Open thomelane opened 4 years ago
sorry for the slow response here as well.
for the three attributes you mentioned:
entry_point
is covered in #1427, since deploy()
just calls create_model()
name
should be working now (code). The change was made in this recent commit, which was released in v1.55.4predictor_cls
already can be overridden through create_model()
Describe the bug
I'm trying to deploy a trained estimator to two different endpoints, each with their own model (each with their own entry_point). Since I can't override the entry point the deploying the model, I need to create two models. Ideally I'd like to use the create a SKLearnModel directly from the estimator but override a few properties. Unfortunately, this doesn't work as there are hard set arguments used. See
entry_point
as an example: https://github.com/aws/sagemaker-python-sdk/blob/master/src/sagemaker/sklearn/estimator.py#L186.Expected behavior
Should be able to set entry_point, name and predictor_cls as follows.
Unfortunately you currently have to do something like:
Which uses a private property (_
current_job_name
) or be verbose and create a model from scratch:Screenshots or logs If applicable, add screenshots or logs to help explain your problem.
System information A description of your system. Please provide:
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