Cuda Tensorflow Opencv Versions Save

Support for TF 2.9.1, OpenCV 3.4.16 and 4.6.0, as well as PyTorch 1.12.1

Full list of available containers (up to this release) can be seen at: https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md

For list of installed pip packages, for example PyTorch, see the matching entry in the BuildInfo directory.

Support for TF 2.9.1, OpenCV 3.4.16 and 4.5.5, as well as PyTorch 1.11 As of 20220530, PyTorch is also built from source

Full list of available containers (up to this release) can be seen at: https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md

For list of installed pip packages, for example PyTorch, see the matching entry in the BuildInfo-TensorFlow directory.

Support for TF 2.9.1, OpenCV 3.4.16 and 4.5.5. For CUDA 11, using 11.3.1 at minimum for PyTorch support, with max CUDA of 11.6.2

Full list of available containers (up to this release) can be seen at: https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md

For list of installed pip packages, for example PyTorch, see the matching entry in the BuildInfo-TensorFlow directory.

Support for TF 2.9.0, OpenCV 3.4.16 and 4.5.5. For CUDA 11, using 11.3.1 at minimum for PyTorch support, with max CUDA of 11.5.2

Full list of available containers (up to this release) can be seen at: https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md

For list of installed pip packages, for example PyTorch, see the matching entry in the BuildInfo-TensorFlow directory.

Update base container, including Nvidia's new package signing key

Support for TF 2.8.0, OpenCV 3.4.16 and 4.5.5. For CUDA 11, using 11.3 at minimum for PyTorch support, with max CUDA of 11.5.1

Full list of available containers (up to this release) can be seen at: https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md

For list of installed pip packages, for example PyTorch, see the matching entry in the BuildInfo-TensorFlow directory.

Support for TF 2.8.0, OpenCV 3.4.16 and 4.5.5. For CUDA 11, using 11.3 at minimum for PyTorch support, with max CUDA of 11.5.1

Full list of available containers (up to this release) can be seen at: https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md

For list of installed pip packages, for example PyTorch, see the matching entry in the BuildInfo-TensorFlow directory.

Support for TF 2.7.0, OpenCV 3.4.16 and 4.5.5. For CUDA 11, using 11.3 at minimum for PyTorch support, with max CUDA of 11.4.2

Full list of available containers (up to this release) can be seen at: https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md

For list of installed pip packages, for example PyTorch, see the matching entry in the BuildInfo-TensorFlow directory.

Support for TF 2.7.0, OpenCV 3.4.16 and 4.5.4. For CUDA 11, using 11.3 at minimum for PyTorch support, with max CUDA of 11.4.2

Full list of available containers (up to this release) can be seen at: https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md

For list of installed pip packages, for example PyTorch, see the matching entry in the BuildInfo-TensorFlow directory.

Support for TF 1.15.5 and 2.6.3, OpenCV 3.4.16 and 4.5.4. For CUDA 11, using 11.3 at minimum for PyTorch support, with max CUDA of 11.4.2

Expected to be last release to built TF1 or Ubuntu 18.04 based images.

Full list of available containers (up to this release) can be seen at: https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md

For list of installed pip packages, for example PyTorch, see the matching entry in the BuildInfo-TensorFlow directory.

Support for TF 2.6.0, OpenCV 3.4.16 and 4.5.4. For CUDA 11, using 11.3 at minimum for PyTorch support, with max CUDA of 11.4.2

Full list of available containers (up to this release) can be seen at: https://github.com/datamachines/cuda_tensorflow_opencv/blob/master/Builds-DockerHub.md

For list of installed pip packages, for example PyTorch, see the matching entry in the BuildInfo-TensorFlow directory.