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Update DLAMI BASE AMI Logic to switch between OSS and Proprietary Nvidia Driver AMI #3760
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test/dlc_tests/benchmark/ec2/pytorch/inference/test_performance_pytorch_inference.py
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UBUNTU_20_BASE_DLAMI_US_WEST_2 = get_ami_id_boto3( | ||
region_name="us-west-2", ami_name_pattern="Deep Learning Base GPU AMI (Ubuntu 20.04) ????????" | ||
# DLAMI Base is split between OSS Nvidia Driver and Propietary Nvidia Driver. see https://docs.aws.amazon.com/dlami/latest/devguide/important-changes.html | ||
UBUNTU_20_BASE_OSS_DLAMI_US_WEST_2 = get_ami_id_boto3( |
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looked at scope of removing these, and it will over-scope this PR. We can proceed with this for now
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Nice work and thorough testing
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…idia Driver AMI (aws#3760) * Update DLAMI BASE AMI Logic to switch between OSS and Proprietary Nvidia Driver AMI * update gdrcopy to 2.4 * formatting * disable buiild and fix sm local test instance ami * use proprietary drier dlami as default * fix ul20 and aml2 dlami name logic and test only ec2 * allow test efa * update oss dlami list * test curand * ensure ec2 instance type fixture is ran before ec2 instance ami * alter ami pulling logic * usefixtures * use parametrize * use instance ami in parametrize * add instace ami ad parametrize * fix curand test * correct ami name * correct ami format * use proprietary dlami for curand * rebuild * logging debug * remove parametrize ami * flip logic * formatting * print instance ami * fix typo * remove parametrize logic and fix proprietary dlami name pattern * revert gdr copy * update test with gdrcopy 2.4 * build test pt ec2 * build test pt sm * remove gdrcopy ami * sanity and sm local testonly * build test pt sm * formatting * test pt sm * build test pt sm * disable build * build test pt sm * use get-login-password * remove () from get-login * test tensorflow * use login_to_ecr_registry function * use dict for base dlami logic * use image uri instead * fix aml2 dlami logic * revert toml file
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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
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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.