Merge pull request #203 from changeworld/patch-1

Court72 · web-flow · commit 19b4fbcdcabe · 2025-02-03T09:43:24.000-07:00
Fix typo
diff --git a/articles/ai-services/computer-vision/spatial-analysis-container.md b/articles/ai-services/computer-vision/spatial-analysis-container.md
@@ -159,9 +159,9 @@ Follow these steps to remotely connect from a Windows client.
 
 Follow these instructions if your host computer isn't an Azure Stack Edge device.
 
-#### Install NVIDIA CUDA Toolkit and Nvidia graphics drivers on the host computer
+#### Install NVIDIA CUDA Toolkit and NVIDIA graphics drivers on the host computer
 
-Use the following bash script to install the required Nvidia graphics drivers, and CUDA Toolkit.
+Use the following bash script to install the required NVIDIA graphics drivers, and CUDA Toolkit.
 
 ```bash
 wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/cuda-ubuntu1804.pin
diff --git a/articles/ai-services/content-safety/how-to/containers/install-run-container.md b/articles/ai-services/content-safety/how-to/containers/install-run-container.md
@@ -86,7 +86,7 @@ Even with identical GPUs, performance can fluctuate based on the GPU load and th
 
 ## Install the NVIDIA container toolkit
 
-The `host` is the computer that runs the docker container. The host must support Nvidia container toolkit. Follow the below guidance to install the toolkit in your environment.
+The `host` is the computer that runs the docker container. The host must support NVIDIA container toolkit. Follow the below guidance to install the toolkit in your environment.
 
 [Install the NVIDIA Container Toolkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html)
 
diff --git a/articles/ai-services/language-service/summarization/how-to/use-containers.md b/articles/ai-services/language-service/summarization/how-to/use-containers.md
@@ -35,7 +35,7 @@ The following table describes the minimum and recommended specifications for the
 | Container Type             | Recommended number of CPU cores  | Recommended memory | Notes |
 |----------------------------|----------------------------------|--------------------|-------|
 | Summarization CPU container| 16                               | 48 GB              |       |
-| Summarization GPU container| 2                                | 24 GB              | Requires an Nvidia GPU that supports Cuda 11.8 with 16GB VRAM.|
+| Summarization GPU container| 2                                | 24 GB              | Requires an NVIDIA GPU that supports Cuda 11.8 with 16GB VRAM.|
 
 CPU core and memory correspond to the `--cpus` and `--memory` settings, which are used as part of the `docker run` command.
 
diff --git a/articles/ai-studio/how-to/deploy-models-mistral-nemo.md b/articles/ai-studio/how-to/deploy-models-mistral-nemo.md
@@ -37,7 +37,7 @@ Mistral Nemo is a 12B model, making it a powerful drop-in replacement for any sy
 
 Additionally, Mistral Nemo is:
 
-* **Jointly developed with Nvidia**. This collaboration has resulted in a powerful 12B model that pushes the boundaries of language understanding and generation.
+* **Jointly developed with NVIDIA**. This collaboration has resulted in a powerful 12B model that pushes the boundaries of language understanding and generation.
 * **Multilingual proficient**. Mistral Nemo is equipped with a tokenizer called Tekken, which is designed for multilingual applications. It supports over 100 languages, such as English, French, German, and Spanish. Tekken is more efficient than the Llama 3 tokenizer in compressing text for approximately 85% of all languages, with significant improvements in Malayalam, Hindi, Arabic, and prevalent European languages.
 * **Agent-centric**. Mistral Nemo possesses top-tier agentic capabilities, including native function calling and JSON outputting.
 * **Advanced in reasoning**. Mistral Nemo demonstrates state-of-the-art mathematical and reasoning capabilities within its size category.
@@ -477,7 +477,7 @@ Mistral Nemo is a 12B model, making it a powerful drop-in replacement for any sy
 
 Additionally, Mistral Nemo is:
 
-* **Jointly developed with Nvidia**. This collaboration has resulted in a powerful 12B model that pushes the boundaries of language understanding and generation.
+* **Jointly developed with NVIDIA**. This collaboration has resulted in a powerful 12B model that pushes the boundaries of language understanding and generation.
 * **Multilingual proficient**. Mistral Nemo is equipped with a tokenizer called Tekken, which is designed for multilingual applications. It supports over 100 languages, such as English, French, German, and Spanish. Tekken is more efficient than the Llama 3 tokenizer in compressing text for approximately 85% of all languages, with significant improvements in Malayalam, Hindi, Arabic, and prevalent European languages.
 * **Agent-centric**. Mistral Nemo possesses top-tier agentic capabilities, including native function calling and JSON outputting.
 * **Advanced in reasoning**. Mistral Nemo demonstrates state-of-the-art mathematical and reasoning capabilities within its size category.
@@ -936,7 +936,7 @@ Mistral Nemo is a 12B model, making it a powerful drop-in replacement for any sy
 
 Additionally, Mistral Nemo is:
 
-* **Jointly developed with Nvidia**. This collaboration has resulted in a powerful 12B model that pushes the boundaries of language understanding and generation.
+* **Jointly developed with NVIDIA**. This collaboration has resulted in a powerful 12B model that pushes the boundaries of language understanding and generation.
 * **Multilingual proficient**. Mistral Nemo is equipped with a tokenizer called Tekken, which is designed for multilingual applications. It supports over 100 languages, such as English, French, German, and Spanish. Tekken is more efficient than the Llama 3 tokenizer in compressing text for approximately 85% of all languages, with significant improvements in Malayalam, Hindi, Arabic, and prevalent European languages.
 * **Agent-centric**. Mistral Nemo possesses top-tier agentic capabilities, including native function calling and JSON outputting.
 * **Advanced in reasoning**. Mistral Nemo demonstrates state-of-the-art mathematical and reasoning capabilities within its size category.
@@ -1417,7 +1417,7 @@ Mistral Nemo is a 12B model, making it a powerful drop-in replacement for any sy
 
 Additionally, Mistral Nemo is:
 
-* **Jointly developed with Nvidia**. This collaboration has resulted in a powerful 12B model that pushes the boundaries of language understanding and generation.
+* **Jointly developed with NVIDIA**. This collaboration has resulted in a powerful 12B model that pushes the boundaries of language understanding and generation.
 * **Multilingual proficient**. Mistral Nemo is equipped with a tokenizer called Tekken, which is designed for multilingual applications. It supports over 100 languages, such as English, French, German, and Spanish. Tekken is more efficient than the Llama 3 tokenizer in compressing text for approximately 85% of all languages, with significant improvements in Malayalam, Hindi, Arabic, and prevalent European languages.
 * **Agent-centric**. Mistral Nemo possesses top-tier agentic capabilities, including native function calling and JSON outputting.
 * **Advanced in reasoning**. Mistral Nemo demonstrates state-of-the-art mathematical and reasoning capabilities within its size category.
diff --git a/articles/machine-learning/azure-machine-learning-ci-image-release-notes.md b/articles/machine-learning/azure-machine-learning-ci-image-release-notes.md
@@ -36,7 +36,7 @@ gnomeshell: removed
 
 ml: '2.32.4'
 
-Nvidia Driver:  `535.216.03`
+NVIDIA Driver:  `535.216.03`
 
 `CUDA`: `12.2`
 
@@ -50,7 +50,7 @@ SDK Version: `1.57.0`
 
 Python: `3.10.11`
 
-Nvidia Driver:  `535.183.06`
+NVIDIA Driver:  `535.183.06`
 
 `CUDA`: `12.2`
 
@@ -223,7 +223,7 @@ Python: `3.9`
 
 CUDnn==`9.1.1`
 
-Nvidia Driver: `535.171.04`
+NVIDIA Driver: `535.171.04`
 
 PyTorch: `1.13.1`
 
diff --git a/articles/machine-learning/concept-model-catalog.md b/articles/machine-learning/concept-model-catalog.md
@@ -17,7 +17,7 @@ ms.date: 12/11/2024
 
 # Model Catalog and Collections
 
-The model catalog in Azure Machine Learning studio is the hub to discover and use a wide range of models that enable you to build Generative AI applications. The model catalog features hundreds of models from model providers such as Azure OpenAI service, Mistral, Meta, Cohere, Nvidia, Hugging Face, including models trained by Microsoft. Models from providers other than Microsoft are Non-Microsoft Products, as defined in [Microsoft's Product Terms](https://www.microsoft.com/licensing/terms/welcome/welcomepage), and subject to the terms provided with the model.   
+The model catalog in Azure Machine Learning studio is the hub to discover and use a wide range of models that enable you to build Generative AI applications. The model catalog features hundreds of models from model providers such as Azure OpenAI service, Mistral, Meta, Cohere, NVIDIA, Hugging Face, including models trained by Microsoft. Models from providers other than Microsoft are Non-Microsoft Products, as defined in [Microsoft's Product Terms](https://www.microsoft.com/licensing/terms/welcome/welcomepage), and subject to the terms provided with the model.   
 
 ## Model Collections 
 
diff --git a/articles/machine-learning/concept-onnx.md b/articles/machine-learning/concept-onnx.md
@@ -29,7 +29,7 @@ This ONNX flow diagram shows available frameworks and deployment options.
 
 [ONNX Runtime](https://onnxruntime.ai) is a high-performance inference engine for deploying ONNX models to production. ONNX Runtime is optimized for both cloud and edge, and works on Linux, Windows, and macOS. ONNX is written in C++, but also has C, Python, C#, Java, and JavaScript (Node.js) APIs to use in those environments.
 
-ONNX Runtime supports both deep neural networks (DNN) and traditional machine learning models, and it integrates with accelerators on different hardware such as TensorRT on Nvidia GPUs, OpenVINO on Intel processors, and DirectML on Windows. By using ONNX Runtime, you can benefit from extensive production-grade optimizations, testing, and ongoing improvements.
+ONNX Runtime supports both deep neural networks (DNN) and traditional machine learning models, and it integrates with accelerators on different hardware such as TensorRT on NVIDIA GPUs, OpenVINO on Intel processors, and DirectML on Windows. By using ONNX Runtime, you can benefit from extensive production-grade optimizations, testing, and ongoing improvements.
 
 High-scale Microsoft services such as Bing, Office, and Azure AI use ONNX Runtime. Although performance gains depend on many factors, these Microsoft services report an average 2x performance gain on CPU by using ONNX. ONNX Runtime runs in Azure Machine Learning and other Microsoft products that support machine learning workloads, including:
 
diff --git a/articles/machine-learning/data-science-virtual-machine/overview.md b/articles/machine-learning/data-science-virtual-machine/overview.md
@@ -30,7 +30,7 @@ Additionally, we offer Azure DSVM for PyTorch - an Ubuntu 20.04 image from Azure
 - MSCCL​
 - ORTMoE​
 - Fairscale​
-- Nvidia Apex​
+- NVIDIA Apex​
 - An up-to-date stack with the latest compatible versions of Ubuntu, Python, PyTorch, and CUDA
 
 ## Comparison with Azure Machine Learning
diff --git a/articles/machine-learning/data-science-virtual-machine/release-notes.md b/articles/machine-learning/data-science-virtual-machine/release-notes.md
@@ -518,7 +518,7 @@ Version: `21.08.11`
 Primary changes:
 
 - Windows Security update
-- Update of Nvidia CuDNN to 8.1.0
+- Update of NVIDIA CuDNN to 8.1.0
 - Update of Jupyter Lab -to 3.0.16
 - Added MLFLow for experiment tracking
 - Improvement of stability and minor bug fixes
diff --git a/articles/machine-learning/how-to-deploy-models-mistral.md b/articles/machine-learning/how-to-deploy-models-mistral.md
@@ -90,7 +90,7 @@ Mistral Nemo is a 12B model, making it a powerful drop-in replacement for any sy
 
 Additionally, Mistral Nemo is:
 
-- **Jointly developed with Nvidia.** This collaboration has resulted in a powerful 12B model that pushes the boundaries of language understanding and generation.
+- **Jointly developed with NVIDIA.** This collaboration has resulted in a powerful 12B model that pushes the boundaries of language understanding and generation.
 - **Multilingual proficient.** Mistral Nemo is equipped with a tokenizer called Tekken, which is designed for multilingual applications. It supports over 100 languages, such as English, French, German, and Spanish. Tekken is more efficient than the Llama 3 tokenizer in compressing text for approximately 85% of all languages, with significant improvements in Malayalam, Hindi, Arabic, and prevalent European languages.
 - **Agent-centric.** Mistral Nemo possesses top-tier agentic capabilities, including native function calling and JSON outputting.
 - **Advanced in reasoning.** Mistral Nemo demonstrates state-of-the-art mathematical and reasoning capabilities within its size category.
diff --git a/articles/machine-learning/how-to-troubleshoot-environments.md b/articles/machine-learning/how-to-troubleshoot-environments.md
@@ -121,7 +121,7 @@ System vulnerabilities in an environment are usually introduced from the base im
 - Microsoft Artifact Registry (MAR) aka Microsoft Container Registry (mcr.microsoft.com). 
     - Images can be listed from MAR homepage, calling _catalog API, or [/tags/list](https://mcr.microsoft.com/v2/azureml/openmpi4.1.0-ubuntu20.04/tags/list)_
     - Source and release notes for training base images from AzureML can be found in [Azure/AzureML-Containers](https://github.com/Azure/AzureML-Containers)
-- Nvidia (nvcr.io, or [nvidia's Profile](https://hub.docker.com/u/nvidia/#!))
+- NVIDIA (nvcr.io, or [nvidia's Profile](https://hub.docker.com/u/nvidia/#!))
 
 If the latest version of your base image does not resolve your vulnerabilities, base image vulnerabilities can be addressed by installing versions recommended by a vulnerability scan:
 
diff --git a/articles/machine-learning/overview-what-is-azure-machine-learning.md b/articles/machine-learning/overview-what-is-azure-machine-learning.md
@@ -79,7 +79,7 @@ Both Azure Machine Learning studio and Azure AI Foundry allow you to work with L
 
 ### Model catalog
 
-The model catalog in Azure Machine Learning studio is the hub to discover and use a wide range of models that enable you to build Generative AI applications. The model catalog features hundreds of models from model providers such as Azure OpenAI service, Mistral, Meta, Cohere, Nvidia, Hugging Face, including models trained by Microsoft. Models from providers other than Microsoft are Non-Microsoft Products, as defined in [Microsoft's Product Terms](https://www.microsoft.com/licensing/terms/welcome/welcomepage), and subject to the terms provided with the model.
+The model catalog in Azure Machine Learning studio is the hub to discover and use a wide range of models that enable you to build Generative AI applications. The model catalog features hundreds of models from model providers such as Azure OpenAI service, Mistral, Meta, Cohere, NVIDIA, Hugging Face, including models trained by Microsoft. Models from providers other than Microsoft are Non-Microsoft Products, as defined in [Microsoft's Product Terms](https://www.microsoft.com/licensing/terms/welcome/welcomepage), and subject to the terms provided with the model.
 
 ### Prompt flow
 
diff --git a/articles/machine-learning/reference-checkpoint-performance-for-large-models.md b/articles/machine-learning/reference-checkpoint-performance-for-large-models.md
@@ -53,7 +53,7 @@ With Nebula you can:
 
   This example shows the checkpoint and end-to-end training time reduction for four checkpoints saving of Hugging Face GPT2, GPT2-Large, and GPT-XL training jobs. For the medium-sized Hugging Face GPT2-XL checkpoint saves (20.6 GB), Nebula achieved a 96.9% time reduction for one checkpoint.
   
-  The checkpoint speed gain can still increase with model size and GPU numbers. For example, testing a training point checkpoint save of 97 GB on 128 A100 Nvidia GPUs can shrink from 20 minutes to 1 second.
+  The checkpoint speed gain can still increase with model size and GPU numbers. For example, testing a training point checkpoint save of 97 GB on 128 A100 NVIDIA GPUs can shrink from 20 minutes to 1 second.
 
 * **Reduce end-to-end training time and computation costs for large models** by minimizing checkpoint overhead and reducing the number of GPU hours wasted on job recovery. Nebula saves checkpoints asynchronously, and unblocks the training process, to shrink the end-to-end training time. It also allows for more frequent checkpoint saves. This way, you can resume your training from the latest checkpoint after any interruption, and save time and money wasted on job recovery and GPU training hours.
 
diff --git a/articles/machine-learning/reference-yaml-deployment-kubernetes-online.md b/articles/machine-learning/reference-yaml-deployment-kubernetes-online.md
@@ -86,15 +86,15 @@ The source JSON schema can be found at https://azuremlschemas.azureedge.net/late
 | --- | ---- | ----------- |
 | `cpu` | string | The number of CPU cores requested for the container. |
 | `memory` | string | The memory size requested for the container |
-| `nvidia.com/gpu` | string | The number of Nvidia GPU cards requested for the container |
+| `nvidia.com/gpu` | string | The number of NVIDIA GPU cards requested for the container |
 
 ### ContainerResourceLimits
 
 | Key | Type | Description |
 | --- | ---- | ----------- |
 | `cpu` | string | The limit for the number of CPU cores for the container. |
 | `memory` | string | The limit for the memory size for the container. |
-| `nvidia.com/gpu` | string | The limit for the number of Nvidia GPU cards for the container |
+| `nvidia.com/gpu` | string | The limit for the number of NVIDIA GPU cards for the container |
 
 ### DataCollector
 
