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Merge pull request #2334 from annietllnd/updates
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content/install-guides/dcperf.md

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- Neoverse
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test_images:
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- ubuntu:22.04
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- ubuntu:latest
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test_maintenance: false
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layout: installtoolsall
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DCPerf is an open-source benchmarking and microbenchmarking suite originally developed by Meta. It faithfully replicates the characteristics of general-purpose data center workloads, with particular attention to microarchitectural fidelity. DCPerf stands out for accurate simulation of behaviors such as cache misses and branch mispredictions, which are details that many other benchmarking tools overlook.
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You can use DCPerf to generate performance data to inform procurement decisions, and for regression testing to detect changes in the environment, such as kernel and compiler changes.
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You can use DCPerf to generate performance data to inform procurement decisions, and for regression testing to detect changes in the environment, such as kernel and compiler changes.
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DCPerf runs on Arm-based servers. The examples below have been tested on an AWS `c7g.metal` instance running Ubuntu 22.04 LTS.
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DCPerf runs on Arm-based servers. The examples below have been tested on an AWS `c7g.metal` instance running Ubuntu 22.04 LTS.
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{{% notice Note %}}
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When running on a server provided by a cloud service, you have limited access to some parameters, such as UEFI settings, which can affect performance.
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When running on a server provided by a cloud service, you have limited access to some parameters, such as UEFI settings, which can affect performance.
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{{% /notice %}}
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## Install prerequisites
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sudo apt install -y python-is-python3 python3-pip python3-venv git
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```
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It is recommended that you install Python packages in a Python virtual environment.
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It is recommended that you install Python packages in a Python virtual environment.
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Set up your virtual environment:
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```bash
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python3 -m venv venv
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source venv/bin/activate
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```
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If requested, restart the recommended services.
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If requested, restart the recommended services.
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Install the required packages:
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## Running the MediaWiki benchmark
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DCPerf offers many benchmarks. See the official documentation for the benchmark of your choice.
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DCPerf offers many benchmarks. See the official documentation for the benchmark of your choice.
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One example is the MediaWiki benchmark, designed to faithfully reproduce the workload of the Facebook social networking site.
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One example is the MediaWiki benchmark, designed to faithfully reproduce the workload of the Facebook social networking site.
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Install HipHop Virtual Machine (HHVM), a virtual machine used to execute the web application code:
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Repo schema: 4239d11395efb06bee3ab2923797fedfee64738e
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```
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Confirm security-enhanced Linux (SELinux) is disabled with the following commands:
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Confirm security-enhanced Linux (SELinux) is disabled with the following commands:
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```bash
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sudo apt install selinux-utils
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getenforce
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```
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You should see the following response:
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You should see the following response:
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```output
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Disabled
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These metrics help you evaluate the performance and reliability of the system under test. Higher values for successful requests and RPS, and lower response times, generally indicate better performance. The score provides a single value for easy comparison across runs or systems.
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## Next steps
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## Next steps
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These are some activities you might like to try next:
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content/learning-paths/cross-platform/intrinsics/_index.md

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test_images:
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- amd64/ubuntu:latest
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- arm64v8/ubuntu:latest
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test_link: https://github.com/armflorentlebeau/arm-learning-paths/actions/runs/4312122327
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test_maintenance: true
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test_maintenance: false
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### Tags
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skilllevels: Advanced

content/learning-paths/embedded-and-microcontrollers/tfm/_index.md

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test_images:
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- armswdev/arm-tools:bare-metal-compilers
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test_link: null
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test_maintenance: true
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test_maintenance: false
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### Tags
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skilllevels: Introductory

content/learning-paths/embedded-and-microcontrollers/zephyr/_index.md

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test_images:
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- amd64/ubuntu:latest
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test_link: null
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test_maintenance: true
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test_maintenance: false
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### Tags
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skilllevels: Introductory

content/learning-paths/mobile-graphics-and-gaming/nss-unreal/2-emulation-layer.md

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```
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![Add user-defined Vulkan layers path in Vulkan Configurator#center](./images/load_layers.png "Figure 1: Add Vulkan layer path.")
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5. Ensure the Graph layer is listed *above* the Tensor layer, and that you've set up the configuration scope as shown in the image.
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5. Switch back to the **Vulkan Loader Management** tab. Ensure the Graph layer is listed *above* the Tensor layer, and that you've set up the configuration scope as shown in the image.
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![Layer configuration showing Graph above Tensor#center](./images/verify_layers.png "Figure 2: Verify layer ordering and scope.")
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content/learning-paths/mobile-graphics-and-gaming/vulkan-ml-sample/2-ml-ext-for-vulkan.md

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Under the **Vulkan Layers Location** tab, add the path to your `MLEmulationLayerForVulkan` folder.
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In the **Apply a Vulkan Loader Configuration** list, right-click and choose **Create a new Configuration**. You can give the new configuration any name, for example `tensor_and_data_graph`.
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Switch back to the **Vulkan Loader Management** tab. In the **Apply a Vulkan Loader Configuration** list, right-click and choose **Create a new Configuration**. You can give the new configuration any name, for example `tensor_and_data_graph`.
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![Screenshot of the Vulkan Configurator showing the Vulkan Layers Location tab, where the emulation layer path (MLEmulationLayerForVulkan) is added to enable VK_ARM_data_graph and VK_ARM_tensors alt-text#center](./images/load_layers.png "Add emulation layers in Vulkan Configurator")
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tools/check_open_category.py

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# ----------------------------
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def main():
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if len(sys.argv) != 2:
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print("Usage: python tools/check_tools_software_languages.py /path/to/content/<section>/<category>/<lp>/_index.md")
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sys.exit(2)
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lp_index_path = Path(sys.argv[1]).resolve()
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if not lp_index_path.exists():
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print(f"[ERROR] File not found: {lp_index_path}")
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sys.exit(2)
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# Single sequential flow so you can follow the variables:
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problems = check_entries(lp_index_path, model="gpt-4o-2024-08-06")
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files = sys.argv[1:]
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problems = 0
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for f in files:
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if "content/" in f and f.endswith("_index.md"):
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lp_index_path = Path(f).resolve()
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if not lp_index_path.exists():
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print(f"[ERROR] File not found: {lp_index_path}")
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sys.exit(2)
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print("Checking file:", f)
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# Single sequential flow so you can follow the variables:
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problems = problems + check_entries(lp_index_path, model="gpt-4o-2024-08-06")
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# Exit codes: 0 ok, 1 issues found
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sys.exit(1 if problems > 0 else 0)

tools/requirements.txt

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better-profanity
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setuptools
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alive-progress
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openai

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