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https://xuanyuan.cloud/agents.md
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https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html#gs.9bos9m enhances performance and can improve your program speed from 10 to 100 times faster. It is a Python* distribution that includes the https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html (oneMKL) and other Intel performance libraries to enable near-native performance through acceleration of core numerical and machine learning packages.
Intel® Distribution for Python* is also available for Intel® dGPUs, that include the latest https://dgpu-docs.intel.com/releases/releases.html and Intel® OneAPI runtime libraries such as https://www.intel.com/content/www/us/en/developer/tools/oneapi/onemkl.html, https://www.intel.com/content/www/us/en/developer/tools/oneapi/dpc-compiler-download.html and https://www.intel.com/content/www/us/en/developer/tools/oneapi/oneccl.html that enable Machine Learning frameworks leverage the XPU device plugin.
The images below include variations for only the core packages in the https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html#gs.9bos9m installation, or all of the packages.
| Tag(s) | IDP |
|---|---|
3.11-full, latest | 2025.0.0 |
3.11-xpu-full | 2025.0.0 |
3.10-full, | 2024.2.0 |
3.10-core | 2024.2.0 |
To run a performance sample run the following commands:
bashgit clone https://github.com/intel/ai-containers cd ai-containers/python docker run --rm -it \ -v $PWD/tests:/tests \ intel/python:latest \ python /tests/perf_sample.py
In the previous command, you should see a result at the bottom like: Time Consuming: 0.03897857666015625. We can compare this against python:3.11-slim-bullseye
bash# Use the working directory from the above command docker run --rm -it \ -v $PWD/tests:/tests \ python:3.10-slim-bullseye \ bash pip install numpy python /tests/perf_sample.py
Use the following command to check the availability of Intel dGPU devices on your system and the presence of Intel® OneAPI runtime libraries.
bash# Use the working directory from the first command docker run --rm -it \ -v $PWD/tests:/tests \ --device /dev/dri \ intel/python:3.11-xpu-full \ bash /tests/xpu_base_layers_test.sh
To build the images from source, clone the https://github.com/intel/ai-containers repository, follow the main README.md file to setup your environment, and run the following command:
bashcd python docker compose build idp docker compose run idp
To build the xpu variant of the image, run the following commands:
bashcd python docker compose build xpu docker compose run xpu
You can find the list of services below for each container in the group:
| Service Name | Description |
|---|---|
idp | Base image with https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html#gs.9bos9m |
pip | Equivalent python image without https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html#gs.9bos9m |
xpu | Base Image for Intel XPU plugin with https://www.intel.com/content/www/us/en/developer/tools/oneapi/distribution-for-python.html#gs.9bos9m |
View the https://github.com/intel/ai-containers/blob/main/LICENSE for the [Intel® Distribution for Python].
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.
您可以使用以下命令拉取该镜像。请将 <标签> 替换为具体的标签版本。如需查看所有可用标签版本,请访问 标签列表页面。
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以下是 intel/python 相关的常用 Docker 镜像,适用于 Web 开发、数据科学、机器学习 等不同场景: