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| if specified_tests: | ||
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| # Skip telemetry tests for sglang images | ||
| if is_sglang_image and specific_test_type == "ec2": |
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Have you tried removing this statement? I don't think this is needed since we already put a skip logic prior.
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yeah I've tried skipping this and just using the upper skip block as suggested : b1df749
but the telemetry tests are failing without this skip logic for TEST_TYPE=ec2
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Then the logic should support specified_tests as well. So It'd look something like this
if specified_tests:
test_expr = " or ".join(f"test_{t}" for t in specified_tests)
if is_sglang_image and specific_test_type == "ec2":
test_expr += " not telemetry"
LOGGER.info("Excluding telemetry tests from sglang ec2 suite")
pytest_cmd.extend(["-k", f"({test_expr})"])
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Description
Tests Run
By default, docker image builds and tests are disabled. Two ways to run builds and tests:
How to use the helper utility for updating dlc_developer_config.toml
Assuming your remote is called
origin(you can find out more withgit remote -v)...python src/prepare_dlc_dev_environment.py -b </path/to/buildspec.yml> -cp originpython src/prepare_dlc_dev_environment.py -b </path/to/buildspec.yml> -t sanity_tests -cp originpython src/prepare_dlc_dev_environment.py -rcp originNOTE: If you are creating a PR for a new framework version, please ensure success of the local, standard, rc, and efa sagemaker tests by updating the dlc_developer_config.toml file:
sagemaker_remote_tests = truesagemaker_efa_tests = truesagemaker_rc_tests = truesagemaker_local_tests = trueHow to use PR description
Use the code block below to uncomment commands and run the PR CodeBuild jobs. There are two commands available:# /buildspec <buildspec_path># /buildspec pytorch/training/buildspec.yml# /tests <test_list># /tests sanity security ec2sanity, security, ec2, ecs, eks, sagemaker, sagemaker-local.Formatting
black -l 100on my code (formatting tool: https://black.readthedocs.io/en/stable/getting_started.html)PR Checklist
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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.