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35 lines
1.1 KiB
Markdown
35 lines
1.1 KiB
Markdown
Test Nvidia CUDA environment in relation to TensorFlow
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======================================================
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* `./run` tests the native system. One of tf1 or tf2 is expected to have no
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GPU available due to CUDA library incompatibility
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* `./run-docker` tests Docker support. Both TensorFlow versions should work
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as we're using a base image compatible to the respective version.
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* `./run-docker-compatibility-matrix` tests combinations of (pip-installable)
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TensorFlow versions and `nvidia/cuda` images.
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Example output
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==============
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~~~
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% ./run-docker
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== tf1
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GPU 0: GeForce RTX 2080 (UUID: GPU-612ce75c-1340-772b-039c-2a83a3ea5c95)
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TensorFlow 1.15.3
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GPU available: True
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== tf2
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GPU 0: GeForce RTX 2080 (UUID: GPU-612ce75c-1340-772b-039c-2a83a3ea5c95)
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TensorFlow 2.3.0
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GPU available: True
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~~~
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Results
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=======
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As of 2020-09, the only combinations that are working:
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* TensorFlow 1.15.3 using CUDA Toolkit 10.0
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* TensorFlow 2.3.0 using CUDA Toolkit 10.1
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This is only for pip-installable TensorFlow, not self-compiled nor Anaconda.
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We also did not test other TensorFlow versions.
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