Intel® Optimized ML🔗
Intel® Extension for Scikit-learn* enhances the performance of Scikit-learn* by accelerating the training and inference of machine learning models on Intel® hardware.
XGBoost* is an optimized distributed gradient boosting library designed to be highly efficient, flexible and portable.
Images🔗
The images below include Intel® Extension for Scikit-learn* and XGBoost*.
| Tag(s) | Intel SKLearn | Scikit-learn | XGBoost | Dockerfile |
|---|---|---|---|---|
2024.7.0-pip-base, latest |
v2024.7.0 | v1.5.2 | v2.1.1 | v0.4.0 |
2024.6.0-pip-base |
v2024.6.0 | v1.5.0 | v2.1.0 | v0.4.0 |
2024.5.0-pip-base |
v2024.5.0 | v1.5.0 | v2.1.0 | v0.4.0 |
2024.3.0-pip-base |
v2024.3.0 | v1.4.2 | v2.0.3 | v0.4.0-Beta |
2024.2.0-xgboost-2.0.3-pip-base |
v2024.2.0 | v1.4.1 | v2.0.3 | v0.4.0-Beta |
scikit-learning-2024.0.0-xgboost-2.0.2-pip-base |
v2024.0.0 | v1.3.2 | v2.0.2 | v0.3.4 |
The images below additionally include Jupyter Notebook server:
| Tag(s) | Intel SKLearn | Scikit-learn | XGBoost | Dockerfile |
|---|---|---|---|---|
2024.7.0-pip-jupyter |
v2024.7.0 | v1.5.2 | v2.1.1 | v0.4.0 |
2024.6.0-pip-jupyter |
v2024.6.0 | v1.5.1 | v2.1.1 | v0.4.0 |
2024.5.0-pip-jupyter |
v2024.5.0 | v1.5.0 | v2.1.0 | v0.4.0 |
2024.3.0-pip-jupyter |
v2024.3.0 | v1.4.2 | v2.0.3 | v0.4.0-Beta |
2024.2.0-xgboost-2.0.3-pip-jupyter |
v2024.2.0 | v1.4.1 | v2.0.3 | v0.4.0-Beta |
scikit-learning-2024.0.0-xgboost-2.0.2-pip-jupyter |
v2024.0.0 | v1.3.2 | v2.0.2 | v0.3.4 |
Run the Jupyter Container🔗
docker run -it --rm \
-p 8888:8888 \
--net=host \
-v $PWD/workspace:/workspace \
-w /workspace \
intel/intel-optimized-ml:2024.2.0-xgboost-2.0.3-pip-jupyter
After running the command above, copy the URL (something like http://127.0.0.1:$PORT/?token=***) into your browser to access the notebook server.
Images with Intel® Distribution for Python*🔗
The images below include [Intel® Distribution for Python*]:
| Tag(s) | Intel SKLearn | Scikit-learn | XGBoost | Dockerfile |
|---|---|---|---|---|
2024.7.0-idp-base |
v2024.7.0 | v1.5.2 | v2.1.1 | v0.4.0 |
2024.6.0-idp-base |
v2024.6.0 | v1.5.1 | v2.1.1 | v0.4.0 |
2024.5.0-idp-base |
v2024.5.0 | v1.5.0 | v2.1.0 | v0.4.0 |
2024.3.0-idp-base |
v2024.3.0 | v1.4.1 | v2.1.0 | v0.4.0 |
2024.2.0-xgboost-2.0.3-idp-base |
v2024.2.0 | v1.4.1 | v2.0.3 | v0.4.0-Beta |
scikit-learning-2024.0.0-xgboost-2.0.2-idp-base |
v2024.0.0 | v1.3.2 | v2.0.2 | v0.3.4 |
The images below additionally include Jupyter Notebook server:
| Tag(s) | Intel SKLearn | Scikit-learn | XGBoost | Dockerfile |
|---|---|---|---|---|
2024.7.0-idp-jupyter |
v2024.7.0 | v1.5.2 | v2.1.1 | v0.4.0 |
2024.6.0-idp-jupyter |
v2024.6.0 | v1.5.1 | v2.1.1 | v0.4.0 |
2024.5.0-idp-jupyter |
v2024.5.0 | v1.5.0 | v2.1.0 | v0.4.0 |
2024.3.0-idp-jupyter |
v2024.3.0 | [v1.4.0] | v2.1.0 | v0.4.0 |
2024.2.0-xgboost-2.0.3-idp-jupyter |
v2024.2.0 | v1.4.1 | v2.0.3 | v0.4.0-Beta |
scikit-learning-2024.0.0-xgboost-2.0.2-idp-jupyter |
v2024.0.0 | v1.3.2 | v2.0.2 | v0.3.4 |
Build from Source🔗
To build the images from source, clone the AI Containers repository, follow the main README.md file to setup your environment, and run the following command:
cd classical-ml
docker compose build ml-base
docker compose run ml-base
You can find the list of services below for each container in the group:
| Service Name | Description |
|---|---|
ml-base |
Base image with Intel® Extension for Scikit-learn* and XGBoost* |
jupyter |
Adds Jupyter Notebook server |
License🔗
View the License for the Intel® Distribution for Python.
The images below also contain other software which may be under other licenses (such as Pytorch, Jupyter, Bash, etc. from the base).
It is the image user's responsibility to ensure that any use of The images below comply with any relevant licenses for all software contained within.
* Other names and brands may be claimed as the property of others.