Make code_location to be S3 URI instead of bucket in training_config() #501
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@@ Coverage Diff @@
## master #501 +/- ##
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+ Coverage 94.29% 94.29% +<.01%
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Files 59 59
Lines 4610 4613 +3
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+ Hits 4347 4350 +3
Misses 263 263
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jesterhazy
suggested changes
Nov 19, 2018
src/sagemaker/estimator.py
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| If not specified, default bucket created by ``sagemaker.session.Session`` is used. | ||
| The default S3 path is default_bucket/job-name/source/. |
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including s3 protocol makes this clearer (s3://default_bucket/...)
src/sagemaker/estimator.py
Outdated
| @@ -670,8 +670,9 @@ def __init__(self, entry_point, source_dir=None, hyperparameters=None, enable_cl | |||
| training jobs. This will be ignored for now and removed in a further release. | |||
| container_log_level (int): Log level to use within the container (default: logging.INFO). | |||
| Valid values are defined in the Python logging module. | |||
| code_location (str): Name of the S3 bucket where custom code is uploaded (default: None). | |||
| code_location (str): The S3 URI where custom code is uploaded (default: None). | |||
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let's fix this to make it clear we expect a prefix and will append /source/sourcedir.tar.gz
jesterhazy
approved these changes
Nov 19, 2018
metrizable
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Dec 1, 2020
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Issue #, if available:
Description of changes:
The docstring for code_location is not correct. code_location can be a S3 URI, not just a bucket. And I need to change related Airflow API because it treats code_location as a bucket.
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