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.
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 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.* and 11.* (the version depends on the framework)
- CuDNN 7.* and NCCL 2.* (the version depends on the CUDA version)
JupyterLab
What's next
You can get started with Deep Learning Containers by walking through the How-to guides, which provide instructions on create and work with deep learning containers.