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Skip EFA tests for PT 1.13 #3908
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test/dlc_tests/ec2/test_efa.py
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@@ -76,6 +78,12 @@ def test_pytorch_efa( | |||
:param region: str Region in which EFA-enabled instances are launched | |||
:param gpu_only: pytest fixture to limit test only to GPU DLCs | |||
""" | |||
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# NOTE: Skip PT1.13 autopatching |
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putting skip condition inside the test function is not a good idea, because test setup will still launch instances. Instead, you can probably use an existing fixture like pt21_and_above_only
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* Skip EFA tests for PT 1.13 * formatting * fix comment * use pt201 and above fixture * test 1.13 * revert toml * revert toml * revert gdrcopy skip * revert unused efa import * revert gdrcopy skip * test sagemaker only fixture * fix comments * revert toml * split ec2 sm buildspec * split ec2 sm * add sm buildspec --------- Co-authored-by: arjkesh <[email protected]>
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Description
Tests run
NOTE: By default, docker builds are disabled. In order to build your container, please update dlc_developer_config.toml and specify the framework to build in "build_frameworks"
NOTE: If you are creating a PR for a new framework version, please ensure success of the standard, rc, and efa sagemaker remote tests by updating the dlc_developer_config.toml file:
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sagemaker_remote_tests = true
sagemaker_efa_tests = true
sagemaker_rc_tests = true
Additionally, please run the sagemaker local tests in at least one revision:
sagemaker_local_tests = true
Formatting
black -l 100
on my code (formatting tool: https://black.readthedocs.io/en/stable/getting_started.html)DLC image/dockerfile
Builds to Execute
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Click the checkbox to enable a build to execute upon merge.
Note: By default, pipelines are set to "latest". Replace with major.minor framework version if you do not want "latest".
Additional context
PR Checklist
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NEURON/GRAVITON Testing Checklist
dlc_developer_config.toml
in my PR branch by settingneuron_mode = true
orgraviton_mode = true
Benchmark Testing Checklist
dlc_developer_config.toml
in my PR branch by settingec2_benchmark_tests = true
orsagemaker_benchmark_tests = true
Pytest Marker Checklist
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@pytest.mark.model("<model-type>")
to the new tests which I have added, to specify the Deep Learning model that is used in the test (use"N/A"
if the test doesn't use a model)@pytest.mark.integration("<feature-being-tested>")
to the new tests which I have added, to specify the feature that will be tested@pytest.mark.multinode(<integer-num-nodes>)
to the new tests which I have added, to specify the number of nodes used on a multi-node test@pytest.mark.processor(<"cpu"/"gpu"/"eia"/"neuron">)
to the new tests which I have added, if a test is specifically applicable to only one processor typeBy submitting this pull request, I confirm that my contribution is made under the terms of the Apache 2.0 license. I confirm that you can use, modify, copy, and redistribute this contribution, under the terms of your choice.