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Test Nvidia environment in relation to TensorFlow
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Gerber, Mike 8d312119ad 📝 README: Mention CUDA driver version 4 years ago
2019-12-tensorflow2-keras-cnn+lstm 🧹 2019-12-tensorflow2-keras-cnn+lstm 4 years ago
2020-03-tensorflow-vs-tensorflow-gpu 🧹 2020-03-tensorflow-vs-tensorflow-gpu/tensorflow-vs-tensorflow-gpu 4 years ago
assets ✨ Test TensorFlow 2 4 years ago
README.md 📝 README: Mention CUDA driver version 4 years ago
run ✨ Test TensorFlow 2 4 years ago
run-docker ✨ Add run-docker-compatibility-matrix to test more combinations 4 years ago
run-docker-compatibility-matrix ✨ Add run-docker-compatibility-matrix to test more combinations 4 years ago
test-nvidia ✨ Add run-docker-compatibility-matrix to test more combinations 4 years ago

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. Note that these are the CUDA Toolkit versions, not the CUDA version the driver supports (reported by nvidia-smi).