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Support optional input channels in local mode. #466

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Merged
merged 3 commits into from
Nov 9, 2018
Merged

Support optional input channels in local mode. #466

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5611512
Support optional input channels in local mode.
nadiaya Nov 8, 2018
1938558
Merge branch 'master' into local-mode-input
nadiaya Nov 8, 2018
7fa29b0
Merge branch 'master' into local-mode-input
nadiaya Nov 9, 2018
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1 change: 1 addition & 0 deletions CHANGELOG.rst
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@ CHANGELOG
==========

* doc-fix: fix rendering error in README.rst
* enhancement: Local Mode: support optional input channels
* build: added pylint

1.14.1
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1 change: 0 additions & 1 deletion src/sagemaker/local/image.py
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Expand Up @@ -262,7 +262,6 @@ def write_config_files(self, host, hyperparameters, input_data_config):
'hosts': self.hosts
}

print(input_data_config)
json_input_data_config = {}
for c in input_data_config:
channel_name = c['ChannelName']
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6 changes: 3 additions & 3 deletions src/sagemaker/local/local_session.py
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Expand Up @@ -53,8 +53,8 @@ def __init__(self, sagemaker_session=None):
"""
self.sagemaker_session = sagemaker_session or LocalSession()

def create_training_job(self, TrainingJobName, AlgorithmSpecification, InputDataConfig, OutputDataConfig,
ResourceConfig, **kwargs):
def create_training_job(self, TrainingJobName, AlgorithmSpecification, OutputDataConfig,
ResourceConfig, InputDataConfig=None, **kwargs):
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if the first line of this method becomes

InputDataConfig = InputDataConfig or {}

Then you don't need to actually touch any other part of the code as all the for loops will just not happen.

"""
Create a training job in Local Mode
Args:
Expand All @@ -66,7 +66,7 @@ def create_training_job(self, TrainingJobName, AlgorithmSpecification, InputData
HyperParameters (dict) [optional]: Specifies these algorithm-specific parameters to influence the quality of
the final model.
"""

InputDataConfig = InputDataConfig or {}
container = _SageMakerContainer(ResourceConfig['InstanceType'], ResourceConfig['InstanceCount'],
AlgorithmSpecification['TrainingImage'], self.sagemaker_session)
training_job = _LocalTrainingJob(container)
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12 changes: 6 additions & 6 deletions tests/unit/test_local_session.py
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Expand Up @@ -61,8 +61,8 @@ def test_create_training_job(train, LocalSession):
resource_config = {'InstanceType': 'local', 'InstanceCount': instance_count}
hyperparameters = {'a': 1, 'b': 'bee'}

local_sagemaker_client.create_training_job('my-training-job', algo_spec, input_data_config,
output_data_config, resource_config, HyperParameters=hyperparameters)
local_sagemaker_client.create_training_job('my-training-job', algo_spec, output_data_config, resource_config,
InputDataConfig=input_data_config, HyperParameters=hyperparameters)

expected = {
'ResourceConfig': {'InstanceCount': instance_count},
Expand Down Expand Up @@ -111,8 +111,8 @@ def test_create_training_job_invalid_data_source(train, LocalSession):
hyperparameters = {'a': 1, 'b': 'bee'}

with pytest.raises(ValueError):
local_sagemaker_client.create_training_job('my-training-job', algo_spec, input_data_config,
output_data_config, resource_config, HyperParameters=hyperparameters)
local_sagemaker_client.create_training_job('my-training-job', algo_spec, output_data_config, resource_config,
InputDataConfig=input_data_config, HyperParameters=hyperparameters)


@patch('sagemaker.local.image._SageMakerContainer.train', return_value="/some/path/to/model")
Expand Down Expand Up @@ -141,8 +141,8 @@ def test_create_training_job_not_fully_replicated(train, LocalSession):
hyperparameters = {'a': 1, 'b': 'bee'}

with pytest.raises(RuntimeError):
local_sagemaker_client.create_training_job('my-training-job', algo_spec, input_data_config,
output_data_config, resource_config, HyperParameters=hyperparameters)
local_sagemaker_client.create_training_job('my-training-job', algo_spec, output_data_config, resource_config,
InputDataConfig=input_data_config, HyperParameters=hyperparameters)


@patch('sagemaker.local.local_session.LocalSession')
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