Merge branch 'master-jumpstart' into feat/hyperparameter-validation

Author: evakravi

src/sagemaker/algorithm.py

@@ -174,7 +174,7 @@ def __init__(
         self.validate_train_spec()
         self.hyperparameter_definitions = self._parse_hyperparameters()
 
-        self.hyperparam_dict = {}
+        self._hyperparameters = {}
         if hyperparameters:
             self.set_hyperparameters(**hyperparameters)
 
@@ -215,7 +215,7 @@ def set_hyperparameters(self, **kwargs):
         """Placeholder docstring"""
         for k, v in kwargs.items():
             value = self._validate_and_cast_hyperparameter(k, v)
-            self.hyperparam_dict[k] = value
+            self._hyperparameters[k] = value
 
         self._validate_and_set_default_hyperparameters()
 
@@ -225,7 +225,7 @@ def hyperparameters(self):
         The fit() method, that does the model training, calls this method to
         find the hyperparameters you specified.
         """
-        return self.hyperparam_dict
+        return self._hyperparameters
 
     def training_image_uri(self):
         """Returns the docker image to use for training.
@@ -464,10 +464,10 @@ def _validate_and_set_default_hyperparameters(self):
         # Check if all the required hyperparameters are set. If there is a default value
         # for one, set it.
         for name, definition in self.hyperparameter_definitions.items():
-            if name not in self.hyperparam_dict:
+            if name not in self._hyperparameters:
                 spec = definition["spec"]
                 if "DefaultValue" in spec:
-                    self.hyperparam_dict[name] = spec["DefaultValue"]
+                    self._hyperparameters[name] = spec["DefaultValue"]
                 elif "IsRequired" in spec and spec["IsRequired"]:
                     raise ValueError("Required hyperparameter: %s is not set" % name)
 

src/sagemaker/chainer/estimator.py

@@ -15,7 +15,7 @@
 
 import logging
 
-from sagemaker.estimator import Framework
+from sagemaker.estimator import Framework, EstimatorBase
 from sagemaker.fw_utils import (
     framework_name_from_image,
     framework_version_from_tag,
@@ -158,7 +158,9 @@ def hyperparameters(self):
 
         # remove unset keys.
         additional_hyperparameters = {k: v for k, v in additional_hyperparameters.items() if v}
-        hyperparameters.update(Framework._json_encode_hyperparameters(additional_hyperparameters))
+        hyperparameters.update(
+            EstimatorBase._json_encode_hyperparameters(additional_hyperparameters)
+        )
         return hyperparameters
 
     def create_model(

src/sagemaker/chainer/model.py

@@ -168,7 +168,7 @@ def prepare_container_def(self, instance_type=None, accelerator_type=None):
         deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
         self._upload_code(deploy_key_prefix)
         deploy_env = dict(self.env)
-        deploy_env.update(self._framework_env_vars())
+        deploy_env.update(self._script_mode_env_vars())
 
         if self.model_server_workers:
             deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = str(self.model_server_workers)

src/sagemaker/estimator.py

@@ -17,7 +17,7 @@
 import re
 
 from sagemaker.deprecations import renamed_kwargs
-from sagemaker.estimator import Framework
+from sagemaker.estimator import Framework, EstimatorBase
 from sagemaker.fw_utils import (
     framework_name_from_image,
     warn_if_parameter_server_with_multi_gpu,
@@ -246,13 +246,13 @@ def hyperparameters(self):
             distribution=self.distribution
         )
         hyperparameters.update(
-            Framework._json_encode_hyperparameters(distributed_training_hyperparameters)
+            EstimatorBase._json_encode_hyperparameters(distributed_training_hyperparameters)
         )
 
         if self.compiler_config:
             training_compiler_hyperparameters = self.compiler_config._to_hyperparameter_dict()
             hyperparameters.update(
-                Framework._json_encode_hyperparameters(training_compiler_hyperparameters)
+                EstimatorBase._json_encode_hyperparameters(training_compiler_hyperparameters)
             )
 
         return hyperparameters

src/sagemaker/huggingface/estimator.py

@@ -273,7 +273,7 @@ def prepare_container_def(self, instance_type=None, accelerator_type=None):
         deploy_key_prefix = model_code_key_prefix(self.key_prefix, self.name, deploy_image)
         self._upload_code(deploy_key_prefix, repack=True)
         deploy_env = dict(self.env)
-        deploy_env.update(self._framework_env_vars())
+        deploy_env.update(self._script_mode_env_vars())
 
         if self.model_server_workers:
             deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = str(self.model_server_workers)