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Intel® AI Analytics Toolkit (AI Kit) Container Getting Started sample readme update (#1492)
* Fixes for 2023.1 AI Kit (#1409) * Intel Python Numpy Numba_dpes kNN sample (#1292) * *.py and *.ipynb files with implementation * README.md and sample.json files with documentation * License and thir party programs * Adding PyTorch Training Optimizations with AMX BF16 oneAPI sample (#1293) * add IntelPytorch Quantization code samples (#1301) * add IntelPytorch Quantization code samples * fix the spelling error in the README file * use john's README with grammar fix and title change * Rename third-party-grograms.txt to third-party-programs.txt Co-authored-by: Jimmy Wei <[email protected]> * AMX bfloat16 mixed precision learning TensorFlow Transformer sample (#1317) * [New Sample] Intel Extension for TensorFlow Getting Started (#1313) * first draft * Update README.md * remove redunant file * [New Sample] [oneDNN] Benchdnn tutorial (#1315) * New Sample: benchDNN tutorial * Update readme: new sample * Rename sample to benchdnn_tutorial * Name fix * Add files via upload (#1320) * [New Sample] oneCCL Bindings for PyTorch Getting Started (#1316) * Update README.md * [New Sample] oneCCL Bindings for PyTorch Getting Started * Update README.md * add torch-ccl version check * [New Sample] Intel Extension for PyTorch Getting Started (#1314) * add new ipex GSG notebook for dGPU * Update sample.json for expertise field * Update requirements.txt Update package versions to comply with Snyk tool * Updated title field in sample.json in TF Transformer AMX bfloat16 Mixed Precision sample to fit within character length range (#1327) * add arch checker class (#1332) * change gpu.patch to convert the code samples from cpu to gpu correctly (#1334) * Fixes for spelling in AMX bfloat16 transformer sample and printing error in python code in numpy vs numba sample (#1335) * 2023.1 ai kit itex get started example fix (#1338) * Fix the typo * Update ResNet50_Inference.ipynb * fix resnet inference demo link (#1339) * Fix printing issue in numpy vs numba AI sample (#1356) * Fix Invalid Kmeans parameters on oneAPI 2023 (#1345) * Update README to add new samples into the list (#1366) * PyTorch AMX BF16 Training sample: remove graphs and performance numbers (#1408) * Adding PyTorch Training Optimizations with AMX BF16 oneAPI sample * remove performance graphs, update README * remove graphs from README and folder * update top README in Features and Functionality --------- Co-authored-by: krzeszew <[email protected]> Co-authored-by: alexsin368 <[email protected]> Co-authored-by: ZhaoqiongZ <[email protected]> Co-authored-by: Louie Tsai <[email protected]> Co-authored-by: Orel Yehuda <[email protected]> Co-authored-by: yuning <[email protected]> Co-authored-by: Wang, Kai Lawrence <[email protected]> Co-authored-by: xiguiw <[email protected]> * readme up text --------- Co-authored-by: Jimmy Wei <[email protected]> Co-authored-by: krzeszew <[email protected]> Co-authored-by: alexsin368 <[email protected]> Co-authored-by: ZhaoqiongZ <[email protected]> Co-authored-by: Louie Tsai <[email protected]> Co-authored-by: Orel Yehuda <[email protected]> Co-authored-by: yuning <[email protected]> Co-authored-by: Wang, Kai Lawrence <[email protected]> Co-authored-by: xiguiw <[email protected]>
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‎AI-and-Analytics/Getting-Started-Samples/IntelAIKitContainer_GettingStarted/README.md

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# Intel&reg; AI Analytics Toolkit (AI Kit) Container Sample
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# `Intel® AI Analytics Toolkit (AI Kit) Container Getting Started` Sample
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Containers allow you to set up and configure environments for
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building, running, and profiling AI applications and distribute
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them using images. You can also use Kubernetes* to automate the
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deployment and management of containers in the cloud.
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The `Intel® AI Analytics Toolkit (AI Kit) Container Getting Started` sample demonstrates how to use AI Kit containers.
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This get started sample shows the easiest way to start using any of
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the [Intel® AI Analytics
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Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html)
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components without the hassle of installing the toolkit, configuring
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networking and file sharing.
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| Area | Description
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|:--- |:---
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| What you will learn | How to start using the Intel® oneapi-aikit container
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| Time to complete | 10 minutes
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| Category | Tutorial
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| Optimized for | Description
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|:--- |:---
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| OS | Linux* Ubuntu* 18.04
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| Hardware | Intel® Xeon® Scalable processor family or newer
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| Software | Intel® AI Analytics Toolkit
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| What you will learn | How to start using the Intel® oneapi-aikit container
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| Time to complete | 10 minutes
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For more information on the **oneapi-aikit** container, see [Intel AI Analytics Toolkit](https://hub.docker.com/r/intel/oneapi-aikit) Docker Hub location.
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## Purpose
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This sample provides a Bash script to help users configure their Intel&reg; AI Analytics Toolkit
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container environment. Developers can
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quickly build and train deep learning models using this Docker*
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environment.
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This sample provides a Bash script to help you configure an AI Kit container environment. You can build and train deep learning models using this Docker* environment.
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Containers allow you to set up and configure environments for building, running, and profiling AI applications and distribute them using images. You can also use Kubernetes* to automate the deployment and management of containers in the cloud.
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Read the [Get Started with the Intel® AI Analytics Toolkit for Linux*](https://www.intel.com/content/www/us/en/develop/documentation/get-started-with-ai-linux/top.html) to find out how you can achieve performance gains for popular deep-learning and machine-learning frameworks through Intel optimizations.
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This sample shows an easy way to start using any of the [Intel® AI Analytics Toolkit (AI Kit)](https://www.intel.com/content/www/us/en/developer/tools/oneapi/ai-analytics-toolkit.html) components without the hassle of installing the toolkit, configuring networking and file sharing.
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For more information on the one API AIKit container, see [AI Kit
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Container Repository](https://hub.docker.com/r/intel/oneapi-aikit).
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## Prerequisites
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| Optimized for | Description
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|:--- |:---
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| OS | Ubuntu* 20.04 (or newer)
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| Hardware | Intel® Xeon® Scalable processor family
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| Software | Intel® AI Analytics Toolkit (AI Kit)
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## Key Implementation Details
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The Bash script provided in this sample performs the following
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configuration steps:
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- Mounts the `/home` folder from host machine into the Docker
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container. You can share files between the host machine and the
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Docker container via the `/home` folder.
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- Mounts the `/home` folder from host machine into the Docker container. You can share files between the host machine and the Docker container through the `/home` folder.
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- Applies proxy settings from the host machine into the Docker
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container.
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- Uses the same IP addresses between the host machine and the Docker
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container.
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- Applies proxy settings from the host machine into the Docker container.
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- Forwards ports 8888, 6006, 6543, and 12345 from the host machine to
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the Docker container for some popular network services, such as
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Jupyter notebook and TensorBoard.
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- Uses the same IP addresses between the host machine and the Docker container.
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## Run the Sample
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- Forwards ports 8888, 6006, 6543, and 12345 from the host machine to the Docker container for some popular network services, such as Jupyter* Notebook and TensorFlow* TensorBoard.
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## Run the `Intel® AI Analytics Toolkit (AI Kit) Container Getting Started` Sample
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This sample uses a configuration script to automatically configure the
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environment. This provides fast and less error prone setup. For
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complete instructions for using the AI Kit containers see
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the [Getting Started Guide](https://www.intel.com/content/www/us/en/develop/documentation/get-started-with-ai-linux/top/using-containers.html.)
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This sample uses a configuration script to automatically configure the environment. This provides fast and less error prone setup. For complete instructions for using the AI Kit containers, see the [Getting Started Guide](https://www.intel.com/content/www/us/en/develop/documentation/get-started-with-ai-linux/top/using-containers.html).
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To run the configuration script on Linux*, type the following command
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in the terminal with [Docker](https://docs.docker.com/engine/install/)
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installed:
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### On Linux*
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You must have [Docker](https://docs.docker.com/engine/install/)
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installed.
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1. Navigate to the directory with the IntelAIKitContainer sample and pull the oneapi-aikit docker image:
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```
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docker pull intel/oneapi-aikit
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```
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> Please apply the below command and login again if a permisson denied error occurs.
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```
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sudo usermod -aG docker $USER
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```
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2. Use the `run_oneapi_docker.sh` Bash script to run the Docker image:
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1. Open a terminal.
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2. Change to the sample folder, and pull the oneapi-aikit Docker image.
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```
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docker pull intel/oneapi-aikit
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```
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>**Note**: If a permission denied error occurs, run the following command.
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>```
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>sudo usermod -aG docker $USER
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>```
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```bash
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3. Run the Docker images using the `run_oneapi_docker.sh` Bash script.
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```
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./run_oneapi_docker.sh intel/oneapi-aikit
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```
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The script opens a Bash shell inside the Docker container.
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> Note : Users could install additional packages by adding them into requirements.txt.
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> Please copy the modified requirements.txt into /tmp folder, so the bash script will install those packages for you.
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To create a new Bash session in the running container from outside
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the Docker container, use the following:
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```bash
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> **Note**: Install additional packages by adding them into requirements.txt file in the sample. Copy the modified requirements.txt into /tmp folder, so the bash script will install those packages for you.
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To create a Bash session in the running container from outside the Docker container, enter a command similar to the following.
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```
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docker exec -it aikit_container /bin/bash
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```
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3. In the Bash shell inside the Docker container, activate the oneAPI
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environment:
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```bash
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4. In the Bash shell inside the Docker container, activate the specialized environment.
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```
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source activate tensorflow
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```
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or
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```bash
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```
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source activate pytorch
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```
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Now you can start using Intel® Optimization for TensorFlow* or Intel
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Optimization for PyTorch inside the Docker container.
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To verify the activated environment, navigate to the directory with
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the IntelAIKitContainer sample and run the `version_check.py` script:
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```bash
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python version_check.py
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```
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You can start using Intel® Optimization for TensorFlow* or Intel® Optimization for PyTorch* inside the Docker container.
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## Example of Output
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Output from TensorFlow Environment
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```
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TensorFlow version: 2.6.0
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MKL enabled : True
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```
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Output from PyTorch Environment
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```
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PyTorch Version: 1.8.0a0+37c1f4a
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mkldnn : True, mkl : True, openmp : True
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```
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## Next Steps
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Explore the [Get Started
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Guide](https://www.intel.com/content/www/us/en/develop/documentation/get-started-with-ai-linux/top.html).
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to find out how you can achieve performance gains for popular
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deep-learning and machine-learning frameworks through Intel
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optimizations.
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>**Note**: You can verify the activated environment. Change to the directory with the IntelAIKitContainer sample and run the `version_check.py` script.
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>```
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>python version_check.py
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>```
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### Manage Docker* Images
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You can install additional packages, upload the workloads via the
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`/tmp` folder, and then commit your changes into a new Docker image,
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for example, `intel/oneapi-aikit-v1`:
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```bash
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You can install additional packages, upload the workloads via the `/tmp` folder, and then commit your changes into a new Docker image, for example, `intel/oneapi-aikit-v1`.
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```
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docker commit -a "intel" -m "test" DOCKER_ID intel/oneapi-aikit-v1
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```
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>**Note**: Replace `DOCKER_ID` with the ID of your container. Use `docker ps` to get the DOCKER_ID of your Docker container.
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**NOTE:** Replace `DOCKER_ID` with the ID of your container. Use
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`docker ps` to get the DOCKER_ID of your Docker container.
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You can then use the new image name to start Docker:
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```bash
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You can use the new image name to start Docker.
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```
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./run_oneapi_docker.sh intel/oneapi-aikit-v1
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```
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To save the Docker image as a tar file:
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```bash
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You can save the Docker image as a tar file.
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```
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docker save -o oneapi-aikit-v1.tar intel/oneapi-aikit-v1
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```
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To load the tar file on other machines:
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```bash
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You can load the tar file on other machines.
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```
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docker load -i oneapi-aikit-v1.tar
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```
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## Troubleshooting
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### Docker Proxy
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#### Ubuntu
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For docker proxy related problem, you could follow below instructions to setup proxy for your docker client.
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For Docker proxy related problem, you could follow below instructions to configure proxy settings for your Docker client.
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1. Create a directory for the Docker service configurations.
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```
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sudo mkdir -p /etc/systemd/system/docker.service.d
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```
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2. Create a file called `proxy.conf` in our configuration directory.
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```
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sudo vi /etc/systemd/system/docker.service.d/proxy.conf
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```
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3. Add the contents similar to the following to the `.conf` file. Change the values to match your environment.
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```
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[Service]
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Environment="HTTP_PROXY=http://proxy-hostname:911/"
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Environment="HTTPS_PROXY="http://proxy-hostname:911/
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Environment="NO_PROXY="10.0.0.0/8,192.168.0.0/16,localhost,127.0.0.0/8,134.134.0.0/16"
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```
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4. Save your changes and exit the text editor.
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5. Reload the daemon configuration.
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```
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sudo systemctl daemon-reload
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```
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6. Restart Docker to apply our changes.
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```
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sudo systemctl restart docker.service
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```
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## Example Output
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### Output from TensorFlow* Environment
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1. Create a new directory for our Docker service configurations
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```bash
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sudo mkdir -p /etc/systemd/system/docker.service.d
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```
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2. Create a file called proxy.conf in our configuration directory.
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```bash
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sudo vi /etc/systemd/system/docker.service.d/proxy.conf
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```
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3. Add the following contents, changing the values to match your environment.
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```bash
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[Service]
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Environment="HTTP_PROXY=http://proxy-hostname:911/"
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Environment="HTTPS_PROXY="http://proxy-hostname:911/
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Environment="NO_PROXY="10.0.0.0/8,192.168.0.0/16,localhost,127.0.0.0/8,134.134.0.0/16"
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TensorFlow version: 2.6.0
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MKL enabled : True
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```
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4. Save your changes and exit the text editor.
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5. Reload the daemon configuration
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```bash
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sudo systemctl daemon-reload
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### Output from PyTorch* Environment
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```
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6. Restart Docker to apply our changes
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```bash
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sudo systemctl restart docker.service
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PyTorch Version: 1.8.0a0+37c1f4a
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mkldnn : True, mkl : True, openmp : True
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```
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## License
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Code samples are licensed under the MIT license. See
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[License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt)
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for details.
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[License.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/License.txt) for details.
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Third party program Licenses can be found here: [third-party-programs.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/third-party-programs.txt)
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Third party program Licenses can be found here: [third-party-programs.txt](https://github.com/oneapi-src/oneAPI-samples/blob/master/third-party-programs.txt).

‎AI-and-Analytics/Getting-Started-Samples/IntelAIKitContainer_GettingStarted/sample.json

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{
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"guid": "0F95DA9E-0A5D-4CF2-B791-885B09675004",
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"name": "IntelAIKitContainer_GettingStarted",
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"name": "Intel(R) AI Analytics Toolkit (AI Kit) Container Getting Started",
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"categories": ["Toolkit/oneAPI AI And Analytics/AI Getting Started Samples"],
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"description": "This sample illustrates how to utilize the oneAPI AI Kit container.",
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"builder": ["cli"],

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