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No good way to test, just can check docker build on the images itself. Since it is broken anyway, it is totally fine to try directly.
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| # Install TensorRT. Requires that libcudnn7 is installed above. | ||
| RUN apt-get install -y --no-install-recommends \ | ||
| libnvinfer6=6.0.1-1+cuda10.1 \ |
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nit: CUDA_VERSION=6.0.1-1+cuda10.1 at start of command and then use in three places as $CUDA_VERSION ?
Same for libcudnn7 above.
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thanks for the suggestion! I don't think that would improve readability a lot, plus it's not CUDA version (cuda is 10.1), so I'm just leaving this like suggested in the docs: https://www.tensorflow.org/install/gpu#ubuntu_1604_cuda_101
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I'm not quite sure how to test this. I did try to install things manually on a bot and that worked, but we have slightly different setup for deploying CF bots.
One idea might be to update Chromium's ml-with-gpu image and just re-create those bots. Even if they break, we won't lose much.
Another option is to set up a separate GCP project with a ml-with-gpu bot. It'll take a bit longer but won't be "testing in prod".
Maybe there are any other tricks that I don't remember?