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Description
Updating docs for available autogluon images post-release.
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"
Confused on how to run tests? Try using the helper utility...
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 origin
python src/prepare_dlc_dev_environment.py -b </path/to/buildspec.yml> -t sanity_tests -cp origin
python src/prepare_dlc_dev_environment.py -rcp origin
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:
Expand
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
Expand
Fill out the template and click the checkbox of the builds you'd like to execute
Note: Replace with <X.Y> with the major.minor framework version (i.e. 2.2) you would like to start.
build_pytorch_training_<X.Y>_sm
build_pytorch_training_<X.Y>_ec2
build_pytorch_inference_<X.Y>_sm
build_pytorch_inference_<X.Y>_ec2
build_pytorch_inference_<X.Y>_graviton
build_tensorflow_training_<X.Y>_sm
build_tensorflow_training_<X.Y>_ec2
build_tensorflow_inference_<X.Y>_sm
build_tensorflow_inference_<X.Y>_ec2
build_tensorflow_inference_<X.Y>_graviton
Additional context
PR Checklist
Expand
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
Expand
@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.