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…euronx (aws#5278) * Update ['dlc_developer_config.toml'] dlc_developer_config.toml: { 'build': { 'build_frameworks': ['huggingface_pytorch'], 'build_inference': False, 'build_training': True}, 'buildspec_override': { 'dlc-pr-huggingface-pytorch-neuronx-training': 'huggingface/pytorch/training/buildspec-neuronx.yml'}, 'dev': { 'arm64_mode': False, 'deep_canary_mode': False, 'graviton_mode': False, 'neuronx_mode': True}, 'test': { 'ec2_tests': False, 'ecs_tests': False, 'eks_tests': False, 'sagemaker_local_tests': False, 'sagemaker_remote_tests': False, 'sanity_tests': True, 'security_tests': False}} * patch: linux-libc-dev vulnerability in huggingface-pytorch-training-neuronx:2.1.2-transformers4.48.1-neuronx-py310-sdk2.20.0-ubuntu20.04 * Restore ['dlc_developer_config.toml'] dlc_developer_config.toml: ('Restore to ' 'https://raw.githubusercontent.com/aws/deep-learning-containers/master/dlc_developer_config.toml') --------- Co-authored-by: Jonathan Lee <jonwlee@amazon.com> Co-authored-by: varunmoris <176621270+varunmoris@users.noreply.github.com>
* update 0.10.2 * update 0.10.2
* changelog update * update changelog to 0.10.2
* Add Neuronx SDK 2.26.0 images to release_images.md * Remove transformers
* rebuild pytorch sm arm64 2.6 image * revert toml * Rebuild pt 2.5 * Rebuild pt 2.6 * rebuild and test pt2.5 * rebuild and test pt2.5 arm64 ec2 * rebuild and test pt2.5 arm64 ec2 * test --cache error * test --cache error * fix --cache problem * fix --cache problem * fix --cache problem * revert toml and dlc py scans * remove py scan files
* patch tf training 2.18 ec2 * patch tf training 2.18 sm * autopatch tf 2.18 sm * autopatch tf 2.18 sm * add docker login * add docker login * retest tensorflow 2.18 * build tf 2.18 * build tf2.18 * revert toml
* patch pytorch 2.5 inference ARM64 sagemaker image
* test tf 2.18 inference amr64 sm * test tf 2.18 inference amr64 ec2 * test tf 2.18 inference amr64 sm * revert toml
* Patch tensoflow 2.19 inference and training images
* fix: update neuronx huggingface tests * black formatter * Add inference_ami_version=al2-ami-sagemaker-inference-neuron-2 to all neuronx tests --------- Co-authored-by: Sirut Buasai <73297481+sirutBuasai@users.noreply.github.com>
* Allowlisting for PyTorch 2.6 inference ARM image
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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.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.