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Launch efa instances from heterogenous reservations #3768

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merged 25 commits into from
Mar 27, 2024

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arjkesh
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@arjkesh arjkesh commented Mar 13, 2024

GitHub Issue #, if available:

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  • If merging this PR should also close the associated Issue, please also add that Issue # to the Linked Issues section on the right.

  • All PR's are checked weekly for staleness. This PR will be closed if not updated in 30 days.

Description

Update EFA tests to use a heterogenous approach for launching instances from capacity reservation. This allows us to make use of reservations where there may be < min number of instances available in a particular reservation, but there are enough instances available in multiple reservations or in reservations + open capacity

Tests run

  • Successfully launch p5 instances (3100f27)
  • Successfully launch with p4d instances (9bb1796)
  • Test launching from one (cbb3eab)
  • ...and then launching quickly again to see failure mode (01f5ee6)
  • test default PR behavior (8731694)
  • additional sanity test for deepcopy update - d904077

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"

  • I have run builds/tests on commit for my changes.

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

DLC image/dockerfile

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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".

  • build_pytorch_training_latest
  • build_pytorch_inference_latest
  • build_tensorflow_training_latest
  • build_tensorflow_inference_latest

Additional context

PR Checklist

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  • I've prepended PR tag with frameworks/job this applies to : [mxnet, tensorflow, pytorch] | [ei/neuron/graviton] | [build] | [test] | [benchmark] | [ec2, ecs, eks, sagemaker]
  • If the PR changes affects SM test, I've modified dlc_developer_config.toml in my PR branch by setting sagemaker_tests = true and efa_tests = true
  • If this PR changes existing code, the change fully backward compatible with pre-existing code. (Non backward-compatible changes need special approval.)
  • (If applicable) I've documented below the DLC image/dockerfile this relates to
  • (If applicable) I've documented below the tests I've run on the DLC image
  • (If applicable) I've reviewed the licenses of updated and new binaries and their dependencies to make sure all licenses are on the Apache Software Foundation Third Party License Policy Category A or Category B license list. See https://www.apache.org/legal/resolved.html.
  • (If applicable) I've scanned the updated and new binaries to make sure they do not have vulnerabilities associated with them.

NEURON/GRAVITON Testing Checklist

  • When creating a PR:
  • I've modified dlc_developer_config.toml in my PR branch by setting neuron_mode = true or graviton_mode = true

Benchmark Testing Checklist

  • When creating a PR:
  • I've modified dlc_developer_config.toml in my PR branch by setting ec2_benchmark_tests = true or sagemaker_benchmark_tests = true

Pytest Marker Checklist

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  • (If applicable) I have added the marker @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)
  • (If applicable) I have added the marker @pytest.mark.integration("<feature-being-tested>") to the new tests which I have added, to specify the feature that will be tested
  • (If applicable) I have added the marker @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
  • (If applicable) I have added the marker @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 type

By 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.

@aws-deep-learning-containers-ci aws-deep-learning-containers-ci bot added build Reflects file change in build folder ec2 Reflects file change in dlc_tests/ec2 folder pytorch Reflects file change in pytorch folder sagemaker_tests Size:S Determines the size of the PR src Reflects file change in src folder test Reflects file change in test folder labels Mar 13, 2024
def launch_efa_with_heterogenous_reservations(
ec2_client, ec2_instance_type, ec2_run_instances_definition, fn_name=""
):
minimum_number_of_instances = ec2_run_instances_definition["MinCount"]
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let's add a high level description on what we try to do here

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updated fn description

test/test_utils/ec2.py Show resolved Hide resolved
pass


def launch_efa_with_heterogenous_reservations(
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if this works, we can deprecate launch_efa_with_reservations, I can't think of a use case where 2 instances must come from same cr

@@ -35,7 +37,10 @@ def can_run_pytorchddp(ecr_image):
return Version(image_framework_version) in SpecifierSet(">=1.10")


# Skip due to known issue: https://github.com/pytorch/pytorch/issues/99074
@pytest.mark.skipif(
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I can remove these conditional skips if we still want to run these

@@ -35,6 +37,10 @@ def can_run_pytorchddp(ecr_image):
return Version(image_framework_version) in SpecifierSet(">=1.10")


@pytest.mark.skipif(
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I can remove these conditional skips if we still want to run these

@arjkesh arjkesh changed the title Test heterogenous CR approach for efa tests Launch efa instances from heterogenous reservations Mar 19, 2024
@arjkesh arjkesh marked this pull request as ready for review March 19, 2024 06:27
@arjkesh arjkesh requested review from a team as code owners March 19, 2024 06:27
@arjkesh arjkesh enabled auto-merge (squash) March 27, 2024 00:41
@arjkesh arjkesh merged commit 6adcb21 into aws:master Mar 27, 2024
28 checks passed
evakravi pushed a commit to evakravi/deep-learning-containers that referenced this pull request Sep 5, 2024
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3 participants