Build and Run a Sample Using the Command Line
AI Tools
In this section, you will run a simple "Hello World" project to familiarize yourself with the process of building projects, and then build your own project.
NOTE:
If you have not already configured your development environment, go to Configure your system then return to this page. If you have already completed the steps to configure your system, continue with the steps below.
You can use either a terminal window or Visual Studio Code* when working from the command line. For details on how to use VS Code locally, see Basic Usage of Visual Studio Code with oneAPI on Linux*. To use VS Code remotely, see Remote Visual Studio Code Development with oneAPI on Linux*.
Build and Run a Sample Project
Samples must be cloned to your system before you can build the sample project. To build and run a sample, start by cloning the sample, then follow the directions in README.md to build and run the sample. For more samples, browse the full GitHub repository: AI Tools Code Samples.
Getting Started Samples for AI Tools
| Component | Folder | Description | |
| Classical Machine Learning | Modin* | Modin_GettingStarted | Run Modin*-accelerated Pandas functions and note the performance gain. |
| Modin_Vs_Pandas | Compares the performance of Intel® Distribution of Modin* and the performance of Pandas. | ||
| Intel® Optimization for XGBoost* | IntelPython_XGBoost_GettingStarted | Set up and trains an XGBoost* model on datasets for prediction. | |
| Scikit-learn* | Intel_Extension_For_SKLearn_GettingStarted | Speed up a Scikit-learn* application using Intel oneDAL. | |
| IntelPython_daal4py_GettingStarted | Batch linear regression using the Python API package daal4py from oneAPI Data Analytics Library (oneDAL). | ||
| Deep Learning | Intel® Extension for PyTorch* | Getting Started with Intel® Extension for PyTorch* | A simple training example for Intel® Extension for PyTorch*. For the Intel® Extension for PyTorch* CPU sample, first clone the repository using the following command: git clone https://github.com/intel/intel-extension-for-pytorch.git Then activate the environment using the following command: conda activate pytorch Finally, follow the instructions in the README file: Inference notebooks For the Intel® Extension for PyTorch* GPU sample, first clone the repository using the following command: git clone https://github.com/intel/intel-extension-for-pytorch.gitThen activate the environment using the following command: conda activate pytorch Finally, follow the instructions in the README file: |
| Intel_oneCCL_Bindings_For_PyTorch_GettingStarted | Guides users through the process of running a simple PyTorch* distributed workload on both GPU and CPU. | ||
| Intel® Neural Compressor (INC) | Intel® Neural Compressor (INC) Sample-for-PyTorch | Performs INT8 quantization on a Hugging Face BERT model. | |
| Intel® Neural Compressor (INC) Sample-for-Tensorflow | Quantizes a FP32 model into INT8 by Intel® Neural Compressor (INC) and compares the performance between FP32 and INT8. | ||
| ONNX Runtime* | Quickstart Examples for PyTorch*, TensorFlow*, and SciKit Learn* | Train a model using your favorite framework, export to ONNX format and inference in any supported ONNX Runtime* language. | |
| Intel® Extension for TensorFlow* | IntelTensorFlow_GettingStarted | A simple training example for TensorFlow*. | |
| Intel® Extension For TensorFlow GettingStarted | Guides users how to run a TensorFlow* inference workload on both GPU and CPU. | ||
| JAX* | IntelJAX GettingStarted | The JAX* Getting Started sample demonstrates how to train a JAX* model and run inference on Intel® hardware. |
To see a list of components that support CMake, see Use CMake to with oneAPI Applications.
Build Your Own Project
No special modifications to your existing Python projects are required to start using them with these tools. For new projects, the process closely follows the process used for the Getting Started Samples. Refer to the TensorFlow* Getting Started Sample README file for instructions.
Maximizing Performance
You can get documentation to help you maximize performance for either TensorFlow* or PyTorch*.
Activate the AI Tools
. shell source %HOME/intel/oneapi/intelpython/bin/activate
Create your own Environment
- To create an environment:
. shell conda create --name <my-env>
This creates the virtual environment. No packages will be installed in this environment.
- To create an environment with a specific package:
. shell conda create -n myenv intel-extension-for-tensorflow -c https://software.repos.intel.com/python -c conda-forge
Or:
. shell conda create --name myenv conda install -n myenv intel-extension-for-tensorflow -c https://software.repos.intel.com/python -c conda-forge
NOTE:
For more personalized package selection, please visit the AI Tools Selector.
Using JupyterLab*
- Activate the AI Tools:
. shell source %HOME/intel/oneapi/intelpython/bin/activate
- Run JupyterLab:
. shell jupyter lab --ip 0.0.0.0 --no-browser --allow-root
Parent topic: Get Started with the AI Tools