Machine Learning using oneAPI
Machine Learning using oneAPI
Preparation to run on Intel Developer Cloud
To Access via Intel(R) Developer Cloud:
- Go to the Intel Developer Cloud by visiting https://cloud.intel.com/
- Click "Get Started"
- Subscribe to the “Standard – Free” service tier and complete your cloud registration.
- To start up a free and quick Jupyter notebook session with the latest 4th Gen Intel Xeon CPU and Intel Data Center GPU Max 1100, click the "Training and Workshops" icon and then "Launch JupyterLab", or one of the specific training materials launches.
From a Jupyterhub terminal instance:
- mkdir MLoneAPI
- cd MLoneAPI
- git clone https://github.com/IntelSoftware/Machine-Learning-using-oneAPI.git
- cd Machine-Learning-using-oneAPI
- . lab_setup.sh
- pip install -r requirements.txt
Preparation to run on Intel DevCloud (aka Colfax)
mkdir MLoneAPI cd MLoneAPI source /glob/development-tools/versions/oneapi/2022.3.1/inteloneapi/setvars.sh --force conda activate base pip install ipykernel python -m ipykernel install --user --name 2022.3.1 --display-name "oneAPI 2022.3.1"
git clone https://github.com/IntelSoftware/Machine-Learning-using-oneAPI.git cd Machine-Learning-using-oneAPI pip install -r requirements.txt
Currently Known Issues:
Known issue:
Purpose
The Jupyter Notebooks in this training are intended to give instructors an accesible but challenging introduction to machine learning using oneAPI. It enumerates and describes many commonly used Scikit-learn* allgorithms which are used daily to address machine learning challenges. The primary purpose is to accelerate commonly used Scikit-learn algorithms for Intel CPUs and GPU's using Intel Extensions for Scikit-learn* which is part of the Intel AI Analytics Toolkit powered by oneAPI.
This workshop is designed to be used on the Intel Developer Cloud.
License
Code samples are licensed under the MIT license. See License.txt for details. Third party program Licenses can be found here: third-party-programs.txt
Content Details
Pre-requisites
- Python* Programming
- Calculus
- Linear algebra
- Statistics
Content Structure
Each module folder has a Jupyter Notebook file (*.ipynb), this can be opened in Jupyter Lab to view the training contant, edit code and compile/run.
Install Directions
The training content can be accessed locally on the computer after installing necessary tools, or you can directly access using Intel Developer Cloud without any installation.
Local Installation of JupyterLab and oneAPI Tools
The Jupyter Notebooks can be downloaded locally to computer and accessed:
- Install Jupyter Lab on local computer: Installation Guide
- Install Intel oneAPI Base Toolkit on local computer: Installation Guide
- git clone the repo and access the Notebooks using Jupyter Lab
Access using Intel Developer Cloud
The Jupyter notebooks are tested and can be run on Intel Developer Cloud without any installation necessary, below are the steps to access these Jupyter notebooks on Intel DevCloud:
- Register on Intel Developer Cloud
- Login, Get Started and Launch Jupyter Lab
- Open Terminal in Jupyter Lab and git clone the repo and access the Notebooks