Refactor LocalSageMakerClient #375
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Codecov Report
@@ Coverage Diff @@
## master #375 +/- ##
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- Coverage 92.99% 92.96% -0.04%
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Files 51 52 +1
Lines 3568 3652 +84
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+ Hits 3318 3395 +77
- Misses 250 257 +7
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local sagemakerclient was not very useful beyond creating a single estimator/endpoint. doing workflows such as training and later creating and endpoint was really awkward. These changes make it both resemble the API a bit more and allow persisting objects across LocalSessions. This is important because most of the time the SDK classes create sessions behind the scenes.
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this fixes integration tests when we pass custom local sessions.
Codecov Report
@@ Coverage Diff @@
## master #375 +/- ##
==========================================
- Coverage 92.94% 92.83% -0.12%
==========================================
Files 51 52 +1
Lines 3572 3656 +84
==========================================
+ Hits 3320 3394 +74
- Misses 252 262 +10
Continue to review full report at Codecov.
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laurenyu
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Sep 25, 2018
src/sagemaker/local/entities.py
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| 'ModelName': self.model_name, | ||
| 'CreationTime': self.creation_time, | ||
| 'ExecutionRoleArn': 'local:arn-does-not-matter', | ||
| 'ModelArn': 'local:arn-does-not-matter', |
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might be better to have these as constants
src/sagemaker/local/entities.py
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| _FAILED = 'Failed' | ||
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| def __init__(self, endpoint_name, endpoint_config_name, local_session=None): | ||
| from sagemaker.local import LocalSagemakerClient |
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could you add a comment explaining why this has to be imported down here? also, I think you could move it to the else block if you wanted to
src/sagemaker/local/local_session.py
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| InputDataConfig (dict): Describes the training dataset and the location where it is stored. | ||
| OutputDataConfig (dict): Identifies the location where you want to save the results of model training. | ||
| ResourceConfig (dict): Identifies the resources to use for local model traininig. | ||
| HyperParameters (dict): Specify these algorithm-specific parameters to influence the quality of the final |
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nitpick: s/Specify/Specifies to make it consistent with the other ones
src/sagemaker/local/local_session.py
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| container = _SageMakerContainer(ResourceConfig['InstanceType'], ResourceConfig['InstanceCount'], | ||
| AlgorithmSpecification['TrainingImage'], self.sagemaker_session) | ||
| train_job = _LocalTrainingJob(container) | ||
| train_job.start(InputDataConfig, HyperParameters) |
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nitpick: s/train_job/training_job
| assert train_container is not None | ||
| assert train_container.image == image | ||
| assert train_container.instance_count == instance_count | ||
| local_sagemaker_client.create_training_job('my-training-job', algo_spec, input_data_config, |
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maybe constants for some of these strings as well?
- Fix Hyperparameters being mandatory in local mode - Fix serving container timeout time to match SageMaker
This was referenced Sep 26, 2018
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laurenyu
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Sep 26, 2018
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Nov 1, 2018
Local sagemakerclient was not very useful beyond creating a single estimator/endpoint. doing workflows such as training and later creating and endpoint was really awkward. These changes make it both resemble the API a bit more and allow persisting objects across LocalSessions. This is important because most of the time the SDK classes create sessions behind the scenes. includes 2 bug fixes: - Fix Hyperparameters being mandatory in local mode - Fix serving container timeout time to match SageMaker
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local sagemakerclient was not very useful beyond creating a single
estimator/endpoint. doing workflows such as training and later creating
and endpoint was really awkward.
These changes make it both resemble the API a bit more and allow
persisting objects across LocalSessions. This is important because most
of the time the SDK classes create sessions behind the scenes.
Issue #, if available:
Description of changes:
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