Add support for async fit()

README.rst

@@ -97,7 +97,7 @@ SageMaker Python SDK provides several high-level abstractions for working with A
 - **Estimators**: Encapsulate training on SageMaker. Can be ``fit()`` to run training, then the resulting model ``deploy()`` ed to a SageMaker Endpoint.
 - **Models**: Encapsulate built ML models. Can be ``deploy()`` ed to a SageMaker Endpoint.
 - **Predictors**: Provide real-time inference and transformation using Python data-types against a SageMaker Endpoint.
-- **Session**: Provides a collection of convience methods for working with SageMaker resources.
+- **Session**: Provides a collection of convenience methods for working with SageMaker resources.
 
 Estimator and Model implementations for MXNet, TensorFlow, and Amazon ML algorithms are included. There's also an Estimator that runs SageMaker compatible custom Docker containers, allowing you to run your own ML algorithms via SageMaker Python SDK.
 
@@ -1150,6 +1150,7 @@ Optional arguments
 
 -  ``wait (bool)``: Defaults to True, whether to block and wait for the
    training script to complete before returning.
+   If set to False, it will return immediately, and can later be attached to.
 -  ``logs (bool)``: Defaults to True, whether to show logs produced by training
    job in the Python session. Only meaningful when wait is True.
 - ``run_tensorboard_locally (bool)``: Defaults to False. Executes TensorBoard in a different
@@ -1178,9 +1179,25 @@ the ``TensorFlow`` estimator parameter ``training_steps`` is finished or when th
 job execution time reaches the ``TensorFlow`` estimator parameter ``train_max_run``.
 
 When the training job finishes, a `TensorFlow serving <https://www.tensorflow.org/serving/serving_basic>`_
-with the result of the training is generated and saved to the S3 location define by
+with the result of the training is generated and saved to the S3 location defined by
 the ``TensorFlow`` estimator parameter ``output_path``.
 
+
+If the ``wait=False`` flag is passed to ``fit``, then it will return immediately. The training job will continue running
+asynchronously. At a later time, a Tensorflow Estimator can be obtained by attaching to the existing training job. If
+the training job is not finished it will start showing the standard output of training and wait until it completes.
+After attaching, the estimator can be deployed as usual.
+
+.. code:: python
+
+    tf_estimator.fit(your_input_data, wait=False)
+    training_job_name = tf_estimator.latest_training_job.name
+
+    # after some time, or in a separate python notebook, we can attach to it again.
+
+    tf_estimator = TensorFlow.attach(training_job_name=training_job_name)
+
+
 The evaluation process
 """"""""""""""""""""""
 
@@ -1244,6 +1261,8 @@ You can access TensorBoard locally at http://localhost:6006 or using your SakeMa
 `https*workspace_base_url*proxy/6006/ <proxy/6006/>`_ (TensorBoard will not work if you forget to put the slash,
 '/', in end of the url). If TensorBoard started on a different port, adjust these URLs to match.
 
+Note that TensorBoard is not supported when passing wait=False to ``fit``.
+
 
 Deploying TensorFlow Serving models
 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~

src/sagemaker/amazon/amazon_estimator.py

@@ -64,6 +64,31 @@ def data_location(self, data_location):
             data_location = data_location + '/'
         self._data_location = data_location
 
+    @classmethod
+    def _prepare_init_params_from_job_description(cls, job_details):
+        """Convert the job description to init params that can be handled by the class constructor
+
+        Args:
+            job_details: the returned job details from a describe_training_job API call.
+
+        Returns:
+             dictionary: The transformed init_params
+
+        """
+        init_params = super(AmazonAlgorithmEstimatorBase, cls)._prepare_init_params_from_job_description(job_details)
+
+        # The hyperparam names may not be the same as the class attribute that holds them,
+        # for instance: local_lloyd_init_method is called local_init_method. We need to map these
+        # and pass the correct name to the constructor.
+        for attribute, value in cls.__dict__.items():
+            if isinstance(value, hp):
+                if value.name in init_params['hyperparameters']:
+                    init_params[attribute] = init_params['hyperparameters'][value.name]
+
+        del init_params['hyperparameters']
+        del init_params['image']
+        return init_params
+
     def fit(self, records, mini_batch_size=None, **kwargs):
         """Fit this Estimator on serialized Record objects, stored in S3.
 

src/sagemaker/estimator.py

@@ -152,8 +152,60 @@ def fit(self, inputs, wait=True, logs=True, job_name=None):
         self.latest_training_job = _TrainingJob.start_new(self, inputs)
         if wait:
             self.latest_training_job.wait(logs=logs)
-        else:
-            raise NotImplemented('Asynchronous fit not available')
+
+    @classmethod
+    def _from_training_job(cls, init_params, hyperparameters, image, sagemaker_session):
+        """Create an Estimator from existing training job data.
+
+        Args:
+            init_params (dict): The init_params the training job was created with.
+            hyperparameters (dict):  The hyperparameters the training job was created with.
+            image (str): Container image (if any) the training job was created with
+            sagemaker_session (sagemaker.session.Session): A sagemaker Session to pass to the estimator.
+
+        Returns: An instance of the calling Estimator Class.
+
+        """
+        raise NotImplementedError()
+
+    @classmethod
+    def attach(cls, training_job_name, sagemaker_session=None, job_details=None):
+        """Attach to an existing training job.
+
+        Create an Estimator bound to an existing training job, each subclass is responsible to implement
+        ``_prepare_init_params_from_job_description()`` as this method delegates the actual conversion of a training
+        job description to the arguments that the class constructor expects. After attaching, if the training job has a
+        Complete status, it can be ``deploy()`` ed to create a SageMaker Endpoint and return a ``Predictor``.
+
+        If the training job is in progress, attach will block and display log messages
+        from the training job, until the training job completes.
+
+        Args:
+            training_job_name (str): The name of the training job to attach to.
+            sagemaker_session (sagemaker.session.Session): Session object which manages interactions with
+                Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one
+                using the default AWS configuration chain.
+
+        Examples:
+            >>> my_estimator.fit(wait=False)
+            >>> training_job_name = my_estimator.latest_training_job.name
+            Later on:
+            >>> attached_estimator = Estimator.attach(training_job_name)
+            >>> attached_estimator.deploy()
+
+        Returns:
+            Instance of the calling ``Estimator`` Class with the attached training job.
+        """
+        sagemaker_session = sagemaker_session or Session()
+
+        job_details = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=training_job_name)
+        init_params = cls._prepare_init_params_from_job_description(job_details)
+
+        estimator = cls(sagemaker_session=sagemaker_session, **init_params)
+        estimator.latest_training_job = _TrainingJob(sagemaker_session=sagemaker_session,
+                                                     training_job_name=init_params['base_job_name'])
+        estimator.latest_training_job.wait()
+        return estimator
 
     def deploy(self, initial_instance_count, instance_type, endpoint_name=None, **kwargs):
         """Deploy the trained model to an Amazon SageMaker endpoint and return a ``sagemaker.RealTimePredictor`` object.
@@ -202,21 +254,33 @@ def create_model(self, **kwargs):
         """
         pass
 
-    @staticmethod
-    def _prepare_estimator_params_from_job_description(job_details):
-        estimator_params = dict()
+    @classmethod
+    def _prepare_init_params_from_job_description(cls, job_details):
+        """Convert the job description to init params that can be handled by the class constructor
+
+        Args:
+            job_details: the returned job details from a describe_training_job API call.
+
+        Returns:
+             dictionary: The transformed init_params
+
+        """
+        init_params = dict()
 
-        estimator_params['role'] = job_details['RoleArn']
-        estimator_params['train_instance_count'] = job_details['ResourceConfig']['InstanceCount']
-        estimator_params['train_instance_type'] = job_details['ResourceConfig']['InstanceType']
-        estimator_params['train_volume_size'] = job_details['ResourceConfig']['VolumeSizeInGB']
-        estimator_params['train_max_run'] = job_details['StoppingCondition']['MaxRuntimeInSeconds']
-        estimator_params['input_mode'] = job_details['AlgorithmSpecification']['TrainingInputMode']
-        estimator_params['base_job_name'] = job_details['TrainingJobName']
-        estimator_params['output_path'] = job_details['OutputDataConfig']['S3OutputPath']
-        estimator_params['output_kms_key'] = job_details['OutputDataConfig']['KmsKeyId']
+        init_params['role'] = job_details['RoleArn']
+        init_params['train_instance_count'] = job_details['ResourceConfig']['InstanceCount']
+        init_params['train_instance_type'] = job_details['ResourceConfig']['InstanceType']
+        init_params['train_volume_size'] = job_details['ResourceConfig']['VolumeSizeInGB']
+        init_params['train_max_run'] = job_details['StoppingCondition']['MaxRuntimeInSeconds']
+        init_params['input_mode'] = job_details['AlgorithmSpecification']['TrainingInputMode']
+        init_params['base_job_name'] = job_details['TrainingJobName']
+        init_params['output_path'] = job_details['OutputDataConfig']['S3OutputPath']
+        init_params['output_kms_key'] = job_details['OutputDataConfig']['KmsKeyId']
 
-        return estimator_params, job_details['HyperParameters'], job_details['AlgorithmSpecification']['TrainingImage']
+        init_params['hyperparameters'] = job_details['HyperParameters']
+        init_params['image'] = job_details['AlgorithmSpecification']['TrainingImage']
+
+        return init_params
 
     def delete_endpoint(self):
         """Delete an Amazon SageMaker ``Endpoint``.
@@ -333,7 +397,8 @@ class Estimator(EstimatorBase):
 
     def __init__(self, image_name, role, train_instance_count, train_instance_type,
                  train_volume_size=30, train_max_run=24 * 60 * 60, input_mode='File',
-                 output_path=None, output_kms_key=None, base_job_name=None, sagemaker_session=None):
+                 output_path=None, output_kms_key=None, base_job_name=None, sagemaker_session=None,
+                 hyperparameters=None):
         """Initialize an ``Estimator`` instance.
 
         Args:
@@ -365,9 +430,10 @@ def __init__(self, image_name, role, train_instance_count, train_instance_type,
             sagemaker_session (sagemaker.session.Session): Session object which manages interactions with
                 Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one
                 using the default AWS configuration chain.
+            hyperparameters (dict): Dictionary containing the hyperparameters to initialize this estimator with.
         """
         self.image_name = image_name
-        self.hyperparam_dict = {}
+        self.hyperparam_dict = hyperparameters.copy() if hyperparameters else {}
         super(Estimator, self).__init__(role, train_instance_count, train_instance_type,
                                         train_volume_size, train_max_run, input_mode,
                                         output_path, output_kms_key, base_job_name, sagemaker_session)
@@ -422,6 +488,22 @@ def predict_wrapper(endpoint, session):
         return Model(self.model_data, image or self.train_image(), self.role, sagemaker_session=self.sagemaker_session,
                      predictor_cls=predictor_cls, **kwargs)
 
+    @classmethod
+    def _prepare_init_params_from_job_description(cls, job_details):
+        """Convert the job description to init params that can be handled by the class constructor
+
+        Args:
+            job_details: the returned job details from a describe_training_job API call.
+
+        Returns:
+             dictionary: The transformed init_params
+
+        """
+        init_params = super(Estimator, cls)._prepare_init_params_from_job_description(job_details)
+
+        init_params['image_name'] = init_params.pop('image')
+        return init_params
+
 
 class Framework(EstimatorBase):
     """Base class that cannot be instantiated directly.
@@ -528,12 +610,37 @@ def hyperparameters(self):
         return self._json_encode_hyperparameters(self._hyperparameters)
 
     @classmethod
-    def attach(cls, training_job_name, sagemaker_session=None, **kwargs):
+    def _prepare_init_params_from_job_description(cls, job_details):
+        """Convert the job description to init params that can be handled by the class constructor
+
+        Args:
+            job_details: the returned job details from a describe_training_job API call.
+
+        Returns:
+             dictionary: The transformed init_params
+
+        """
+        init_params = super(Framework, cls)._prepare_init_params_from_job_description(job_details)
+
+        init_params['entry_point'] = json.loads(init_params['hyperparameters'].get(SCRIPT_PARAM_NAME))
+        init_params['source_dir'] = json.loads(init_params['hyperparameters'].get(DIR_PARAM_NAME))
+        init_params['enable_cloudwatch_metrics'] = json.loads(
+            init_params['hyperparameters'].get(CLOUDWATCH_METRICS_PARAM_NAME))
+        init_params['container_log_level'] = json.loads(
+            init_params['hyperparameters'].get(CONTAINER_LOG_LEVEL_PARAM_NAME))
+
+        init_params['hyperparameters'] = {k: json.loads(v) for k, v in init_params['hyperparameters'].items()}
+
+        return init_params
+
+    @classmethod
+    def attach(cls, training_job_name, sagemaker_session=None):
         """Attach to an existing training job.
 
-        Create an Estimator bound to an existing training job. After attaching, if
-        the training job has a Complete status, it can be ``deploy()`` ed to create
-        a SageMaker Endpoint and return a ``Predictor``.
+        Create an Estimator bound to an existing training job, each subclass is responsible to implement
+        ``_prepare_init_params_from_job_description()`` as this method delegates the actual conversion of a training
+        job description to the arguments that the class constructor expects. After attaching, if the training job has a
+        Complete status, it can be ``deploy()`` ed to create a SageMaker Endpoint and return a ``Predictor``.
 
         If the training job is in progress, attach will block and display log messages
         from the training job, until the training job completes.
@@ -543,41 +650,18 @@ def attach(cls, training_job_name, sagemaker_session=None, **kwargs):
             sagemaker_session (sagemaker.session.Session): Session object which manages interactions with
                 Amazon SageMaker APIs and any other AWS services needed. If not specified, the estimator creates one
                 using the default AWS configuration chain.
-            **kwargs: Additional kwargs passed to the :class:`~sagemaker.estimator.Estimator` constructor.
+
+        Examples:
+            >>> my_estimator.fit(wait=False)
+            >>> training_job_name = my_estimator.latest_training_job.name
+            Later on:
+            >>> attached_estimator = Estimator.attach(training_job_name)
+            >>> attached_estimator.deploy()
 
         Returns:
-            sagemaker.estimator.Framework: ``Estimator`` with the attached training job.
+            Instance of the calling ``Estimator`` Class with the attached training job.
         """
-        sagemaker_session = sagemaker_session or Session()
-
-        if training_job_name is not None:
-            job_details = sagemaker_session.sagemaker_client.describe_training_job(TrainingJobName=training_job_name)
-            init_params, hp, _ = cls._prepare_estimator_params_from_job_description(job_details)
-
-        else:
-            # this case is only valid when called from inheriting class and then the class must declare framework
-            if not hasattr(cls, '__framework_name__'):
-                raise ValueError('must specify training_job name')
-            init_params = dict(kwargs)
-            hp = init_params.pop('hyperparameters')
-
-        # parameters for framework classes
-        framework_init_params = dict()
-        framework_init_params['entry_point'] = json.loads(hp.get(SCRIPT_PARAM_NAME))
-        framework_init_params['source_dir'] = json.loads(hp.get(DIR_PARAM_NAME))
-        framework_init_params['enable_cloudwatch_metrics'] = json.loads(hp.get(CLOUDWATCH_METRICS_PARAM_NAME))
-        framework_init_params['container_log_level'] = json.loads(hp.get(CONTAINER_LOG_LEVEL_PARAM_NAME))
-
-        # drop json and remove other SageMaker specific additions
-        hyperparameters = {entry: json.loads(hp[entry]) for entry in hp}
-        framework_init_params['hyperparameters'] = hyperparameters
-
-        init_params.update(framework_init_params)
-
-        estimator = cls(sagemaker_session=sagemaker_session, **init_params)
-        estimator.latest_training_job = _TrainingJob(sagemaker_session=sagemaker_session,
-                                                     training_job_name=init_params['base_job_name'])
-        estimator.latest_training_job.wait()
+        estimator = super(Framework, cls).attach(training_job_name, sagemaker_session)
         estimator.uploaded_code = UploadedCode(estimator.source_dir, estimator.entry_point)
         return estimator