``` +{{% notice Note %}}At the time of publication, llama.cpp only supports up to 16 backend workers.{{% /notice %}}
The model file for this experiment is hosted on Arm’s private AWS S3 bucket. If you don’t have access to it, you can find a publicly available version of the model on Hugging Face. The output: ```output @@ -201,7 +202,7 @@ That's it! You have sucessfully run the llama-3.1-8B model on CPUs with the powe Lastly to set up OpenAI compatible API, you can use the `llama-server` functionality. The process of implementing this is described [here](/learning-paths/servers-and-cloud-computing/llama-cpu) under the "Access the chatbot using the OpenAI-compatible API" section. Here is a snippet, for how to set up llama-server for disributed inference: ```bash -bin/llama-server -m /home/ubuntu/model.gguf --port 8080 --rpc 172.31.110.11:50052,172.31.110.12:50052 -ngl 99 +bin/llama-server -m /home/ubuntu/model.gguf --port 8080 --rpc "$worker_ips" -ngl 99 ``` At the very end of the output to the above command, you will see somethin like the following: ```output From 4006bba0345bb64376d802ce9338d3ddf95f0a33 Mon Sep 17 00:00:00 2001 From: Arnaud de Grandmaison Date: Wed, 11 Jun 2025 16:57:28 +0200 Subject: [PATCH 03/42] First sketch of the CCA DA LP. --- .../cca-device-attach/1-introduction.md | 30 ++ .../cca-device-attach/2-virtio.md | 339 ++++++++++++++++++ .../cca-device-attach/_index.md | 57 +++ .../cca-device-attach/_next-steps.md | 8 + .../cca-essentials/example.md | 45 ++- 5 files changed, 456 insertions(+), 23 deletions(-) create mode 100644 content/learning-paths/servers-and-cloud-computing/cca-device-attach/1-introduction.md create mode 100644 content/learning-paths/servers-and-cloud-computing/cca-device-attach/2-virtio.md create mode 100644 content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md create mode 100644 content/learning-paths/servers-and-cloud-computing/cca-device-attach/_next-steps.md diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/1-introduction.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/1-introduction.md new file mode 100644 index 0000000000..d2b20b5892 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/1-introduction.md @@ -0,0 +1,30 @@ +--- +title: "About CCA Realms" +weight: 2 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +Arm’s *Realm Management Extension (RME)* is a key security feature introduced in +the Armv9-A architecture. It enables a new form of hardware-enforced isolation +designed to support _confidential computing_. It defines the set of hardware +features and properties that are required to comply with the Arm *Confidential +Computing Architecture (CCA)* architecture. + +At the heart of RME is the concept of a *Realm*, a protected execution +environment that operates independently from the conventional *Normal World* +(used by operating systems and applications) and the *Secure World* (used by +trusted firmware or TEE). Realms are managed by a new privileged entity called +the *Realm Management Monitor (RMM)* and are enforced by the hardware via the +*Granule Protection Table (GPT)* and *Granule Transitioning* mechanism. + +Realms allow lower-privileged software, such as an application or a virtual +machine, to protect its content and execution from attacks by higher-privileged +software, such as an OS or a hypervisor. Realms provide an environment for +confidential computing, without requiring the Realm owner to trust the software +components that manage the resources that the Realm uses. + +To be useful, a Realm has to interact with the rest of the world at some point. +For example, a network interface is likely to be needed. This learning path will +teach you how devices are attached and used by Realms. \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/2-virtio.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/2-virtio.md new file mode 100644 index 0000000000..bdbf057d16 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/2-virtio.md @@ -0,0 +1,339 @@ +--- +title: "Device attach - part 1: virtio" +weight: 2 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +This section is a high level overview of VirtIO and Bounce Buffers, and how they +relate to CCA Realms. + +A Realm has to use physical devices at some point to interact with the external +and / or physical world. The easiest way to do this is by using VirtIO, which +provides a fast high level emulation layer. This can be seen as the level-0 of +device attach. + +As will be seen later in this learning path, more evolved device attach can be +performed leveraging hardware security features like PCIe-TDISP (**T**EE +**D**evice **I**nterface **S**ecurity **P**rotocol) and PCIe-IDE (**I**ntegrity +and **D**ata **E**ncryption), where the host OS can assign a physical device to +a realm, which will be able to make security measurements on the physical device +and include it in its base and measurements. + +## VirtIO + +### What is VirtIO ? + +VirtIO is an abstraction layer for virtual devices in virtualized environments. +It provides standardized and efficient interfaces between guest virtual machines +(VMs) and host devices, making it easier to develop _paravirtualized_ drivers. +_Paravirtualized_ means that the guest OS is aware it’s running in a virtualized +environment and can use optimized drivers (VirtIO) to communicate with virtual +hardware. Emulating hardware devices (like NICs or disks) for VMs is slow and +inefficient. VirtIO provides a standardized and efficient interface that allows +VMs to bypass full device emulation and instead use optimized drivers. + +VirtIO is most commonly used with KVM/QEMU virtualization. Example drivers are: +- `virtio-net`: Paravirtualized networking +- `virtio-blk`: Block device (disk) access +- `virtio-fs`: File sharing (host ↔ guest) +- `virtio-balloon`: Dynamic memory management +- `virtio-rng`: Random number source +- `virtio-console`: Simple console interface +- ... + +### How VirtIO works in VMs + +1. The Host Hypervisor (e.g., QEMU/KVM) exposes VirtIO “backend” devices. +2. The guest OS loads VirtIO _frontend_ drivers (e.g., `virtio_net`, + `virtio_blk`) that speak the VirtIO protocol. +3. Communication happens via shared memory (`virtqueues`) for I/O operations, + avoiding full device emulation. +4. Devices are exposed over the PCI or MMIO bus to the guest. + +For example, instead of emulating an Intel e1000 NIC, the host exposes a +`virtio-net` interface to the guest OS and the guest OS uses the `virtio-net` +driver to send/receive packets via shared buffers. + +## Bounce buffers + +### What are bounce buffers? + +Bounce buffers are a generic mechanism part of the Linux kernel. They are +temporary memory buffers used in the Linux kernel to handle situations where +direct memory access (DMA) can’t be performed directly on the original data +buffer. This often happens because: +1. The original buffer is not physically contiguous. +2. The buffer is in high memory or not accessible to the device. +3. The buffer doesn’t meet alignment or boundary requirements of the device. + +### Why _bounce_ buffers? + +Data _bounces_ between: +- The original buffer (in user/kernel space) and +- The DMA-capable bounce buffer (used for I/O with the device) + +This ensures that data transfers can still happen even when the original memory +is not suitable or accessible for transfers. + +## CCA Realms, VirtIO and bounce buffers + +The defining feature of a Realm is that its memory (called *Realm memory*) is +cryptographically isolated from both the Normal and Secure Worlds. This means +that: +- Realm memory is encrypted using keys that are unique to each Realm. +- Non-Realm entities (like the host OS or hypervisor) cannot directly read or + write Realm memory. +- Even Direct Memory Access (DMA) from peripherals or untrusted drivers cannot + access Realm data. + +This design ensures confidentiality but introduces a problem: *how can Realms +interact with untrusted components*, such as: +- Network stacks in the host OS, +- Storage subsystems, +- I/O devices managed by untrusted drivers? + +The solution to safely exchange data between a Realm and the outside World is to +use _bounce buffers_ as an intermediary ! + +### How bounce buffers are used with RME + +1. Exporting Data: + - A Realm application prepares some data (e.g., results of computation). + - It copies this data from protected Realm memory into a bounce buffer. + - The Realm notifies the untrusted host or hypervisor that the data is ready. + - The host retrieves the data from the bounce buffer. + +2. Importing Data: + - The host places data (e.g., input from a file or device) into a bounce buffer. + - The Realm is notified and validates the source. + - The Realm copies the data from the bounce buffer into its protected memory. + +This pattern preserves confidentiality and integrity of Realm data, since: +- The Realm never allows direct access to its memory. +- It can validate and sanitize any data received via bounce buffers. +- No sensitive data is exposed without explicit copying. + +### Confidentiality preserved with bounce buffers, really? + +In the previous section, it was mentioned that _bounce buffers preserves +confidentiality_, a sentence which deserves a bit more thoughts. Bounce buffers +are nothing more than an explicitly shared temporary area between the Realm +world and the outside world. This does indeed preserve the confidentiality of +all the rest of the Realm data. On the other hand, for the data being +transferred, it is leaving the Realm world and will only remain confidential if it +is encrypted in some ways, e.g. for network traffic, TLS should be used. + +## Seeing a Realm's bounce buffers at work + +Let's put this to work and check for ourselves that bounce buffers are used. This +will build on the Key Broker demo that was used in the [CCA +Essentials learning path](/learning-paths/servers-and-cloud-computing/cca-essentials/example/), +demonstrating an end-to-end attestation. + +### Start the **K**eyb **B**roker **Server** (KBS) + +First, pull the docker container image with the pre-built KBS, and then run the container: + +```bash +docker pull armswdev/cca-learning-path:cca-key-broker-v2 +docker run --rm -it armswdev/cca-learning-path:cca-key-broker-v2 +``` + +Now within your running docker container, get a list of network interfaces: + +```bash +ip -c a +``` + +The output should look like: + +```output +1: lo: mtu 65536 qdisc noqueue state UNKNOWN group default qlen 1000 + link/loopback 00:00:00:00:00:00 brd 00:00:00:00:00:00 + inet 127.0.0.1/8 scope host lo + valid_lft forever preferred_lft forever + inet6 ::1/128 scope host + valid_lft forever preferred_lft forever +20: eth0@if21: mtu 1500 qdisc noqueue state UP group default + link/ether 02:42:ac:11:00:02 brd ff:ff:ff:ff:ff:ff link-netnsid 0 + inet 172.17.0.2/16 brd 172.17.255.255 scope global eth0 + valid_lft forever preferred_lft forever +``` + +Start the KBS on the `eth0` network interface, and replace 172.17.0.2 shown in +the command below with the IP address corresponding to eth0 in the output of `ip +-c a` above. + +```bash +./keybroker-server -v --addr 172.17.0.2 +``` + +The output should look like: + +```output +INFO starting 16 workers +INFO Actix runtime found; starting in Actix runtime +INFO starting service: "actix-web-service-172.17.0.2:8088", workers: 16, listening on: 172.17.0.2:8088 +``` + +### Get into a Realm + +With the Key Broker Server running in one terminal, open up a new terminal in +which you will run the **K**ey **B**roker **C**lient (KBC). The intent is to +observe that the data transmitted over the network (thru `virtio_net`) are +indeed using bounce buffers. + +Pull the docker container image with the pre-built KBC, and then run the container: + +```bash +docker pull armswdev/cca-learning-path:cca-simulation-v2 +docker run --rm -it armswdev/cca-learning-path:cca-simulation-v2 +``` + +Within the running container, launch the `run-cca-fvp.sh` script to run the Arm +CCA pre-built binaries on the FVP: + +```bash +./run-cca-fvp.sh +``` +The `run-cca-fvp.sh` script uses the screen command to connect to the different +UARTs in the FVP. + +You should see the host Linux kernel boot on your terminal and you will be +prompted to log in to the host. Enter root as the username: + +```output +[ 4.169458] Run /sbin/init as init process +[ 4.273748] EXT4-fs (vda): re-mounted 64d1bcff-5d03-412c-83c6-48ec4253590e r/w. Quota mode: none. +Starting syslogd: OK +Starting klogd: OK +Running sysctl: OK +Starting network: [ 5.254843] smc91x 1a000000.ethernet eth0: link up, 10Mbps, half-duplex, lpa 0x0000 +udhcpc: started, v1.36.1 +udhcpc: broadcasting discover +udhcpc: broadcasting select for 172.20.51.1, server 172.20.51.254 +udhcpc: lease of 172.20.51.1 obtained from 172.20.51.254, lease time 86400 +deleting routers +adding dns 172.20.51.254 +OK + +Welcome to the CCA host +host login: root +(host) # +``` + +Change directory to `/cca` and use `lkvm` to launch a guest Linux in a Realm: +```bash +cd /cca +./lkvm run --realm --disable-sve --irqchip=gicv3-its --firmware KVMTOOL_EFI.fd -c 1 -m 512 --no-pvtime --disk guest-disk.img --restricted_mem --virtio-transport pci --pmu --network mode=user +``` + +You should see the realm boot. Note that `lkvm` is invoked with `--network +mode=user`, which makes the guest see the network thru a VirtIO device. + +After boot up, which might take some time, you will be prompted to log in at the +guest Linux prompt. Use root again as the username: + +```output +Starting syslogd: OK +Starting klogd: OK +Running sysctl: OK +Starting network: udhcpc: started, v1.36.1 +udhcpc: broadcasting discover +udhcpc: broadcasting select for 192.168.33.15, server 192.168.33.1 +udhcpc: lease of 192.168.33.15 obtained from 192.168.33.1, lease time 14400 +deleting routers +adding dns 172.20.51.254 +OK + +Welcome to the CCA realm +realm login: root +(realm) # +``` + +### Observe bounce buffer usage in the realm + +First, check that the Linux kernel has tracing support: + +```bash { output_lines="2-46" } +ls /sys/kernel/debug/tracing/events/ +9p i2c_slave qcom_glink +alarmtimer icmp qcom_smp2p +asoc initcall qdisc +block interconnect ras +bpf_test_run io_uring raw_syscalls +bpf_trace iomap rcu +bridge iommu regmap +capability ipi regulator +cgroup irq rpcgss +chipidea jbd2 rpm +clk kmem rpmh +cma ksm rseq +compaction kvm rtc +cpuhp kyber sched +cros_ec libata scmi +csd lock scsi +dev lockd signal +devfreq maple_tree skb +devlink mdio smbus +dma memcg sock +dma_fence migrate spi +dpaa2_eth mmap spmi +dpaa_eth mmap_lock sunrpc +dwc3 mmc swiotlb +e1000e_trace module task +enable mtu3 tcp +error_report musb tegra_apb_dma +ext4 napi thermal +fib neigh thermal_power_allocator +filelock net thp +filemap netfs timer +fsl_edma netlink timer_migration +ftrace nfs timestamp +gadget nfs4 tlb +gpio notifier udp +gpu_mem oom ufs +handshake optee vmalloc +header_event page_isolation vmscan +header_page page_pool watchdog +hns3 pagemap workqueue +huge_memory percpu writeback +hugetlbfs power xdp +hw_pressure printk xhci-hcd +hwmon pwm +i2c qcom_aoss +``` + +As shown in the above transcript, you should get a list of the available trace +points. + +Now, enable the kernel tracing infrastructure together with the bounce buffer +tracing, read the trace in the background (filtering on `keybroker-app-`) and +run the Key Broker Client application in the realm, using the endpoint address +that the Key Broker Server is listening on (from the other terminal): + +```bash +echo 1 > /sys/kernel/debug/tracing/tracing_on +echo 1 > /sys/kernel/debug/tracing/events/swiotlb/enable +grep keybroker-app- /sys/kernel/debug/tracing/trace_pipe & +keybroker-app -v --endpoint http://172.17.0.2:8088 skywalker +``` + +In the `keybroker-app`command above, `skywalker` is the key name that is +requested from the KBS. + +You should get an output looking like: + +```output +INFO Requesting key named 'skywalker' from the keybroker server with URL http://172.17.0.2:8088/keys/v1/key/skywalker +INFO Challenge (64 bytes) = [5c, ec, 1e, f5, 93, 54, 4a, 8a, ee, 2e, 46, a0, 50, 0d, 41, dd, d4, 60, b0, 58, 5b, 51, 71, 76, d1, 66, d3, b7, 38, e8, af, ae, 0a, 07, 4e, c5, 60, dc, 4a, c0, b8, 73, 98, d9, bd, af, 41, 96, 99, 6d, 74, cc, 19, 70, 24, c4, c9, 5c, 21, 61, 1a, cb, 76, 75] +INFO Submitting evidence to URL http://172.17.0.2:8088/keys/v1/evidence/1928844131 +INFO Attestation success :-) ! The key returned from the keybroker is 'May the force be with you.' + keybroker-app-143 [000] b..2. 1772.607321: swiotlb_bounced: dev_name: 0000:00:00.0 dma_mask=ffffffffffffffff dev_addr=80b6717e size=66 FORCE + keybroker-app-143 [000] b..2. 1772.644478: swiotlb_bounced: dev_name: 0000:00:00.0 dma_mask=ffffffffffffffff dev_addr=80b6717e size=66 FORCE +``` + +Note that the interleaving of the trace messages and KBC messages might differ +from one run to another. diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md new file mode 100644 index 0000000000..0bea76e9e0 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md @@ -0,0 +1,57 @@ +--- +title: CCA device attach + +minutes_to_complete: 90 + +who_is_this_for: This Learning Path is for developers who want to learn about CCA Realms, VirtIO, Bounce Buffers and Device Attach. + +learning_objectives: + - Have a high level understanding of VirtIO and Bounce Buffers in CCA Realms + - Understand Device Attach in general, and specifically for different types of devices + +prerequisites: + - An AArch64 or x86_64 computer running Linux or macOS. You can use cloud instances, see this list of [Arm cloud service providers](/learning-paths/servers-and-cloud-computing/csp/). + - Completion of [Get Started with CCA Attestation and Veraison](/learning-paths/servers-and-cloud-computing/cca-veraison) Learning Path. + - Completion of the [Run an application in a Realm using the Arm Confidential Computing Architecture (CCA)](/learning-paths/servers-and-cloud-computing/cca-container/) Learning Path. + - Completion of the [Run an end-to-end Attestation Flow](/learning-paths/servers-and-cloud-computing/cca-essentials/) Learning Path. + +author: Arnaud de Grandmaison + +### Tags +skilllevels: Advanced +subjects: Performance and Architecture +armips: + - Neoverse + - Cortex-A +operatingsystems: + - Linux + - macOS +tools_software_languages: + - CCA + - RME + - Docker + +further_reading: + - resource: + title: Arm Confidential Compute Architecture + link: https://www.arm.com/architecture/security-features/arm-confidential-compute-architecture + type: website + - resource: + title: Arm Confidential Compute Architecture open source enablement + link: https://www.youtube.com/watch?v=JXrNkYysuXw + type: video + - resource: + title: Learn the architecture - Realm Management Extension + link: https://developer.arm.com/documentation/den0126 + type: documentation + - resource: + title: Realm Management Monitor specification + link: https://developer.arm.com/documentation/den0137/latest/ + type: documentation + +### FIXED, DO NOT MODIFY +# ================================================================================ +weight: 1 # _index.md always has weight of 1 to order correctly +layout: "learningpathall" # All files under learning paths have this same wrapper +learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. +--- diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_next-steps.md new file mode 100644 index 0000000000..c3db0de5a2 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_next-steps.md @@ -0,0 +1,8 @@ +--- +# ================================================================================ +# FIXED, DO NOT MODIFY THIS FILE +# ================================================================================ +weight: 21 # Set to always be larger than the content in this path to be at the end of the navigation. +title: "Next Steps" # Always the same, html page title. +layout: "learningpathall" # All files under learning paths have this same wrapper for Hugo processing. +--- diff --git a/content/learning-paths/servers-and-cloud-computing/cca-essentials/example.md b/content/learning-paths/servers-and-cloud-computing/cca-essentials/example.md index 276279ced7..2d7283e55d 100644 --- a/content/learning-paths/servers-and-cloud-computing/cca-essentials/example.md +++ b/content/learning-paths/servers-and-cloud-computing/cca-essentials/example.md @@ -12,17 +12,17 @@ layout: "learningpathall" #### Background -The concept of a Key Broker Server (KBS) is a common one in confidential computing, and there are multiple open-source implementations, including the [Trustee](https://github.com/confidential-containers/trustee) from the [CNCF Confidential Containers](https://confidentialcontainers.org/) project. +The concept of a Key Broker Server (KBS) is a common one in confidential computing, and there are multiple open-source implementations, including the [Trustee](https://github.com/confidential-containers/trustee) from the [CNCF Confidential Containers](https://confidentialcontainers.org/) project. -The KBS in this Learning Path is part of the [Veraison](https://github.com/veraison) project. It has been created specifically for educational purposes, so is intentionally small and simple to understand, and is not designed for production use. +The KBS in this Learning Path is part of the [Veraison](https://github.com/veraison) project. It has been created specifically for educational purposes, so is intentionally small and simple to understand, and is not designed for production use. #### Get started First, pull the docker container image with the pre-built KBS, and then run the container: ```bash -docker pull armswdev/cca-learning-path:cca-key-broker-v1 -docker run --rm -it armswdev/cca-learning-path:cca-key-broker-v1 +docker pull armswdev/cca-learning-path:cca-key-broker-v2 +docker run --rm -it armswdev/cca-learning-path:cca-key-broker-v2 ``` Now within your running docker container, get a list of network interfaces: @@ -45,7 +45,7 @@ The output should look like: inet 172.17.0.2/16 brd 172.17.255.255 scope global eth0 valid_lft forever preferred_lft forever ``` -Start the KBS on the `eth0` network interface, and replace 172.17.0.2 shown in the command below with the IP address corresponding to eth0 in the output of "ip -c a" above. +Start the KBS on the `eth0` network interface, and replace 172.17.0.2 shown in the command below with the IP address corresponding to eth0 in the output of `ip -c a` above. ```bash ./keybroker-server -v --addr 172.17.0.2 @@ -66,12 +66,12 @@ With the Key Broker Server running in one terminal, open up a new terminal in wh In the new terminal that you have just opened, pull the docker container image that contains the FVP and pre-built software binaries to run the Key Broker Client in a realm. ```bash -docker pull armswdev/cca-learning-path:cca-simulation-v1 +docker pull armswdev/cca-learning-path:cca-simulation-v2 ``` Now run the docker container: ```bash -docker run --rm -it armswdev/cca-learning-path:cca-simulation-v1 +docker run --rm -it armswdev/cca-learning-path:cca-simulation-v2 ``` Within your running container, launch the `run-cca-fvp.sh` script to run the Arm CCA pre-built binaries on the FVP: @@ -79,11 +79,11 @@ Within your running container, launch the `run-cca-fvp.sh` script to run the Arm ```bash ./run-cca-fvp.sh ``` -The run-cca-fvp.sh script uses the screen command to connect to the different UARTs in the FVP. +The `run-cca-fvp.sh` script uses the screen command to connect to the different UARTs in the FVP. -You should see the host Linux kernel boot on your terminal. +You should see the host Linux kernel boot on your terminal. -You will be prompted to log in to the host. +You will be prompted to log in to the host. Enter root as the username: @@ -109,11 +109,11 @@ host login: root Use kvmtool to launch guest Linux in a Realm: ```bash cd /cca -./lkvm run --realm --disable-sve --irqchip=gicv3-its --firmware KVMTOOL_EFI.fd -c 1 -m 512 --no-pvtime --force-pci --disk guest-disk.img --measurement-algo=sha256 --restricted_mem +./lkvm run --realm --disable-sve --irqchip=gicv3-its --firmware KVMTOOL_EFI.fd -c 1 -m 512 --no-pvtime --force-pci --disk guest-disk.img --measurement-algo=sha256 --restricted_mem ``` -You should see the realm boot. +You should see the realm boot. -After boot up, you will be prompted to log in at the guest Linux prompt. +After boot up, which might take some time, you will be prompted to log in at the guest Linux prompt. Use root again as the username: @@ -134,15 +134,14 @@ realm login: root (realm) # ``` -Now run the Key Broker Client application in the realm. +Now run the Key Broker Client application in the realm. Use the endpoint address that the Key Broker Server is listening in on the other terminal: ```bash -cd /cca -./keybroker-app -v --endpoint http://172.17.0.2:8088 skywalker +keybroker-app -v --endpoint http://172.17.0.2:8088 skywalker ``` -In the command above, `skywalker` is the key name that is requested from the Key Broker Server. +In the command above, `skywalker` is the key name that is requested from the Key Broker Server. After some time, you should see the following output: ``` @@ -151,7 +150,7 @@ INFO Challenge (64 bytes) = [0f, ea, c4, e2, 24, 4e, fa, dc, 1d, ea, ea, 3d, 60, INFO Submitting evidence to URL http://172.17.0.2:8088/keys/v1/evidence/3974368321 INFO Attestation failure :-( ! AttestationFailure: No attestation result was obtained. No known-good reference values. ``` -You can see from the Key Broker client application output that the `skywalker` key is requested from the Key Broker Server, which did send a challenge. +You can see from the Key Broker client application output that the `skywalker` key is requested from the Key Broker Server, which did send a challenge. The Key Broker Client application uses the challenge to submit its evidence back to the Key Broker Server, but it receives an attestation failure. This is because the server does not have any known good reference values. @@ -164,15 +163,15 @@ command-line option to populate it with known-good RIM values: --reference-values <(echo '{ "reference-values": [ "tiA66VOokO071FfsCHr7es02vUbtVH5FpLLqTzT7jps=" ] }') INFO Evidence submitted for challenge 1302147796: no attestation result was obtained. No known-good reference values. ``` -From the server output, you can see that it did create the challenge for the Key Broker application, but it reports that it has no known good reference values. +From the server output, you can see that it did create the challenge for the Key Broker application, but it reports that it has no known good reference values. -It does however provide a way to provision the Key Broker Server with known good values if the client is trusted. +It does however provide a way to provision the Key Broker Server with known good values if the client is trusted. In a production environment, the known good reference value is generated using a deployment- specific process, but for demonstration purposes and simplification, you will use the value proposed by the Key Broker Server. -Now go ahead and terminate the running instance of the Key Broker Server using Ctrl+C and restart it with the known good reference value. +Now go ahead and terminate the running instance of the Key Broker Server using Ctrl+C and restart it with the known good reference value. -Notice here that you need to copy the `--reference-values` argument directly from the previous error message reported by the Key Broker. +Notice here that you need to copy the `--reference-values` argument directly from the previous error message reported by the Key Broker. When running the next command, ensure that you are copying the exact value reported, for example: @@ -183,7 +182,7 @@ When running the next command, ensure that you are copying the exact value repor On the terminal with the running realm, rerun the Key Broker Client application with the exact same command line parameters as before: ```bash -./keybroker-app -v --endpoint http://172.17.0.2:8088 skywalker +keybroker-app -v --endpoint http://172.17.0.2:8088 skywalker ``` You should now get a successful attestation as shown: From 18b710f2c8b59eb9924825e7e0f4342f0f1cd6fe Mon Sep 17 00:00:00 2001 From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Sat, 26 Jul 2025 09:34:20 +0100 Subject: [PATCH 04/42] Update _index.md Removed extra spaces --- .../automotive/openadkit2_safetyisolation/_index.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md index 55245d22ff..75f3bf2cbe 100644 --- a/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md +++ b/content/learning-paths/automotive/openadkit2_safetyisolation/_index.md @@ -8,7 +8,7 @@ who_is_this_for: This Learning Path is for automotive engineers developing safet learning_objectives: - Apply functional safety principles, including risk prevention, fault detection, and ASIL compliance, to build robust, certifiable automotive systems - Use DDS and a publish-subscribe architecture for low-latency, scalable, and fault-tolerant communication in autonomous driving systems - - Implement distributed development by separating the simulation platform into independent, safety-isolated components + - Implement distributed development by separating the simulation platform into independent, safety-isolated components prerequisites: - Access to two Arm-based Neoverse cloud instances, or a local Arm Neoverse Linux system with at least 16 CPUs and 32 GB of RAM From f62db05e24b39778e16c2f8616bd80277c60a9e7 Mon Sep 17 00:00:00 2001 From: yinyu01 Date: Sun, 27 Jul 2025 11:17:17 +0800 Subject: [PATCH 05/42] add Java Performance Analysis - FlameGraph --- .../java-perf-flamegraph/1_setup.md | 100 ++++++++++++++++++ .../java-perf-flamegraph/2_async-profiler.md | 30 ++++++ .../java-perf-flamegraph/3_agent.md | 38 +++++++ .../_images/LP-flamegraph-agent.png | Bin 0 -> 2202574 bytes .../_images/LP-flamegraph-async.png | Bin 0 -> 1252583 bytes .../_images/LP-tomcat-examples.png | Bin 0 -> 195686 bytes .../_images/LP-tomcat-homepage.png | Bin 0 -> 768199 bytes .../java-perf-flamegraph/_index.md | 57 ++++++++++ .../java-perf-flamegraph/_next-steps.md | 8 ++ 9 files changed, 233 insertions(+) create mode 100644 content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md create mode 100644 content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md create mode 100644 content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md create mode 100644 content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-flamegraph-agent.png create mode 100644 content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-flamegraph-async.png create mode 100644 content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-tomcat-examples.png create mode 100644 content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-tomcat-homepage.png create mode 100644 content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md create mode 100644 content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_next-steps.md diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md new file mode 100644 index 0000000000..b055f74c63 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md @@ -0,0 +1,100 @@ +--- +title: Setup Tomcat Benchmark Environment +weight: 2 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + + +## Before You Begin +- There are numerous performance analysis methods and tools for Java applications, among which the call stack flame graph method is regarded as a conventional entry-level approach. Therefore, generating flame graphs is considered a basic operation. +- Various methods and tools are available for generating Java flame graphs, including async-profiler, Java Agent, jstack, JFR (Java Flight Recorder), etc. +- This LP (Learning Path) focuses on introducing two simple and easy-to-use methods: async-profiler and Java Agent. + + +## Setup Benchmark Server - Tomcat +- [Apache Tomcat](https://tomcat.apache.org/) is an open-source Java Servlet container that enables running Java web applications, handling HTTP requests and serving dynamic content. +- As a core component in Java web development, Apache Tomcat supports Servlet, JSP, and WebSocket technologies, providing a lightweight runtime environment for web apps. + +1. So you should install Java Development Kit (JDK) first. +```bash +sudo apt update +sudo apt install -y openjdk-21-jdk +``` + +2. Second, you can install Tomcat by either [building it from source](https://github.com/apache/tomcat) or downloading the pre-built package simply from [the official website](https://tomcat.apache.org/whichversion.html) +```bash +wget -c https://dlcdn.apache.org/tomcat/tomcat-11/v11.0.9/bin/apache-tomcat-11.0.9.tar.gz +tar xzf apache-tomcat-11.0.9.tar.gz +``` + +3. If you intend to access the built-in Examples of Tomcat via an intranet IP or even an external IP, you need to modify a configuration file. +```bash +vim apache-tomcat-11.0.9/webapps/examples/META-INF/context.xml +# change +# to +# + +# now you can start Tomcat Server +./apache-tomcat-11.0.9/bin/startup.sh +``` + +4. If you can access the page at "http://${tomcat_ip}:8080/examples" via a browser, congratulations-you can proceed to the next benchmarking step. + +![example image alt-text#center](./_images/LP-tomcat-homepage.png "Tomcat-HomePage") + +![example image alt-text#center](./_images/LP-tomcat-examples.png "Tomcat-Examples") + +## Setup Benchmark Client - [wrk2](https://github.com/giltene/wrk2) +- wrk2 is a high-performance HTTP benchmarking tool specialized in generating constant throughput loads and measuring latency percentiles for web services. +- As an enhanced version of wrk, wrk2 provides accurate latency statistics under controlled request rates, ideal for performance testing of HTTP servers. + +1. If you intend to use wrk2, you should install some essential tools before build it. +```bash +sudo apt-get update +sudo apt-get install -y build-essential libssl-dev git zlib1g-dev +``` + +2. Now you can clone and build it from source. +```bash +sudo git clone https://github.com/giltene/wrk2.git +cd wrk2 +sudo make +# move the executable to somewhere in your PATH +sudo cp wrk /usr/local/bin +``` + +3. Finally, you can run the benchamrk of Tomcat through wrk2. +```bash +wrk -c32 -t16 -R50000 -d60 http://${tomcat_ip}:8080/examples/servlets/servlet/HelloWorldExample +``` + Below is the output of wrk2 +```console +Running 1m test @ http://172.26.203.139:8080/examples/servlets/servlet/HelloWorldExample + 16 threads and 32 connections + Thread calibration: mean lat.: 0.986ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.984ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.999ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.994ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.983ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.989ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.991ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.993ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.985ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.990ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.987ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.990ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.984ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.991ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.978ms, rate sampling interval: 10ms + Thread calibration: mean lat.: 0.976ms, rate sampling interval: 10ms + Thread Stats Avg Stdev Max +/- Stdev + Latency 1.00ms 454.90us 5.09ms 63.98% + Req/Sec 3.31k 241.68 4.89k 63.83% + 2999817 requests in 1.00m, 1.56GB read +Requests/sec: 49997.08 +Transfer/sec: 26.57MB +``` + + diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md new file mode 100644 index 0000000000..0aa0b78825 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md @@ -0,0 +1,30 @@ +--- +title: Java FlameGraph - Async-profiler +weight: 3 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +## Java Flame Graph Generation via async-profiler [async-profiler](https://github.com/async-profiler/async-profiler) (Recommended) +- async-profiler is a low-overhead sampling profiler for JVM applications, capable of capturing CPU, allocation, and lock events to generate actionable performance insights. +- A lightweight tool for Java performance analysis, async-profiler produces flame graphs and detailed stack traces with minimal runtime impact, suitable for production environments. + +You should deploy async-profiler on the same machine where Tomcat is running to ensure accurate performance profiling. +1. Download async-profiler-4.0 and uncompress +```bash +wget -c https://github.com/async-profiler/async-profiler/releases/download/v4.0/async-profiler-4.0-linux-arm64.tar.gz +tar xzf async-profiler-4.0-linux-arm64.tar.gz +``` + +2. Run async-profiler to profile the Tomcat instance under benchmarking +```bash +cd async-profiler-4.0-linux-arm64/bin +./asprof -d 10 -f profile.html $(jps | awk /Bootstrap/'{print $1}') +# or +./asprof -d 10 -f profile.html ${tomcat_process_id} +``` + +3. Launch profile.html in a browser to analyse the profiling result + +![example image alt-text#center](_images/LP-flamegraph-async.png "Java Flame Graph via async-profiler") diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md new file mode 100644 index 0000000000..de00a7d369 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md @@ -0,0 +1,38 @@ +--- +title: Java FlameGraph - Java Agent +weight: 4 + + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +## Java Flame Graph Generation via Java agent and perf +To profile a Java application with perf and ensure proper symbol resolution, you must include libperf-jvmti.so when launching the Java application. +- libperf-jvmti.so is a JVM TI agent library enabling perf to resolve Java symbols, facilitating accurate profiling of Java applications. +- A specialized shared library, libperf-jvmti.so bridges perf and the JVM, enabling proper translation of memory addresses to Java method names during profiling. + +1. Find and add libperf-jvmti.so to Java option +```bash +vi apache-tomcat-11.0.9/bin/catalina.sh +# add JAVA_OPTS="$JAVA_OPTS -agentpath:/usr/lib/linux-tools-6.8.0-63/libperf-jvmti.so -XX:+PreserveFramePointer" +cd apache-tomcat-11.0.9/bin +./shutdown.sh +./startup.sh +``` + +2. Use perf to profile Tomcat, and restart wrk if necessary +```bash +sudo perf record -g -k1 -p $(jps | awk /Bootstrap/'{print $1}') -- sleep 10 +``` + +3. Convert the collected perf.data file into a Java flame graph using FlameGraph +```bash +git clone https://github.com/brendangregg/FlameGraph.git +export PATH=$PATH:/root/FlameGraph +sudo perf inject -j -i perf.data | perf script | stackcollapse-perf.pl | flamegraph.pl &> profile.svg +``` + +4. 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a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md new file mode 100644 index 0000000000..1ed9fbcc58 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md @@ -0,0 +1,57 @@ +--- +title: Java Performance Analysis - FlameGraph + +minutes_to_complete: 30 + +who_is_this_for: This is an introductory guide for individuals aiming to perform performance analysis of Java applications on the ARM Neoverse platform using flame graphs. + +learning_objectives: + - How to set up tomcat benchmark environment + - How to generate flame graphs for Java applications using async-profiler + - How to generate flame graphs for Java applications using Java agent + +prerequisites: + - Basic familiarity with Java applications + - Basic familiarity with flame graphs + - Basic familiarity with Tomcat, wrk, etc + +author: Ying Yu, Martin Ma + +### Tags +skilllevels: Introductory +subjects: Java Performance Analysis +armips: + - Neoverse + +tools_software_languages: + - OpenJDK-21 + - Tomcat + - Async-profiler + - FlameGraph + - wrk2 +operatingsystems: + - Ubuntu 24 + + +further_reading: + - resource: + title: PLACEHOLDER MANUAL + link: PLACEHOLDER MANUAL LINK + type: documentation + - resource: + title: PLACEHOLDER BLOG + link: PLACEHOLDER BLOG LINK + type: blog + - resource: + title: PLACEHOLDER GENERAL WEBSITE + link: PLACEHOLDER GENERAL WEBSITE LINK + type: website + + + +### FIXED, DO NOT MODIFY +# ================================================================================ +weight: 1 # _index.md always has weight of 1 to order correctly +layout: "learningpathall" # All files under learning paths have this same wrapper +learning_path_main_page: "yes" # This should be surfaced when looking for related content. Only set for _index.md of learning path content. +--- diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_next-steps.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_next-steps.md new file mode 100644 index 0000000000..c3db0de5a2 --- /dev/null +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_next-steps.md @@ -0,0 +1,8 @@ +--- +# ================================================================================ +# FIXED, DO NOT MODIFY THIS FILE +# ================================================================================ +weight: 21 # Set to always be larger than the content in this path to be at the end of the navigation. +title: "Next Steps" # Always the same, html page title. +layout: "learningpathall" # All files under learning paths have this same wrapper for Hugo processing. +--- From 289750cb0700a804e7dc7e4f4d39fb123828881b Mon Sep 17 00:00:00 2001 From: yinyu01 Date: Sun, 27 Jul 2025 12:19:17 +0800 Subject: [PATCH 06/42] Rename image files to lowercase to comply with naming conventions --- .../java-perf-flamegraph/1_setup.md | 4 ++-- .../java-perf-flamegraph/2_async-profiler.md | 2 +- .../java-perf-flamegraph/3_agent.md | 2 +- 3 files changed, 4 insertions(+), 4 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md index b055f74c63..ba0ffd9868 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md @@ -42,9 +42,9 @@ vim apache-tomcat-11.0.9/webapps/examples/META-INF/context.xml 4. If you can access the page at "http://${tomcat_ip}:8080/examples" via a browser, congratulations-you can proceed to the next benchmarking step. -![example image alt-text#center](./_images/LP-tomcat-homepage.png "Tomcat-HomePage") +![example image alt-text#center](./_images/lp-tomcat-homepage.png "Tomcat-HomePage") -![example image alt-text#center](./_images/LP-tomcat-examples.png "Tomcat-Examples") +![example image alt-text#center](./_images/lp-tomcat-examples.png "Tomcat-Examples") ## Setup Benchmark Client - [wrk2](https://github.com/giltene/wrk2) - wrk2 is a high-performance HTTP benchmarking tool specialized in generating constant throughput loads and measuring latency percentiles for web services. diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md index 0aa0b78825..4e834176e9 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md @@ -27,4 +27,4 @@ cd async-profiler-4.0-linux-arm64/bin 3. Launch profile.html in a browser to analyse the profiling result -![example image alt-text#center](_images/LP-flamegraph-async.png "Java Flame Graph via async-profiler") +![example image alt-text#center](_images/lp-flamegraph-async.png "Java Flame Graph via async-profiler") diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md index de00a7d369..13fade565e 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md @@ -35,4 +35,4 @@ sudo perf inject -j -i perf.data | perf script | stackcollapse-perf.pl | flamegr 4. Launch profile.svg in a browser to analyse the profiling result -![example image alt-text#center](_images/LP-flamegraph-agent.png "Java Flame Graph via Java agent and perf") +![example image alt-text#center](_images/lp-flamegraph-agent.png "Java Flame Graph via Java agent and perf") From db21e7faa0287e347c8df141eeda4cb9163e16fa Mon Sep 17 00:00:00 2001 From: yinyu01 Date: Sun, 27 Jul 2025 12:30:16 +0800 Subject: [PATCH 07/42] Rename image files to lowercase for naming convention compliance --- ...flamegraph-agent.png => lp-flamegraph-agent.png} | Bin ...flamegraph-async.png => lp-flamegraph-async.png} | Bin ...P-tomcat-examples.png => lp-tomcat-examples.png} | Bin ...P-tomcat-homepage.png => lp-tomcat-homepage.png} | Bin 4 files changed, 0 insertions(+), 0 deletions(-) rename content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/{LP-flamegraph-agent.png => lp-flamegraph-agent.png} (100%) rename content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/{LP-flamegraph-async.png => lp-flamegraph-async.png} (100%) rename content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/{LP-tomcat-examples.png => lp-tomcat-examples.png} (100%) rename content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/{LP-tomcat-homepage.png => lp-tomcat-homepage.png} (100%) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-flamegraph-agent.png b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-flamegraph-agent.png similarity index 100% rename from content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-flamegraph-agent.png rename to content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-flamegraph-agent.png diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-flamegraph-async.png b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-flamegraph-async.png similarity index 100% rename from content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-flamegraph-async.png rename to content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-flamegraph-async.png diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-tomcat-examples.png b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-tomcat-examples.png similarity index 100% rename from content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-tomcat-examples.png rename to content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-tomcat-examples.png diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-tomcat-homepage.png b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-tomcat-homepage.png similarity index 100% rename from content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/LP-tomcat-homepage.png rename to content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_images/lp-tomcat-homepage.png From 28d4b2b4cbc6a748e78c92e6774728880b2615ea Mon Sep 17 00:00:00 2001 From: Maddy Underwood Date: Sun, 27 Jul 2025 23:02:15 +0000 Subject: [PATCH 08/42] Refactored Godot performance profiling tutorial into modular Learning Path steps --- .../godot_packages/_index.md | 12 +++---- .../godot_packages/add-markers.md | 28 +++++++++++++++ .../arm_mobile_studio_integrations.md | 12 ++++--- .../godot_packages/create-counters.md | 27 ++++++++++++++ .../godot_packages/define-regions.md | 23 ++++++++++++ .../godot_packages/install-extension.md | 15 ++++++++ .../godot_packages/use-channels.md | 28 +++++++++++++++ .../use-custom-activity-maps.md | 36 +++++++++++++++++++ 8 files changed, 169 insertions(+), 12 deletions(-) create mode 100644 content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md create mode 100644 content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md create mode 100644 content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md create mode 100644 content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md create mode 100644 content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md create mode 100644 content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md index 9b272c76ab..b9fbecd3f9 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/_index.md @@ -1,17 +1,13 @@ --- -title: Use the Arm Performance Studio Integration extension in Godot - -draft: true -cascade: - draft: true - +title: Profile Android game performance in Godot with Arm Performance Studio + minutes_to_complete: 15 -who_is_this_for: This is an introductory topic for Godot developers who are targeting Android devices and want to get more insight into how their game performs on devices with Arm CPUs and GPUs. +who_is_this_for: This is an introductory topic for Godot developers targeting Android devices who want to optimize game performance on Arm CPUs and Mali GPUs using Arm Performance Studio tools. learning_objectives: - Install the Arm Performance Studio Integration extension in Godot - - Annotate your Godot game with markers that give context to a profile in Arm Performance Studio tools + - Annotate your Godot game with performance markers for profiling in Streamline and Performance Advisor prerequisites: - Familiarity with Godot diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md new file mode 100644 index 0000000000..4882c5bc44 --- /dev/null +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md @@ -0,0 +1,28 @@ +--- +title: Add performance markers in Godot +weight: 5 +layout: learningpathall +--- + +## Add single markers to highlight key game events + +All functionality in the extension is provided by the `PerformanceStudio` class. First, create an instance: + +```gdscript +var performance_studio = PerformanceStudio.new() +``` + +The simplest annotations are single markers, which can have a name and a color. For example: + +```gdscript +performance_studio.marker("Game Started") +``` + +This will emit a timestamped marker labeled "Game Started." When you capture a profile in Streamline, you’ll see this marker at the point the game starts. + +![Marker annotation in Streamline](sl_marker.png "Figure 4. Marker annotation in Streamline") + +To assign a color: + +```gdscript +performance_studio.marker_color("Game Started", Color8(0, 255, 0)) diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md index f345ac8828..c1d6bf5652 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md @@ -6,7 +6,13 @@ weight: 3 layout: learningpathall --- -## Arm Performance Studio Godot integrations +## Profile and analyze Godot game performance with Arm Performance Studio + +Learn how to annotate and analyze performance in your Godot game using the Arm Performance Studio extension. You’ll use markers, counters, and timelines to highlight game events, then visualize them in Streamline and Performance Advisor to identify CPU and GPU bottlenecks. + +{{% notice Note %}} + This extension is compatible with **Godot 4.3 and later**. +{{% /notice %}} [Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) is a free suite of analysis tools to help you profile game performance on mobile devices with Arm CPUs and GPUs. Arm provides a Godot extension to make data from [Godot games](https://godotengine.org/) visible in the Arm Performance Studio tools, [Streamline](https://developer.arm.com/Tools%20and%20Software/Streamline%20Performance%20Analyzer) and [Performance Advisor](https://developer.arm.com/Tools%20and%20Software/Performance%20Advisor). @@ -14,9 +20,7 @@ This package provides a simple way to incorporate annotations into your Godot pr ![Marker annotations in Streamline](sl_annotation.png "Figure 1. Marker annotations in Streamline") -{{% notice Note %}} -The Arm Performance Studio Integration extension is supported with Godot version 4.3 and later. -{{% /notice %}} + ### How to install the Arm Performance Studio Integration extension diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md new file mode 100644 index 0000000000..469302177c --- /dev/null +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md @@ -0,0 +1,27 @@ +--- +title: Create and track custom counters in Godot +weight: 8 +layout: learningpathall +--- + +## Track numerical performance data using counters + +Counters are float values plotted as charts in the Streamline timeline. You can use **absolute counters** (e.g., frame rate) or **delta counters** (e.g., events per frame). + +### Create a counter: + +```gdscript +var counter = performance_studio.create_counter("Title", "Series", false) +``` + +- `Title` = name of the chart +- `Series` = label for the data series +- `false` = absolute (set `true` for delta counters) + +### Update counter values: + +```gdscript +counter.setValue(42.2) +``` + +Multiple series can share the same chart title to group related values together. diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md new file mode 100644 index 0000000000..4548a35f70 --- /dev/null +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md @@ -0,0 +1,23 @@ +--- +title: Define performance regions in Godot +weight: 6 +layout: learningpathall +--- + +## Define regions of interest for performance analysis + +To define regions of interest within the game, use marker pairs prefixed with “Region Start” and “Region End”: + +```gdscript +performance_studio.marker("Region Start Times Square") +# Do work +performance_studio.marker("Region End Times Square") +``` + +These regions are shown in the **frame rate analysis chart** in Performance Advisor. + +![Regions in Performance Advisor](pa_frame_rate_regions.png "Figure 5. Regions in Performance Advisor") + +Each region gets a dedicated chart in the final report, so you can analyze them independently. + +![Dedicated region charts in Performance Advisor](pa_dedicated_region_charts.png "Figure 6. Dedicated region charts in Performance Advisor") diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md new file mode 100644 index 0000000000..4ca0731152 --- /dev/null +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md @@ -0,0 +1,15 @@ +--- +title: Install the Arm Performance Studio extension in Godot +weight: 4 +layout: learningpathall +--- + +## Install the Arm Performance Studio Integration extension + +1. In Godot, click **AssetLib** to see the available extensions. +2. Find the **Arm Performance Studio Integration** extension, then double-click to open the extension. +3. The extension opens in a dialog box. Click **Download**. + +![Installing the Arm Performance Studio Integration extension in Godot](godot_install_performance_studio_extension.png "Figure 2. Installing the Arm Performance Studio Integration extension in Godot") + +4. A new dialog box opens where you can change the install folder if required. Click **Install**. \ No newline at end of file diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md new file mode 100644 index 0000000000..80e5512919 --- /dev/null +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md @@ -0,0 +1,28 @@ +--- +title: Use channels for threaded performance annotations +weight: 7 +layout: learningpathall +--- + +## Use channels to annotate threaded events + +Channels are custom timelines tied to software threads. You can annotate them with labels and colors over a span of time. + +### Create and use a channel: + +```gdscript +var channel : PerformanceStudio_Channel + +func _ready(): + channel = performance_studio.create_channel("Spawner") + +func _on_new_wave_started(): + channel.annotate_color("Spawning Wave", Color8(255, 0, 0)) + +func _on_wave_completed(): + channel.end() +``` + +To view channels in Streamline, open the **Core Map** view and expand the **VkThread** thread. + +![Channel annotations in Streamline](sl_channel.png "Figure 7. Channel annotations in Streamline") diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md new file mode 100644 index 0000000000..fea008a62b --- /dev/null +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md @@ -0,0 +1,36 @@ +--- +title: Use Custom Activity Maps in Godot profiling +weight: 9 +layout: learningpathall +--- + +## Visualize jobs and activity with Custom Activity Maps (CAM) + +Custom Activity Maps (CAMs) help track complex jobs across multiple non-threaded timelines. Great for modeling gameplay layers like UI, audio, and AI. + +### Create a CAM with multiple tracks: + +```gdscript +var game_cam : PerformanceStudio_CAM +var wave_track : PerformanceStudio_CAMTrack +var ui_track : PerformanceStudio_CAMTrack + +func _ready(): + game_cam = performance_studio.create_cam("Game Activity") + wave_track = game_cam.create_track("Wave Activity") + ui_track = game_cam.create_track("UI Activity") +``` + +### Add jobs to tracks: + +```gdscript +var wave_job : PerformanceStudio_CAMJob + +func _on_new_wave_started(): + wave_job = wave_track.create_job("Spawning Wave", Color8(255, 0, 0)) + +func _on_wave_completed(): + wave_job.stop() +``` + +![Custom activity maps in Streamline](sl_cam.png "Figure 8. Custom activity maps in Streamline") From fa5f1e84c1a3292070136c12ae9fc334301eaff6 Mon Sep 17 00:00:00 2001 From: GitHub Actions Stats Bot <> Date: Mon, 28 Jul 2025 01:30:03 +0000 Subject: [PATCH 09/42] automatic update of stats files --- data/stats_current_test_info.yml | 2 +- data/stats_weekly_data.yml | 117 +++++++++++++++++++++++++++++++ 2 files changed, 118 insertions(+), 1 deletion(-) diff --git a/data/stats_current_test_info.yml b/data/stats_current_test_info.yml index 48c5619c87..d3a145316b 100644 --- a/data/stats_current_test_info.yml +++ b/data/stats_current_test_info.yml @@ -1,5 +1,5 @@ summary: - content_total: 387 + content_total: 391 content_with_all_tests_passing: 0 content_with_tests_enabled: 61 sw_categories: diff --git a/data/stats_weekly_data.yml b/data/stats_weekly_data.yml index ad533ae6fa..34051a8dca 100644 --- a/data/stats_weekly_data.yml +++ b/data/stats_weekly_data.yml @@ -6895,3 +6895,120 @@ avg_close_time_hrs: 0 num_issues: 21 percent_closed_vs_total: 0.0 +- a_date: '2025-07-28' + content: + automotive: 3 + cross-platform: 34 + embedded-and-microcontrollers: 43 + install-guides: 105 + iot: 6 + laptops-and-desktops: 38 + mobile-graphics-and-gaming: 34 + servers-and-cloud-computing: 128 + total: 391 + contributions: + external: 98 + internal: 516 + github_engagement: + num_forks: 30 + num_prs: 14 + individual_authors: + adnan-alsinan: 2 + alaaeddine-chakroun: 2 + albin-bernhardsson: 1 + alex-su: 1 + alexandros-lamprineas: 1 + andrew-choi: 2 + andrew-kilroy: 1 + annie-tallund: 4 + arm: 3 + arnaud-de-grandmaison: 5 + aude-vuilliomenet: 1 + avin-zarlez: 1 + barbara-corriero: 1 + basma-el-gaabouri: 1 + ben-clark: 1 + bolt-liu: 2 + brenda-strech: 1 + bright-edudzi-gershon-kordorwu: 1 + chaodong-gong: 1 + chen-zhang: 1 + chenying-kuo: 1 + christophe-favergeon: 1 + christopher-seidl: 7 + cyril-rohr: 1 + daniel-gubay: 1 + daniel-nguyen: 2 + david-spickett: 2 + dawid-borycki: 33 + diego-russo: 2 + dominica-abena-o.-amanfo: 1 + elham-harirpoush: 2 + florent-lebeau: 5 + "fr\xE9d\xE9ric--lefred--descamps": 2 + gabriel-peterson: 5 + gayathri-narayana-yegna-narayanan: 2 + georgios-mermigkis: 1 + geremy-cohen: 3 + gian-marco-iodice: 1 + graham-woodward: 1 + han-yin: 1 + iago-calvo-lista: 1 + james-whitaker: 1 + jason-andrews: 105 + jeff-young: 1 + joana-cruz: 1 + joe-stech: 6 + johanna-skinnider: 2 + jonathan-davies: 2 + jose-emilio-munoz-lopez: 1 + julie-gaskin: 5 + julien-jayat: 1 + julio-suarez: 6 + jun-he: 1 + kasper-mecklenburg: 1 + kieran-hejmadi: 12 + koki-mitsunami: 2 + konstantinos-margaritis: 8 + kristof-beyls: 1 + leandro-nunes: 1 + liliya-wu: 1 + mark-thurman: 1 + masoud-koleini: 1 + mathias-brossard: 1 + michael-hall: 5 + na-li: 1 + nader-zouaoui: 2 + nikhil-gupta: 1 + nina-drozd: 1 + nobel-chowdary-mandepudi: 6 + odin-shen: 9 + owen-wu: 2 + pareena-verma: 46 + paul-howard: 3 + peter-harris: 1 + pranay-bakre: 5 + preema-merlin-dsouza: 1 + przemyslaw-wirkus: 2 + qixiang-xu: 1 + rani-chowdary-mandepudi: 1 + rin-dobrescu: 1 + roberto-lopez-mendez: 2 + ronan-synnott: 45 + shuheng-deng: 1 + thirdai: 1 + tianyu-li: 2 + tom-pilar: 1 + uma-ramalingam: 1 + varun-chari: 2 + visualsilicon: 1 + willen-yang: 1 + william-liang: 1 + ying-yu: 2 + yiyang-fan: 1 + zach-lasiuk: 2 + zhengjun-xing: 2 + issues: + avg_close_time_hrs: 0 + num_issues: 21 + percent_closed_vs_total: 0.0 From 664dec1e0feab08c344494b1159a7b9b0af48431 Mon Sep 17 00:00:00 2001 From: GitHub Actions Stats Bot <> Date: Mon, 28 Jul 2025 02:59:23 +0000 Subject: [PATCH 10/42] automatic update of stats files --- data/stats_current_test_info.yml | 2 +- data/stats_weekly_data.yml | 116 +++++++++++++++++++++++++++++++ 2 files changed, 117 insertions(+), 1 deletion(-) diff --git a/data/stats_current_test_info.yml b/data/stats_current_test_info.yml index 48c5619c87..d3a145316b 100644 --- a/data/stats_current_test_info.yml +++ b/data/stats_current_test_info.yml @@ -1,5 +1,5 @@ summary: - content_total: 387 + content_total: 391 content_with_all_tests_passing: 0 content_with_tests_enabled: 61 sw_categories: diff --git a/data/stats_weekly_data.yml b/data/stats_weekly_data.yml index ad533ae6fa..b31ae7b1c2 100644 --- a/data/stats_weekly_data.yml +++ b/data/stats_weekly_data.yml @@ -6895,3 +6895,119 @@ avg_close_time_hrs: 0 num_issues: 21 percent_closed_vs_total: 0.0 +- a_date: '2025-07-28' + content: + automotive: 3 + cross-platform: 33 + embedded-and-microcontrollers: 43 + install-guides: 105 + iot: 6 + laptops-and-desktops: 38 + mobile-graphics-and-gaming: 34 + servers-and-cloud-computing: 129 + total: 391 + contributions: + external: 97 + internal: 515 + github_engagement: + num_forks: 30 + num_prs: 14 + individual_authors: + adnan-alsinan: 2 + alaaeddine-chakroun: 2 + albin-bernhardsson: 1 + alex-su: 1 + alexandros-lamprineas: 1 + andrew-choi: 2 + andrew-kilroy: 1 + annie-tallund: 4 + arm: 3 + arnaud-de-grandmaison: 5 + aude-vuilliomenet: 1 + avin-zarlez: 1 + barbara-corriero: 1 + basma-el-gaabouri: 1 + ben-clark: 1 + bolt-liu: 2 + brenda-strech: 1 + bright-edudzi-gershon-kordorwu: 1 + chaodong-gong: 1 + chen-zhang: 1 + christophe-favergeon: 1 + christopher-seidl: 7 + cyril-rohr: 1 + daniel-gubay: 1 + daniel-nguyen: 2 + david-spickett: 2 + dawid-borycki: 33 + diego-russo: 2 + dominica-abena-o.-amanfo: 1 + elham-harirpoush: 2 + florent-lebeau: 5 + "fr\xE9d\xE9ric--lefred--descamps": 2 + gabriel-peterson: 5 + gayathri-narayana-yegna-narayanan: 2 + georgios-mermigkis: 1 + geremy-cohen: 3 + gian-marco-iodice: 1 + graham-woodward: 1 + han-yin: 1 + iago-calvo-lista: 1 + james-whitaker: 1 + jason-andrews: 105 + jeff-young: 1 + joana-cruz: 1 + joe-stech: 6 + johanna-skinnider: 2 + jonathan-davies: 2 + jose-emilio-munoz-lopez: 1 + julie-gaskin: 5 + julien-jayat: 1 + julio-suarez: 6 + jun-he: 1 + kasper-mecklenburg: 1 + kieran-hejmadi: 12 + koki-mitsunami: 2 + konstantinos-margaritis: 8 + kristof-beyls: 1 + leandro-nunes: 1 + liliya-wu: 1 + mark-thurman: 1 + masoud-koleini: 1 + mathias-brossard: 1 + michael-hall: 5 + na-li: 1 + nader-zouaoui: 2 + nikhil-gupta: 1 + nina-drozd: 1 + nobel-chowdary-mandepudi: 6 + odin-shen: 8 + owen-wu: 2 + pareena-verma: 46 + paul-howard: 3 + peter-harris: 1 + pranay-bakre: 5 + preema-merlin-dsouza: 1 + przemyslaw-wirkus: 2 + qixiang-xu: 1 + rani-chowdary-mandepudi: 1 + rin-dobrescu: 1 + roberto-lopez-mendez: 2 + ronan-synnott: 45 + shuheng-deng: 1 + thirdai: 1 + tianyu-li: 2 + tom-pilar: 1 + uma-ramalingam: 1 + varun-chari: 2 + visualsilicon: 1 + willen-yang: 1 + ying-yu: 2 + ying-yu,-martin-ma: 1 + yiyang-fan: 1 + zach-lasiuk: 2 + zhengjun-xing: 2 + issues: + avg_close_time_hrs: 0 + num_issues: 21 + percent_closed_vs_total: 0.0 From bde0065ae4d749fd78b37cfcdb798bbb1a048eaa Mon Sep 17 00:00:00 2001 From: birju Date: Mon, 28 Jul 2025 08:39:48 -0400 Subject: [PATCH 11/42] remove the stats file from the PR --- data/stats_current_test_info.yml | 198 - data/stats_weekly_data.yml | 7013 ------------------------------ 2 files changed, 7211 deletions(-) delete mode 100644 data/stats_current_test_info.yml delete mode 100644 data/stats_weekly_data.yml diff --git a/data/stats_current_test_info.yml b/data/stats_current_test_info.yml deleted file mode 100644 index d3a145316b..0000000000 --- a/data/stats_current_test_info.yml +++ /dev/null @@ -1,198 +0,0 @@ -summary: - content_total: 391 - content_with_all_tests_passing: 0 - content_with_tests_enabled: 61 -sw_categories: - automotive: {} - cross-platform: - dynamic-memory-allocator: - readable_title: Write a Dynamic Memory Allocator - tests_and_status: [] - intrinsics: - readable_title: Porting architecture specific intrinsics - tests_and_status: [] - embedded-and-microcontrollers: - tfm: - readable_title: Get started with Trusted Firmware-M - tests_and_status: [] - zephyr: - readable_title: Run the Zephyr RTOS on Arm Corstone-300 - tests_and_status: [] - install-guides: - acfl: - readable_title: Arm Compiler for Linux - tests_and_status: [] - ambaviz: - readable_title: Arm AMBA Viz - tests_and_status: [] - ams: - readable_title: Arm Performance Studio - tests_and_status: [] - anaconda: - readable_title: Anaconda - tests_and_status: [] - ansible: - readable_title: Ansible - tests_and_status: [] - aperf: - readable_title: AWS Perf (APerf) - tests_and_status: [] - arduino-pico: - readable_title: Arduino core for the Raspberry Pi Pico - tests_and_status: [] - arm-gnu: - readable_title: Arm GNU Toolchain - tests_and_status: [] - armclang: - readable_title: Arm Compiler for Embedded - tests_and_status: [] - armds: - readable_title: Arm Development Studio - tests_and_status: [] - armie: - readable_title: Arm Instruction Emulator (armie) - tests_and_status: [] - armpl: - readable_title: Arm Performance Libraries - tests_and_status: [] - aws-cli: - readable_title: AWS CLI - tests_and_status: [] - aws-copilot: - readable_title: AWS Copilot CLI - tests_and_status: [] - aws-q-cli: - readable_title: Amazon Q Developer CLI - tests_and_status: [] - azure-cli: - readable_title: Azure CLI - tests_and_status: [] - bedrust: - readable_title: Bedrust - invoke models on Amazon Bedrock - tests_and_status: [] - bolt: - readable_title: BOLT - tests_and_status: [] - cross: - readable_title: Cross-compiler - tests_and_status: [] - cyclonedds: - readable_title: Cyclone DDS - tests_and_status: [] - dotnet: - readable_title: .NET SDK - tests_and_status: [] - eksctl: - readable_title: AWS EKS CLI (eksctl) - tests_and_status: [] - forge: - readable_title: Linaro Forge - tests_and_status: [] - gcloud: - readable_title: Google Cloud Platform (GCP) CLI - tests_and_status: [] - gfortran: - readable_title: GFortran - tests_and_status: [] - go: - readable_title: Go - tests_and_status: [] - kubectl: - readable_title: Kubectl - tests_and_status: [] - multipass: - readable_title: Multipass - tests_and_status: [] - native: - readable_title: Native compiler - tests_and_status: [] - oci-cli: - readable_title: Oracle Cloud Infrastructure (OCI) CLI - tests_and_status: [] - papi: - readable_title: Performance API (PAPI) - tests_and_status: [] - perf: - readable_title: Perf for Linux on Arm (LinuxPerf) - tests_and_status: [] - pulumi: - readable_title: Pulumi - tests_and_status: [] - pytorch: - readable_title: PyTorch - tests_and_status: [] - ros2: - readable_title: ROS - Robot Operating System - tests_and_status: [] - rust: - readable_title: Rust for Linux Applications - tests_and_status: [] - rust_embedded: - readable_title: Rust for Embedded Applications - tests_and_status: [] - ssh: - readable_title: SSH - tests_and_status: [] - streamline-cli: - readable_title: Streamline CLI Tools - tests_and_status: [] - swift: - readable_title: Swift - tests_and_status: [] - sysbox: - readable_title: Sysbox - tests_and_status: [] - terraform: - readable_title: Terraform - tests_and_status: [] - topdown-tool: - readable_title: Telemetry Solution (Topdown Methodology) - tests_and_status: [] - vnc: - readable_title: VNC on Arm Linux - tests_and_status: [] - iot: {} - laptops-and-desktops: {} - mobile-graphics-and-gaming: {} - servers-and-cloud-computing: - clickhouse: - readable_title: Measure performance of ClickHouse on Arm servers - tests_and_status: [] - codec: - readable_title: Run x265 (H.265 codec) on Arm servers - tests_and_status: [] - geekbench: - readable_title: Get started with Geekbench on Arm - tests_and_status: [] - memcached: - readable_title: Run memcached on Arm servers and measure its performance - tests_and_status: [] - memcached_cache: - readable_title: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm - based servers - tests_and_status: [] - ml-perf: - readable_title: Measure Machine Learning Inference Performance on Arm servers - tests_and_status: [] - mysql_tune: - readable_title: Learn how to Tune MySQL - tests_and_status: [] - nginx: - readable_title: Learn how to deploy Nginx - tests_and_status: [] - postgresql_tune: - readable_title: Learn how to Tune PostgreSQL - tests_and_status: [] - ran: - readable_title: Get started with the Arm 5G RAN Acceleration Library (ArmRAL) - tests_and_status: [] - snappy: - readable_title: Measure performance of compression libraries on Arm servers - tests_and_status: [] - vectorscan: - readable_title: Install Vectorscan (Hyperscan on Arm) and use it with Snort - 3 - tests_and_status: [] - zlib: - readable_title: Learn how to build and use Cloudflare zlib on Arm servers - tests_and_status: [] diff --git a/data/stats_weekly_data.yml b/data/stats_weekly_data.yml deleted file mode 100644 index b31ae7b1c2..0000000000 --- a/data/stats_weekly_data.yml +++ /dev/null @@ -1,7013 +0,0 @@ -- a_date: '2023-05-15' - content: - cross-platform: 7 - embedded-systems: 11 - install-guides: 59 - laptops-and-desktops: 7 - microcontrollers: 18 - servers-and-cloud-computing: 36 - smartphones-and-mobile: 3 - total: 141 - contributions: - external: 1 - internal: 140 - github_engagement: - num_forks: 30 - num_prs: 4 - individual_authors: - brenda-strech: 1 - christopher-seidl: 2 - florent-lebeau: 5 - gabriel-peterson: 1 - jason-andrews: 66 - julio-suarez: 2 - kasper-mecklenburg: 1 - liliya-wu: 1 - michael-hall: 1 - pareena-verma: 27 - ronan-synnott: 34 - issues: - avg_close_time_hrs: 0 - num_issues: 4 - percent_closed_vs_total: 0.0 -- a_date: '2023-05-15' - content: - cross-platform: 7 - embedded-systems: 11 - install-guides: 59 - laptops-and-desktops: 8 - microcontrollers: 18 - servers-and-cloud-computing: 36 - smartphones-and-mobile: 3 - total: 142 - contributions: - external: 1 - internal: 141 - github_engagement: - num_forks: 30 - num_prs: 2 - individual_authors: - brenda-strech: 1 - christopher-seidl: 2 - florent-lebeau: 5 - gabriel-peterson: 1 - jason-andrews: 69 - julio-suarez: 2 - kasper-mecklenburg: 1 - liliya-wu: 1 - michael-hall: 1 - pareena-verma: 27 - ronan-synnott: 32 - issues: - avg_close_time_hrs: 0 - num_issues: 2 - percent_closed_vs_total: 0.0 -- a_date: '2023-06-01' - content: - cross-platform: 7 - embedded-systems: 11 - install-guides: 60 - laptops-and-desktops: 9 - microcontrollers: 19 - servers-and-cloud-computing: 36 - smartphones-and-mobile: 3 - total: 145 - contributions: - external: 1 - internal: 144 - github_engagement: - num_forks: 30 - num_prs: 3 - individual_authors: - brenda-strech: 1 - christopher-seidl: 2 - florent-lebeau: 5 - gabriel-peterson: 1 - jason-andrews: 71 - julio-suarez: 2 - kasper-mecklenburg: 1 - liliya-wu: 1 - michael-hall: 1 - pareena-verma: 28 - ronan-synnott: 32 - issues: - avg_close_time_hrs: 0 - num_issues: 3 - percent_closed_vs_total: 0.0 -- a_date: '2023-06-05' - content: - cross-platform: 7 - embedded-systems: 11 - install-guides: 60 - laptops-and-desktops: 9 - microcontrollers: 19 - servers-and-cloud-computing: 36 - smartphones-and-mobile: 3 - total: 145 - contributions: - external: 1 - internal: 144 - github_engagement: - num_forks: 30 - num_prs: 3 - individual_authors: - brenda-strech: 1 - christopher-seidl: 2 - florent-lebeau: 5 - gabriel-peterson: 1 - jason-andrews: 71 - julio-suarez: 2 - kasper-mecklenburg: 1 - liliya-wu: 1 - michael-hall: 1 - pareena-verma: 28 - ronan-synnott: 32 - issues: - avg_close_time_hrs: 0 - num_issues: 3 - percent_closed_vs_total: 0.0 -- a_date: '2023-06-12' - content: - cross-platform: 7 - embedded-systems: 11 - install-guides: 61 - laptops-and-desktops: 9 - microcontrollers: 19 - servers-and-cloud-computing: 36 - smartphones-and-mobile: 5 - total: 148 - contributions: - external: 1 - internal: 147 - github_engagement: - num_forks: 30 - num_prs: 5 - individual_authors: - brenda-strech: 1 - christopher-seidl: 2 - florent-lebeau: 5 - gabriel-peterson: 1 - jason-andrews: 73 - julio-suarez: 2 - kasper-mecklenburg: 1 - liliya-wu: 1 - michael-hall: 1 - pareena-verma: 29 - ronan-synnott: 32 - issues: - avg_close_time_hrs: 0 - num_issues: 5 - percent_closed_vs_total: 0.0 -- a_date: '2023-06-19' - content: - cross-platform: 7 - embedded-systems: 11 - install-guides: 61 - laptops-and-desktops: 9 - microcontrollers: 19 - servers-and-cloud-computing: 36 - smartphones-and-mobile: 5 - total: 148 - contributions: - external: 1 - internal: 147 - github_engagement: - num_forks: 30 - num_prs: 2 - individual_authors: - brenda-strech: 1 - christopher-seidl: 2 - florent-lebeau: 5 - gabriel-peterson: 1 - jason-andrews: 73 - julio-suarez: 2 - kasper-mecklenburg: 1 - liliya-wu: 1 - michael-hall: 1 - pareena-verma: 29 - ronan-synnott: 32 - issues: - avg_close_time_hrs: 0 - num_issues: 2 - percent_closed_vs_total: 0.0 -- a_date: '2023-06-28' - content: - cross-platform: 7 - embedded-systems: 11 - install-guides: 62 - laptops-and-desktops: 9 - microcontrollers: 19 - servers-and-cloud-computing: 36 - smartphones-and-mobile: 6 - total: 150 - contributions: - external: 1 - internal: 149 - github_engagement: - num_forks: 30 - num_prs: 3 - individual_authors: - brenda-strech: 1 - christopher-seidl: 2 - florent-lebeau: 5 - gabriel-peterson: 2 - jason-andrews: 71 - julio-suarez: 2 - kasper-mecklenburg: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 1 - pareena-verma: 29 - ronan-synnott: 34 - issues: - avg_close_time_hrs: 0 - num_issues: 3 - percent_closed_vs_total: 0.0 -- a_date: '2023-07-03' - content: - cross-platform: 7 - embedded-systems: 11 - install-guides: 63 - laptops-and-desktops: 9 - microcontrollers: 19 - servers-and-cloud-computing: 36 - smartphones-and-mobile: 6 - total: 151 - contributions: - external: 1 - internal: 149 - github_engagement: - num_forks: 30 - num_prs: 4 - individual_authors: - brenda-strech: 1 - christopher-seidl: 2 - daniel-gubay: 1 - florent-lebeau: 5 - gabriel-peterson: 2 - jason-andrews: 71 - julio-suarez: 2 - kasper-mecklenburg: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 1 - pareena-verma: 29 - ronan-synnott: 34 - issues: - avg_close_time_hrs: 0 - num_issues: 4 - percent_closed_vs_total: 0.0 -- a_date: '2023-07-10' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 63 - laptops-and-desktops: 9 - microcontrollers: 19 - servers-and-cloud-computing: 36 - smartphones-and-mobile: 6 - total: 154 - contributions: - external: 1 - internal: 152 - github_engagement: - num_forks: 30 - num_prs: 3 - individual_authors: - brenda-strech: 1 - christopher-seidl: 2 - daniel-gubay: 1 - florent-lebeau: 5 - gabriel-peterson: 3 - jason-andrews: 71 - julio-suarez: 2 - kasper-mecklenburg: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - ronan-synnott: 34 - issues: - avg_close_time_hrs: 0 - num_issues: 3 - percent_closed_vs_total: 0.0 -- a_date: '2023-07-17' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 63 - laptops-and-desktops: 9 - microcontrollers: 20 - servers-and-cloud-computing: 37 - smartphones-and-mobile: 6 - total: 156 - contributions: - external: 1 - internal: 154 - github_engagement: - num_forks: 30 - num_prs: 3 - individual_authors: - brenda-strech: 1 - christopher-seidl: 3 - daniel-gubay: 1 - florent-lebeau: 5 - gabriel-peterson: 3 - jason-andrews: 72 - julio-suarez: 2 - 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laptops-and-desktops: 9 - microcontrollers: 21 - servers-and-cloud-computing: 38 - smartphones-and-mobile: 6 - total: 159 - contributions: - external: 1 - internal: 157 - github_engagement: - num_forks: 30 - num_prs: 2 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - florent-lebeau: 5 - gabriel-peterson: 3 - jason-andrews: 72 - julio-suarez: 3 - kasper-mecklenburg: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - ronan-synnott: 35 - issues: - avg_close_time_hrs: 0 - num_issues: 2 - percent_closed_vs_total: 0.0 -- a_date: '2023-08-07' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 64 - laptops-and-desktops: 9 - microcontrollers: 21 - servers-and-cloud-computing: 38 - smartphones-and-mobile: 7 - total: 160 - contributions: - external: 1 - internal: 158 - github_engagement: - num_forks: 30 - num_prs: 4 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - florent-lebeau: 5 - gabriel-peterson: 3 - jason-andrews: 72 - julie-gaskin: 1 - julio-suarez: 3 - kasper-mecklenburg: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - ronan-synnott: 35 - issues: - avg_close_time_hrs: 0 - num_issues: 4 - percent_closed_vs_total: 0.0 -- a_date: '2023-08-14' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 67 - laptops-and-desktops: 9 - microcontrollers: 21 - servers-and-cloud-computing: 39 - smartphones-and-mobile: 7 - total: 164 - contributions: - external: 1 - internal: 162 - github_engagement: - num_forks: 30 - num_prs: 4 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - florent-lebeau: 5 - gabriel-peterson: 3 - jason-andrews: 73 - julie-gaskin: 1 - julio-suarez: 4 - kasper-mecklenburg: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - ronan-synnott: 37 - issues: - avg_close_time_hrs: 0 - num_issues: 4 - percent_closed_vs_total: 0.0 -- a_date: '2023-08-21' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 67 - laptops-and-desktops: 9 - microcontrollers: 21 - servers-and-cloud-computing: 39 - smartphones-and-mobile: 7 - total: 164 - contributions: - external: 1 - internal: 162 - github_engagement: - num_forks: 30 - num_prs: 5 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - florent-lebeau: 5 - gabriel-peterson: 3 - jason-andrews: 73 - julie-gaskin: 1 - julio-suarez: 4 - kasper-mecklenburg: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 28 - ronan-synnott: 38 - issues: - avg_close_time_hrs: 0 - num_issues: 5 - percent_closed_vs_total: 0.0 -- a_date: '2023-08-28' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 69 - laptops-and-desktops: 9 - microcontrollers: 21 - servers-and-cloud-computing: 41 - smartphones-and-mobile: 7 - total: 168 - contributions: - external: 2 - internal: 165 - github_engagement: - num_forks: 30 - 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percent_closed_vs_total: 0.0 -- a_date: '2023-09-18' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 70 - laptops-and-desktops: 9 - microcontrollers: 21 - servers-and-cloud-computing: 45 - smartphones-and-mobile: 6 - total: 172 - contributions: - external: 3 - internal: 168 - github_engagement: - num_forks: 30 - num_prs: 3 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 72 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - pranay-bakre: 1 - ronan-synnott: 39 - issues: - avg_close_time_hrs: 0 - num_issues: 3 - percent_closed_vs_total: 0.0 -- a_date: '2023-09-25' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 70 - laptops-and-desktops: 9 - microcontrollers: 21 - servers-and-cloud-computing: 45 - smartphones-and-mobile: 6 - total: 172 - contributions: - external: 3 - internal: 168 - github_engagement: - num_forks: 30 - num_prs: 6 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 72 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - pranay-bakre: 1 - ronan-synnott: 39 - issues: - avg_close_time_hrs: 0 - num_issues: 6 - percent_closed_vs_total: 0.0 -- a_date: '2023-10-02' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 70 - laptops-and-desktops: 9 - microcontrollers: 22 - servers-and-cloud-computing: 45 - smartphones-and-mobile: 8 - total: 175 - contributions: - external: 3 - internal: 170 - github_engagement: - num_forks: 30 - num_prs: 5 - individual_authors: - ben-clark,-ported-to-learning-path-by-dominique-louis-of-visualsilicon: 1 - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 73 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - pranay-bakre: 1 - ronan-synnott: 39 - uma-ramalingam: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 5 - percent_closed_vs_total: 0.0 -- a_date: '2023-10-09' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 73 - laptops-and-desktops: 9 - microcontrollers: 22 - servers-and-cloud-computing: 45 - smartphones-and-mobile: 7 - total: 177 - contributions: - external: 3 - internal: 173 - github_engagement: - num_forks: 30 - num_prs: 7 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 76 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - pranay-bakre: 1 - ronan-synnott: 39 - uma-ramalingam: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 8 - percent_closed_vs_total: 0.0 -- a_date: '2023-10-16' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 73 - laptops-and-desktops: 9 - microcontrollers: 22 - servers-and-cloud-computing: 46 - smartphones-and-mobile: 7 - total: 178 - contributions: - external: 3 - internal: 174 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 77 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - kristof-beyls: 1 - liliya-wu: 1 - 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num_issues: 7 - percent_closed_vs_total: 0.0 -- a_date: '2023-10-30' - content: - cross-platform: 7 - embedded-systems: 14 - install-guides: 73 - laptops-and-desktops: 9 - microcontrollers: 22 - servers-and-cloud-computing: 47 - smartphones-and-mobile: 7 - total: 179 - contributions: - external: 3 - internal: 174 - github_engagement: - num_forks: 30 - num_prs: 6 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - dawid-borycki: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 77 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - pranay-bakre: 1 - ronan-synnott: 39 - uma-ramalingam: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 7 - percent_closed_vs_total: 0.0 -- a_date: '2023-11-06' - content: - cross-platform: 7 - embedded-systems: 15 - install-guides: 73 - laptops-and-desktops: 10 - microcontrollers: 22 - servers-and-cloud-computing: 47 - smartphones-and-mobile: 7 - total: 181 - contributions: - external: 3 - internal: 174 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 77 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis,-vectorcamp: 1 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - pranay-bakre: 1 - ronan-synnott: 39 - uma-ramalingam: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 10 - percent_closed_vs_total: 0.0 -- a_date: '2023-11-13' - content: - cross-platform: 9 - embedded-systems: 14 - install-guides: 73 - laptops-and-desktops: 9 - microcontrollers: 22 - servers-and-cloud-computing: 48 - smartphones-and-mobile: 7 - total: 182 - contributions: - external: 3 - internal: 176 - github_engagement: - num_forks: 30 - num_prs: 9 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 77 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis,-vectorcamp: 1 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 29 - pranay-bakre: 2 - ronan-synnott: 39 - uma-ramalingam: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 10 - percent_closed_vs_total: 0.0 -- a_date: '2023-11-20' - content: - cross-platform: 11 - embedded-systems: 14 - install-guides: 73 - laptops-and-desktops: 9 - microcontrollers: 22 - servers-and-cloud-computing: 50 - smartphones-and-mobile: 7 - total: 186 - contributions: - external: 5 - internal: 179 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 1 - diego-russo: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 77 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 2 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - ronan-synnott: 40 - uma-ramalingam: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 9 - percent_closed_vs_total: 0.0 -- a_date: '2023-11-27' - content: - cross-platform: 11 - embedded-systems: 14 - install-guides: 73 - laptops-and-desktops: 10 - microcontrollers: 22 - servers-and-cloud-computing: 52 - smartphones-and-mobile: 7 - total: 189 - contributions: - external: 5 - internal: 181 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 1 - diego-russo: 2 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 78 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 2 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - ronan-synnott: 40 - uma-ramalingam: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 9 - percent_closed_vs_total: 0.0 -- a_date: '2023-12-04' - content: - cross-platform: 11 - embedded-systems: 14 - install-guides: 73 - laptops-and-desktops: 10 - microcontrollers: 22 - servers-and-cloud-computing: 52 - smartphones-and-mobile: 7 - total: 189 - contributions: - external: 5 - internal: 181 - github_engagement: - num_forks: 30 - num_prs: 10 - individual_authors: - brenda-strech: 1 - christopher-seidl: 4 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 1 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 3 - jason-andrews: 77 - jonathan-davies: 1 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 2 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - ronan-synnott: 40 - uma-ramalingam: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 12 - percent_closed_vs_total: 0.0 -- a_date: '2023-12-11' - content: - cross-platform: 11 - embedded-systems: 14 - install-guides: 73 - laptops-and-desktops: 11 - microcontrollers: 24 - servers-and-cloud-computing: 58 - smartphones-and-mobile: 7 - total: 198 - contributions: - external: 5 - internal: 184 - github_engagement: - num_forks: 30 - num_prs: 10 - individual_authors: - bolt-liu: 1 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - 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david-spickett: 1 - dawid-borycki: 5 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 78 - jonathan-davies: 2 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 2 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - ronan-synnott: 40 - uma-ramalingam: 1 - ying-yu: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 16 - percent_closed_vs_total: 0.0 -- a_date: '2023-12-25' - content: - cross-platform: 11 - embedded-systems: 15 - install-guides: 74 - laptops-and-desktops: 11 - microcontrollers: 24 - servers-and-cloud-computing: 58 - smartphones-and-mobile: 7 - total: 200 - contributions: - external: 10 - internal: 188 - github_engagement: - num_forks: 30 - num_prs: 10 - individual_authors: - bolt-liu: 1 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 5 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 78 - jonathan-davies: 2 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 2 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - ronan-synnott: 40 - uma-ramalingam: 1 - ying-yu: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 12 - percent_closed_vs_total: 0.0 -- a_date: '2024-01-01' - content: - cross-platform: 11 - embedded-systems: 15 - install-guides: 74 - laptops-and-desktops: 11 - microcontrollers: 24 - servers-and-cloud-computing: 58 - smartphones-and-mobile: 7 - total: 200 - contributions: - external: 10 - internal: 188 - github_engagement: - num_forks: 30 - num_prs: 13 - individual_authors: - bolt-liu: 1 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 5 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 78 - jonathan-davies: 2 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 2 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - ronan-synnott: 40 - uma-ramalingam: 1 - ying-yu: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 15 - percent_closed_vs_total: 0.0 -- a_date: '2024-01-08' - content: - cross-platform: 11 - embedded-systems: 15 - install-guides: 74 - laptops-and-desktops: 15 - microcontrollers: 24 - servers-and-cloud-computing: 59 - smartphones-and-mobile: 8 - total: 206 - contributions: - external: 14 - internal: 189 - github_engagement: - num_forks: 30 - num_prs: 11 - individual_authors: - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 9 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 78 - jonathan-davies: 2 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 2 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 40 - uma-ramalingam: 1 - ying-yu: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 15 - percent_closed_vs_total: 0.0 -- a_date: '2024-01-15' - content: - cross-platform: 12 - embedded-systems: 15 - install-guides: 74 - laptops-and-desktops: 15 - microcontrollers: 24 - servers-and-cloud-computing: 59 - smartphones-and-mobile: 8 - total: 207 - contributions: - external: 15 - internal: 189 - github_engagement: - num_forks: 30 - num_prs: 12 - individual_authors: - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 9 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 78 - jonathan-davies: 2 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 3 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 40 - uma-ramalingam: 1 - ying-yu: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 17 - percent_closed_vs_total: 0.0 -- a_date: '2024-01-22' - content: - cross-platform: 12 - embedded-systems: 15 - install-guides: 74 - laptops-and-desktops: 15 - microcontrollers: 24 - servers-and-cloud-computing: 60 - smartphones-and-mobile: 8 - total: 208 - contributions: - external: 15 - internal: 190 - github_engagement: - num_forks: 30 - num_prs: 9 - individual_authors: - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 9 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 78 - jonathan-davies: 2 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 3 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 40 - uma-ramalingam: 1 - ying-yu: 1 - ying-yu,-arm: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 11 - percent_closed_vs_total: 0.0 -- a_date: '2024-01-29' - content: - cross-platform: 12 - embedded-systems: 15 - install-guides: 74 - laptops-and-desktops: 15 - microcontrollers: 24 - servers-and-cloud-computing: 60 - smartphones-and-mobile: 8 - total: 208 - contributions: - external: 15 - internal: 190 - github_engagement: - num_forks: 30 - num_prs: 12 - individual_authors: - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 9 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 78 - jonathan-davies: 2 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 3 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 40 - uma-ramalingam: 1 - ying-yu: 1 - ying-yu,-arm: 1 - issues: - avg_close_time_hrs: 0 - num_issues: 13 - percent_closed_vs_total: 0.0 -- a_date: '2024-02-05' - content: - cross-platform: 14 - embedded-systems: 15 - install-guides: 75 - laptops-and-desktops: 20 - microcontrollers: 24 - servers-and-cloud-computing: 63 - smartphones-and-mobile: 9 - total: 220 - contributions: - external: 21 - internal: 218 - github_engagement: - num_forks: 30 - num_prs: 5 - individual_authors: - arm: 1 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 14 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 80 - jonathan-davies: 2 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 4 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 3 - pareena-verma: 30 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 40 - stephen-theobald: 1 - uma-ramalingam: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 6 - percent_closed_vs_total: 0.0 -- a_date: '2024-02-12' - content: - cross-platform: 14 - embedded-systems: 15 - install-guides: 76 - laptops-and-desktops: 20 - microcontrollers: 25 - servers-and-cloud-computing: 64 - smartphones-and-mobile: 9 - total: 223 - contributions: - external: 21 - internal: 221 - github_engagement: - num_forks: 30 - num_prs: 11 - individual_authors: - arm: 1 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 14 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 80 - jonathan-davies: 2 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 4 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - pareena-verma: 31 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 40 - stephen-theobald: 1 - uma-ramalingam: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 12 - percent_closed_vs_total: 0.0 -- a_date: '2024-02-19' - content: - cross-platform: 13 - embedded-systems: 15 - install-guides: 76 - laptops-and-desktops: 20 - microcontrollers: 24 - servers-and-cloud-computing: 64 - smartphones-and-mobile: 9 - total: 221 - contributions: - external: 21 - internal: 220 - github_engagement: - num_forks: 30 - num_prs: 7 - individual_authors: - arm: 1 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 14 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 80 - jonathan-davies: 2 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 4 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 4 - pareena-verma: 31 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - 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konstantinos-margaritis: 5 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 4 - pareena-verma: 31 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 42 - uma-ramalingam: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 13 - percent_closed_vs_total: 0.0 -- a_date: '2024-03-11' - content: - cross-platform: 15 - embedded-systems: 15 - install-guides: 77 - laptops-and-desktops: 20 - microcontrollers: 23 - servers-and-cloud-computing: 65 - smartphones-and-mobile: 10 - total: 225 - contributions: - external: 22 - internal: 223 - github_engagement: - num_forks: 30 - num_prs: 11 - individual_authors: - arm: 1 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 14 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 4 - jason-andrews: 79 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 1 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 5 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 4 - pareena-verma: 32 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 42 - uma-ramalingam: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 13 - percent_closed_vs_total: 0.0 -- a_date: '2024-03-18' - content: - cross-platform: 15 - embedded-systems: 16 - install-guides: 77 - laptops-and-desktops: 20 - microcontrollers: 24 - servers-and-cloud-computing: 64 - smartphones-and-mobile: 12 - total: 228 - contributions: - external: 23 - internal: 225 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - arm: 1 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 1 - dawid-borycki: 15 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - jason-andrews: 78 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 2 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 5 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - pareena-verma: 32 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 42 - uma-ramalingam: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 10 - percent_closed_vs_total: 0.0 -- a_date: '2024-03-25' - content: - cross-platform: 15 - embedded-systems: 16 - install-guides: 77 - laptops-and-desktops: 22 - microcontrollers: 24 - servers-and-cloud-computing: 64 - smartphones-and-mobile: 12 - total: 230 - contributions: - external: 24 - internal: 227 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - arm: 1 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 2 - dawid-borycki: 16 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - jason-andrews: 78 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 2 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 5 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - pareena-verma: 32 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 42 - uma-ramalingam: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 11 - percent_closed_vs_total: 0.0 -- a_date: '2024-04-01' - content: - cross-platform: 15 - embedded-systems: 16 - install-guides: 77 - laptops-and-desktops: 22 - microcontrollers: 24 - servers-and-cloud-computing: 65 - smartphones-and-mobile: 15 - total: 234 - contributions: - external: 24 - internal: 279 - github_engagement: - num_forks: 30 - num_prs: 10 - individual_authors: - arm: 3 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 2 - dawid-borycki: 16 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - james-whitaker,-arm: 1 - jason-andrews: 78 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 2 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 5 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - pareena-verma: 32 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 42 - uma-ramalingam: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 14 - percent_closed_vs_total: 0.0 -- a_date: '2024-04-08' - content: - cross-platform: 15 - embedded-systems: 16 - install-guides: 78 - laptops-and-desktops: 22 - microcontrollers: 24 - servers-and-cloud-computing: 65 - smartphones-and-mobile: 15 - total: 235 - contributions: - external: 24 - internal: 280 - github_engagement: - num_forks: 30 - num_prs: 10 - individual_authors: - arm: 3 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 2 - dawid-borycki: 16 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - james-whitaker,-arm: 1 - jason-andrews: 78 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 2 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 5 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - pareena-verma: 32 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 43 - uma-ramalingam: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 14 - percent_closed_vs_total: 0.0 -- a_date: '2024-04-15' - content: - cross-platform: 15 - embedded-systems: 16 - install-guides: 78 - laptops-and-desktops: 24 - microcontrollers: 24 - servers-and-cloud-computing: 66 - smartphones-and-mobile: 15 - total: 238 - contributions: - external: 26 - internal: 284 - github_engagement: - num_forks: 30 - num_prs: 6 - individual_authors: - arm: 3 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 6 - daniel-gubay: 1 - david-spickett: 2 - dawid-borycki: 18 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - james-whitaker,-arm: 1 - jason-andrews: 80 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 2 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 5 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - 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- julie-gaskin: 2 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 5 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - pareena-verma: 32 - pareena-verma-and-zach-lasiuk: 1 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 42 - uma-ramalingam: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 12 - percent_closed_vs_total: 0.0 -- a_date: '2024-04-29' - content: - cross-platform: 15 - embedded-systems: 16 - install-guides: 80 - laptops-and-desktops: 24 - microcontrollers: 24 - servers-and-cloud-computing: 67 - smartphones-and-mobile: 15 - total: 241 - contributions: - external: 26 - internal: 286 - github_engagement: - num_forks: 30 - num_prs: 9 - individual_authors: - arm: 3 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 7 - daniel-gubay: 1 - david-spickett: 2 - dawid-borycki: 18 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - 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10 - individual_authors: - arm: 3 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 7 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 19 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - james-whitaker,-arm: 1 - jason-andrews: 80 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 2 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 5 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - pareena-verma: 32 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - ronan-synnott: 43 - tom-pilar: 1 - uma-ramalingam: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 14 - percent_closed_vs_total: 0.0 -- a_date: '2024-05-13' - content: - cross-platform: 15 - embedded-systems: 16 - install-guides: 80 - laptops-and-desktops: 26 - microcontrollers: 24 - servers-and-cloud-computing: 69 - smartphones-and-mobile: 15 - total: 245 - contributions: - external: 28 - internal: 286 - github_engagement: - num_forks: 30 - num_prs: 11 - individual_authors: - arm: 3 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 7 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 20 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - james-whitaker,-arm: 1 - jason-andrews: 80 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 2 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 5 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - pareena-verma: 32 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 1 - 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num_issues: 12 - percent_closed_vs_total: 0.0 -- a_date: '2024-06-10' - content: - cross-platform: 19 - embedded-systems: 17 - install-guides: 83 - laptops-and-desktops: 29 - microcontrollers: 24 - servers-and-cloud-computing: 73 - smartphones-and-mobile: 16 - total: 261 - contributions: - external: 31 - internal: 304 - github_engagement: - num_forks: 30 - num_prs: 9 - individual_authors: - arm: 3 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 7 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 22 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - james-whitaker,-arm: 1 - jason-andrews: 83 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 2 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 6 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - pareena-verma: 35 - 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- install-guides: 85 - laptops-and-desktops: 31 - microcontrollers: 24 - servers-and-cloud-computing: 81 - smartphones-and-mobile: 23 - total: 285 - contributions: - external: 39 - internal: 331 - github_engagement: - num_forks: 30 - num_prs: 7 - individual_authors: - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - basma-el-gaabouri: 1 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 28 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 84 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 35 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 10 - percent_closed_vs_total: 0.0 -- a_date: '2024-09-23' - content: - cross-platform: 22 - embedded-systems: 19 - install-guides: 85 - laptops-and-desktops: 31 - microcontrollers: 24 - servers-and-cloud-computing: 82 - smartphones-and-mobile: 23 - total: 286 - contributions: - external: 40 - internal: 331 - github_engagement: - num_forks: 30 - num_prs: 6 - individual_authors: - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - 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roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 10 - percent_closed_vs_total: 0.0 -- a_date: '2024-09-30' - content: - cross-platform: 25 - embedded-systems: 19 - install-guides: 85 - laptops-and-desktops: 31 - microcontrollers: 24 - servers-and-cloud-computing: 82 - smartphones-and-mobile: 23 - total: 289 - contributions: - external: 42 - internal: 335 - github_engagement: - num_forks: 30 - num_prs: 4 - individual_authors: - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - basma-el-gaabouri: 1 - bolt-liu: 2 - brenda-strech: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 31 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 84 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 35 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - pranay-bakre: 2 - przemyslaw-wirkus: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 6 - percent_closed_vs_total: 0.0 -- a_date: '2024-11-04' - content: - cross-platform: 25 - embedded-systems: 19 - install-guides: 89 - laptops-and-desktops: 31 - microcontrollers: 24 - servers-and-cloud-computing: 87 - smartphones-and-mobile: 24 - total: 299 - contributions: - external: 43 - internal: 348 - github_engagement: - num_forks: 30 - num_prs: 12 - individual_authors: - alaaeddine-chakroun: 1 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - basma-el-gaabouri: 1 - bolt-liu: 2 - brenda-strech: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 30 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 88 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - koki-mitsunami: 1 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 35 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - pranay-bakre: 4 - przemyslaw-wirkus: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 16 - percent_closed_vs_total: 0.0 -- a_date: '2024-11-11' - content: - cross-platform: 25 - embedded-systems: 19 - install-guides: 89 - laptops-and-desktops: 32 - microcontrollers: 24 - servers-and-cloud-computing: 88 - smartphones-and-mobile: 24 - total: 301 - contributions: - external: 43 - internal: 353 - github_engagement: - num_forks: 30 - num_prs: 13 - individual_authors: - alaaeddine-chakroun: 1 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - basma-el-gaabouri: 1 - bolt-liu: 2 - brenda-strech: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 30 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 89 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - koki-mitsunami: 1 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 35 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - pranay-bakre: 4 - przemyslaw-wirkus: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 17 - percent_closed_vs_total: 0.0 -- a_date: '2024-11-18' - content: - cross-platform: 25 - embedded-systems: 19 - install-guides: 89 - laptops-and-desktops: 32 - microcontrollers: 25 - servers-and-cloud-computing: 88 - smartphones-and-mobile: 25 - total: 303 - contributions: - external: 43 - internal: 353 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - alaaeddine-chakroun: 1 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 30 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 89 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - koki-mitsunami: 1 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 35 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - pranay-bakre: 4 - przemyslaw-wirkus: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 12 - percent_closed_vs_total: 0.0 -- a_date: '2024-11-25' - content: - cross-platform: 25 - embedded-systems: 19 - install-guides: 89 - laptops-and-desktops: 32 - microcontrollers: 25 - servers-and-cloud-computing: 88 - smartphones-and-mobile: 25 - total: 303 - contributions: - external: 43 - internal: 353 - github_engagement: - num_forks: 30 - num_prs: 9 - individual_authors: - alaaeddine-chakroun: 1 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 30 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 89 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - koki-mitsunami: 1 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 35 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - pranay-bakre: 4 - przemyslaw-wirkus: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 13 - percent_closed_vs_total: 0.0 -- a_date: '2024-12-02' - content: - cross-platform: 25 - embedded-systems: 19 - install-guides: 90 - laptops-and-desktops: 33 - microcontrollers: 25 - servers-and-cloud-computing: 88 - smartphones-and-mobile: 25 - total: 305 - contributions: - external: 43 - internal: 355 - github_engagement: - num_forks: 30 - num_prs: 14 - individual_authors: - alaaeddine-chakroun: 1 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 30 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 90 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - koki-mitsunami: 2 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 35 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - pranay-bakre: 4 - przemyslaw-wirkus: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 18 - percent_closed_vs_total: 0.0 -- a_date: '2024-12-09' - content: - cross-platform: 26 - embedded-systems: 19 - install-guides: 90 - laptops-and-desktops: 33 - microcontrollers: 24 - servers-and-cloud-computing: 89 - smartphones-and-mobile: 25 - total: 306 - contributions: - external: 44 - internal: 354 - github_engagement: - num_forks: 30 - num_prs: 7 - individual_authors: - alaaeddine-chakroun: 2 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - basma-el-gaabouri: 1 - bolt-liu: 2 - brenda-strech: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 30 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis-and-konstantinos-margaritis,-vectorcamp: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 90 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 1 - koki-mitsunami: 1 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 35 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - pranay-bakre: 4 - przemyslaw-wirkus: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-albin-bernhardsson: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 10 - percent_closed_vs_total: 0.0 -- a_date: '2024-12-16' - content: - cross-platform: 26 - embedded-systems: 19 - install-guides: 90 - laptops-and-desktops: 33 - microcontrollers: 25 - servers-and-cloud-computing: 89 - smartphones-and-mobile: 26 - total: 308 - contributions: - external: 44 - internal: 358 - github_engagement: - num_forks: 30 - num_prs: 11 - individual_authors: - alaaeddine-chakroun: 2 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 30 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis-and-konstantinos-margaritis,-vectorcamp: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 90 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 1 - koki-mitsunami: 1 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 35 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - pranay-bakre: 4 - przemyslaw-wirkus: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-albin-bernhardsson: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 14 - percent_closed_vs_total: 0.0 -- a_date: '2024-12-23' - content: - cross-platform: 26 - embedded-systems: 19 - install-guides: 90 - laptops-and-desktops: 34 - microcontrollers: 25 - servers-and-cloud-computing: 93 - smartphones-and-mobile: 25 - total: 312 - contributions: - external: 45 - internal: 362 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - alaaeddine-chakroun: 2 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - arnaud-de-grandmaison,-paul-howard,-and-pareena-verma: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 31 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis-and-konstantinos-margaritis,-vectorcamp: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 91 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 1 - koki-mitsunami: 2 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 34 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - paul-howard: 1 - pranay-bakre: 4 - przemyslaw-wirkus: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-albin-bernhardsson: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 12 - percent_closed_vs_total: 0.0 -- a_date: '2024-12-30' - content: - cross-platform: 26 - embedded-systems: 19 - install-guides: 90 - laptops-and-desktops: 34 - microcontrollers: 25 - servers-and-cloud-computing: 94 - smartphones-and-mobile: 25 - total: 313 - contributions: - external: 45 - internal: 362 - github_engagement: - num_forks: 30 - num_prs: 9 - individual_authors: - alaaeddine-chakroun: 2 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - arnaud-de-grandmaison,-paul-howard,-and-pareena-verma: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 31 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis-and-konstantinos-margaritis,-vectorcamp: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 91 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 1 - koki-mitsunami: 2 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 34 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - paul-howard: 1 - pranay-bakre: 4 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-albin-bernhardsson: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 13 - percent_closed_vs_total: 0.0 -- a_date: '2025-01-06' - content: - cross-platform: 27 - embedded-systems: 19 - install-guides: 93 - laptops-and-desktops: 34 - microcontrollers: 25 - servers-and-cloud-computing: 94 - smartphones-and-mobile: 25 - total: 317 - contributions: - external: 45 - internal: 366 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - alaaeddine-chakroun: 2 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - arnaud-de-grandmaison,-paul-howard,-and-pareena-verma: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 31 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis-and-konstantinos-margaritis,-vectorcamp: 1 - graham-woodward: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 94 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 1 - koki-mitsunami: 2 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 34 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - paul-howard: 1 - pranay-bakre: 4 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 2 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-albin-bernhardsson: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 12 - percent_closed_vs_total: 0.0 -- a_date: '2025-01-13' - content: - automotive: 2 - cross-platform: 28 - embedded-and-microcontrollers: 39 - install-guides: 93 - iot: 5 - laptops-and-desktops: 33 - mobile-graphics-and-gaming: 26 - servers-and-cloud-computing: 97 - total: 323 - contributions: - external: 45 - internal: 373 - github_engagement: - num_forks: 30 - num_prs: 6 - individual_authors: - alaaeddine-chakroun: 2 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - arnaud-de-grandmaison,-paul-howard,-and-pareena-verma: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 31 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis-and-konstantinos-margaritis,-vectorcamp: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 96 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 1 - koki-mitsunami: 2 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 34 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 2 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - paul-howard: 1 - pranay-bakre: 4 - pranay-bakre,-masoud-koleini,-nobel-chowdary-mandepudi,-na-li: 1 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 2 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-albin-bernhardsson: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - willen-yang: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 10 - percent_closed_vs_total: 0.0 -- a_date: '2025-01-20' - content: - automotive: 2 - cross-platform: 28 - embedded-and-microcontrollers: 39 - install-guides: 93 - iot: 5 - laptops-and-desktops: 33 - mobile-graphics-and-gaming: 26 - servers-and-cloud-computing: 97 - total: 323 - contributions: - external: 45 - internal: 373 - github_engagement: - num_forks: 30 - num_prs: 13 - individual_authors: - alaaeddine-chakroun: 2 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - arnaud-de-grandmaison,-paul-howard,-and-pareena-verma: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 31 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis-and-konstantinos-margaritis,-vectorcamp: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 96 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 1 - koki-mitsunami: 2 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - nobel-chowdary-mandepudi: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 34 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - paul-howard: 1 - pranay-bakre: 4 - pranay-bakre,-masoud-koleini,-nobel-chowdary-mandepudi,-na-li: 1 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 2 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-albin-bernhardsson: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - willen-yang: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 16 - percent_closed_vs_total: 0.0 -- a_date: '2025-01-27' - content: - automotive: 1 - cross-platform: 28 - embedded-and-microcontrollers: 40 - install-guides: 93 - iot: 5 - laptops-and-desktops: 33 - mobile-graphics-and-gaming: 26 - servers-and-cloud-computing: 97 - total: 323 - contributions: - external: 45 - internal: 373 - github_engagement: - num_forks: 30 - num_prs: 12 - individual_authors: - alaaeddine-chakroun: 2 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - arnaud-de-grandmaison,-paul-howard,-and-pareena-verma: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 31 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis-and-konstantinos-margaritis,-vectorcamp: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 95 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 1 - koki-mitsunami: 2 - konstantinos-margaritis: 7 - kristof-beyls: 1 - liliya-wu: 1 - mathias-brossard: 1 - michael-hall: 5 - nikhil-gupta,-pareena-verma,-nobel-chowdary-mandepudi,-ravi-malhotra: 1 - nobel-chowdary-mandepudi: 1 - odin-shen: 1 - owen-wu,-arm: 2 - pareena-verma: 34 - pareena-verma,-annie-tallund: 1 - pareena-verma,-jason-andrews,-and-zach-lasiuk: 1 - pareena-verma,-joe-stech,-adnan-alsinan: 1 - paul-howard: 1 - pranay-bakre: 4 - pranay-bakre,-masoud-koleini,-nobel-chowdary-mandepudi,-na-li: 1 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 2 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 46 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari,-albin-bernhardsson: 1 - varun-chari,-pareena-verma: 1 - visualsilicon: 1 - willen-yang: 1 - ying-yu: 1 - ying-yu,-arm: 1 - zach-lasiuk: 1 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 15 - percent_closed_vs_total: 0.0 -- a_date: '2025-02-03' - content: - automotive: 1 - cross-platform: 28 - embedded-and-microcontrollers: 40 - install-guides: 93 - iot: 5 - laptops-and-desktops: 34 - mobile-graphics-and-gaming: 26 - servers-and-cloud-computing: 97 - total: 324 - contributions: - external: 45 - internal: 373 - github_engagement: - num_forks: 30 - num_prs: 9 - individual_authors: - alaaeddine-chakroun: 2 - alexandros-lamprineas: 1 - annie-tallund: 1 - arm: 3 - arnaud-de-grandmaison: 1 - arnaud-de-grandmaison,-paul-howard,-and-pareena-verma: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong,-alex-su,-kieran-hejmadi: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 1 - david-spickett: 2 - dawid-borycki: 31 - diego-russo: 1 - diego-russo-and-leandro-nunes: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis-and-konstantinos-margaritis,-vectorcamp: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista,-arm: 1 - james-whitaker,-arm: 1 - jason-andrews: 95 - joe-stech: 1 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez,-arm: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 1 - koki-mitsunami: 2 - 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cross-platform: 29 - embedded-and-microcontrollers: 41 - install-guides: 94 - iot: 5 - laptops-and-desktops: 34 - mobile-graphics-and-gaming: 27 - servers-and-cloud-computing: 100 - total: 331 - contributions: - external: 87 - internal: 432 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - adnan-alsinan: 1 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - annie-tallund: 2 - arm: 3 - arnaud-de-grandmaison: 3 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 2 - david-spickett: 2 - dawid-borycki: 32 - diego-russo: 2 - dominica-abena-o.-amanfo: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista: 1 - james-whitaker: 1 - 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install-guides: 94 - iot: 5 - laptops-and-desktops: 35 - mobile-graphics-and-gaming: 27 - servers-and-cloud-computing: 102 - total: 334 - contributions: - external: 89 - internal: 433 - github_engagement: - num_forks: 30 - num_prs: 14 - individual_authors: - adnan-alsinan: 1 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - annie-tallund: 2 - arm: 3 - arnaud-de-grandmaison: 3 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 2 - david-spickett: 2 - dawid-borycki: 32 - diego-russo: 2 - dominica-abena-o.-amanfo: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista: 1 - james-whitaker: 1 - jason-andrews: 97 - joe-stech: 2 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 4 - koki-mitsunami: 2 - konstantinos-margaritis: 9 - kristof-beyls: 1 - leandro-nunes: 1 - liliya-wu: 1 - masoud-koleini: 1 - mathias-brossard: 1 - michael-hall: 5 - na-li: 1 - nader-zouaoui: 2 - nikhil-gupta: 1 - nobel-chowdary-mandepudi: 4 - odin-shen: 2 - owen-wu: 2 - pareena-verma: 41 - paul-howard: 2 - pranay-bakre: 5 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 2 - ravi-malhotra: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 46 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari: 2 - visualsilicon: 1 - willen-yang: 1 - ying-yu: 2 - zach-lasiuk: 2 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 18 - percent_closed_vs_total: 0.0 -- a_date: '2025-03-10' - content: - automotive: 1 - cross-platform: 29 - embedded-and-microcontrollers: 41 - install-guides: 94 - iot: 5 - laptops-and-desktops: 35 - 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servers-and-cloud-computing: 104 - total: 336 - contributions: - external: 88 - internal: 437 - github_engagement: - num_forks: 30 - num_prs: 10 - individual_authors: - adnan-alsinan: 1 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - annie-tallund: 2 - arm: 3 - arnaud-de-grandmaison: 3 - avin-zarlez: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 2 - david-spickett: 2 - dawid-borycki: 32 - diego-russo: 2 - dominica-abena-o.-amanfo: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista: 1 - james-whitaker: 1 - jason-andrews: 97 - joe-stech: 4 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez: 1 - julie-gaskin: 4 - julio-suarez: 5 - kasper-mecklenburg: 1 - kieran-hejmadi: 3 - koki-mitsunami: 2 - konstantinos-margaritis: 9 - kristof-beyls: 1 - leandro-nunes: 1 - liliya-wu: 1 - masoud-koleini: 1 - mathias-brossard: 1 - michael-hall: 5 - na-li: 1 - nader-zouaoui: 2 - nikhil-gupta: 1 - nobel-chowdary-mandepudi: 4 - odin-shen: 3 - owen-wu: 2 - pareena-verma: 41 - paul-howard: 2 - pranay-bakre: 5 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 2 - ravi-malhotra: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 46 - thirdai: 1 - tianyu-li: 1 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari: 2 - visualsilicon: 1 - willen-yang: 1 - ying-yu: 2 - zach-lasiuk: 2 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 14 - percent_closed_vs_total: 0.0 -- a_date: '2025-03-24' - content: - automotive: 1 - cross-platform: 29 - embedded-and-microcontrollers: 42 - install-guides: 94 - iot: 5 - laptops-and-desktops: 35 - mobile-graphics-and-gaming: 29 - servers-and-cloud-computing: 106 - total: 341 - 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- contributions: - external: 91 - internal: 460 - github_engagement: - num_forks: 30 - num_prs: 12 - individual_authors: - adnan-alsinan: 1 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - andrew-choi: 1 - annie-tallund: 3 - arm: 3 - arnaud-de-grandmaison: 3 - avin-zarlez: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 2 - david-spickett: 2 - dawid-borycki: 32 - diego-russo: 2 - dominica-abena-o.-amanfo: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis: 1 - geremy-cohen: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista: 1 - james-whitaker: 1 - jason-andrews: 99 - joe-stech: 4 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez: 1 - julie-gaskin: 5 - julio-suarez: 6 - 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mobile-graphics-and-gaming: 30 - servers-and-cloud-computing: 110 - total: 350 - contributions: - external: 92 - internal: 459 - github_engagement: - num_forks: 30 - num_prs: 11 - individual_authors: - adnan-alsinan: 1 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - andrew-choi: 1 - annie-tallund: 3 - arm: 3 - arnaud-de-grandmaison: 3 - avin-zarlez: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong: 1 - chen-zhang: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 2 - david-spickett: 2 - dawid-borycki: 33 - diego-russo: 2 - dominica-abena-o.-amanfo: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 1 - georgios-mermigkis: 1 - geremy-cohen: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista: 1 - james-whitaker: 1 - jason-andrews: 99 - joe-stech: 4 - johanna-skinnider: 2 - 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- percent_closed_vs_total: 0.0 -- a_date: '2025-05-26' - content: - automotive: 2 - cross-platform: 32 - embedded-and-microcontrollers: 41 - install-guides: 101 - iot: 6 - laptops-and-desktops: 37 - mobile-graphics-and-gaming: 33 - servers-and-cloud-computing: 117 - total: 369 - contributions: - external: 94 - internal: 482 - github_engagement: - num_forks: 30 - num_prs: 6 - individual_authors: - adnan-alsinan: 1 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - andrew-choi: 1 - annie-tallund: 4 - arm: 3 - arnaud-de-grandmaison: 4 - arnaud-de-grandmaison.: 1 - avin-zarlez: 1 - barbara-corriero: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong: 1 - chen-zhang: 1 - christophe-favergeon: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 2 - david-spickett: 2 - dawid-borycki: 33 - diego-russo: 2 - dominica-abena-o.-amanfo: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - 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percent_closed_vs_total: 0.0 -- a_date: '2025-06-30' - content: - automotive: 2 - cross-platform: 33 - embedded-and-microcontrollers: 41 - install-guides: 102 - iot: 6 - laptops-and-desktops: 38 - mobile-graphics-and-gaming: 34 - servers-and-cloud-computing: 124 - total: 380 - contributions: - external: 97 - internal: 505 - github_engagement: - num_forks: 30 - num_prs: 8 - individual_authors: - adnan-alsinan: 2 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - andrew-choi: 2 - andrew-kilroy: 1 - annie-tallund: 4 - arm: 3 - arnaud-de-grandmaison: 4 - arnaud-de-grandmaison.: 1 - aude-vuilliomenet: 1 - avin-zarlez: 1 - barbara-corriero: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong: 1 - chen-zhang: 1 - christophe-favergeon: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 2 - david-spickett: 2 - dawid-borycki: 33 - diego-russo: 2 - dominica-abena-o.-amanfo: 1 - elham-harirpoush: 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- tom-pilar: 1 - uma-ramalingam: 1 - varun-chari: 2 - visualsilicon: 1 - willen-yang: 1 - ying-yu: 2 - yiyang-fan: 1 - zach-lasiuk: 2 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 17 - percent_closed_vs_total: 0.0 -- a_date: '2025-07-07' - content: - automotive: 2 - cross-platform: 32 - embedded-and-microcontrollers: 42 - install-guides: 102 - iot: 6 - laptops-and-desktops: 38 - mobile-graphics-and-gaming: 34 - servers-and-cloud-computing: 126 - total: 382 - contributions: - external: 97 - internal: 509 - github_engagement: - num_forks: 30 - num_prs: 10 - individual_authors: - adnan-alsinan: 2 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - andrew-choi: 2 - andrew-kilroy: 1 - annie-tallund: 4 - arm: 3 - arnaud-de-grandmaison: 4 - aude-vuilliomenet: 1 - avin-zarlez: 1 - barbara-corriero: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong: 1 - chen-zhang: 1 - christophe-favergeon: 1 - 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3 - peter-harris: 1 - pranay-bakre: 5 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 2 - qixiang-xu: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - shuheng-deng: 1 - thirdai: 1 - tianyu-li: 2 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari: 2 - visualsilicon: 1 - willen-yang: 1 - ying-yu: 2 - yiyang-fan: 1 - zach-lasiuk: 2 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 19 - percent_closed_vs_total: 0.0 -- a_date: '2025-07-14' - content: - automotive: 2 - cross-platform: 33 - embedded-and-microcontrollers: 42 - install-guides: 103 - iot: 6 - laptops-and-desktops: 38 - mobile-graphics-and-gaming: 34 - servers-and-cloud-computing: 128 - total: 386 - contributions: - external: 97 - internal: 511 - github_engagement: - num_forks: 30 - num_prs: 7 - individual_authors: - adnan-alsinan: 2 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - andrew-choi: 2 - andrew-kilroy: 1 - annie-tallund: 4 - arm: 3 - arnaud-de-grandmaison: 5 - aude-vuilliomenet: 1 - avin-zarlez: 1 - barbara-corriero: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - chaodong-gong: 1 - chen-zhang: 1 - christophe-favergeon: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 2 - david-spickett: 2 - dawid-borycki: 33 - diego-russo: 2 - dominica-abena-o.-amanfo: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 2 - georgios-mermigkis: 1 - geremy-cohen: 3 - gian-marco-iodice: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista: 1 - james-whitaker: 1 - jason-andrews: 103 - jeff-young: 1 - joana-cruz: 1 - joe-stech: 6 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez: 1 - julie-gaskin: 5 - julio-suarez: 6 - jun-he: 1 - kasper-mecklenburg: 1 - kieran-hejmadi: 12 - koki-mitsunami: 2 - konstantinos-margaritis: 8 - kristof-beyls: 1 - leandro-nunes: 1 - liliya-wu: 1 - mark-thurman: 1 - masoud-koleini: 1 - mathias-brossard: 1 - michael-hall: 5 - na-li: 1 - nader-zouaoui: 2 - nikhil-gupta: 1 - nina-drozd: 1 - nobel-chowdary-mandepudi: 6 - odin-shen: 7 - owen-wu: 2 - pareena-verma: 46 - paul-howard: 3 - peter-harris: 1 - pranay-bakre: 5 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 2 - qixiang-xu: 1 - rani-chowdary-mandepudi: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - shuheng-deng: 1 - thirdai: 1 - tianyu-li: 2 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari: 2 - visualsilicon: 1 - willen-yang: 1 - ying-yu: 2 - yiyang-fan: 1 - zach-lasiuk: 2 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 16 - percent_closed_vs_total: 0.0 -- a_date: '2025-07-21' - content: - automotive: 2 - cross-platform: 33 - embedded-and-microcontrollers: 43 - install-guides: 103 - iot: 6 - laptops-and-desktops: 38 - mobile-graphics-and-gaming: 34 - servers-and-cloud-computing: 128 - total: 387 - contributions: - external: 97 - internal: 511 - github_engagement: - num_forks: 30 - num_prs: 12 - individual_authors: - adnan-alsinan: 2 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - andrew-choi: 2 - andrew-kilroy: 1 - annie-tallund: 4 - arm: 3 - arnaud-de-grandmaison: 5 - aude-vuilliomenet: 1 - avin-zarlez: 1 - barbara-corriero: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - bright-edudzi-gershon-kordorwu: 1 - chaodong-gong: 1 - chen-zhang: 1 - christophe-favergeon: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 2 - david-spickett: 2 - dawid-borycki: 33 - diego-russo: 2 - dominica-abena-o.-amanfo: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 2 - georgios-mermigkis: 1 - geremy-cohen: 3 - gian-marco-iodice: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista: 1 - james-whitaker: 1 - jason-andrews: 103 - jeff-young: 1 - joana-cruz: 1 - joe-stech: 6 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez: 1 - julie-gaskin: 5 - julio-suarez: 6 - jun-he: 1 - kasper-mecklenburg: 1 - kieran-hejmadi: 12 - koki-mitsunami: 2 - konstantinos-margaritis: 8 - kristof-beyls: 1 - leandro-nunes: 1 - liliya-wu: 1 - mark-thurman: 1 - masoud-koleini: 1 - mathias-brossard: 1 - michael-hall: 5 - na-li: 1 - nader-zouaoui: 2 - nikhil-gupta: 1 - nina-drozd: 1 - nobel-chowdary-mandepudi: 6 - odin-shen: 7 - owen-wu: 2 - pareena-verma: 46 - paul-howard: 3 - peter-harris: 1 - pranay-bakre: 5 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 2 - qixiang-xu: 1 - rani-chowdary-mandepudi: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - shuheng-deng: 1 - thirdai: 1 - tianyu-li: 2 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari: 2 - visualsilicon: 1 - willen-yang: 1 - ying-yu: 2 - yiyang-fan: 1 - zach-lasiuk: 2 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 21 - percent_closed_vs_total: 0.0 -- a_date: '2025-07-28' - content: - automotive: 3 - cross-platform: 33 - embedded-and-microcontrollers: 43 - install-guides: 105 - iot: 6 - laptops-and-desktops: 38 - mobile-graphics-and-gaming: 34 - servers-and-cloud-computing: 129 - total: 391 - contributions: - external: 97 - internal: 515 - github_engagement: - num_forks: 30 - num_prs: 14 - individual_authors: - adnan-alsinan: 2 - alaaeddine-chakroun: 2 - albin-bernhardsson: 1 - alex-su: 1 - alexandros-lamprineas: 1 - andrew-choi: 2 - andrew-kilroy: 1 - annie-tallund: 4 - arm: 3 - arnaud-de-grandmaison: 5 - aude-vuilliomenet: 1 - avin-zarlez: 1 - barbara-corriero: 1 - basma-el-gaabouri: 1 - ben-clark: 1 - bolt-liu: 2 - brenda-strech: 1 - bright-edudzi-gershon-kordorwu: 1 - chaodong-gong: 1 - chen-zhang: 1 - christophe-favergeon: 1 - christopher-seidl: 7 - cyril-rohr: 1 - daniel-gubay: 1 - daniel-nguyen: 2 - david-spickett: 2 - dawid-borycki: 33 - diego-russo: 2 - dominica-abena-o.-amanfo: 1 - elham-harirpoush: 2 - florent-lebeau: 5 - "fr\xE9d\xE9ric--lefred--descamps": 2 - gabriel-peterson: 5 - gayathri-narayana-yegna-narayanan: 2 - georgios-mermigkis: 1 - geremy-cohen: 3 - gian-marco-iodice: 1 - graham-woodward: 1 - han-yin: 1 - iago-calvo-lista: 1 - james-whitaker: 1 - jason-andrews: 105 - jeff-young: 1 - joana-cruz: 1 - joe-stech: 6 - johanna-skinnider: 2 - jonathan-davies: 2 - jose-emilio-munoz-lopez: 1 - julie-gaskin: 5 - julien-jayat: 1 - julio-suarez: 6 - jun-he: 1 - kasper-mecklenburg: 1 - kieran-hejmadi: 12 - koki-mitsunami: 2 - konstantinos-margaritis: 8 - kristof-beyls: 1 - leandro-nunes: 1 - liliya-wu: 1 - mark-thurman: 1 - masoud-koleini: 1 - mathias-brossard: 1 - michael-hall: 5 - na-li: 1 - nader-zouaoui: 2 - nikhil-gupta: 1 - nina-drozd: 1 - nobel-chowdary-mandepudi: 6 - odin-shen: 8 - owen-wu: 2 - pareena-verma: 46 - paul-howard: 3 - peter-harris: 1 - pranay-bakre: 5 - preema-merlin-dsouza: 1 - przemyslaw-wirkus: 2 - qixiang-xu: 1 - rani-chowdary-mandepudi: 1 - rin-dobrescu: 1 - roberto-lopez-mendez: 2 - ronan-synnott: 45 - shuheng-deng: 1 - thirdai: 1 - tianyu-li: 2 - tom-pilar: 1 - uma-ramalingam: 1 - varun-chari: 2 - visualsilicon: 1 - willen-yang: 1 - ying-yu: 2 - ying-yu,-martin-ma: 1 - yiyang-fan: 1 - zach-lasiuk: 2 - zhengjun-xing: 2 - issues: - avg_close_time_hrs: 0 - num_issues: 21 - percent_closed_vs_total: 0.0 From c7b186f65000d410b2a0707f204395d60c644a76 Mon Sep 17 00:00:00 2001 From: birju Date: Mon, 28 Jul 2025 08:46:37 -0400 Subject: [PATCH 12/42] Reverting data files --- data/stats_current_test_info.yml | 199 + data/stats_weekly_data.yml | 7015 ++++++++++++++++++++++++++++++ 2 files changed, 7214 insertions(+) create mode 100644 data/stats_current_test_info.yml create mode 100644 data/stats_weekly_data.yml diff --git a/data/stats_current_test_info.yml b/data/stats_current_test_info.yml new file mode 100644 index 0000000000..5e020828c9 --- /dev/null +++ b/data/stats_current_test_info.yml @@ -0,0 +1,199 @@ +summary: + content_total: 391 + content_with_all_tests_passing: 0 + content_with_tests_enabled: 61 +sw_categories: + automotive: {} + cross-platform: + dynamic-memory-allocator: + readable_title: Write a Dynamic Memory Allocator + tests_and_status: [] + intrinsics: + readable_title: Porting architecture specific intrinsics + tests_and_status: [] + embedded-and-microcontrollers: + tfm: + readable_title: Get started with Trusted Firmware-M + tests_and_status: [] + zephyr: + readable_title: Run the Zephyr RTOS on Arm Corstone-300 + tests_and_status: [] + install-guides: + acfl: + readable_title: Arm Compiler for Linux + tests_and_status: [] + ambaviz: + readable_title: Arm AMBA Viz + tests_and_status: [] + ams: + readable_title: Arm Performance Studio + tests_and_status: [] + anaconda: + readable_title: Anaconda + tests_and_status: [] + ansible: + readable_title: Ansible + tests_and_status: [] + aperf: + readable_title: AWS Perf (APerf) + tests_and_status: [] + arduino-pico: + readable_title: Arduino core for the Raspberry Pi Pico + tests_and_status: [] + arm-gnu: + readable_title: Arm GNU Toolchain + tests_and_status: [] + armclang: + readable_title: Arm Compiler for Embedded + tests_and_status: [] + armds: + readable_title: Arm Development Studio + tests_and_status: [] + armie: + readable_title: Arm Instruction Emulator (armie) + tests_and_status: [] + armpl: + readable_title: Arm Performance Libraries + tests_and_status: [] + aws-cli: + readable_title: AWS CLI + tests_and_status: [] + aws-copilot: + readable_title: AWS Copilot CLI + tests_and_status: [] + aws-q-cli: + readable_title: Amazon Q Developer CLI + tests_and_status: [] + azure-cli: + readable_title: Azure CLI + tests_and_status: [] + bedrust: + readable_title: Bedrust - invoke models on Amazon Bedrock + tests_and_status: [] + bolt: + readable_title: BOLT + tests_and_status: [] + cross: + readable_title: Cross-compiler + tests_and_status: [] + cyclonedds: + readable_title: Cyclone DDS + tests_and_status: [] + dotnet: + readable_title: .NET SDK + tests_and_status: [] + eksctl: + readable_title: AWS EKS CLI (eksctl) + tests_and_status: [] + forge: + readable_title: Linaro Forge + tests_and_status: [] + gcloud: + readable_title: Google Cloud Platform (GCP) CLI + tests_and_status: [] + gfortran: + readable_title: GFortran + tests_and_status: [] + go: + readable_title: Go + tests_and_status: [] + kubectl: + readable_title: Kubectl + tests_and_status: [] + multipass: + readable_title: Multipass + tests_and_status: [] + native: + readable_title: Native compiler + tests_and_status: [] + oci-cli: + readable_title: Oracle Cloud Infrastructure (OCI) CLI + tests_and_status: [] + papi: + readable_title: Performance API (PAPI) + tests_and_status: [] + perf: + readable_title: Perf for Linux on Arm (LinuxPerf) + tests_and_status: [] + pulumi: + readable_title: Pulumi + tests_and_status: [] + pytorch: + readable_title: PyTorch + tests_and_status: [] + ros2: + readable_title: ROS - Robot Operating System + tests_and_status: [] + rust: + readable_title: Rust for Linux Applications + tests_and_status: [] + rust_embedded: + readable_title: Rust for Embedded Applications + tests_and_status: [] + ssh: + readable_title: SSH + tests_and_status: [] + streamline-cli: + readable_title: Streamline CLI Tools + tests_and_status: [] + swift: + readable_title: Swift + tests_and_status: [] + sysbox: + readable_title: Sysbox + tests_and_status: [] + terraform: + readable_title: Terraform + tests_and_status: [] + topdown-tool: + readable_title: Telemetry Solution (Topdown Methodology) + tests_and_status: [] + vnc: + readable_title: VNC on Arm Linux + tests_and_status: [] + iot: {} + laptops-and-desktops: {} + mobile-graphics-and-gaming: {} + servers-and-cloud-computing: + clickhouse: + readable_title: Measure performance of ClickHouse on Arm servers + tests_and_status: [] + codec: + readable_title: Run x265 (H.265 codec) on Arm servers + tests_and_status: [] + geekbench: + readable_title: Get started with Geekbench on Arm + tests_and_status: [] + memcached: + readable_title: Run memcached on Arm servers and measure its performance + tests_and_status: [] + memcached_cache: + readable_title: Deploy Memcached as a cache for MySQL and PostgreSQL on Arm + based servers + tests_and_status: [] + ml-perf: + readable_title: Measure Machine Learning Inference Performance on Arm servers + tests_and_status: [] + mysql_tune: + readable_title: Learn how to Tune MySQL + tests_and_status: [] + nginx: + readable_title: Learn how to deploy Nginx + tests_and_status: [] + postgresql_tune: + readable_title: Learn how to Tune PostgreSQL + tests_and_status: [] + ran: + readable_title: Get started with the Arm 5G RAN Acceleration Library (ArmRAL) + tests_and_status: [] + snappy: + readable_title: Measure performance of compression libraries on Arm servers + tests_and_status: [] + vectorscan: + readable_title: Install Vectorscan (Hyperscan on Arm) and use it with Snort + 3 + tests_and_status: [] + zlib: + readable_title: Learn how to build and use Cloudflare zlib on Arm servers + tests_and_status: [] + diff --git a/data/stats_weekly_data.yml b/data/stats_weekly_data.yml new file mode 100644 index 0000000000..b2c3d43996 --- /dev/null +++ b/data/stats_weekly_data.yml @@ -0,0 +1,7015 @@ +- a_date: '2023-05-15' + content: + cross-platform: 7 + embedded-systems: 11 + install-guides: 59 + laptops-and-desktops: 7 + microcontrollers: 18 + servers-and-cloud-computing: 36 + smartphones-and-mobile: 3 + total: 141 + contributions: + external: 1 + internal: 140 + github_engagement: + num_forks: 30 + num_prs: 4 + individual_authors: + brenda-strech: 1 + christopher-seidl: 2 + florent-lebeau: 5 + gabriel-peterson: 1 + jason-andrews: 66 + julio-suarez: 2 + kasper-mecklenburg: 1 + liliya-wu: 1 + michael-hall: 1 + pareena-verma: 27 + ronan-synnott: 34 + issues: + avg_close_time_hrs: 0 + num_issues: 4 + percent_closed_vs_total: 0.0 +- a_date: '2023-05-15' + content: + cross-platform: 7 + embedded-systems: 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aude-vuilliomenet: 1 + avin-zarlez: 1 + barbara-corriero: 1 + basma-el-gaabouri: 1 + ben-clark: 1 + bolt-liu: 2 + brenda-strech: 1 + chaodong-gong: 1 + chen-zhang: 1 + christophe-favergeon: 1 + christopher-seidl: 7 + cyril-rohr: 1 + daniel-gubay: 1 + daniel-nguyen: 2 + david-spickett: 2 + dawid-borycki: 33 + diego-russo: 2 + dominica-abena-o.-amanfo: 1 + elham-harirpoush: 2 + florent-lebeau: 5 + "fr\xE9d\xE9ric--lefred--descamps": 2 + gabriel-peterson: 5 + gayathri-narayana-yegna-narayanan: 1 + georgios-mermigkis: 1 + geremy-cohen: 2 + gian-marco-iodice: 1 + graham-woodward: 1 + han-yin: 1 + iago-calvo-lista: 1 + james-whitaker: 1 + jason-andrews: 103 + joe-stech: 6 + johanna-skinnider: 2 + jonathan-davies: 2 + jose-emilio-munoz-lopez: 1 + julie-gaskin: 5 + julio-suarez: 6 + jun-he: 1 + kasper-mecklenburg: 1 + kieran-hejmadi: 12 + koki-mitsunami: 2 + konstantinos-margaritis: 8 + kristof-beyls: 1 + leandro-nunes: 1 + liliya-wu: 1 + mark-thurman: 1 + masoud-koleini: 1 + mathias-brossard: 1 + michael-hall: 5 + na-li: 1 + nader-zouaoui: 2 + nikhil-gupta: 1 + nina-drozd: 1 + nobel-chowdary-mandepudi: 6 + odin-shen: 7 + owen-wu: 2 + pareena-verma: 46 + paul-howard: 3 + peter-harris: 1 + pranay-bakre: 5 + preema-merlin-dsouza: 1 + przemyslaw-wirkus: 2 + rin-dobrescu: 1 + roberto-lopez-mendez: 2 + ronan-synnott: 45 + shuheng-deng: 1 + thirdai: 1 + tianyu-li: 2 + tom-pilar: 1 + uma-ramalingam: 1 + varun-chari: 2 + visualsilicon: 1 + willen-yang: 1 + ying-yu: 2 + yiyang-fan: 1 + zach-lasiuk: 2 + zhengjun-xing: 2 + issues: + avg_close_time_hrs: 0 + num_issues: 17 + percent_closed_vs_total: 0.0 +- a_date: '2025-07-07' + content: + automotive: 2 + cross-platform: 32 + embedded-and-microcontrollers: 42 + install-guides: 102 + iot: 6 + laptops-and-desktops: 38 + mobile-graphics-and-gaming: 34 + servers-and-cloud-computing: 126 + total: 382 + contributions: + external: 97 + internal: 509 + github_engagement: + num_forks: 30 + num_prs: 10 + individual_authors: + adnan-alsinan: 2 + alaaeddine-chakroun: 2 + albin-bernhardsson: 1 + alex-su: 1 + alexandros-lamprineas: 1 + andrew-choi: 2 + andrew-kilroy: 1 + annie-tallund: 4 + arm: 3 + arnaud-de-grandmaison: 4 + aude-vuilliomenet: 1 + avin-zarlez: 1 + barbara-corriero: 1 + basma-el-gaabouri: 1 + ben-clark: 1 + bolt-liu: 2 + brenda-strech: 1 + chaodong-gong: 1 + chen-zhang: 1 + christophe-favergeon: 1 + christopher-seidl: 7 + cyril-rohr: 1 + daniel-gubay: 1 + daniel-nguyen: 2 + david-spickett: 2 + dawid-borycki: 33 + diego-russo: 2 + dominica-abena-o.-amanfo: 1 + elham-harirpoush: 2 + florent-lebeau: 5 + "fr\xE9d\xE9ric--lefred--descamps": 2 + gabriel-peterson: 5 + gayathri-narayana-yegna-narayanan: 1 + georgios-mermigkis: 1 + geremy-cohen: 3 + gian-marco-iodice: 1 + graham-woodward: 1 + han-yin: 1 + iago-calvo-lista: 1 + james-whitaker: 1 + jason-andrews: 103 + joana-cruz: 1 + joe-stech: 6 + johanna-skinnider: 2 + jonathan-davies: 2 + jose-emilio-munoz-lopez: 1 + julie-gaskin: 5 + julio-suarez: 6 + jun-he: 1 + kasper-mecklenburg: 1 + kieran-hejmadi: 12 + koki-mitsunami: 2 + konstantinos-margaritis: 8 + kristof-beyls: 1 + leandro-nunes: 1 + liliya-wu: 1 + mark-thurman: 1 + masoud-koleini: 1 + mathias-brossard: 1 + michael-hall: 5 + na-li: 1 + nader-zouaoui: 2 + nikhil-gupta: 1 + nina-drozd: 1 + nobel-chowdary-mandepudi: 6 + odin-shen: 7 + owen-wu: 2 + pareena-verma: 46 + paul-howard: 3 + peter-harris: 1 + pranay-bakre: 5 + preema-merlin-dsouza: 1 + przemyslaw-wirkus: 2 + qixiang-xu: 1 + rin-dobrescu: 1 + roberto-lopez-mendez: 2 + ronan-synnott: 45 + shuheng-deng: 1 + thirdai: 1 + tianyu-li: 2 + tom-pilar: 1 + uma-ramalingam: 1 + varun-chari: 2 + visualsilicon: 1 + willen-yang: 1 + ying-yu: 2 + yiyang-fan: 1 + zach-lasiuk: 2 + zhengjun-xing: 2 + issues: + avg_close_time_hrs: 0 + num_issues: 19 + percent_closed_vs_total: 0.0 +- a_date: '2025-07-14' + content: + automotive: 2 + cross-platform: 33 + embedded-and-microcontrollers: 42 + install-guides: 103 + iot: 6 + laptops-and-desktops: 38 + mobile-graphics-and-gaming: 34 + servers-and-cloud-computing: 128 + total: 386 + contributions: + external: 97 + internal: 511 + github_engagement: + num_forks: 30 + num_prs: 7 + individual_authors: + adnan-alsinan: 2 + alaaeddine-chakroun: 2 + albin-bernhardsson: 1 + alex-su: 1 + alexandros-lamprineas: 1 + andrew-choi: 2 + andrew-kilroy: 1 + annie-tallund: 4 + arm: 3 + arnaud-de-grandmaison: 5 + aude-vuilliomenet: 1 + avin-zarlez: 1 + barbara-corriero: 1 + basma-el-gaabouri: 1 + ben-clark: 1 + bolt-liu: 2 + brenda-strech: 1 + chaodong-gong: 1 + chen-zhang: 1 + christophe-favergeon: 1 + christopher-seidl: 7 + cyril-rohr: 1 + daniel-gubay: 1 + daniel-nguyen: 2 + david-spickett: 2 + dawid-borycki: 33 + diego-russo: 2 + dominica-abena-o.-amanfo: 1 + elham-harirpoush: 2 + florent-lebeau: 5 + "fr\xE9d\xE9ric--lefred--descamps": 2 + gabriel-peterson: 5 + gayathri-narayana-yegna-narayanan: 2 + georgios-mermigkis: 1 + geremy-cohen: 3 + gian-marco-iodice: 1 + graham-woodward: 1 + han-yin: 1 + iago-calvo-lista: 1 + james-whitaker: 1 + jason-andrews: 103 + jeff-young: 1 + joana-cruz: 1 + joe-stech: 6 + johanna-skinnider: 2 + jonathan-davies: 2 + jose-emilio-munoz-lopez: 1 + julie-gaskin: 5 + julio-suarez: 6 + jun-he: 1 + kasper-mecklenburg: 1 + kieran-hejmadi: 12 + koki-mitsunami: 2 + konstantinos-margaritis: 8 + kristof-beyls: 1 + leandro-nunes: 1 + liliya-wu: 1 + mark-thurman: 1 + masoud-koleini: 1 + mathias-brossard: 1 + michael-hall: 5 + na-li: 1 + nader-zouaoui: 2 + nikhil-gupta: 1 + nina-drozd: 1 + nobel-chowdary-mandepudi: 6 + odin-shen: 7 + owen-wu: 2 + pareena-verma: 46 + paul-howard: 3 + peter-harris: 1 + pranay-bakre: 5 + preema-merlin-dsouza: 1 + przemyslaw-wirkus: 2 + qixiang-xu: 1 + rani-chowdary-mandepudi: 1 + rin-dobrescu: 1 + roberto-lopez-mendez: 2 + ronan-synnott: 45 + shuheng-deng: 1 + thirdai: 1 + tianyu-li: 2 + tom-pilar: 1 + uma-ramalingam: 1 + varun-chari: 2 + visualsilicon: 1 + willen-yang: 1 + ying-yu: 2 + yiyang-fan: 1 + zach-lasiuk: 2 + zhengjun-xing: 2 + issues: + avg_close_time_hrs: 0 + num_issues: 16 + percent_closed_vs_total: 0.0 +- a_date: '2025-07-21' + content: + automotive: 2 + cross-platform: 33 + embedded-and-microcontrollers: 43 + install-guides: 103 + iot: 6 + laptops-and-desktops: 38 + mobile-graphics-and-gaming: 34 + servers-and-cloud-computing: 128 + total: 387 + contributions: + external: 97 + internal: 511 + github_engagement: + num_forks: 30 + num_prs: 12 + individual_authors: + adnan-alsinan: 2 + alaaeddine-chakroun: 2 + albin-bernhardsson: 1 + alex-su: 1 + alexandros-lamprineas: 1 + andrew-choi: 2 + andrew-kilroy: 1 + annie-tallund: 4 + arm: 3 + arnaud-de-grandmaison: 5 + aude-vuilliomenet: 1 + avin-zarlez: 1 + barbara-corriero: 1 + basma-el-gaabouri: 1 + ben-clark: 1 + bolt-liu: 2 + brenda-strech: 1 + bright-edudzi-gershon-kordorwu: 1 + chaodong-gong: 1 + chen-zhang: 1 + christophe-favergeon: 1 + christopher-seidl: 7 + cyril-rohr: 1 + daniel-gubay: 1 + daniel-nguyen: 2 + david-spickett: 2 + dawid-borycki: 33 + diego-russo: 2 + dominica-abena-o.-amanfo: 1 + elham-harirpoush: 2 + florent-lebeau: 5 + "fr\xE9d\xE9ric--lefred--descamps": 2 + gabriel-peterson: 5 + gayathri-narayana-yegna-narayanan: 2 + georgios-mermigkis: 1 + geremy-cohen: 3 + gian-marco-iodice: 1 + graham-woodward: 1 + han-yin: 1 + iago-calvo-lista: 1 + james-whitaker: 1 + jason-andrews: 103 + jeff-young: 1 + joana-cruz: 1 + joe-stech: 6 + johanna-skinnider: 2 + jonathan-davies: 2 + jose-emilio-munoz-lopez: 1 + julie-gaskin: 5 + julio-suarez: 6 + jun-he: 1 + kasper-mecklenburg: 1 + kieran-hejmadi: 12 + koki-mitsunami: 2 + konstantinos-margaritis: 8 + kristof-beyls: 1 + leandro-nunes: 1 + liliya-wu: 1 + mark-thurman: 1 + masoud-koleini: 1 + mathias-brossard: 1 + michael-hall: 5 + na-li: 1 + nader-zouaoui: 2 + nikhil-gupta: 1 + nina-drozd: 1 + nobel-chowdary-mandepudi: 6 + odin-shen: 7 + owen-wu: 2 + pareena-verma: 46 + paul-howard: 3 + peter-harris: 1 + pranay-bakre: 5 + preema-merlin-dsouza: 1 + przemyslaw-wirkus: 2 + qixiang-xu: 1 + rani-chowdary-mandepudi: 1 + rin-dobrescu: 1 + roberto-lopez-mendez: 2 + ronan-synnott: 45 + shuheng-deng: 1 + thirdai: 1 + tianyu-li: 2 + tom-pilar: 1 + uma-ramalingam: 1 + varun-chari: 2 + visualsilicon: 1 + willen-yang: 1 + ying-yu: 2 + yiyang-fan: 1 + zach-lasiuk: 2 + zhengjun-xing: 2 + issues: + avg_close_time_hrs: 0 + num_issues: 21 + percent_closed_vs_total: 0.0 +- a_date: '2025-07-28' + content: + automotive: 3 + cross-platform: 34 + embedded-and-microcontrollers: 43 + install-guides: 105 + iot: 6 + laptops-and-desktops: 38 + mobile-graphics-and-gaming: 34 + servers-and-cloud-computing: 128 + total: 391 + contributions: + external: 98 + internal: 516 + github_engagement: + num_forks: 30 + num_prs: 14 + individual_authors: + adnan-alsinan: 2 + alaaeddine-chakroun: 2 + albin-bernhardsson: 1 + alex-su: 1 + alexandros-lamprineas: 1 + andrew-choi: 2 + andrew-kilroy: 1 + annie-tallund: 4 + arm: 3 + arnaud-de-grandmaison: 5 + aude-vuilliomenet: 1 + avin-zarlez: 1 + barbara-corriero: 1 + basma-el-gaabouri: 1 + ben-clark: 1 + bolt-liu: 2 + brenda-strech: 1 + bright-edudzi-gershon-kordorwu: 1 + chaodong-gong: 1 + chen-zhang: 1 + chenying-kuo: 1 + christophe-favergeon: 1 + christopher-seidl: 7 + cyril-rohr: 1 + daniel-gubay: 1 + daniel-nguyen: 2 + david-spickett: 2 + dawid-borycki: 33 + diego-russo: 2 + dominica-abena-o.-amanfo: 1 + elham-harirpoush: 2 + florent-lebeau: 5 + "fr\xE9d\xE9ric--lefred--descamps": 2 + gabriel-peterson: 5 + gayathri-narayana-yegna-narayanan: 2 + georgios-mermigkis: 1 + geremy-cohen: 3 + gian-marco-iodice: 1 + graham-woodward: 1 + han-yin: 1 + iago-calvo-lista: 1 + james-whitaker: 1 + jason-andrews: 105 + jeff-young: 1 + joana-cruz: 1 + joe-stech: 6 + johanna-skinnider: 2 + jonathan-davies: 2 + jose-emilio-munoz-lopez: 1 + julie-gaskin: 5 + julien-jayat: 1 + julio-suarez: 6 + jun-he: 1 + kasper-mecklenburg: 1 + kieran-hejmadi: 12 + koki-mitsunami: 2 + konstantinos-margaritis: 8 + kristof-beyls: 1 + leandro-nunes: 1 + liliya-wu: 1 + mark-thurman: 1 + masoud-koleini: 1 + mathias-brossard: 1 + michael-hall: 5 + na-li: 1 + nader-zouaoui: 2 + nikhil-gupta: 1 + nina-drozd: 1 + nobel-chowdary-mandepudi: 6 + odin-shen: 9 + owen-wu: 2 + pareena-verma: 46 + paul-howard: 3 + peter-harris: 1 + pranay-bakre: 5 + preema-merlin-dsouza: 1 + przemyslaw-wirkus: 2 + qixiang-xu: 1 + rani-chowdary-mandepudi: 1 + rin-dobrescu: 1 + roberto-lopez-mendez: 2 + ronan-synnott: 45 + shuheng-deng: 1 + thirdai: 1 + tianyu-li: 2 + tom-pilar: 1 + uma-ramalingam: 1 + varun-chari: 2 + visualsilicon: 1 + willen-yang: 1 + william-liang: 1 + ying-yu: 2 + yiyang-fan: 1 + zach-lasiuk: 2 + zhengjun-xing: 2 + issues: + avg_close_time_hrs: 0 + num_issues: 21 + percent_closed_vs_total: 0.0 + From 88abe561b5825bdc9e9f0e313504c3114b95ac89 Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Mon, 28 Jul 2025 08:48:28 -0400 Subject: [PATCH 13/42] Update _index.md --- .../java-perf-flamegraph/_index.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md index 1ed9fbcc58..80d48d3357 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md @@ -1,6 +1,10 @@ --- title: Java Performance Analysis - FlameGraph +draft: true +cascade: + draft: true + minutes_to_complete: 30 who_is_this_for: This is an introductory guide for individuals aiming to perform performance analysis of Java applications on the ARM Neoverse platform using flame graphs. From 5139efae16897c4126ee950f87392be8f9ee5051 Mon Sep 17 00:00:00 2001 From: birju Date: Mon, 28 Jul 2025 10:31:25 -0400 Subject: [PATCH 14/42] First tech review of java flamegraph LP, wrk2 architecture issue with LuaJIT needs to be resolved --- .../java-perf-flamegraph/1_setup.md | 46 +++++++++++++------ .../java-perf-flamegraph/_index.md | 13 +++--- 2 files changed, 38 insertions(+), 21 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md index ba0ffd9868..264ce3d16e 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md @@ -9,59 +9,76 @@ layout: learningpathall ## Before You Begin - There are numerous performance analysis methods and tools for Java applications, among which the call stack flame graph method is regarded as a conventional entry-level approach. Therefore, generating flame graphs is considered a basic operation. -- Various methods and tools are available for generating Java flame graphs, including async-profiler, Java Agent, jstack, JFR (Java Flight Recorder), etc. -- This LP (Learning Path) focuses on introducing two simple and easy-to-use methods: async-profiler and Java Agent. +- Various methods and tools are available for generating Java flame graphs, including `async-profiler`, `Java Agent`, `jstack`, `JFR` (Java Flight Recorder), etc. +- This Learning Path focuses on introducing two simple and easy-to-use methods: `async-profiler` and `Java Agent`. ## Setup Benchmark Server - Tomcat - [Apache Tomcat](https://tomcat.apache.org/) is an open-source Java Servlet container that enables running Java web applications, handling HTTP requests and serving dynamic content. - As a core component in Java web development, Apache Tomcat supports Servlet, JSP, and WebSocket technologies, providing a lightweight runtime environment for web apps. -1. So you should install Java Development Kit (JDK) first. +1. Start by installing Java Development Kit (JDK) on your Arm-based server: ```bash sudo apt update sudo apt install -y openjdk-21-jdk ``` -2. Second, you can install Tomcat by either [building it from source](https://github.com/apache/tomcat) or downloading the pre-built package simply from [the official website](https://tomcat.apache.org/whichversion.html) +2. Next, you can install Tomcat by either [building it from source](https://github.com/apache/tomcat) or downloading the pre-built package simply from [the official website](https://tomcat.apache.org/whichversion.html) ```bash wget -c https://dlcdn.apache.org/tomcat/tomcat-11/v11.0.9/bin/apache-tomcat-11.0.9.tar.gz tar xzf apache-tomcat-11.0.9.tar.gz ``` -3. If you intend to access the built-in Examples of Tomcat via an intranet IP or even an external IP, you need to modify a configuration file. +3. If you intend to access the built-in examples of Tomcat via an intranet IP or even an external IP, you need to modify a configuration file as shown: ```bash vim apache-tomcat-11.0.9/webapps/examples/META-INF/context.xml +``` +Then change the values: +```console # change # to # - -# now you can start Tomcat Server +``` +Now you can start Tomcat Server: +```bash ./apache-tomcat-11.0.9/bin/startup.sh ``` -4. If you can access the page at "http://${tomcat_ip}:8080/examples" via a browser, congratulations-you can proceed to the next benchmarking step. +The output from starting the server should look like: + +```output +Using CATALINA_BASE: /home/ubuntu/apache-tomcat-11.0.9 +Using CATALINA_HOME: /home/ubuntu/apache-tomcat-11.0.9 +Using CATALINA_TMPDIR: /home/ubuntu/apache-tomcat-11.0.9/temp +Using JRE_HOME: /usr +Using CLASSPATH: /home/ubuntu/apache-tomcat-11.0.9/bin/bootstrap.jar:/home/ubuntu/apache-tomcat-11.0.9/bin/tomcat-juli.jar +Using CATALINA_OPTS: +Tomcat started. +``` + +4. If you can access the page at "http://${tomcat_ip}:8080/examples" via a browser, you can proceed to the next benchmarking step. ![example image alt-text#center](./_images/lp-tomcat-homepage.png "Tomcat-HomePage") ![example image alt-text#center](./_images/lp-tomcat-examples.png "Tomcat-Examples") ## Setup Benchmark Client - [wrk2](https://github.com/giltene/wrk2) -- wrk2 is a high-performance HTTP benchmarking tool specialized in generating constant throughput loads and measuring latency percentiles for web services. -- As an enhanced version of wrk, wrk2 provides accurate latency statistics under controlled request rates, ideal for performance testing of HTTP servers. +`wrk2` is a high-performance HTTP benchmarking tool specialized in generating constant throughput loads and measuring latency percentiles for web services. `wrk2` is an enhanced version of `wrk` that provides accurate latency statistics under controlled request rates, ideal for performance testing of HTTP servers. -1. If you intend to use wrk2, you should install some essential tools before build it. +1. To use `wrk2`, you will need to install some essential tools before you can build it: ```bash sudo apt-get update sudo apt-get install -y build-essential libssl-dev git zlib1g-dev ``` -2. Now you can clone and build it from source. +2. Now you can clone and build it from source: ```bash sudo git clone https://github.com/giltene/wrk2.git cd wrk2 sudo make -# move the executable to somewhere in your PATH +``` +Move the executable to somewhere in your PATH: +```bash sudo cp wrk /usr/local/bin ``` @@ -69,7 +86,8 @@ sudo cp wrk /usr/local/bin ```bash wrk -c32 -t16 -R50000 -d60 http://${tomcat_ip}:8080/examples/servlets/servlet/HelloWorldExample ``` - Below is the output of wrk2 +Shown below is the output of wrk2: + ```console Running 1m test @ http://172.26.203.139:8080/examples/servlets/servlet/HelloWorldExample 16 threads and 32 connections diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md index 80d48d3357..2363791d5e 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md @@ -1,5 +1,5 @@ --- -title: Java Performance Analysis - FlameGraph +title: Analyze Java Performance on Arm servers using FlameGraphs draft: true cascade: @@ -7,7 +7,7 @@ cascade: minutes_to_complete: 30 -who_is_this_for: This is an introductory guide for individuals aiming to perform performance analysis of Java applications on the ARM Neoverse platform using flame graphs. +who_is_this_for: This is an introductory topic for software developers looking to analyze the performance of their Java applications on the Arm Neoverse based servers using flame graphs. learning_objectives: - How to set up tomcat benchmark environment @@ -17,13 +17,12 @@ learning_objectives: prerequisites: - Basic familiarity with Java applications - Basic familiarity with flame graphs - - Basic familiarity with Tomcat, wrk, etc author: Ying Yu, Martin Ma ### Tags skilllevels: Introductory -subjects: Java Performance Analysis +subjects: Performance and Architecture armips: - Neoverse @@ -34,13 +33,13 @@ tools_software_languages: - FlameGraph - wrk2 operatingsystems: - - Ubuntu 24 + - Linux further_reading: - resource: - title: PLACEHOLDER MANUAL - link: PLACEHOLDER MANUAL LINK + title: OpenJDK Wiki + link: https://wiki.openjdk.org/ type: documentation - resource: title: PLACEHOLDER BLOG From bb8666752100f87c8606c875a54eb12634a4f3e1 Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Mon, 28 Jul 2025 11:09:29 -0400 Subject: [PATCH 15/42] Updates to Executorch 0.7 Kleidi LP --- .../2-executorch-setup.md | 10 ++++---- .../4-prepare-llama-models.md | 3 ++- .../5-run-benchmark-on-android.md | 24 ++++++++++++------- 3 files changed, 22 insertions(+), 15 deletions(-) diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/2-executorch-setup.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/2-executorch-setup.md index a9ac71fcaa..590d49ade7 100755 --- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/2-executorch-setup.md +++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/2-executorch-setup.md @@ -15,8 +15,8 @@ The best practice is to generate an isolated Python environment in which to inst ### Option 1: Create a Python virtual environment ```bash -python3.10 -m venv executorch -source executorch/bin/activate +python3.10 -m venv executorch-venv +source executorch-venv/bin/activate ``` The prompt of your terminal has `executorch` as a prefix to indicate the virtual environment is active. @@ -28,8 +28,8 @@ Install Miniconda on your development machine by following the [Installing conda Once `conda` is installed, create the environment: ```bash -conda create -yn executorch python=3.10.0 -conda activate executorch +conda create -yn executorch-venv python=3.10.0 +conda activate executorch-venv ``` ### Clone ExecuTorch and install the required dependencies @@ -40,7 +40,7 @@ From within the conda environment, run the commands below to download the ExecuT git clone https://github.com/pytorch/executorch.git cd executorch git submodule sync -git submodule update --init +git submodule update --init --recursive ./install_executorch.sh ./examples/models/llama/install_requirements.sh ``` diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/4-prepare-llama-models.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/4-prepare-llama-models.md index 116bb3f364..a6c05c4e64 100755 --- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/4-prepare-llama-models.md +++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/4-prepare-llama-models.md @@ -46,7 +46,8 @@ python3 -m examples.models.llama.export_llama \ --metadata '{"get_bos_id":128000, "get_eos_ids":[128009, 128001, 128006, 128007]}' \ --embedding-quantize 4,32 \ --output_name="llama3_1B_kv_sdpa_xnn_qe_4_64_1024_embedding_4bit.pte" \ ---max_seq_length 1024 +--max_seq_length 1024 \ +--max_context_length 1024 ``` Due to the larger vocabulary size of Llama 3, you should quantize the embeddings with `--embedding-quantize 4,32` to further reduce the model size. diff --git a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/5-run-benchmark-on-android.md b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/5-run-benchmark-on-android.md index fe1bd9981e..4f07675eac 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/5-run-benchmark-on-android.md +++ b/content/learning-paths/mobile-graphics-and-gaming/build-llama3-chat-android-app-using-executorch-and-xnnpack/5-run-benchmark-on-android.md @@ -38,18 +38,23 @@ cmake -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \ -DEXECUTORCH_BUILD_EXTENSION_DATA_LOADER=ON \ -DEXECUTORCH_BUILD_EXTENSION_MODULE=ON \ -DEXECUTORCH_BUILD_EXTENSION_TENSOR=ON \ + -DEXECUTORCH_BUILD_EXTENSION_FLAT_TENSOR=ON \ -DEXECUTORCH_BUILD_XNNPACK=ON \ -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \ -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON \ -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON \ + -DEXECUTORCH_BUILD_KERNELS_LLM=ON \ + -DEXECUTORCH_BUILD_EXTENSION_LLM_RUNNER=ON \ + -DEXECUTORCH_BUILD_EXTENSION_RUNNER_UTIL=ON \ -DEXECUTORCH_XNNPACK_ENABLE_KLEIDI=ON \ -DXNNPACK_ENABLE_ARM_BF16=OFF \ + -DBUILD_TESTING=OFF \ -Bcmake-out-android . cmake --build cmake-out-android -j7 --target install --config Release ``` {{% notice Note %}} -Make sure you add -DEXECUTORCH_XNNPACK_ENABLE_KLEIDI=ON option to enable support for KleidiAI kernels in ExecuTorch with XNNPack. +Starting with Executorch version 0.7 beta, KleidiAI is enabled by default. The -DEXECUTORCH_XNNPACK_ENABLE_KLEIDI=ON option is enabled and adds default support for KleidiAI kernels in ExecuTorch with XNNPack. {{% /notice %}} ### 3. Build Llama runner for Android @@ -67,7 +72,8 @@ cmake -DCMAKE_TOOLCHAIN_FILE=$ANDROID_NDK/build/cmake/android.toolchain.cmake \ -DEXECUTORCH_BUILD_KERNELS_OPTIMIZED=ON \ -DEXECUTORCH_BUILD_KERNELS_QUANTIZED=ON \ -DEXECUTORCH_BUILD_KERNELS_CUSTOM=ON \ - -DEXECUTORCH_USE_TIKTOKEN=ON \ + -DSUPPORT_REGEX_LOOKAHEAD=ON \ + -DBUILD_TESTING=OFF \ -Bcmake-out-android/examples/models/llama \ examples/models/llama @@ -144,13 +150,13 @@ Reached to the end of generation I 00:00:05.399314 executorch:runner.cpp:257] RSS after finishing text generation: 1269.445312 MiB (0 if unsupported) PyTorchObserver {"prompt_tokens":54,"generated_tokens":51,"model_load_start_ms":1710296339487,"model_load_end_ms":1710296343047,"inference_start_ms":1710296343370,"inference_end_ms":1710296344877,"prompt_eval_end_ms":1710296343556,"first_token_ms":1710296343556,"aggregate_sampling_time_ms":49,"SCALING_FACTOR_UNITS_PER_SECOND":1000} -I 00:00:05.399342 executorch:stats.h:111] Prompt Tokens: 54 Generated Tokens: 51 -I 00:00:05.399344 executorch:stats.h:117] Model Load Time: 3.560000 (seconds) -I 00:00:05.399346 executorch:stats.h:127] Total inference time: 1.507000 (seconds) Rate: 33.842070 (tokens/second) -I 00:00:05.399348 executorch:stats.h:135] Prompt evaluation: 0.186000 (seconds) Rate: 290.322581 (tokens/second) -I 00:00:05.399350 executorch:stats.h:146] Generated 51 tokens: 1.321000 (seconds) Rate: 38.607116 (tokens/second) -I 00:00:05.399352 executorch:stats.h:154] Time to first generated token: 0.186000 (seconds) -I 00:00:05.399354 executorch:stats.h:161] Sampling time over 105 tokens: 0.049000 (seconds) +I 00:00:04.530945 executorch:stats.h:108] Prompt Tokens: 54 Generated Tokens: 69 +I 00:00:04.530947 executorch:stats.h:114] Model Load Time: 1.196000 (seconds) +I 00:00:04.530949 executorch:stats.h:124] Total inference time: 1.934000 (seconds) Rate: 35.677353 (tokens/second) +I 00:00:04.530952 executorch:stats.h:132] Prompt evaluation: 0.176000 (seconds) Rate: 306.818182 (tokens/second) +I 00:00:04.530954 executorch:stats.h:143] Generated 69 tokens: 1.758000 (seconds) Rate: 39.249147 (tokens/second) +I 00:00:04.530956 executorch:stats.h:151] Time to first generated token: 0.176000 (seconds) +I 00:00:04.530959 executorch:stats.h:158] Sampling time over 123 tokens: 0.067000 (seconds) ``` You have successfully run the Llama 3.1 1B Instruct model on your Android smartphone with ExecuTorch using KleidiAI kernels. From b35cb7967dbf2d15e2560b0ce41c427cc8c36ab5 Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Mon, 28 Jul 2025 11:33:05 -0400 Subject: [PATCH 16/42] Update _index.md --- .../servers-and-cloud-computing/cca-device-attach/_index.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md index 0bea76e9e0..e1ff1ae7a6 100644 --- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md @@ -1,6 +1,10 @@ --- title: CCA device attach +draft: true +cascade: + draft: true + minutes_to_complete: 90 who_is_this_for: This Learning Path is for developers who want to learn about CCA Realms, VirtIO, Bounce Buffers and Device Attach. From af1f0c255e1b5450083ac64a7bc78534d72a6d82 Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Mon, 28 Jul 2025 11:54:33 -0500 Subject: [PATCH 17/42] Tech review of Azure Linux Learning Path --- content/install-guides/azure-cli.md | 14 ++--- .../azure-vm/_index.md | 2 +- .../azure-vm/azure-vm.md | 19 +++++-- .../azure-vm/background.md | 4 +- .../azure-vm/save-image.md | 54 +++++++++++-------- .../azure-vm/start-vm.md | 12 +++++ 6 files changed, 65 insertions(+), 40 deletions(-) diff --git a/content/install-guides/azure-cli.md b/content/install-guides/azure-cli.md index d1625082c8..ce40f2b4b1 100644 --- a/content/install-guides/azure-cli.md +++ b/content/install-guides/azure-cli.md @@ -22,11 +22,7 @@ It is available for a variety of operating systems and Linux distributions and h ## How do I prepare to install the Azure CLI on Ubuntu? -[General installation information](https://learn.microsoft.com/en-us/cli/azure/install-azure-cli-linux?pivots=apt) is available which covers all supported Linux distributions. Starting with version 2.46.0, Azure CLI supports Arm64 Linux distributions. The 'apt' package manager contains both x86_64 and Arm64 packages for the following linux distributions - -```output -Ubuntu 20.04, Ubuntu 22.04, Ubuntu 24.04 -``` +[General installation information](https://learn.microsoft.com/en-us/cli/azure/install-azure-cli-linux?pivots=apt) is available which covers all supported Linux distributions. Starting with version 2.46.0, Azure CLI supports Arm64 Linux distributions. The 'apt' package manager contains both x86_64 and Arm64 packages for Ubuntu 20.04, Ubuntu 22.04, and Ubuntu 24.04. ## How do I install the Azure CLI using the installation script? @@ -60,8 +56,8 @@ You should see an output similar to: ```output { - "azure-cli": "2.61.0", - "azure-cli-core": "2.61.0", + "azure-cli": "2.75.0", + "azure-cli-core": "2.75.0", "azure-cli-telemetry": "1.1.0", "extensions": {} } @@ -98,8 +94,8 @@ You should see an output similar to: ```output { - "azure-cli": "2.61.0", - "azure-cli-core": "2.61.0", + "azure-cli": "2.75.0", + "azure-cli-core": "2.75.0", "azure-cli-telemetry": "1.1.0", "extensions": {} } diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md index 8a7c2f5afa..357a8bdcd5 100644 --- a/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/_index.md @@ -17,7 +17,7 @@ learning_objectives: prerequisites: - A [Microsoft Azure](https://azure.microsoft.com/) account with permission to create resources, including instances using Cobalt 100 processors. - - A local Linux machine with [QEMU](https://www.qemu.org/download/) and the [Azure CLI](/install-guides/azure-cli/) installed and authenticated. + - A Linux machine with [QEMU](https://www.qemu.org/download/) and the [Azure CLI](/install-guides/azure-cli/) installed and authenticated. author: Jason Andrews diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/azure-vm.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/azure-vm.md index f7c1a0e446..0159ccae7d 100644 --- a/content/learning-paths/servers-and-cloud-computing/azure-vm/azure-vm.md +++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/azure-vm.md @@ -8,7 +8,7 @@ layout: learningpathall You can view the Azure Linux 3.0 project on [GitHub](https://github.com/microsoft/azurelinux). There are links to the ISO downloads in the project README. -Using QEMU, you can create a raw disk image and boot a virtual machine with the ISO to install the OS onto the disk. +Using QEMU, you can create a raw disk image and boot a virtual machine with the ISO to install the OS on the disk. Once the installation is complete, you can convert the raw disk to a fixed-size VHD, upload it to Azure Blob Storage, and then use the Azure CLI to create a custom Arm image. @@ -20,9 +20,9 @@ Use `wget` to download the Azure Linux ISO image file. wget https://aka.ms/azurelinux-3.0-aarch64.iso ``` -Use `qemu-img` to create a 32 GB raw disk image. +Use `qemu-img` to create a 32 GB empty raw disk image to install the OS. -This step creates a 32 GB empty raw disk image to install the OS. You can increase the disk size by modifying the value passed to `qemu-img`. +You can increase the disk size by modifying the value passed to `qemu-img`. ```bash qemu-img create -f raw azurelinux-arm64.raw 34359738368 @@ -46,7 +46,14 @@ qemu-system-aarch64 \ -device virtio-net-device,netdev=net0 ``` -Once the OS boots successfully, install the Azure Linux Agent for VM provisioning, and power off the VM. +Navigate through the installer by entering the hostname, username, and password for the custom image. +You should use the username of `azureuser` if you want match the instructions on the following pages. + +Be patient, it takes some time to complete the full installation. + +At the end of installation you are prompted for confirmation to reboot the system. + +Once the newly installed OS boots successfully, install the Azure Linux Agent for VM provisioning, and power off the VM. ```bash sudo dnf install WALinuxAgent -y @@ -55,6 +62,8 @@ sudo systemctl start waagent sudo poweroff ``` +Be patient, it takes some time to install the packages and power off. + ## Convert the raw disk to VHD Format Now that the raw disk image is ready to be used, convert the image to fixed-size VHD, making it compatible with Azure. @@ -64,7 +73,7 @@ qemu-img convert -f raw -o subformat=fixed,force_size -O vpc azurelinux-arm64.ra ``` {{% notice Note %}} -VHD files have 512 bytes of footer attached at the end. The `force_size` flag ensures that the exact virtual size specified is used for the final VHD file. Without this, QEMU may round the size or adjust for footer overhead (especially when converting from raw to VHD). The `force_size` flag forces the final image to match the original size. This flag helps make the final VHD size a clean, whole number in MB or GiB, which is required for Azure. +VHD files have 512 bytes of footer attached at the end. The `force_size` flag ensures that the exact virtual size specified is used for the final VHD file. Without this, QEMU may round the size or adjust for footer overhead (especially when converting from raw to VHD). The `force_size` flag forces the final image to match the original size. This flag makes the final VHD size a whole number in MB or GB, which is required for Azure. {{% /notice %}} Next, you can save the image in your Azure account. diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/background.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/background.md index dda13eaf11..fa9b4854f7 100644 --- a/content/learning-paths/servers-and-cloud-computing/azure-vm/background.md +++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/background.md @@ -36,8 +36,8 @@ You should see an output similar to: ```output { - "azure-cli": "2.61.0", - "azure-cli-core": "2.61.0", + "azure-cli": "2.75.0", + "azure-cli-core": "2.75.0", "azure-cli-telemetry": "1.1.0", "extensions": {} } diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/save-image.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/save-image.md index a9f52f461b..ab66336077 100644 --- a/content/learning-paths/servers-and-cloud-computing/azure-vm/save-image.md +++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/save-image.md @@ -37,6 +37,14 @@ VM_SIZE="Standard_D4ps_v6" You can modify the environment variables such as RESOURCE_GROUP, VM_NAME, and LOCATION based on your naming preferences, region, and resource requirements. {{% /notice %}} +Make sure to login to Azure using the CLI. + +```bash +az login +``` + +If a link is printed, open it in a browser and enter the provided code to authenticate. + Create a new resource group. If you are using an existing resource group for the RESOURCE_GROUP environment variable you can skip this step. ```bash @@ -88,41 +96,41 @@ az sig create \ Create the image definition. ```bash -az sig image-definition create - --resource-group "$RESOURCE_GROUP" - --gallery-name "$GALLERY_NAME" - --gallery-image-definition "$IMAGE_DEF_NAME" - --publisher "$PUBLISHER" - --offer "$OFFER" - --sku "$SKU" - --os-type "$OS_TYPE" - --architecture "$ARCHITECTURE" +az sig image-definition create \ + --resource-group "$RESOURCE_GROUP" \ + --gallery-name "$GALLERY_NAME" \ + --gallery-image-definition "$IMAGE_DEF_NAME" \ + --publisher "$PUBLISHER" \ + --offer "$OFFER" \ + --sku "$SKU" \ + --os-type "$OS_TYPE" \ + --architecture "$ARCHITECTURE" \ --hyper-v-generation "$HYPERV_GEN" ``` Create the image version to register the VHD as a version of the custom image. ```bash -az sig image-version create - --resource-group "$RESOURCE_GROUP" - --gallery-name "$GALLERY_NAME" - --gallery-image-definition "$IMAGE_DEF_NAME" - --gallery-image-version "$IMAGE_VERSION" - --location "$LOCATION" - --os-vhd-uri "[https://${STORAGE_ACCOUNT}.blob.core.windows.net/${CONTAINER_NAME}/${VHD_NAME](https://${storage_account}.blob.core.windows.net/$%7BCONTAINER_NAME%7D/$%7BVHD_NAME)}" - --os-vhd-storage-account "$STORAGE_ACCOUNT" +az sig image-version create \ + --resource-group "$RESOURCE_GROUP" \ + --gallery-name "$GALLERY_NAME" \ + --gallery-image-definition "$IMAGE_DEF_NAME" \ + --gallery-image-version "$IMAGE_VERSION" \ + --location "$LOCATION" \ + --os-vhd-uri "https://${STORAGE_ACCOUNT}.blob.core.windows.net/${CONTAINER_NAME}/${VHD_NAME}" \ + --os-vhd-storage-account "$STORAGE_ACCOUNT" \ --storage-account-type "$STORAGE_ACCOUNT_TYPE" ``` Once the image has been versioned, you can retrieve the unique image ID for use in VM creation. ```bash -IMAGE_ID=$(az sig image-version show - --resource-group "$RESOURCE_GROUP" - --gallery-name "$GALLERY_NAME" - --gallery-image-definition "$IMAGE_DEF_NAME" - --gallery-image-version "$IMAGE_VERSION" +IMAGE_ID=$(az sig image-version show \ + --resource-group "$RESOURCE_GROUP" \ + --gallery-name "$GALLERY_NAME" \ + --gallery-image-definition "$IMAGE_DEF_NAME" \ + --gallery-image-version "$IMAGE_VERSION" \ --query "id" -o tsv) ``` -Next, you can create a virtual machine with the new image using the image ID. \ No newline at end of file +Next, you can create a virtual machine with the new image using the image ID. \ No newline at end of file diff --git a/content/learning-paths/servers-and-cloud-computing/azure-vm/start-vm.md b/content/learning-paths/servers-and-cloud-computing/azure-vm/start-vm.md index cb28839882..c8592c1f96 100644 --- a/content/learning-paths/servers-and-cloud-computing/azure-vm/start-vm.md +++ b/content/learning-paths/servers-and-cloud-computing/azure-vm/start-vm.md @@ -38,4 +38,16 @@ Use the public IP address to SSH to the VM. Replace `` with t ssh azureuser@ ``` +After you login, print the machine information. + +```bash +uname -a +``` + +The output is similar to: + +```output +Linux MyAzureLinuxARMVM 6.6.92.2-2.azl3 #1 SMP Wed Jul 2 02:43:35 UTC 2025 aarch64 aarch64 aarch64 GNU/Linux +``` + You are ready to use your Azure Linux virtual machine. From 327ff1026fa4a8b63c366d9c208feec528e281fd Mon Sep 17 00:00:00 2001 From: Maddy Underwood Date: Mon, 28 Jul 2025 18:17:43 +0000 Subject: [PATCH 18/42] refactored content --- .../godot_packages/add-markers.md | 9 +- .../arm_mobile_studio_integrations.md | 150 +----------------- .../godot_packages/create-counters.md | 20 ++- .../godot_packages/define-regions.md | 15 +- .../godot_packages/use-channels.md | 20 +-- .../use-custom-activity-maps.md | 25 +-- 6 files changed, 60 insertions(+), 179 deletions(-) diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md index 4882c5bc44..e8d29e17a6 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md @@ -1,9 +1,16 @@ --- -title: Add performance markers in Godot +title: Annotate Game Events for Profiling in Godot weight: 5 layout: learningpathall --- +## Using the extension + +All functionality in the extension is provided by the PerformanceStudio class, so first create an instance of it: + +```console +var performance_studio = PerformanceStudio.new() +``` ## Add single markers to highlight key game events All functionality in the extension is provided by the `PerformanceStudio` class. First, create an instance: diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md index c1d6bf5652..17aebec65d 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md @@ -1,159 +1,25 @@ --- -title: Arm Performance Studio Godot integrations +title: Profile your Godot game with Arm Performance Studio weight: 3 ### FIXED, DO NOT MODIFY layout: learningpathall --- -## Profile and analyze Godot game performance with Arm Performance Studio +## Overview -Learn how to annotate and analyze performance in your Godot game using the Arm Performance Studio extension. You’ll use markers, counters, and timelines to highlight game events, then visualize them in Streamline and Performance Advisor to identify CPU and GPU bottlenecks. +This Learning Path shows you how to annotate and analyze performance in your Godot game using the Arm Performance Studio extension. You’ll use markers, counters, and timelines to highlight game events, then visualize them in Streamline and Performance Advisor to identify CPU and GPU bottlenecks. {{% notice Note %}} This extension is compatible with **Godot 4.3 and later**. {{% /notice %}} -[Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) is a free suite of analysis tools to help you profile game performance on mobile devices with Arm CPUs and GPUs. Arm provides a Godot extension to make data from [Godot games](https://godotengine.org/) visible in the Arm Performance Studio tools, [Streamline](https://developer.arm.com/Tools%20and%20Software/Streamline%20Performance%20Analyzer) and [Performance Advisor](https://developer.arm.com/Tools%20and%20Software/Performance%20Advisor). - -This package provides a simple way to incorporate annotations into your Godot project. These annotations enable you to mark the timeline with events or custom counters which provides valuable context alongside the performance data in Streamline, so you can see what was happening in the game when bottlenecks occur. For example, here you can see markers that highlight where a wave of enemies is spawning: - -![Marker annotations in Streamline](sl_annotation.png "Figure 1. Marker annotations in Streamline") - - - -### How to install the Arm Performance Studio Integration extension - -1. In Godot, click **AssetLib** to see the available extensions. - -2. Find the **Arm Performance Studio Integration** extension, then double-click to open the extension. - -3. The extension opens in a dialog box. Click **Download**. - -![Installing the Arm Performance Studio Integration extension in Godot](godot_install_performance_studio_extension.png "Figure 2. Installing the Arm Performance Studio Integration extension in Godot") - -4. A new dialog box opens where you can change the install folder if required. Click Install. - -### Using the extension - -All functionality in the extension is provided by the PerformanceStudio class, so first create an instance of it: - -```console -var performance_studio = PerformanceStudio.new() -``` - -### Adding single markers to a Godot project - -The simplest annotations are single markers, which can have a name and a color. To use them in a Godot project where you have installed this extension, simply call into the Performance Studio library as follows: - -```console -performance_studio.marker("Game Started") -``` - -This will emit a timestamped marker with the label "Game Started". When you capture a profile in Streamline, you can see this marker along the top of the timeline at the point that the game starts. - -![Marker annotation in Streamline](sl_marker.png "Figure 4. Marker annotation in Streamline") - -To give the annotation a color, use the `marker_color` method: - -```console -performance_studio.marker_color("Game Started", Color8(0, 255, 0)) -``` - -### Defining regions in a Godot project - -To define regions of interest within the game, you can specify a pair of markers prefixed with “Region Start” and “Region End”, for example: - -```console -performance_studio.marker("Region Start Times Square") -# Do work -performance_studio.marker("Region End Times Square") -``` - -These regions are shown on the frame rate analysis chart in the Performance Advisor report. - -![Regions in Performance Advisor](pa_frame_rate_regions.png "Figure 5. Regions in Performance Advisor") - -Also, dedicated charts for each region are appended to the end of the report, so you can analyze each region independently. +## What is Arm Performance Studio? -![Dedicated region charts in Performance Advisor](pa_dedicated_region_charts.png "Figure 6. Dedicated region charts in Performance Advisor") - -### Using channels in a Godot project - -Channels are custom event timelines associated with a software thread. You can create channels and place annotations within them. A channel annotation has a text label and a color but, unlike markers, they span a range of time. - -To create a channel called "Spawner" and insert an annotation called "Spawning Wave", with the color red: - -```console -var channel : PerformanceStudio_Channel - -func _ready() -> void: - channel = performance_studio.create_channel("Spawner") - -# Annotations can then be inserted into a channel: -func _on_new_wave_started() -> void: - channel.annotate_color("Spawning Wave", Color8(255, 0, 0)) - -func _on_wave_completed() -> void: - channel.end() -``` - -To see channels in Streamline, select the **Core Map** view, and expand the **VkThread** thread: - -![Channel annotations in Streamline](sl_channel.png "Figure 7. Channel annotations in Streamline") - -### Creating counters - -Counters are numerical data points that can be plotted as a chart in the Streamline timeline view. Counters can be created as either absolute counters, where every value is an absolute value, or as a delta counter, where values are the number of instances of an event since the last value was emitted. All values are floats and will be presented to 2 decimal places. - -When charts are first defined, you can specify a title and series name. The title names the chart, the series names the data series. - -Multiple counter series can use the same title, which means that they will be plotted on the same chart in the Streamline timeline. - -To create a counter: - -```console -var counter = performance_studio.create_counter("Title", "Series", false) -``` - -Counter values are set easily as shown below: - -```console -counter.setValue(42.2) -``` - -### Custom Activity Maps - -[Custom Activity Map (CAM)](https://developer.arm.com/documentation/101816/latest/Annotate-your-code/User-space-annotations/Custom-Activity-Map-annotations) views allow execution information to be plotted on a hierarchical set of timelines. Like channel annotations, CAM views plot jobs on tracks, but unlike channel annotations, CAM views are not associated with a specific thread. Each CAM view contains one or more tracks and each track contains one or more jobs. - -![Custom activity maps in Streamline](sl_cam.png "Figure 8. Custom activity maps in Streamline") - -To create a custom activity map and add tracks to it: - -```console -var game_cam : PerformanceStudio_CAM -var wave_track : PerformanceStudio_CAMTrack -var ui_track : PerformanceStudio_CAMTrack - -func _ready() -> void: - # Create the CAM - game_cam = performance_studio.create_cam("Game Activity") - - # Add tracks to the CAM - wave_track = game_cam.create_track("Wave Activity") - ui_track = game_cam.create_track("UI Activity") -``` - -To create a job within a track: - -```console -var wave_job : PerformanceStudio_CAMJob +[Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) is a free suite of analysis tools to help you profile game performance on mobile devices with Arm CPUs and GPUs. Arm provides a Godot extension to make data from [Godot games](https://godotengine.org/) visible in the Arm Performance Studio tools, [Streamline](https://developer.arm.com/Tools%20and%20Software/Streamline%20Performance%20Analyzer) and [Performance Advisor](https://developer.arm.com/Tools%20and%20Software/Performance%20Advisor). -func _on_new_wave_started() -> void: - wave_job = wave_track.create_job("Spawning Wave", Color8(255, 0, 0)) +## Add annotations to your Godot project -func _on_wave_completed() -> void: - wave_job.stop() -``` +This package provides a simple way to incorporate annotations into your Godot project. These annotations enable you to mark the timeline with events or custom counters which provides valuable context alongside the performance data in Streamline, so you can see what was happening in the game when bottlenecks occur. For example, here you can see markers that highlight where a wave of enemies is spawning: -You can now annotate your Godot game and analyze the performance with markers that give context to a profile in Arm Performance Studio tools. +![Marker annotations in Streamline#center](sl_annotation.png "Marker annotations in Streamline") diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md index 469302177c..17f3ddac7c 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md @@ -3,25 +3,23 @@ title: Create and track custom counters in Godot weight: 8 layout: learningpathall --- +### Creating counters -## Track numerical performance data using counters +Counters are numerical data points that can be plotted as a chart in the Streamline timeline view. Counters can be created as either absolute counters, where every value is an absolute value, or as a delta counter, where values are the number of instances of an event since the last value was emitted. All values are floats and will be presented to 2 decimal places. -Counters are float values plotted as charts in the Streamline timeline. You can use **absolute counters** (e.g., frame rate) or **delta counters** (e.g., events per frame). +When charts are first defined, you can specify a title and series name. The title names the chart, the series names the data series. -### Create a counter: +Multiple counter series can use the same title, which means that they will be plotted on the same chart in the Streamline timeline. -```gdscript +To create a counter: + +```console var counter = performance_studio.create_counter("Title", "Series", false) ``` -- `Title` = name of the chart -- `Series` = label for the data series -- `false` = absolute (set `true` for delta counters) - -### Update counter values: +Counter values are set easily as shown below: -```gdscript +```console counter.setValue(42.2) ``` -Multiple series can share the same chart title to group related values together. diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md index 4548a35f70..25632a10a3 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md @@ -4,20 +4,25 @@ weight: 6 layout: learningpathall --- -## Define regions of interest for performance analysis +## Defining regions in a Godot project -To define regions of interest within the game, use marker pairs prefixed with “Region Start” and “Region End”: +To define regions of interest within the game, you can specify a pair of markers prefixed with “Region Start” and “Region End”, for example: -```gdscript +```console performance_studio.marker("Region Start Times Square") # Do work performance_studio.marker("Region End Times Square") ``` -These regions are shown in the **frame rate analysis chart** in Performance Advisor. +These regions are shown on the frame rate analysis chart in the Performance Advisor report. ![Regions in Performance Advisor](pa_frame_rate_regions.png "Figure 5. Regions in Performance Advisor") -Each region gets a dedicated chart in the final report, so you can analyze them independently. +Also, dedicated charts for each region are appended to the end of the report, so you can analyze each region independently. ![Dedicated region charts in Performance Advisor](pa_dedicated_region_charts.png "Figure 6. Dedicated region charts in Performance Advisor") + + + + + diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md index 80e5512919..d54abed597 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md @@ -3,26 +3,26 @@ title: Use channels for threaded performance annotations weight: 7 layout: learningpathall --- +### Using channels in a Godot project -## Use channels to annotate threaded events +Channels are custom event timelines associated with a software thread. You can create channels and place annotations within them. A channel annotation has a text label and a color but, unlike markers, they span a range of time. -Channels are custom timelines tied to software threads. You can annotate them with labels and colors over a span of time. +To create a channel called "Spawner" and insert an annotation called "Spawning Wave", with the color red: -### Create and use a channel: - -```gdscript +```console var channel : PerformanceStudio_Channel -func _ready(): +func _ready() -> void: channel = performance_studio.create_channel("Spawner") -func _on_new_wave_started(): +# Annotations can then be inserted into a channel: +func _on_new_wave_started() -> void: channel.annotate_color("Spawning Wave", Color8(255, 0, 0)) -func _on_wave_completed(): +func _on_wave_completed() -> void: channel.end() ``` -To view channels in Streamline, open the **Core Map** view and expand the **VkThread** thread. +To see channels in Streamline, select the **Core Map** view, and expand the **VkThread** thread: -![Channel annotations in Streamline](sl_channel.png "Figure 7. Channel annotations in Streamline") +![Channel annotations in Streamline](sl_channel.png "Figure 7. Channel annotations in Streamline") \ No newline at end of file diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md index fea008a62b..73d736be09 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md @@ -4,33 +4,38 @@ weight: 9 layout: learningpathall --- -## Visualize jobs and activity with Custom Activity Maps (CAM) +### Custom Activity Maps -Custom Activity Maps (CAMs) help track complex jobs across multiple non-threaded timelines. Great for modeling gameplay layers like UI, audio, and AI. +[Custom Activity Map (CAM)](https://developer.arm.com/documentation/101816/latest/Annotate-your-code/User-space-annotations/Custom-Activity-Map-annotations) views allow execution information to be plotted on a hierarchical set of timelines. Like channel annotations, CAM views plot jobs on tracks, but unlike channel annotations, CAM views are not associated with a specific thread. Each CAM view contains one or more tracks and each track contains one or more jobs. -### Create a CAM with multiple tracks: +![Custom activity maps in Streamline](sl_cam.png "Figure 8. Custom activity maps in Streamline") + +To create a custom activity map and add tracks to it: -```gdscript +```console var game_cam : PerformanceStudio_CAM var wave_track : PerformanceStudio_CAMTrack var ui_track : PerformanceStudio_CAMTrack -func _ready(): +func _ready() -> void: + # Create the CAM game_cam = performance_studio.create_cam("Game Activity") + + # Add tracks to the CAM wave_track = game_cam.create_track("Wave Activity") ui_track = game_cam.create_track("UI Activity") ``` -### Add jobs to tracks: +To create a job within a track: -```gdscript +```console var wave_job : PerformanceStudio_CAMJob -func _on_new_wave_started(): +func _on_new_wave_started() -> void: wave_job = wave_track.create_job("Spawning Wave", Color8(255, 0, 0)) -func _on_wave_completed(): +func _on_wave_completed() -> void: wave_job.stop() ``` -![Custom activity maps in Streamline](sl_cam.png "Figure 8. Custom activity maps in Streamline") +You can now annotate your Godot game and analyze the performance with markers that give context to a profile in Arm Performance Studio tools. \ No newline at end of file From 43886d50ba78f0bff3a24dba8751d91a4a8a7bbf Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Mon, 28 Jul 2025 14:32:10 -0400 Subject: [PATCH 19/42] Tech review of DA virtio CCA LP --- .../cca-device-attach/1-introduction.md | 18 +++--- .../cca-device-attach/2-virtio.md | 58 +++++++++---------- .../cca-device-attach/_index.md | 2 +- 3 files changed, 39 insertions(+), 39 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/1-introduction.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/1-introduction.md index d2b20b5892..364ce03d32 100644 --- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/1-introduction.md +++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/1-introduction.md @@ -8,16 +8,16 @@ layout: learningpathall Arm’s *Realm Management Extension (RME)* is a key security feature introduced in the Armv9-A architecture. It enables a new form of hardware-enforced isolation -designed to support _confidential computing_. It defines the set of hardware -features and properties that are required to comply with the Arm *Confidential -Computing Architecture (CCA)* architecture. +designed to support Confidential Computing. It defines the set of hardware +features and properties that are required to comply with the Arm's Confidential +Computing Architecture (CCA) architecture. -At the heart of RME is the concept of a *Realm*, a protected execution -environment that operates independently from the conventional *Normal World* -(used by operating systems and applications) and the *Secure World* (used by +At the heart of RME is the concept of a Realm, a protected execution +environment that operates independently from the conventional Normal World +(used by operating systems and applications) and the Secure World (used by trusted firmware or TEE). Realms are managed by a new privileged entity called -the *Realm Management Monitor (RMM)* and are enforced by the hardware via the -*Granule Protection Table (GPT)* and *Granule Transitioning* mechanism. +the Realm Management Monitor (RMM) and are enforced by the hardware via the +Granule Protection Table (GPT) and Granule Transitioning mechanism. Realms allow lower-privileged software, such as an application or a virtual machine, to protect its content and execution from attacks by higher-privileged @@ -27,4 +27,4 @@ components that manage the resources that the Realm uses. To be useful, a Realm has to interact with the rest of the world at some point. For example, a network interface is likely to be needed. This learning path will -teach you how devices are attached and used by Realms. \ No newline at end of file +show you how devices are attached and used by Realms. diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/2-virtio.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/2-virtio.md index bdbf057d16..d9c1aeef5a 100644 --- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/2-virtio.md +++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/2-virtio.md @@ -1,20 +1,20 @@ --- -title: "Device attach - part 1: virtio" +title: "Device attach: virtio" weight: 2 ### FIXED, DO NOT MODIFY layout: learningpathall --- -This section is a high level overview of VirtIO and Bounce Buffers, and how they +This section provides a high level overview of VirtIO and Bounce Buffers, and how they relate to CCA Realms. A Realm has to use physical devices at some point to interact with the external -and / or physical world. The easiest way to do this is by using VirtIO, which -provides a fast high level emulation layer. This can be seen as the level-0 of +and or physical world. The easiest way to do this is by using VirtIO, which +provides a fast high level emulation layer. This can be viewed as the first level of device attach. -As will be seen later in this learning path, more evolved device attach can be +More evolved device attach features can be performed leveraging hardware security features like PCIe-TDISP (**T**EE **D**evice **I**nterface **S**ecurity **P**rotocol) and PCIe-IDE (**I**ntegrity and **D**ata **E**ncryption), where the host OS can assign a physical device to @@ -27,8 +27,8 @@ and include it in its base and measurements. VirtIO is an abstraction layer for virtual devices in virtualized environments. It provides standardized and efficient interfaces between guest virtual machines -(VMs) and host devices, making it easier to develop _paravirtualized_ drivers. -_Paravirtualized_ means that the guest OS is aware it’s running in a virtualized +(VMs) and host devices, making it easier to develop paravirtualized drivers. +Paravirtualized means that the guest OS is aware it’s running in a virtualized environment and can use optimized drivers (VirtIO) to communicate with virtual hardware. Emulating hardware devices (like NICs or disks) for VMs is slow and inefficient. VirtIO provides a standardized and efficient interface that allows @@ -45,9 +45,11 @@ VirtIO is most commonly used with KVM/QEMU virtualization. Example drivers are: ### How VirtIO works in VMs +Here is an overview of how VirtIO works in Virtual Machines: + 1. The Host Hypervisor (e.g., QEMU/KVM) exposes VirtIO “backend” devices. 2. The guest OS loads VirtIO _frontend_ drivers (e.g., `virtio_net`, - `virtio_blk`) that speak the VirtIO protocol. + `virtio_blk`) that communicate using the VirtIO protocol. 3. Communication happens via shared memory (`virtqueues`) for I/O operations, avoiding full device emulation. 4. Devices are exposed over the PCI or MMIO bus to the guest. @@ -60,15 +62,12 @@ driver to send/receive packets via shared buffers. ### What are bounce buffers? -Bounce buffers are a generic mechanism part of the Linux kernel. They are -temporary memory buffers used in the Linux kernel to handle situations where -direct memory access (DMA) can’t be performed directly on the original data -buffer. This often happens because: +Bounce buffers are temporary memory buffers used in the Linux kernel to handle situations where direct memory access (DMA) can’t be performed directly on the original data buffer. This often happens because: 1. The original buffer is not physically contiguous. 2. The buffer is in high memory or not accessible to the device. 3. The buffer doesn’t meet alignment or boundary requirements of the device. -### Why _bounce_ buffers? +### Why bounce buffers? Data _bounces_ between: - The original buffer (in user/kernel space) and @@ -88,14 +87,14 @@ that: - Even Direct Memory Access (DMA) from peripherals or untrusted drivers cannot access Realm data. -This design ensures confidentiality but introduces a problem: *how can Realms -interact with untrusted components*, such as: +This design ensures confidentiality but introduces a problem: How can Realms +interact with untrusted components, such as: - Network stacks in the host OS, - Storage subsystems, - I/O devices managed by untrusted drivers? The solution to safely exchange data between a Realm and the outside World is to -use _bounce buffers_ as an intermediary ! +use bounce buffers as an intermediary. ### How bounce buffers are used with RME @@ -117,27 +116,27 @@ This pattern preserves confidentiality and integrity of Realm data, since: ### Confidentiality preserved with bounce buffers, really? -In the previous section, it was mentioned that _bounce buffers preserves -confidentiality_, a sentence which deserves a bit more thoughts. Bounce buffers +In the previous section, it was mentioned that bounce buffers preserve +confidentiality. Lets dive a little deeper into that. Bounce buffers are nothing more than an explicitly shared temporary area between the Realm world and the outside world. This does indeed preserve the confidentiality of all the rest of the Realm data. On the other hand, for the data being transferred, it is leaving the Realm world and will only remain confidential if it -is encrypted in some ways, e.g. for network traffic, TLS should be used. +is encrypted in some way, e.g. for network traffic, TLS should be used. ## Seeing a Realm's bounce buffers at work -Let's put this to work and check for ourselves that bounce buffers are used. This -will build on the Key Broker demo that was used in the [CCA +Let's put this to work and check for ourselves that bounce buffers are used. The +steps in this section will build on the Key Broker demo that was used in the [CCA Essentials learning path](/learning-paths/servers-and-cloud-computing/cca-essentials/example/), demonstrating an end-to-end attestation. -### Start the **K**eyb **B**roker **Server** (KBS) +### Start the Key Broker Server (KBS) First, pull the docker container image with the pre-built KBS, and then run the container: ```bash -docker pull armswdev/cca-learning-path:cca-key-broker-v2 +docker pull armswdev/cca-learning-path:cca-key-broker-v2 docker run --rm -it armswdev/cca-learning-path:cca-key-broker-v2 ``` @@ -181,7 +180,7 @@ INFO starting service: "actix-web-service-172.17.0.2:8088", workers: 16, listeni ### Get into a Realm With the Key Broker Server running in one terminal, open up a new terminal in -which you will run the **K**ey **B**roker **C**lient (KBC). The intent is to +which you will run the Key Broker Client (KBC). The intent is to observe that the data transmitted over the network (thru `virtio_net`) are indeed using bounce buffers. @@ -231,7 +230,7 @@ cd /cca ``` You should see the realm boot. Note that `lkvm` is invoked with `--network -mode=user`, which makes the guest see the network thru a VirtIO device. +mode=user`, which makes the guest see the network through a VirtIO device. After boot up, which might take some time, you will be prompted to log in at the guest Linux prompt. Use root again as the username: @@ -306,10 +305,10 @@ hwmon pwm i2c qcom_aoss ``` -As shown in the above transcript, you should get a list of the available trace +As shown above, you should see a list of the available trace points. -Now, enable the kernel tracing infrastructure together with the bounce buffer +Now, enable the kernel tracing infrastructure together with bounce buffer tracing, read the trace in the background (filtering on `keybroker-app-`) and run the Key Broker Client application in the realm, using the endpoint address that the Key Broker Server is listening on (from the other terminal): @@ -324,7 +323,7 @@ keybroker-app -v --endpoint http://172.17.0.2:8088 skywalker In the `keybroker-app`command above, `skywalker` is the key name that is requested from the KBS. -You should get an output looking like: +The output should look like: ```output INFO Requesting key named 'skywalker' from the keybroker server with URL http://172.17.0.2:8088/keys/v1/key/skywalker @@ -336,4 +335,5 @@ INFO Attestation success :-) ! The key returned from the keybroker is 'May the f ``` Note that the interleaving of the trace messages and KBC messages might differ -from one run to another. +from one run to another. With the `switlb_bounced` messages above you can successfully observe that the bounce buffers are being used in the realm. + diff --git a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md index e1ff1ae7a6..922c1278fa 100644 --- a/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/cca-device-attach/_index.md @@ -7,7 +7,7 @@ cascade: minutes_to_complete: 90 -who_is_this_for: This Learning Path is for developers who want to learn about CCA Realms, VirtIO, Bounce Buffers and Device Attach. +who_is_this_for: This is an advanced topic for developers who want to learn about Arm CCA Realms, VirtIO, Bounce Buffers and Device Attach. learning_objectives: - Have a high level understanding of VirtIO and Bounce Buffers in CCA Realms From 742f41c4123ba599b81e69f758578065727a57db Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Mon, 28 Jul 2025 14:44:21 -0400 Subject: [PATCH 20/42] Update _index.md --- .../distributed-inference-with-llama-cpp/_index.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md index b7e478b5fe..4871cd65e6 100644 --- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/_index.md @@ -1,6 +1,10 @@ --- title: Distributed inference using llama.cpp +draft: true +cascade: + draft: true + minutes_to_complete: 30 who_is_this_for: This learning path is for developers with some experience using llama.cpp who want to learn about distributed inference. From 8e35e5089b20665dbbb78555e11eac2549bb6d02 Mon Sep 17 00:00:00 2001 From: Maddy Underwood Date: Mon, 28 Jul 2025 20:29:09 +0000 Subject: [PATCH 21/42] Rename intro file. Streamlining language. --- .../arm_mobile_studio_integrations.md | 25 ------------- .../godot_packages/install-extension.md | 8 +++-- .../godot_packages/profile-godot-with-aps.md | 35 +++++++++++++++++++ 3 files changed, 41 insertions(+), 27 deletions(-) delete mode 100644 content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md create mode 100644 content/learning-paths/mobile-graphics-and-gaming/godot_packages/profile-godot-with-aps.md diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md deleted file mode 100644 index 17aebec65d..0000000000 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/arm_mobile_studio_integrations.md +++ /dev/null @@ -1,25 +0,0 @@ ---- -title: Profile your Godot game with Arm Performance Studio -weight: 3 - -### FIXED, DO NOT MODIFY -layout: learningpathall ---- - -## Overview - -This Learning Path shows you how to annotate and analyze performance in your Godot game using the Arm Performance Studio extension. You’ll use markers, counters, and timelines to highlight game events, then visualize them in Streamline and Performance Advisor to identify CPU and GPU bottlenecks. - -{{% notice Note %}} - This extension is compatible with **Godot 4.3 and later**. -{{% /notice %}} - -## What is Arm Performance Studio? - -[Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) is a free suite of analysis tools to help you profile game performance on mobile devices with Arm CPUs and GPUs. Arm provides a Godot extension to make data from [Godot games](https://godotengine.org/) visible in the Arm Performance Studio tools, [Streamline](https://developer.arm.com/Tools%20and%20Software/Streamline%20Performance%20Analyzer) and [Performance Advisor](https://developer.arm.com/Tools%20and%20Software/Performance%20Advisor). - -## Add annotations to your Godot project - -This package provides a simple way to incorporate annotations into your Godot project. These annotations enable you to mark the timeline with events or custom counters which provides valuable context alongside the performance data in Streamline, so you can see what was happening in the game when bottlenecks occur. For example, here you can see markers that highlight where a wave of enemies is spawning: - -![Marker annotations in Streamline#center](sl_annotation.png "Marker annotations in Streamline") diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md index 4ca0731152..25978ab88d 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md @@ -4,9 +4,13 @@ weight: 4 layout: learningpathall --- -## Install the Arm Performance Studio Integration extension +## Install the Arm Performance Studio extension in Godot + +To profile performance in your Godot game, first install the Arm Performance Studio extension using the Godot Asset Library. + +Start by opening your project in Godot, then select **AssetLib** from the top menu to browse available extensions. + -1. In Godot, click **AssetLib** to see the available extensions. 2. Find the **Arm Performance Studio Integration** extension, then double-click to open the extension. 3. The extension opens in a dialog box. Click **Download**. diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/profile-godot-with-aps.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/profile-godot-with-aps.md new file mode 100644 index 0000000000..ce97cd51a1 --- /dev/null +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/profile-godot-with-aps.md @@ -0,0 +1,35 @@ +--- +title: Profile your Godot game with Arm Performance Studio +weight: 3 + +### FIXED, DO NOT MODIFY +layout: learningpathall +--- + +## Overview + +In this Learning Path, you'll learn how to annotate and analyze performance in your Godot game using the Arm Performance Studio extension. You’ll add markers, counters, and timelines to capture game events and visualize them in Streamline and Performance Advisor. These tools help you identify CPU and GPU bottlenecks and optimize performance on Arm-based Android devices. + +{{% notice Note %}} + This extension is compatible with **Godot 4.3 and later**. +{{% /notice %}} + +## What is Arm Performance Studio? + +[Arm Performance Studio](https://developer.arm.com/Tools%20and%20Software/Arm%20Performance%20Studio) is a free suite of analysis tools to profile game performance on mobile devices with Arm CPUs and GPUs. It includes: + +- [Streamline](https://developer.arm.com/Tools%20and%20Software/Streamline%20Performance%20Analyzer): a performance analyzer that collects CPU and GPU metrics. + +- [Performance Advisor](https://developer.arm.com/Tools%20and%20Software/Performance%20Advisor): a report generator that offers optimization suggestions. + +Arm provides a Godot extension from [Godot games](https://godotengine.org/) that integrates with these tools, making it easier to capture performance data directly from your game. + +## Add annotations to your Godot project + +The Arm Performance Studio extension lets you add custom annotations to your Godot project. These annotations include timeline markers and counters that describe what's happening during gameplay,such as loading a level or spawning enemies. + +When you record a capture in Streamline, these annotations appear in the timeline alongside CPU and GPU metrics. This context makes it easier to correlate performance issues with in-game events. + +For example, here’s a capture showing a marker for when a wave of enemies spawns: + +![Marker annotations in Streamline#center](sl_annotation.png "Marker annotations in Streamline") From f1697eca071580e7878cbb5b1e42a70c1f75e8c6 Mon Sep 17 00:00:00 2001 From: Maddy Underwood Date: Mon, 28 Jul 2025 22:06:16 +0000 Subject: [PATCH 22/42] Revised sections --- .../godot_packages/add-markers.md | 34 ++++++++++++------- .../godot_packages/create-counters.md | 30 ++++++++++++---- .../godot_packages/define-regions.md | 10 +++--- .../godot_packages/install-extension.md | 15 +++++--- .../godot_packages/use-channels.md | 17 +++++++--- .../use-custom-activity-maps.md | 24 +++++++++---- 6 files changed, 91 insertions(+), 39 deletions(-) diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md index e8d29e17a6..176283b81b 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/add-markers.md @@ -4,32 +4,40 @@ weight: 5 layout: learningpathall --- -## Using the extension +## Use the Performance Studio extension in your project -All functionality in the extension is provided by the PerformanceStudio class, so first create an instance of it: +All annotation features are provided through the `PerformanceStudio` class. To begin, create an instance in your script: -```console +```gdscript var performance_studio = PerformanceStudio.new() -``` -## Add single markers to highlight key game events -All functionality in the extension is provided by the `PerformanceStudio` class. First, create an instance: -```gdscript -var performance_studio = PerformanceStudio.new() -``` +## Add single markers to highlight key game events -The simplest annotations are single markers, which can have a name and a color. For example: +The simplest annotations are single markers. These appear in the Streamline timeline and help you correlate game behavior with performance data. + +To emit a basic marker, use the `marker()` method with a descriptive label: ```gdscript performance_studio.marker("Game Started") ``` -This will emit a timestamped marker labeled "Game Started." When you capture a profile in Streamline, you’ll see this marker at the point the game starts. +This creates a timestamped marker labeled **Game Started**. When you capture a profile in Streamline, you’ll see this marker at the point the game starts. + +![Marker annotation in Streamline#center](sl_marker.png "Marker annotation in Streamline") -![Marker annotation in Streamline](sl_marker.png "Figure 4. Marker annotation in Streamline") -To assign a color: +## Assign a custom color + +You can assign a color to the marker using the `marker_color()` method: ```gdscript performance_studio.marker_color("Game Started", Color8(0, 255, 0)) +``` + +This example displays the Game Started marker in green. Use different colors to visually distinguish important game events. + + + + + diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md index 17f3ddac7c..86929e4a6d 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/create-counters.md @@ -3,23 +3,41 @@ title: Create and track custom counters in Godot weight: 8 layout: learningpathall --- -### Creating counters +## What are counters? -Counters are numerical data points that can be plotted as a chart in the Streamline timeline view. Counters can be created as either absolute counters, where every value is an absolute value, or as a delta counter, where values are the number of instances of an event since the last value was emitted. All values are floats and will be presented to 2 decimal places. +Counters are floating-point values plotted as line charts in Streamline. Each value appears with two decimal places of precision. -When charts are first defined, you can specify a title and series name. The title names the chart, the series names the data series. +There are two types of counters: -Multiple counter series can use the same title, which means that they will be plotted on the same chart in the Streamline timeline. +- Absolute counters: every value is treated as an independent measurement -To create a counter: +- Delta counters: each value represents the change since the last measurement (for example, the number of enemy spawns since the last update) + +## Define your counter chart + +When charts are first defined, you can specify: + +- A title: this names the chart in Streamline + +- A series name: this labels the specific data stream within the chart + +You can group multiple counter series under the same title to plot them on the same chart. + +## Create and update a counter + +Use the `create_counter()` method to define a counter in your script. For example: ```console var counter = performance_studio.create_counter("Title", "Series", false) ``` -Counter values are set easily as shown below: +The third parameter sets whether the counter is a delta counter `(true)` or absolute counter `(false)`. + +To update the counter value, use: ```console counter.setValue(42.2) ``` +This value will appear in the timeline alongside other profiling data during a Streamline capture. + diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md index 25632a10a3..73e3846105 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/define-regions.md @@ -4,9 +4,9 @@ weight: 6 layout: learningpathall --- -## Defining regions in a Godot project +## Defining regions in your Godot project -To define regions of interest within the game, you can specify a pair of markers prefixed with “Region Start” and “Region End”, for example: +To define regions of interest within the game, you can specify a pair of markers prefixed with **Region Start** and **Region End**, for example: ```console performance_studio.marker("Region Start Times Square") @@ -16,11 +16,11 @@ performance_studio.marker("Region End Times Square") These regions are shown on the frame rate analysis chart in the Performance Advisor report. -![Regions in Performance Advisor](pa_frame_rate_regions.png "Figure 5. Regions in Performance Advisor") +![Regions in Performance Advisor#center](pa_frame_rate_regions.png "Regions in Performance Advisor") -Also, dedicated charts for each region are appended to the end of the report, so you can analyze each region independently. +Performance Advisor also includes dedicated charts for each region at the end of the report, allowing you to analyze them independently. -![Dedicated region charts in Performance Advisor](pa_dedicated_region_charts.png "Figure 6. Dedicated region charts in Performance Advisor") +![Dedicated region charts in Performance Advisor#center](pa_dedicated_region_charts.png "Dedicated region charts in Performance Advisor") diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md index 25978ab88d..18938803a3 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/install-extension.md @@ -10,10 +10,17 @@ To profile performance in your Godot game, first install the Arm Performance Stu Start by opening your project in Godot, then select **AssetLib** from the top menu to browse available extensions. +Search for **Arm Performance Studio Integration**, then double-click the result to open its details. + +In the extension dialog that appears, select **Download** to begin the installation. + +![Extension download dialog in Godot#center](godot_install_performance_studio_extension.png "Download dialog for the Arm Performance Studio Integration extension in Godot") + +When prompted, you can change the install folder if needed. To complete the setup, select **Install**. + +The extension will now be added to your project and ready to use for adding markers and counters. + + -2. Find the **Arm Performance Studio Integration** extension, then double-click to open the extension. -3. The extension opens in a dialog box. Click **Download**. -![Installing the Arm Performance Studio Integration extension in Godot](godot_install_performance_studio_extension.png "Figure 2. Installing the Arm Performance Studio Integration extension in Godot") -4. A new dialog box opens where you can change the install folder if required. Click **Install**. \ No newline at end of file diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md index d54abed597..056d2e78c0 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-channels.md @@ -3,11 +3,13 @@ title: Use channels for threaded performance annotations weight: 7 layout: learningpathall --- -### Using channels in a Godot project +## Use channels for threaded annotations in Godot -Channels are custom event timelines associated with a software thread. You can create channels and place annotations within them. A channel annotation has a text label and a color but, unlike markers, they span a range of time. +Channels are custom event timelines associated with a specific software thread. Unlike single-point markers, channel annotations span a duration and include a label and optional color. You can use them to trace task execution or track long-running operations, such as asset loading or enemy spawning. -To create a channel called "Spawner" and insert an annotation called "Spawning Wave", with the color red: +## Create and annotate a channel + +To define a new channel named **Spawner** and insert an annotation labeled **Spawning Wave**, use the following approach: ```console var channel : PerformanceStudio_Channel @@ -22,7 +24,14 @@ func _on_new_wave_started() -> void: func _on_wave_completed() -> void: channel.end() ``` +In this example: + +- The `annotate_color()` method begins a red-colored annotation labeled Spawning Wave + +- The end() method marks when the annotation finishes + +## View channels in Streamline To see channels in Streamline, select the **Core Map** view, and expand the **VkThread** thread: -![Channel annotations in Streamline](sl_channel.png "Figure 7. Channel annotations in Streamline") \ No newline at end of file +![Channel annotations in Streamline#center](sl_channel.png "Channel annotations in Streamline") \ No newline at end of file diff --git a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md index 73d736be09..166724ee88 100644 --- a/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md +++ b/content/learning-paths/mobile-graphics-and-gaming/godot_packages/use-custom-activity-maps.md @@ -1,16 +1,20 @@ --- -title: Use Custom Activity Maps in Godot profiling +title: Use custom activity maps in Godot profiling weight: 9 layout: learningpathall --- -### Custom Activity Maps +## Use custom activity maps to organize game profiling -[Custom Activity Map (CAM)](https://developer.arm.com/documentation/101816/latest/Annotate-your-code/User-space-annotations/Custom-Activity-Map-annotations) views allow execution information to be plotted on a hierarchical set of timelines. Like channel annotations, CAM views plot jobs on tracks, but unlike channel annotations, CAM views are not associated with a specific thread. Each CAM view contains one or more tracks and each track contains one or more jobs. +[Custom Activity Map (CAM)](https://developer.arm.com/documentation/101816/latest/Annotate-your-code/User-space-annotations/Custom-Activity-Map-annotations) views allow execution information to be plotted on a hierarchical set of timelines. -![Custom activity maps in Streamline](sl_cam.png "Figure 8. Custom activity maps in Streamline") +Like channel annotations, CAM views plot jobs on tracks, but unlike channel annotations, CAM views are not associated with a specific thread. Each CAM view contains one or more tracks and each track contains one or more jobs. -To create a custom activity map and add tracks to it: +![Custom activity maps in Streamline#center](sl_cam.png "Custom activity maps in Streamline") + +## Create a CAM and add tracks + +To define a custom activity map and add tracks for different systems: ```console var game_cam : PerformanceStudio_CAM @@ -26,7 +30,9 @@ func _ready() -> void: ui_track = game_cam.create_track("UI Activity") ``` -To create a job within a track: +## Add jobs to tracks + +You can use jobs to represent specific time-bound tasks on a track. Here’s how to create and stop a job: ```console var wave_job : PerformanceStudio_CAMJob @@ -38,4 +44,8 @@ func _on_wave_completed() -> void: wave_job.stop() ``` -You can now annotate your Godot game and analyze the performance with markers that give context to a profile in Arm Performance Studio tools. \ No newline at end of file +## Analyze custom activity maps in Streamline + +Once you capture a profiling session, you can view your CAM data in Streamline under the Custom Activity Map section. Each job appears on its assigned track, enabling you to inspect how long each task ran and when overlapping operations occurred. + +Use CAMs to structure your profiling data around gameplay concepts, not just threads, making your performance analysis more intuitive and actionable. \ No newline at end of file From 61c735173e41ea22980941e337abdf7fc6e9b439 Mon Sep 17 00:00:00 2001 From: Arnaud de Grandmaison Date: Tue, 29 Jul 2025 08:43:31 +0200 Subject: [PATCH 23/42] Only run the weekly stats update for ArmDeveloperEcosystem/arm-learning-paths repository. This commit prevents the update from running on the (many) clones. I suspect the stats update has very little benefits on the clones and could even be considered an annoyance as it introduces stats update commits. --- .github/workflows/weekly_stats_update.yml | 1 + 1 file changed, 1 insertion(+) diff --git a/.github/workflows/weekly_stats_update.yml b/.github/workflows/weekly_stats_update.yml index 59ed5ad721..1cc1b21e54 100644 --- a/.github/workflows/weekly_stats_update.yml +++ b/.github/workflows/weekly_stats_update.yml @@ -17,6 +17,7 @@ on: jobs: # This workflow contains a single job update_stats: + if: github.repository == 'ArmDeveloperEcosystem/arm-learning-paths' runs-on: ubuntu-latest steps: - name: Check out current repo From c14c5cd3995411b78c098849cde77fab5b553420 Mon Sep 17 00:00:00 2001 From: Julien Simon Date: Wed, 30 Jul 2025 17:09:43 +0200 Subject: [PATCH 24/42] - Add the URL of the final model - Update the Hugging Face CLI to hf - Update model download procedure - Update performance and perplexity numbers --- .../00_overview.md | 4 +- .../05_downloading_and_optimizing_afm45b.md | 36 ++++++++++++-- .../06_running_inference.md | 47 ++++++++++--------- .../07_evaluating_the_quantized_models.md | 47 ++++++++++--------- .../08_conclusion.md | 8 ++-- 5 files changed, 86 insertions(+), 56 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/00_overview.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/00_overview.md index 2e3ddadafe..2fbf1b961e 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/00_overview.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/00_overview.md @@ -8,9 +8,9 @@ layout: learningpathall ## The AFM-4.5B model -AFM-4.5B is a 4.5-billion-parameter foundation model designed to balance accuracy, efficiency, and broad language coverage. Trained on nearly 7 trillion tokens of carefully filtered data, it performs well across a wide range of languages, including Arabic, English, French, German, Hindi, Italian, Korean, Mandarin, Portuguese, Russian, and Spanish. +[AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) is a 4.5-billion-parameter foundation model designed to balance accuracy, efficiency, and broad language coverage. Trained on nearly 8 trillion tokens of carefully filtered data, it performs well across a wide range of languages, including Arabic, English, French, German, Hindi, Italian, Korean, Mandarin, Portuguese, Russian, and Spanish. -In this Learning Path, you'll deploy AFM-4.5B using [Llama.cpp](https://github.com/ggerganov/llama.cpp) on an Arm-based AWS Graviton4 instance. You’ll walk through the full workflow, from setting up your environment and compiling the runtime, to downloading, quantizing, and running inference on the model. You'll also evaluate model quality using perplexity, a common metric for measuring how well a language model predicts text. +In this Learning Path, you'll deploy [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) using [Llama.cpp](https://github.com/ggerganov/llama.cpp) on an Arm-based AWS Graviton4 instance. You’ll walk through the full workflow, from setting up your environment and compiling the runtime, to downloading, quantizing, and running inference on the model. You'll also evaluate model quality using perplexity, a common metric for measuring how well a language model predicts text. This hands-on guide helps developers build cost-efficient, high-performance LLM applications on modern Arm server infrastructure using open-source tools and real-world deployment practices. diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md index 3feb85647e..ec980c9f03 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md @@ -6,10 +6,19 @@ weight: 7 layout: learningpathall --- -In this step, you’ll download the AFM-4.5B model from Hugging Face, convert it to the GGUF format for compatibility with `llama.cpp`, and generate quantized versions to optimize memory usage and improve inference speed. +In this step, you’ll download the [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) model from Hugging Face, convert it to the GGUF format for compatibility with `llama.cpp`, and generate quantized versions to optimize memory usage and improve inference speed. + +**Note: if you want to skip the model optimization process, [GGUF](https://huggingface.co/arcee-ai/AFM-4.5B-GGUF) versions are available.** Make sure to activate your virtual environment before running any commands. The instructions below walk you through downloading and preparing the model for efficient use on AWS Graviton4. +## Signing up to Hugging Face + +In order to download AFM-4.5B, you will need: +- a Hugging Face account: you can sign up at [https://huggingface.co](https://huggingface.co) +- a read-only Hugging Face token: once logged in, you can create one at [https://huggingface.co/settings/tokens](https://huggingface.co/settings/tokens). Don't forget to store it, as you will only be able to view it once. +- to accept the terms of AFM-4.5B at [https://huggingface.co/arcee-ai/AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) + ## Install the Hugging Face libraries ```bash @@ -19,14 +28,31 @@ pip install huggingface_hub hf_xet This command installs: - `huggingface_hub`: Python client for downloading models and datasets -- `hf_xet`: Git extension for fetching large model files stored on Hugging Face +- `hf_xet`: Git extension for fetching large model files stored on Hugging Face + +These tools include the `hf` command-line interface you'll use next. + +## Login to the Hugging Face Hub + +```bash +hf auth login + + _| _| _| _| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _|_|_|_| _|_| _|_|_| _|_|_|_| + _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _| + _|_|_|_| _| _| _| _|_| _| _|_| _| _| _| _| _| _|_| _|_|_| _|_|_|_| _| _|_|_| + _| _| _| _| _| _| _| _| _| _| _|_| _| _| _| _| _| _| _| + _| _| _|_| _|_|_| _|_|_| _|_|_| _| _| _|_|_| _| _| _| _|_|_| _|_|_|_| + + To login, `huggingface_hub` requires a token generated from https://huggingface.co/settings/tokens . +Enter your token (input will not be visible): +``` -These tools include the `huggingface-cli` command-line interface you'll use next. +Please enter the token you created above, and answer 'n' to "Add token as git credential? (Y/n)". ## Download the AFM-4.5B model ```bash -huggingface-cli download arcee-ai/afm-4.5B --local-dir models/afm-4-5b +hf download arcee-ai/afm-4.5B --local-dir models/afm-4-5b ``` This command downloads the model to the `models/afm-4-5b` directory: @@ -89,7 +115,7 @@ Similar to Q4_0, Arm has contributed optimized kernels for Q8_0 quantization tha ## Model files ready for inference -After completing these steps, you'll have three versions of the AFM-4.5B model: +After completing these steps, you'll have three versions of the AFM-4.5B model in `models/afm-4-5b`: - `afm-4-5B-F16.gguf` - The original full-precision model (~15GB) - `afm-4-5B-Q4_0.gguf` - 4-bit quantized version (~4.4GB) for memory-constrained environments - `afm-4-5B-Q8_0.gguf` - 8-bit quantized version (~8GB) for balanced performance and memory usage diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/06_running_inference.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/06_running_inference.md index 0e84e97f56..a4fd44830e 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/06_running_inference.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/06_running_inference.md @@ -6,7 +6,7 @@ weight: 8 layout: learningpathall --- -Now that you have the AFM-4.5B models in GGUF format, you can run inference using various Llama.cpp tools. In this step, you'll explore how to generate text, benchmark performance, and interact with the model through both command-line and HTTP APIs. +Now that you have the [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) models in GGUF format, you can run inference using various Llama.cpp tools. In this step, you'll explore how to generate text, benchmark performance, and interact with the model through both command-line and HTTP APIs. ## Use llama-cli for interactive text generation @@ -55,14 +55,15 @@ To exit the session, type `Ctrl+C` or `/bye`. You'll then see performance metrics like this: ```bash -llama_perf_sampler_print: sampling time = 26.66 ms / 356 runs ( 0.07 ms per token, 13352.84 tokens per second) -llama_perf_context_print: load time = 782.72 ms -llama_perf_context_print: prompt eval time = 392.40 ms / 24 tokens ( 16.35 ms per token, 61.16 tokens per second) -llama_perf_context_print: eval time = 13173.66 ms / 331 runs ( 39.80 ms per token, 25.13 tokens per second) -llama_perf_context_print: total time = 129945.08 ms / 355 tokens +llama_perf_sampler_print: sampling time = 9.47 ms / 119 runs ( 0.08 ms per token, 12569.98 tokens per second) +llama_perf_context_print: load time = 616.69 ms +llama_perf_context_print: prompt eval time = 344.39 ms / 23 tokens ( 14.97 ms per token, 66.79 tokens per second) +llama_perf_context_print: eval time = 9289.81 ms / 352 runs ( 26.39 ms per token, 37.89 tokens per second) +llama_perf_context_print: total time = 17446.13 ms / 375 tokens +llama_perf_context_print: graphs reused = 0 ``` -In this example, the 8-bit model running on 16 threads generated 355 tokens, at ~25 tokens per second (`eval time`). +In this example, the 8-bit model running on 16 threads generated 375 tokens, at ~37 tokens per second (`eval time`). ## Run a non-interactive prompt @@ -77,7 +78,7 @@ This command: - Sends a one-time prompt using `-p` - Prints the generated response and exits -The 4-bit model delivers faster generation—expect around 40 tokens per second on Graviton4. This shows how a more aggressive quantization recipe helps deliver faster performance. +The 4-bit model delivers faster generation—expect around 60 tokens per second on Graviton4. This shows how a more aggressive quantization recipe helps deliver faster performance. ## Use llama-server for API access @@ -130,29 +131,29 @@ The response includes the model’s reply and performance metrics: "index": 0, "message": { "role": "assistant", - "content": "Quantum computing uses quantum-mechanical phenomena, such as superposition and entanglement, to perform calculations. It allows for multiple possibilities to exist simultaneously, which can speed up certain processes. Unlike classical computers, quantum computers can solve complex problems and simulate systems more efficiently. Quantum bits (qubits) store information, and quantum gates perform operations. Quantum computing has potential applications in fields like cryptography, optimization, and materials science. Its development is an active area of research, with companies like IBM, Google, and Microsoft investing in quantum computing technology." + "content": "Quantum computing uses quantum-mechanical phenomena like superposition and entanglement to solve complex problems much faster than classical computers. Instead of binary bits (0 or 1), quantum bits (qubits) can exist in multiple states simultaneously, allowing for parallel processing of vast combinations of possibilities. This enables quantum computers to perform certain calculations exponentially faster, particularly in areas like cryptography, optimization, and drug discovery. However, quantum systems are fragile and prone to errors, requiring advanced error correction techniques. Current quantum computers are still in early stages but show promise for transformative applications." } } ], - "created": 1750929895, + "created": 1753876147, "model": "afm-4-5b", - "system_fingerprint": "b5757-716301d1", + "system_fingerprint": "b6030-1e15bfd4", "object": "chat.completion", "usage": { - "completion_tokens": 111, + "completion_tokens": 115, "prompt_tokens": 20, - "total_tokens": 131 + "total_tokens": 135 }, - "id": "chatcmpl-tb93ww9iYCErwLJmsV0YLrIadVvpBk4m", + "id": "chatcmpl-0Zwzu03zbu77MFx4ogBsqz8E4IdxHOLU", "timings": { - "prompt_n": 11, - "prompt_ms": 105.651, - "prompt_per_token_ms": 9.604636363636363, - "prompt_per_second": 104.11638318615064, - "predicted_n": 111, - "predicted_ms": 2725.982, - "predicted_per_token_ms": 24.558396396396397, - "predicted_per_second": 40.719271073690145 + "prompt_n": 20, + "prompt_ms": 68.37, + "prompt_per_token_ms": 3.4185000000000003, + "prompt_per_second": 292.525961679099, + "predicted_n": 115, + "predicted_ms": 1884.943, + "predicted_per_token_ms": 16.390808695652172, + "predicted_per_second": 61.00980241842857 } } ``` @@ -161,7 +162,7 @@ The response includes the model’s reply and performance metrics: You’ve now successfully: -- Run AFM-4.5B in interactive and non-interactive modes +- Run [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) in interactive and non-interactive modes - Tested performance with different quantized models - Served the model as an OpenAI-compatible API endpoint diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/07_evaluating_the_quantized_models.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/07_evaluating_the_quantized_models.md index f23b06cc3a..b4d3de7eab 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/07_evaluating_the_quantized_models.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/07_evaluating_the_quantized_models.md @@ -26,9 +26,9 @@ bin/llama-bench -m models/afm-4-5b/afm-4-5B-Q4_0.gguf ``` Typical results on a 16 vCPU instance: -- **F16 model**: ~15-16 tokens/second, ~15GB memory usage -- **Q8_0 model**: ~25 tokens/second, ~8GB memory usage -- **Q4_0 model**: ~40 tokens/second, ~4.4GB memory usage +- **F16 model**: ~25 tokens/second, ~9GB memory usage +- **Q8_0 model**: ~40 tokens/second, ~5GB memory usage +- **Q4_0 model**: ~60 tokens/second, ~3GB memory usage Your actual results might vary depending on your specific instance configuration and system load. @@ -40,28 +40,31 @@ Use this command to benchmark performance across prompt sizes and thread counts: bin/llama-bench -m models/afm-4-5b/afm-4-5B-Q4_0.gguf \ -p 128,256,512 \ -n 128 \ - -t 8,16,24 + -t 4,8,16 ``` This command does the following: - Loads the 4-bit model and runs inference benchmarks - `-p`: evaluates prompt lengths of 128, 256, and 512 tokens - `-n`: generates 128 tokens -- `-t`: runs inference using 4, 8, and 24 threads +- `-t`: runs inference using 4, 8, and 16 threads -Here’s an example of how performance scales across threads and prompt sizes: +Here’s an example of how performance scales across threads and prompt sizes (pp = prompt processing, tg = text generation): | model | size | params | backend | threads | test | t/s | | ------------------------------ | ---------: | ---------: | ---------- | ------: | --------------: | -------------------: | -| llama 8B Q4_0 | 4.33 GiB | 8.03 B | CPU | 4 | pp128 | 62.90 ± 0.08 | -| llama 8B Q4_0 | 4.33 GiB | 8.03 B | CPU | 4 | pp512 | 57.63 ± 0.06 | -| llama 8B Q4_0 | 4.33 GiB | 8.03 B | CPU | 4 | tg128 | 15.18 ± 0.02 | -| llama 8B Q4_0 | 4.33 GiB | 8.03 B | CPU | 8 | pp128 | 116.23 ± 0.04 | -| llama 8B Q4_0 | 4.33 GiB | 8.03 B | CPU | 8 | pp512 | 106.39 ± 0.03 | -| llama 8B Q4_0 | 4.33 GiB | 8.03 B | CPU | 8 | tg128 | 25.29 ± 0.05 | -| llama 8B Q4_0 | 4.33 GiB | 8.03 B | CPU | 16 | pp128 | 206.67 ± 0.10 | -| llama 8B Q4_0 | 4.33 GiB | 8.03 B | CPU | 16 | pp512 | 190.18 ± 0.03 | -| llama 8B Q4_0 | 4.33 GiB | 8.03 B | CPU | 16 | tg128 | 40.99 ± 0.36 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 4 | pp128 | 106.03 ± 0.21 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 4 | pp256 | 102.82 ± 0.05 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 4 | pp512 | 95.41 ± 0.18 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 4 | tg128 | 24.15 ± 0.02 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 8 | pp128 | 196.02 ± 0.42 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 8 | pp256 | 190.23 ± 0.34 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 8 | pp512 | 177.14 ± 0.31 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 8 | tg128 | 40.86 ± 0.11 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 16 | pp128 | 346.08 ± 0.62 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 16 | pp256 | 336.72 ± 1.43 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 16 | pp512 | 315.83 ± 0.22 | +| arcee 4B Q4_0 | 2.50 GiB | 4.62 B | CPU | 16 | tg128 | 62.39 ± 0.20 | Even with just four threads, the Q4_0 model achieves comfortable generation speeds. On larger instances, you can run multiple concurrent model processes to support parallel workloads. @@ -102,7 +105,7 @@ To reduce runtime, add the `--chunks` flag to evaluate a subset of the data. For ## Run the evaluation as a background script -Running a full perplexity evaluation on all three models takes about 5 hours. To avoid SSH timeouts and keep the process running after logout, wrap the commands in a shell script and run it in the background. +Running a full perplexity evaluation on all three models takes about 3 hours. To avoid SSH timeouts and keep the process running after logout, wrap the commands in a shell script and run it in the background. Create a script named ppl.sh: @@ -119,13 +122,13 @@ bin/llama-perplexity -m models/afm-4-5b/afm-4-5B-Q4_0.gguf -f wikitext-2-raw/wik tail -f ppl.sh.log ``` -Here are the full results. +| Model | Generation speed (batch size 1, 16 vCPUs) | Memory Usage | Perplexity (Wikitext-2) | Perplexity Increase | +|:-------:|:----------------------:|:------------:|:----------:|:----------------------:| +| F16 | ~25 tokens per second | ~9 GB | 8.4612 +/- 0.06112 | 0 (baseline) | +| Q8_0 | ~40 tokens per second | ~5 GB | 8.4776 +/- 0.06128 | +0.19% | +| Q4_0 | ~60 tokens per second | ~3 GB | 9.1897 +/- 0.06604 | +8.6% | -| Model | Generation speed (tokens/s, 16 vCPUs) | Memory Usage | Perplexity (Wikitext-2) | -|:-------:|:----------------------:|:------------:|:----------:| -| F16 | ~15–16 | ~15 GB | TODO | -| Q8_0 | ~25 | ~8 GB | TODO | -| Q4_0 | ~40 | ~4.4 GB | TODO | +We can see that 8-bit quantization introduces negligible degradation. The 4-bit model does suffer more, but may still serve its purpose for simpler use cases. As always, you should run your own tests and make up your own mind. When you have finished your benchmarking and evaluation, make sure to terminate your AWS EC2 instance in the AWS Management Console to avoid incurring unnecessary charges for unused compute resources. diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/08_conclusion.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/08_conclusion.md index 2faa4f371d..fa343771a4 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/08_conclusion.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/08_conclusion.md @@ -9,7 +9,7 @@ layout: learningpathall ## Wrap up your AFM-4.5B deployment -Congratulations! You have completed the process of deploying the Arcee AFM-4.5B foundation model on AWS Graviton4. +Congratulations! You have completed the process of deploying the Arcee [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) foundation model on AWS Graviton4. Here’s a summary of what you built and how you can take your knowledge forward. @@ -29,8 +29,8 @@ Using this Learning Path, you have: The benchmarking results demonstrate the power of quantization and Arm-based computing: -- **Memory efficiency** – the 4-bit model uses only ~4.4 GB of RAM compared to ~15 GB for the full-precision version -- **Speed improvements** – inference with Q4_0 is 2–3x faster (40+ tokens/sec vs. 15–16 tokens/sec) +- **Memory efficiency** – the 4-bit model uses only ~3 GB of RAM compared to ~9 GB for the full-precision version +- **Speed improvements** – inference with Q4_0 is 2.5x faster (~60+ tokens/sec vs. 25 tokens/sec) - **Cost optimization** – lower memory needs enable smaller, more affordable instances - **Quality preservation** – the quantized models maintain strong perplexity scores, showing minimal quality loss @@ -63,4 +63,4 @@ Together, Arcee AI’s foundation models, Llama.cpp’s efficient runtime, and G From chatbots and content generation to research tools, this stack strikes a balance between performance, cost, and developer control. -For more information on Arcee AI, and how you can build high-quality, secure, and cost-efficient AI solutions, please visit www.arcee.ai. +For more information on Arcee AI, and how you can build high-quality, secure, and cost-efficient AI solutions, please visit [www.arcee.ai](https://www.arcee.ai). From 6c6cdd253e7f82a0ad1763a716936dd0a2dbc18c Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Wed, 30 Jul 2025 22:34:14 -0500 Subject: [PATCH 25/42] remove draft from Arcee Learning Path --- .../arcee-foundation-model-on-aws/_index.md | 4 ---- 1 file changed, 4 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md index 7a7b23f235..9988ed3cb3 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/_index.md @@ -1,10 +1,6 @@ --- title: Deploy Arcee AFM-4.5B on Arm-based AWS Graviton4 with Llama.cpp -draft: true -cascade: - draft: true - minutes_to_complete: 30 who_is_this_for: This Learning Path is for developers and ML engineers who want to deploy Arcee's AFM-4.5B small language model on AWS Graviton4 instances using Llama.cpp. From 6c791718e352491553796844000d1833b4ea26b9 Mon Sep 17 00:00:00 2001 From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 31 Jul 2025 16:56:50 +0100 Subject: [PATCH 26/42] Update 02_setting_up_the_instance.md MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Made AFM-4.5B naming (not “Arcee Foundation Model”) consistent. --- .../arcee-foundation-model-on-aws/02_setting_up_the_instance.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/02_setting_up_the_instance.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/02_setting_up_the_instance.md index 8b8c53c779..a81fd12c81 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/02_setting_up_the_instance.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/02_setting_up_the_instance.md @@ -6,7 +6,7 @@ weight: 4 layout: learningpathall --- -In this step, you'll set up the Graviton4 instance with the tools and dependencies required to build and run the Arcee Foundation Model. This includes installing system packages and a Python environment. +In this step, you'll set up the Graviton4 instance with the tools and dependencies required to build and run the AFM-4.5B model. This includes installing system packages and a Python environment. ## Update the package list From 79f1ec8962b552f19915617be3095d6cfc560f89 Mon Sep 17 00:00:00 2001 From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 31 Jul 2025 16:59:51 +0100 Subject: [PATCH 27/42] Update 03_building_llama_cpp.md Added missing space. --- .../arcee-foundation-model-on-aws/03_building_llama_cpp.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/03_building_llama_cpp.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/03_building_llama_cpp.md index 95fa1e416f..1cdd269a23 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/03_building_llama_cpp.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/03_building_llama_cpp.md @@ -7,7 +7,7 @@ layout: learningpathall --- ## Build the Llama.cpp inference engine -In this step, you'll build Llama.cpp from source. Llama.cpp is a high-performance C++ implementation of the LLaMA model, optimized for inference on a range of hardware platforms,including Arm-based processors like AWS Graviton4. +In this step, you'll build Llama.cpp from source. Llama.cpp is a high-performance C++ implementation of the LLaMA model, optimized for inference on a range of hardware platforms, including Arm-based processors like AWS Graviton4. Even though AFM-4.5B uses a custom model architecture, you can still use the standard Llama.cpp repository - Arcee AI has contributed the necessary modeling code upstream. From 9296a99fe0b4cf07634be0bb78915de9739f956c Mon Sep 17 00:00:00 2001 From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 31 Jul 2025 17:02:11 +0100 Subject: [PATCH 28/42] Update 04_install_python_dependencies_for_llama_cpp.md Removed superfluous word --- .../04_install_python_dependencies_for_llama_cpp.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/04_install_python_dependencies_for_llama_cpp.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/04_install_python_dependencies_for_llama_cpp.md index b680dcf7eb..0af62f97fb 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/04_install_python_dependencies_for_llama_cpp.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/04_install_python_dependencies_for_llama_cpp.md @@ -32,7 +32,7 @@ This command does the following: - Runs the activation script, which modifies your shell environment - Updates your shell prompt to show `env-llama-cpp`, indicating the environment is active -- Updates `PATH` to use so the environment’s Python interpreter +- Updates `PATH` to use the environment’s Python interpreter - Ensures all `pip` commands install packages into the isolated environment ## Upgrade pip to the latest version From 42b6d88245293622fe259d48cdcb29231ce671ec Mon Sep 17 00:00:00 2001 From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 31 Jul 2025 17:03:19 +0100 Subject: [PATCH 29/42] Update 04_install_python_dependencies_for_llama_cpp.md Editorial fix. --- .../04_install_python_dependencies_for_llama_cpp.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/04_install_python_dependencies_for_llama_cpp.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/04_install_python_dependencies_for_llama_cpp.md index 0af62f97fb..db8d79dc36 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/04_install_python_dependencies_for_llama_cpp.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/04_install_python_dependencies_for_llama_cpp.md @@ -72,7 +72,8 @@ After the installation completes, your virtual environment includes: - **NumPy**: for numerical computations and array operations - **Requests**: for HTTP operations and API calls - **Other dependencies**: additional packages required by llama.cpp's Python bindings and utilities -Your environment is now ready to run Python scripts that integrate with the compiled Llama.cpp binaries + +Your environment is now ready to run Python scripts that integrate with the compiled Llama.cpp binaries. {{< notice Tip >}} Before running any Python commands, make sure your virtual environment is activated. {{< /notice >}} From ad6b97bcfc44b713f5b58c0bddac0d623d173a8e Mon Sep 17 00:00:00 2001 From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 31 Jul 2025 17:18:13 +0100 Subject: [PATCH 30/42] Update 05_downloading_and_optimizing_afm45b.md Editorial --- .../05_downloading_and_optimizing_afm45b.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md index ec980c9f03..fe51bca885 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md @@ -28,11 +28,11 @@ pip install huggingface_hub hf_xet This command installs: - `huggingface_hub`: Python client for downloading models and datasets -- `hf_xet`: Git extension for fetching large model files stored on Hugging Face +- `hf_xet`: Git extension for fetching large model files hosted on Hugging Face These tools include the `hf` command-line interface you'll use next. -## Login to the Hugging Face Hub +## Log in to the Hugging Face Hub ```bash hf auth login @@ -86,7 +86,7 @@ This command creates a 4-bit quantized version of the model: - `llama-quantize` is the quantization tool from Llama.cpp. - `afm-4-5B-F16.gguf` is the input GGUF model file in 16-bit precision. - `Q4_0` applies zero-point 4-bit quantization. -- This reduces the model size by approximately 45% (from ~15GB to ~8GB). +- This reduces the model size by approximately ~70% (from ~15GB to ~4.4GB). - The quantized model will use less memory and run faster, though with a small reduction in accuracy. - The output file will be `afm-4-5B-Q4_0.gguf`. @@ -104,7 +104,7 @@ bin/llama-quantize models/afm-4-5b/afm-4-5B-F16.gguf models/afm-4-5b/afm-4-5B-Q8 This command creates an 8-bit quantized version of the model: - `Q8_0` specifies 8-bit quantization with zero-point compression. -- This reduces the model size by approximately 70% (from ~15GB to ~4.4GB). +- This reduces the model size by approximately ~45% (from ~15GB to ~8GB). - The 8-bit version provides a better balance between memory usage and accuracy than 4-bit quantization. - The output file is named `afm-4-5B-Q8_0.gguf`. - Commonly used in production scenarios where memory resources are available. From d2e81e449303360aed41c788d251bcca0578f30b Mon Sep 17 00:00:00 2001 From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 31 Jul 2025 17:22:56 +0100 Subject: [PATCH 31/42] Update 06_running_inference.md From 3391580b3c7cd76e0e02efe1444050761bd20d90 Mon Sep 17 00:00:00 2001 From: Maddy Underwood <167196745+madeline-underwood@users.noreply.github.com> Date: Thu, 31 Jul 2025 17:30:42 +0100 Subject: [PATCH 32/42] Update 05_downloading_and_optimizing_afm45b.md Added note formatting. --- .../05_downloading_and_optimizing_afm45b.md | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md index fe51bca885..5c68008870 100644 --- a/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md +++ b/content/learning-paths/servers-and-cloud-computing/arcee-foundation-model-on-aws/05_downloading_and_optimizing_afm45b.md @@ -8,7 +8,8 @@ layout: learningpathall In this step, you’ll download the [AFM-4.5B](https://huggingface.co/arcee-ai/AFM-4.5B) model from Hugging Face, convert it to the GGUF format for compatibility with `llama.cpp`, and generate quantized versions to optimize memory usage and improve inference speed. -**Note: if you want to skip the model optimization process, [GGUF](https://huggingface.co/arcee-ai/AFM-4.5B-GGUF) versions are available.** +{{% notice Note %}} +If you want to skip the model optimization process, [GGUF](https://huggingface.co/arcee-ai/AFM-4.5B-GGUF) versions are available. {{% /notice %}} Make sure to activate your virtual environment before running any commands. The instructions below walk you through downloading and preparing the model for efficient use on AWS Graviton4. From f46eadc4b8fdb001c7fe043a64cea373ab40f3b0 Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Thu, 31 Jul 2025 14:11:53 -0400 Subject: [PATCH 33/42] Update 3_agent.md --- .../java-perf-flamegraph/3_agent.md | 28 +++++++++++++------ 1 file changed, 19 insertions(+), 9 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md index 13fade565e..963b0a3a56 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md @@ -8,31 +8,41 @@ layout: learningpathall --- ## Java Flame Graph Generation via Java agent and perf -To profile a Java application with perf and ensure proper symbol resolution, you must include libperf-jvmti.so when launching the Java application. -- libperf-jvmti.so is a JVM TI agent library enabling perf to resolve Java symbols, facilitating accurate profiling of Java applications. -- A specialized shared library, libperf-jvmti.so bridges perf and the JVM, enabling proper translation of memory addresses to Java method names during profiling. +To profile a Java application with perf and ensure proper symbol resolution, you must include `libperf-jvmti.so` when launching the Java application. +- `libperf-jvmti.so` is a JVM TI agent library enabling perf to resolve Java symbols, facilitating accurate profiling of Java applications. +- A specialized shared library, `libperf-jvmti.so` bridges perf and the JVM, enabling proper translation of memory addresses to Java method names during profiling. -1. Find and add libperf-jvmti.so to Java option +1. Find where `libperf-jvmti.so` is installed on your Arm-based Linux server: +```bash +pushd /usr/lib +find . -name libperf-jvmti.so` +``` +The output will show the path of the library that you will then include in your Tomcat setup file: ```bash vi apache-tomcat-11.0.9/bin/catalina.sh -# add JAVA_OPTS="$JAVA_OPTS -agentpath:/usr/lib/linux-tools-6.8.0-63/libperf-jvmti.so -XX:+PreserveFramePointer" +``` +Add JAVA_OPTS="$JAVA_OPTS -agentpath:/usr/lib/linux-tools-6.8.0-63/libperf-jvmti.so -XX:+PreserveFramePointer" to `catalina.sh`. Make sure the path matches the location on your machine from the previous step. + +Now shutdown and restart Tomcat: +```bash cd apache-tomcat-11.0.9/bin ./shutdown.sh ./startup.sh ``` -2. Use perf to profile Tomcat, and restart wrk if necessary +2. Use perf to profile Tomcat, and restart wrk that running on your x86 instance if necessary: ```bash sudo perf record -g -k1 -p $(jps | awk /Bootstrap/'{print $1}') -- sleep 10 ``` +This command will record the collected data in a file named `perf.data` -3. Convert the collected perf.data file into a Java flame graph using FlameGraph +3. Convert the collected `perf.data` into a Java flame graph using FlameGraph ```bash git clone https://github.com/brendangregg/FlameGraph.git -export PATH=$PATH:/root/FlameGraph +export PATH=$PATH:`pwd`/FlameGraph sudo perf inject -j -i perf.data | perf script | stackcollapse-perf.pl | flamegraph.pl &> profile.svg ``` -4. Launch profile.svg in a browser to analyse the profiling result +4. You can now successfully launch `profile.svg` in a browser to analyse the profiling result ![example image alt-text#center](_images/lp-flamegraph-agent.png "Java Flame Graph via Java agent and perf") From 6c80a820aa7377e3939b1899dca294eb8bf283f1 Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Thu, 31 Jul 2025 14:14:41 -0400 Subject: [PATCH 34/42] Update 2_async-profiler.md --- .../java-perf-flamegraph/2_async-profiler.md | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md index 4e834176e9..ddef32d6d1 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md @@ -6,11 +6,11 @@ weight: 3 layout: learningpathall --- -## Java Flame Graph Generation via async-profiler [async-profiler](https://github.com/async-profiler/async-profiler) (Recommended) -- async-profiler is a low-overhead sampling profiler for JVM applications, capable of capturing CPU, allocation, and lock events to generate actionable performance insights. -- A lightweight tool for Java performance analysis, async-profiler produces flame graphs and detailed stack traces with minimal runtime impact, suitable for production environments. +## Java Flame Graph Generation via async-profiler [async-profiler](https://github.com/async-profiler/async-profiler) +- `async-profiler` is a low-overhead sampling profiler for JVM applications, capable of capturing CPU, allocation, and lock events to generate actionable performance insights. +- A lightweight tool for Java performance analysis, `async-profiler` produces flame graphs and detailed stack traces with minimal runtime impact, suitable for production environments. -You should deploy async-profiler on the same machine where Tomcat is running to ensure accurate performance profiling. +You should deploy `async-profiler` on the same Arm Linux machine where Tomcat is running to ensure accurate performance profiling. 1. Download async-profiler-4.0 and uncompress ```bash wget -c https://github.com/async-profiler/async-profiler/releases/download/v4.0/async-profiler-4.0-linux-arm64.tar.gz @@ -21,10 +21,12 @@ tar xzf async-profiler-4.0-linux-arm64.tar.gz ```bash cd async-profiler-4.0-linux-arm64/bin ./asprof -d 10 -f profile.html $(jps | awk /Bootstrap/'{print $1}') -# or +``` +You can also run: +``` ./asprof -d 10 -f profile.html ${tomcat_process_id} ``` -3. Launch profile.html in a browser to analyse the profiling result +3. Now launch `profile.html` in a browser to analyse your profiling result ![example image alt-text#center](_images/lp-flamegraph-async.png "Java Flame Graph via async-profiler") From eb6d5eae4f57095bead1363b5d236e97151e3d72 Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Thu, 31 Jul 2025 14:15:21 -0400 Subject: [PATCH 35/42] Update 2_async-profiler.md --- .../java-perf-flamegraph/2_async-profiler.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md index ddef32d6d1..d3da3399d6 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md @@ -6,7 +6,7 @@ weight: 3 layout: learningpathall --- -## Java Flame Graph Generation via async-profiler [async-profiler](https://github.com/async-profiler/async-profiler) +## Java Flame Graph Generation using [async-profiler](https://github.com/async-profiler/async-profiler) - `async-profiler` is a low-overhead sampling profiler for JVM applications, capable of capturing CPU, allocation, and lock events to generate actionable performance insights. - A lightweight tool for Java performance analysis, `async-profiler` produces flame graphs and detailed stack traces with minimal runtime impact, suitable for production environments. From 5f2d3aa2bb4d6be86d94104655e5476eff8ccb9f Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Thu, 31 Jul 2025 14:19:10 -0400 Subject: [PATCH 36/42] Update 1_setup.md --- .../java-perf-flamegraph/1_setup.md | 11 +++++++---- 1 file changed, 7 insertions(+), 4 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md index 264ce3d16e..2f29247fe3 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md @@ -31,13 +31,13 @@ tar xzf apache-tomcat-11.0.9.tar.gz 3. If you intend to access the built-in examples of Tomcat via an intranet IP or even an external IP, you need to modify a configuration file as shown: ```bash -vim apache-tomcat-11.0.9/webapps/examples/META-INF/context.xml +vi apache-tomcat-11.0.9/webapps/examples/META-INF/context.xml ``` -Then change the values: -```console +Then change the allow value as shown and save the changes: +```output # change # to -# + ``` Now you can start Tomcat Server: ```bash @@ -65,6 +65,9 @@ Tomcat started. ## Setup Benchmark Client - [wrk2](https://github.com/giltene/wrk2) `wrk2` is a high-performance HTTP benchmarking tool specialized in generating constant throughput loads and measuring latency percentiles for web services. `wrk2` is an enhanced version of `wrk` that provides accurate latency statistics under controlled request rates, ideal for performance testing of HTTP servers. +Currently `wrk2` is only supported on x86 machines. You will run the Benchmark Client steps shown below on an x86_64 Linux machine. + + 1. To use `wrk2`, you will need to install some essential tools before you can build it: ```bash sudo apt-get update From 1d685d5362961126ffdd2804836e4d5e93b5395e Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Thu, 31 Jul 2025 14:21:02 -0400 Subject: [PATCH 37/42] Update _index.md --- .../java-perf-flamegraph/_index.md | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md index 2363791d5e..be001c6a78 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md @@ -42,16 +42,13 @@ further_reading: link: https://wiki.openjdk.org/ type: documentation - resource: - title: PLACEHOLDER BLOG - link: PLACEHOLDER BLOG LINK - type: blog - - resource: - title: PLACEHOLDER GENERAL WEBSITE - link: PLACEHOLDER GENERAL WEBSITE LINK + title: Java FlameGraphs + link: https://www.brendangregg.com/flamegraphs.html type: website + ### FIXED, DO NOT MODIFY # ================================================================================ weight: 1 # _index.md always has weight of 1 to order correctly From 6a496c55ced48c3680e626612dc81c1bf0aa4cfa Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Thu, 31 Jul 2025 14:23:16 -0400 Subject: [PATCH 38/42] Update _index.md --- .../java-perf-flamegraph/_index.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md index be001c6a78..06a3c9281c 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/_index.md @@ -15,8 +15,8 @@ learning_objectives: - How to generate flame graphs for Java applications using Java agent prerequisites: - - Basic familiarity with Java applications - - Basic familiarity with flame graphs + - An Arm-based and x86 computer running Ubuntu. You can use a server instance from a cloud service provider of your choice. + - Basic familiarity with Java applications and flame graphs author: Ying Yu, Martin Ma From ea35be0be8a0933813835650d1f7513a93546a33 Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Thu, 31 Jul 2025 14:28:30 -0400 Subject: [PATCH 39/42] Update 1_setup.md --- .../java-perf-flamegraph/1_setup.md | 14 ++++++++------ 1 file changed, 8 insertions(+), 6 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md index 2f29247fe3..771a681efc 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md @@ -7,17 +7,17 @@ layout: learningpathall --- -## Before You Begin -- There are numerous performance analysis methods and tools for Java applications, among which the call stack flame graph method is regarded as a conventional entry-level approach. Therefore, generating flame graphs is considered a basic operation. -- Various methods and tools are available for generating Java flame graphs, including `async-profiler`, `Java Agent`, `jstack`, `JFR` (Java Flight Recorder), etc. -- This Learning Path focuses on introducing two simple and easy-to-use methods: `async-profiler` and `Java Agent`. +## Overview +There are numerous performance analysis methods and tools for Java applications, among which the call stack flame graph method is regarded as a conventional entry-level approach. Therefore, generating flame graphs is considered a basic operation. +Various methods and tools are available for generating Java flame graphs, including `async-profiler`, `Java Agent`, `jstack`, `JFR` (Java Flight Recorder), etc. +This Learning Path focuses on introducing two simple and easy-to-use methods: `async-profiler` and `Java Agent`. ## Setup Benchmark Server - Tomcat - [Apache Tomcat](https://tomcat.apache.org/) is an open-source Java Servlet container that enables running Java web applications, handling HTTP requests and serving dynamic content. - As a core component in Java web development, Apache Tomcat supports Servlet, JSP, and WebSocket technologies, providing a lightweight runtime environment for web apps. -1. Start by installing Java Development Kit (JDK) on your Arm-based server: +1. Start by installing Java Development Kit (JDK) on your Arm-based server running Ubuntu: ```bash sudo apt update sudo apt install -y openjdk-21-jdk @@ -62,10 +62,12 @@ Tomcat started. ![example image alt-text#center](./_images/lp-tomcat-examples.png "Tomcat-Examples") +Make sure port 8080 is open in the security group of the IP address for your Arm-based Linux machine. + ## Setup Benchmark Client - [wrk2](https://github.com/giltene/wrk2) `wrk2` is a high-performance HTTP benchmarking tool specialized in generating constant throughput loads and measuring latency percentiles for web services. `wrk2` is an enhanced version of `wrk` that provides accurate latency statistics under controlled request rates, ideal for performance testing of HTTP servers. -Currently `wrk2` is only supported on x86 machines. You will run the Benchmark Client steps shown below on an x86_64 Linux machine. +Currently `wrk2` is only supported on x86 machines. You will run the Benchmark Client steps shown below on an x86_64 server running Ubuntu. 1. To use `wrk2`, you will need to install some essential tools before you can build it: From 7532bada3dabfa3841d8ca477bd439d2b91d1b42 Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Thu, 31 Jul 2025 14:35:00 -0400 Subject: [PATCH 40/42] Update 2_async-profiler.md --- .../java-perf-flamegraph/2_async-profiler.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md index d3da3399d6..5346d45fac 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/2_async-profiler.md @@ -7,8 +7,8 @@ layout: learningpathall --- ## Java Flame Graph Generation using [async-profiler](https://github.com/async-profiler/async-profiler) -- `async-profiler` is a low-overhead sampling profiler for JVM applications, capable of capturing CPU, allocation, and lock events to generate actionable performance insights. -- A lightweight tool for Java performance analysis, `async-profiler` produces flame graphs and detailed stack traces with minimal runtime impact, suitable for production environments. +`async-profiler` is a low-overhead sampling profiler for JVM applications, capable of capturing CPU, allocation, and lock events to generate actionable performance insights. +A lightweight tool for Java performance analysis, `async-profiler` produces flame graphs and detailed stack traces with minimal runtime impact, suitable for production environments. In this section, you will learn how to install and use it to profile your Tomcat instance being benchmarked. You should deploy `async-profiler` on the same Arm Linux machine where Tomcat is running to ensure accurate performance profiling. 1. Download async-profiler-4.0 and uncompress From c2afe85c6f7f64bd7b7e64559aed419a3c4fb8be Mon Sep 17 00:00:00 2001 From: pareenaverma Date: Thu, 31 Jul 2025 14:35:42 -0400 Subject: [PATCH 41/42] Update 3_agent.md --- .../servers-and-cloud-computing/java-perf-flamegraph/3_agent.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md index 963b0a3a56..96ff1ea117 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/3_agent.md @@ -7,7 +7,7 @@ weight: 4 layout: learningpathall --- -## Java Flame Graph Generation via Java agent and perf +## Java Flame Graph Generation using Java agent and perf To profile a Java application with perf and ensure proper symbol resolution, you must include `libperf-jvmti.so` when launching the Java application. - `libperf-jvmti.so` is a JVM TI agent library enabling perf to resolve Java symbols, facilitating accurate profiling of Java applications. - A specialized shared library, `libperf-jvmti.so` bridges perf and the JVM, enabling proper translation of memory addresses to Java method names during profiling. From 511013c160d89550cfe3cca4db8d2838158a948d Mon Sep 17 00:00:00 2001 From: Jason Andrews Date: Thu, 31 Jul 2025 17:56:27 -0500 Subject: [PATCH 42/42] spelling updates --- .wordlist.txt | 115 +++++++++++++++++- .../servers-and-cloud-computing/_index.md | 24 ++-- .../how-to-1.md | 2 +- .../how-to-2.md | 6 +- .../java-perf-flamegraph/1_setup.md | 2 +- 5 files changed, 134 insertions(+), 15 deletions(-) diff --git a/.wordlist.txt b/.wordlist.txt index 796f136e22..5afe320b18 100644 --- a/.wordlist.txt +++ b/.wordlist.txt @@ -4474,4 +4474,117 @@ AssetLib PerformanceStudio VkThread precompiled -rollouts \ No newline at end of file +rollouts +Bhusari +DLLAMA +FlameGraph +FlameGraphs +JSP +KBC +MMIO +Paravirtualized +PreserveFramePointer +Servlet +TDISP +VirtIO +WebSocket +agentpath +alarmtimer +aoss +apb +ata +bpf +brendangregg +chipidea +clk +cma +counterintuitive +cpuhp +cros +csd +devfreq +devlink +dma +dpaa +dwc +ecurity +edma +evice +filelock +filemap +flamegraphs +fsl +glink +gpu +hcd +hns +hw +hwmon +icmp +initcall +iomap +iommu +ipi +irq +jbd +jvmti +kmem +ksm +kvm +kyber +libata +libperf +lockd +mdio +memcg +mmc +mtu +musb +napi +ncryption +netfs +netlink +nfs +ntegrity +nterface +oom +optee +pagemap +paravirtualized +percpu +printk +pwm +qcom +qdisc +ras +rcu +regmap +rgerganov’s +rotocol +rpcgss +rpmh +rseq +rtc +sched +scmi +scsi +skb +smbus +smp +spi +spmi +sunrpc +swiotlb +tegra +thp +tlb +udp +ufs +untrusted +uring +virtio +vmalloc +vmscan +workqueue +xdp +xhci diff --git a/content/learning-paths/servers-and-cloud-computing/_index.md b/content/learning-paths/servers-and-cloud-computing/_index.md index 2cf5628cc6..878d7bd782 100644 --- a/content/learning-paths/servers-and-cloud-computing/_index.md +++ b/content/learning-paths/servers-and-cloud-computing/_index.md @@ -8,8 +8,8 @@ key_ip: maintopic: true operatingsystems_filter: - Android: 2 -- Linux: 154 -- macOS: 10 +- Linux: 157 +- macOS: 11 - Windows: 14 pinned_modules: - module: @@ -22,8 +22,8 @@ subjects_filter: - Containers and Virtualization: 29 - Databases: 15 - Libraries: 9 -- ML: 28 -- Performance and Architecture: 60 +- ML: 29 +- Performance and Architecture: 62 - Storage: 1 - Web: 10 subtitle: Optimize cloud native apps on Arm for performance and cost @@ -47,6 +47,8 @@ tools_software_languages_filter: - ASP.NET Core: 2 - Assembly: 4 - assembly: 1 +- Async-profiler: 1 +- AWS: 1 - AWS CDK: 2 - AWS CodeBuild: 1 - AWS EC2: 2 @@ -65,7 +67,7 @@ tools_software_languages_filter: - C++: 8 - C/C++: 2 - Capstone: 1 -- CCA: 6 +- CCA: 7 - Clair: 1 - Clang: 10 - ClickBench: 1 @@ -77,10 +79,11 @@ tools_software_languages_filter: - Daytona: 1 - Demo: 3 - Django: 1 -- Docker: 17 +- Docker: 18 - Envoy: 2 - ExecuTorch: 1 - FAISS: 1 +- FlameGraph: 1 - Flink: 1 - Fortran: 1 - FunASR: 1 @@ -88,7 +91,7 @@ tools_software_languages_filter: - GCC: 22 - gdb: 1 - Geekbench: 1 -- GenAI: 11 +- GenAI: 12 - GitHub: 6 - GitLab: 1 - Glibc: 1 @@ -114,7 +117,7 @@ tools_software_languages_filter: - Linaro Forge: 1 - Litmus7: 1 - Llama.cpp: 1 -- LLM: 9 +- LLM: 10 - llvm-mca: 1 - LSE: 1 - MariaDB: 1 @@ -132,6 +135,7 @@ tools_software_languages_filter: - Ollama: 1 - ONNX Runtime: 1 - OpenBLAS: 1 +- OpenJDK-21: 1 - OpenShift: 1 - OrchardCore: 1 - PAPI: 1 @@ -144,7 +148,7 @@ tools_software_languages_filter: - RAG: 1 - Redis: 3 - Remote.It: 2 -- RME: 6 +- RME: 7 - Runbook: 71 - Rust: 2 - snappy: 1 @@ -161,6 +165,7 @@ tools_software_languages_filter: - TensorFlow: 2 - Terraform: 11 - ThirdAI: 1 +- Tomcat: 1 - Trusted Firmware: 1 - TSan: 1 - TypeScript: 1 @@ -173,6 +178,7 @@ tools_software_languages_filter: - Whisper: 1 - WindowsPerf: 1 - WordPress: 3 +- wrk2: 1 - x265: 1 - zlib: 1 - Zookeeper: 1 diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-1.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-1.md index 5406146e29..6838a42e06 100644 --- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-1.md +++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-1.md @@ -46,7 +46,7 @@ If everything was built correctly, you should see a list of all the available fl Communication between the master node and the worker nodes occurs through a socket created on each worker. This socket listens for incoming data from the master—such as model parameters, tokens, hidden states, and other inference-related information. {{% notice Note %}}The RPC feature in llama.cpp is not secure by default, so you should never expose it to the open internet. To mitigate this risk, ensure that the security groups for all your EC2 instances are properly configured—restricting access to only trusted IPs or internal VPC traffic. This helps prevent unauthorized access to the RPC endpoints.{{% /notice %}} -Use the following command to start the listeneing on the worker nodes: +Use the following command to start the listening on the worker nodes: ```bash bin/rpc-server -p 50052 -H 0.0.0.0 -t 64 ``` diff --git a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md index 9a41ca72da..65a020ccab 100644 --- a/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md +++ b/content/learning-paths/servers-and-cloud-computing/distributed-inference-with-llama-cpp/how-to-2.md @@ -190,7 +190,7 @@ llama_perf_context_print: eval time = 77429.95 ms / 127 runs ( 609 llama_perf_context_print: total time = 79394.06 ms / 132 tokens llama_perf_context_print: graphs reused = 0 ``` -That's it! You have sucessfully run the llama-3.1-8B model on CPUs with the power of llama.cpp RPC functionality. The following table provides brief description of the metrics from `llama_perf`:

    +That's it! You have successfully run the llama-3.1-8B model on CPUs with the power of llama.cpp RPC functionality. The following table provides brief description of the metrics from `llama_perf`:

    | Log Line | Description | |-------------------|-----------------------------------------------------------------------------| @@ -200,11 +200,11 @@ That's it! You have sucessfully run the llama-3.1-8B model on CPUs with the powe | eval time | Time to generate output tokens by forward-passing through the model. | | total time | Total time for both prompt processing and token generation (excludes model load). | -Lastly to set up OpenAI compatible API, you can use the `llama-server` functionality. The process of implementing this is described [here](/learning-paths/servers-and-cloud-computing/llama-cpu) under the "Access the chatbot using the OpenAI-compatible API" section. Here is a snippet, for how to set up llama-server for disributed inference: +Lastly to set up OpenAI compatible API, you can use the `llama-server` functionality. The process of implementing this is described [here](/learning-paths/servers-and-cloud-computing/llama-cpu) under the "Access the chatbot using the OpenAI-compatible API" section. Here is a snippet, for how to set up llama-server for distributed inference: ```bash bin/llama-server -m /home/ubuntu/model.gguf --port 8080 --rpc "$worker_ips" -ngl 99 ``` -At the very end of the output to the above command, you will see somethin like the following: +At the very end of the output to the above command, you will see something like the following: ```output main: server is listening on http://127.0.0.1:8080 - starting the main loop srv update_slots: all slots are idle diff --git a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md index 771a681efc..6fbe8aeb81 100644 --- a/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md +++ b/content/learning-paths/servers-and-cloud-computing/java-perf-flamegraph/1_setup.md @@ -87,7 +87,7 @@ Move the executable to somewhere in your PATH: sudo cp wrk /usr/local/bin ``` -3. Finally, you can run the benchamrk of Tomcat through wrk2. +3. Finally, you can run the benchmark of Tomcat through wrk2. ```bash wrk -c32 -t16 -R50000 -d60 http://${tomcat_ip}:8080/examples/servlets/servlet/HelloWorldExample ```

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