JSON serializer: predictor.predict accepts dictionaries #62
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mvsusp
reviewed
Feb 2, 2018
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| if isinstance(data, dict): | ||
| if not len(data.keys()) > 0: | ||
| raise ValueError("empty dictionary can't be serialized") |
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Any special reason for handling the empty dictionary case? An empty dictionary is a valid json.
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Valid JSON: sure, but similar to an empty list, what would I attempt to be predicting without data? I cannot think of such a use case where I'd expect my payload to be empty. My prediction response wouldn't depend on my payload. But maybe it's better to be permissive here and with the list. Let me know what you think, I can change this.
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Yeah, I noticed that you are applying the same previously applied pattern to check if it is empty or not. I'm ok with that.
mvsusp
approved these changes
Feb 6, 2018
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| if isinstance(data, dict): | ||
| if not len(data.keys()) > 0: | ||
| raise ValueError("empty dictionary can't be serialized") |
There was a problem hiding this comment.
Yeah, I noticed that you are applying the same previously applied pattern to check if it is empty or not. I'm ok with that.
jalabort
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Mar 1, 2018
* Add data_type to hyperparameters (aws#54) When we describe a training job the data type of the hyper parameters is lost because we use a dict[str, str]. This adds a new field to Hyperparameter so that we can convert the datatypes at runtime. instead of validating with isinstance(), we cast the hp value to the type it is meant to be. This enforces a "strongly typed" value. When we deserialize from the API string responses it becomes easier to deal with too. * Add wrapper for LDA. (aws#56) Update CHANGELOG and bump the version number. * Add support for async fit() (aws#59) when calling fit(wait=False) it will return immediately. The training job will carry on even if the process exits. by using attach() the estimator can be retrieved by providing the training job name. _prepare_init_params_from_job_description() is now a classmethod instead of being a static method. Each class is responsible to implement their specific logic to convert a training job description into arguments that can be passed to its own __init__() * Fix Estimator role expansion (aws#68) Instead of manually constructing the role ARN, use the IAM boto client to do it. This properly expands service-roles and regular roles. * Add FM and LDA to the documentation. (aws#66) * Fix description of an argument of sagemaker.session.train (aws#69) * Fix description of an argument of sagemaker.session.train 'input_config' should be an array which has channel objects. * Add a link to the botocore docs * Use 'list' instead of 'array' in the description * Add ntm algorithm with doc, unit tests, integ tests (aws#73) * JSON serializer: predictor.predict accepts dictionaries (aws#62) Add support for serializing python dictionaries to json Add prediction with dictionary in tf iris integ test * Fixing timeouts for PCA async integration test. (aws#78) Execute tf_cifar test without logs to eliminate delay to detect that job has finished. * Fixes in LinearLearner and unit tests addition. (aws#77) * Print out billable seconds after training completes (aws#30) * Added: print out billable seconds after training completes * Fixed: test_session.py to pass unit tests * Fixed: removed offending tzlocal() * Use sagemaker_timestamp when creating endpoint names in integration tests. (aws#81) * Support TensorFlow-1.5.0 and MXNet-1.0.0 (aws#82) * Update .gitignore to ignore pytest_cache. * Support TensorFlow-1.5.0 and MXNet-1.0.0 * Update and refactor tests. Add tests for fw_utils. * Fix typo. * Update changelog for 1.1.0 (aws#85)
apacker
pushed a commit
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Nov 15, 2018
Scikit bring your own Merging this in preparation for Monday's meeting.
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