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Make code_location to be S3 URI instead of bucket in training_config() #501
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Original file line number | Diff line number | Diff line change |
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@@ -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). | ||
If not specified, default bucket created by ``sagemaker.session.Session`` is used. | ||
The default S3 path is default_bucket/job-name/source/. | ||
There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. including s3 protocol makes this clearer (s3://default_bucket/...) There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Updated. |
||
image_name (str): An alternate image name to use instead of the official Sagemaker image | ||
for the framework. This is useful to run one of the Sagemaker supported frameworks | ||
with an image containing custom dependencies. | ||
|
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let's fix this to make it clear we expect a prefix and will append /source/sourcedir.tar.gz
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Updated.