1a40963d1d
* Add Github Action to run TPU tests. * Trigger new Github Actions run. * Clean up more comments. * Use different fixed version of ml-testing-accelerators and update config to match. * use cluster in us-central1-a * Run 'gcloud logging read' directly without 'echo' to preserve newlines. * cat coverage.xml on the TPU VM side and upload xml on the Github Action side * Use new commit on ml-testing-accelerators so command runs fully. * Preserve newlines in the xml and use if: always() temporarily to upload codecov * Use pytorch_lightning for coverage instead of pytorch-lightning * Remove the debug cat of coverage xml * Apply suggestions from code review * jsonnet rename * name * add codecov flags * add codecov flags * codecov * codecov * revert codecov * Clean up after apt-get and remove old TODOs. * More codefactor cleanups. * drone * drone * disable codecov * cleaning * docker py versions * docker py 3.7 * readme * bash * docker * freeze conda * py3.6 * Stop using apt-get clean. * Dont rm pytorch-lightning * Update docker/tpu/Dockerfile * Longer timeout in the Github Action to wait for GKE to finish. * job1 * job2 * job3 Co-authored-by: Jirka Borovec <Borda@users.noreply.github.com> Co-authored-by: Jirka <jirka@pytorchlightning.ai> |
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| tpu | ||
| Dockerfile | ||
| README.md | ||
README.md
Builds
You can build it on your own, note it takes lots of time, be prepared.
git clone <git-repository>
docker image build -t pytorch-lightning:latest -f docker/Dockerfile .
or with specific arguments
git clone <git-repository>
docker image build \
-t pytorch-lightning:py36 \
-f docker/Dockerfile \
--build-arg PYTHON_VERSION=3.6 \
--build-arg PYTORCH_VERSION=1.3 \
.
To run your docker use
docker image list
docker run --rm -it pytorch-lightning:latest bash
and if you do not need it anymore, just clean it:
docker image list
docker image rm pytorch-lightning:latest