Deep Learning Containers are a set of Docker containers
with key data science frameworks, libraries, and tools pre-installed.
These containers provide you with performance-optimized, consistent
environments that can help you prototype and implement workflows quickly.

To learn
more about containers, see [Containers at Google](https://docs.cloud.google.com/containers).

## Pre-installed software

Deep Learning Containers images can be configured to include the following:

- Frameworks:

  - TensorFlow
  - PyTorch
  - R
  - scikit-learn
  - XGBoost
- Python, including these packages:

  - numpy
  - sklearn
  - scipy
  - pandas
  - nltk
  - pillow
  - [fairness-indicators](https://www.tensorflow.org/responsible_ai/fairness_indicators/guide) for TensorFlow 2.3 and 2.4 Deep Learning Containers instances
  - many others
- Nvidia packages with the latest Nvidia driver for GPU-enabled instances:

  - CUDA 10.\*, 11.\*, and 12.\* (the version depends on the framework)
  - CuDNN 7.\* and NCCL 2.\* (the version depends on the CUDA version)
- JupyterLab

- Model Garden containers

  - vLLM library

## Community support

Ask a question about Deep Learning Containers on [Stack
Overflow](https://stackoverflow.com/questions/tagged/google-dl-platform)
or join the
[google-dl-platform](https://groups.google.com/forum/#!forum/google-dl-platform)
Google group to discuss Deep Learning Containers.

[Learn more about getting support from the
community](https://docs.cloud.google.com/deep-learning-containers/docs/getting-support#get_support_from_the_community).

## What's next

You can get started with Deep Learning Containers by walking through
the [How-to guides](https://docs.cloud.google.com/deep-learning-containers/docs#how-to),
which provide instructions on create and work with deep learning containers.