Local mode pass training env var #411
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SageMaker Training sets various environment variables for every training job, while Local Mode replicates none of these. This change adds the environment variables for the AWS region and training job name.
Codecov Report
@@ Coverage Diff @@
## master #411 +/- ##
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+ Coverage 93.26% 93.45% +0.19%
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Files 52 52
Lines 3665 3668 +3
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+ Hits 3418 3428 +10
+ Misses 247 240 -7
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|
iquintero
reviewed
Sep 27, 2018
src/sagemaker/local/image.py
Outdated
| @@ -102,7 +106,12 @@ def train(self, input_data_config, hyperparameters): | |||
| self.write_config_files(host, hyperparameters, input_data_config) | |||
| shutil.copytree(data_dir, os.path.join(self.container_root, host, 'input', 'data')) | |||
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| compose_data = self._generate_compose_file('train', additional_volumes=volumes) | |||
| training_env_vars = { | |||
| REGION_ENV_NAME: self.sagemaker_session.boto_session.region_name, | |||
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there are cases in which boto_session can be None. I would add an if guard here.
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talked offline - changed to use sagemaker_session.boto_region_name
iquintero
approved these changes
Sep 27, 2018
pdasamzn
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Nov 1, 2018
SageMaker Training sets various environment variables for every training job, while Local Mode replicates none of these. This change adds the environment variables for the AWS region and training job name.
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Description of changes:
SageMaker Training sets various environment variables for every training job, while Local Mode replicates none of these. This change adds the environment variables for the AWS region and training job name.
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