Test Nvidia environment in relation to TensorFlow

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README.md

Test Nvidia CUDA environment in relation to TensorFlow

./run tests the native system. One of tf1 or tf2 is expected to have no GPU available due to CUDA library incompatibility
./run-docker tests Docker support. Both TensorFlow versions should work as we're using a base image compatible to the respective version.
./run-docker-compatibility-matrix tests combinations of (pip-installable) TensorFlow versions and nvidia/cuda images.

Example output

% ./run-docker
== tf1
GPU 0: GeForce RTX 2080 (UUID: GPU-612ce75c-1340-772b-039c-2a83a3ea5c95)
TensorFlow 1.15.3
GPU available: True
== tf2
GPU 0: GeForce RTX 2080 (UUID: GPU-612ce75c-1340-772b-039c-2a83a3ea5c95)
TensorFlow 2.3.0
GPU available: True

Results

As of 2020-09, the only combinations that are working:

TensorFlow 1.15.3 using CUDA Toolkit 10.0
TensorFlow 2.3.0 using CUDA Toolkit 10.1

This is only for pip-installable TensorFlow, not self-compiled nor Anaconda. We also did not test other TensorFlow versions.