This page helps you choose which container image to use.
Choose a container image type
Deep Learning Containers supports each framework version based on a schedule to minimize security vulnerabilities. Review the Deep Learning Containers framework support policy to understand the implications of the end-of-support and end-of-availability dates.
Each container image provides a Python 3 environment and includes the selected data science framework (such as PyTorch or TensorFlow), Conda, the NVIDIA stack for GPU images (CUDA, cuDNN, NCCL2), and many other supporting packages and tools. To find the appropriate container image, see the tables below.
Base versions
| ML framework version | Current patch version | Supported accelerators | End of patch and support date | End of availability date | Image family name |
|---|---|---|---|---|---|
| Base-cu113 (Python 3.10) | CUDA 11.3 | GPU (CUDA 11.3) | Jan 1, 2024 | Jan 1, 2025 | gcr.io/deeplearning-platform-release/base-cu113.py310 |
| Base-cu113 (Python 3.7) | CUDA 11.3 | GPU (CUDA 11.3) | Sep 1, 2023 | Sep 1, 2024 | gcr.io/deeplearning-platform-release/base-cu113.py37 |
| Base-cu110 (Python 3.10 / Debian 11) | CUDA 11.0 | GPU (CUDA 11.0) | Jan 1, 2024 | Jan 1, 2025 | gcr.io/deeplearning-platform-release/base-cu110.py310 |
| Base-cu110 (Python 3.7) | CUDA 11.0 | GPU (CUDA 11.0) | Sep 1, 2023 | Sep 1, 2024 | gcr.io/deeplearning-platform-release/base-cu110.py37 |
TensorFlow versions
| ML framework version | Current patch version | Supported accelerators | End of patch and support date | End of availability date | Image family name |
|---|---|---|---|---|---|
| 2.12 (Python 3.10) | 2.12.0 | CPU only | Jun 30, 2024 | Jun 30, 2025 | gcr.io/deeplearning-platform-release/tf2-cpu.2-12.py310 |
| 2.12 (Python 3.10) | 2.12.0 | GPU (CUDA 11.8) | Jun 30, 2024 | Jun 30, 2025 | gcr.io/deeplearning-platform-release/tf2-gpu.2-12.py310 |
| 2.11 (Python 3.10) | 2.11.0 | CPU only | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf2-cpu.2-11.py310 |
| 2.11 (Python 3.10) | 2.11.0 | GPU (CUDA 11.3) | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf-gpu.2-11.py310 |
| 2.11 | 2.11.0 | CPU only | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf2-cpu.2-11.py37 |
| 2.11 | 2.11.0 | GPU (CUDA 11.3) | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf2-gpu.2-11.py37 |
| 2.10 (Python 3.10) | 2.10.1 | CPU only | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf2-cpu.2-10.py310 |
| 2.10 (Python 3.10) | 2.10.1 | GPU (CUDA 11.3) | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf-gpu.2-10.py310 |
| 2.10 | 2.10.1 | CPU only | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf2-cpu.2-10.py37 |
| 2.10 | 2.10.1 | GPU (CUDA 11.3) | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf2-gpu.2-10.py37 |
| 2.9 | 2.9.3 | CPU only | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf2-cpu.2-9.py37 |
| 2.9 | 2.9.3 | GPU (CUDA 11.3) | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf-gpu.2-9.py37 |
| 2.8 | 2.8.4 | CPU only | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf2-cpu.2-8.py37 |
| 2.8 | 2.8.4 | GPU (CUDA 11.3) | Nov 15, 2023 | Nov 15, 2024 | gcr.io/deeplearning-platform-release/tf2-gpu.2-8.py37 |
| 2.6 (Python 3.9) | 2.6.5 | CPU only | Aug 10, 2023 | Aug 10, 2024 | gcr.io/deeplearning-platform-release/tf-cpu.2-6.py39 |
| 2.6 (Python 3.9) | 2.6.5 | GPU (CUDA 11.3) | Aug 10, 2023 | Aug 10, 2024 | gcr.io/deeplearning-platform-release/tf-gpu.2-6.py39 |
| 2.6 (Python 3.7) | 2.6.5 | CPU only | Sep 1, 2023 | Sep 1, 2024 | gcr.io/deeplearning-platform-release/tf-cpu.2-6.py37 |
| 2.6 (Python 3.7) | 2.6.5 | GPU (CUDA 11.3) | Sep 1, 2023 | Sep 1, 2024 | gcr.io/deeplearning-platform-release/tf-gpu.2-6.py37 |
| 2.3 | 2.3.4 | CPU only | Sep 1, 2023 | Sep 1, 2024 | gcr.io/deeplearning-platform-release/tf-cpu.2-3.py37 |
| 2.3 | 2.3.4 | GPU (CUDA 11.3) | Sep 1, 2023 | Sep 1, 2024 | gcr.io/deeplearning-platform-release/tf-gpu.2-3.py37 |
PyTorch versions
| ML framework version | Current patch version | Supported accelerators | End of patch and support date | End of availability date | Image family name |
|---|---|---|---|---|---|
| 1.13 (Python 3.10) | 1.13.1 | CUDA 11.8 | Dec 8, 2023 | Dec 8, 2024 | gcr.io/deeplearning-platform-release/pytorch-gpu.1-13.py310 |
| 1.13 | 1.13.1 | CUDA 11.8 | Dec 8, 2023 | Dec 8, 2024 | gcr.io/deeplearning-platform-release/pytorch-gpu.1-13.py37 |
| 1.12 (Python 3.10) | 1.12.1 | CUDA 11.3 | Dec 8, 2023 | Dec 8, 2024 | gcr.io/deeplearning-platform-release/pytorch-gpu.1-12.py310 |
| 1.12 | 1.12.1 | CUDA 11.3 | Sep 1, 2023 | Sep 1, 2024 | gcr.io/deeplearning-platform-release/pytorch-gpu.1-12.py37 |
Included dependencies
A list of each Deep Learning Containers release's Python dependencies
is available in Cloud Storage at
gs://deeplearning-platform-release/installed-dependencies/containers/RELEASE_MILESTONE.
Replace RELEASE_MILESTONE with the release milestone, such as m88.
For example, the lists for the M88 release are at
gs://deeplearning-platform-release/installed-dependencies/containers/m88/.
TensorFlow Enterprise container images
TensorFlow Enterprise container images provide a Google Cloud optimized distribution of TensorFlow. For more information about TensorFlow Enterprise including which versions are supported, see TensorFlow Enterprise overview.
Experimental images
Some Deep Learning Containers image families are experimental, as indicated by the table of image families. Experimental images are supported on a best-effort basis, and may not receive refreshes on each new release of the framework.
Listing all available versions
If you need a specific framework or CUDA version, search the complete list of available container images. To list all available Deep Learning Containers images, use the following command in the Google Cloud CLI with your preferred terminal or in Cloud Shell.
gcloud container images list --repository="gcr.io/deeplearning-platform-release"
Using locally
Deep Learning Containers can be pulled and used locally. To do so, see Getting started with a local deep learning container.
What's next
- Read the Deep Learning Containers overview to learn more about what is pre-installed on container images.
- Get started with Deep Learning Containers by walking through the How-to guides, which provide instructions on how to build and push deep learning container images.