Merge branch 'master' into master

Author: andre-marcos-perez

src/sagemaker/estimator.py

@@ -116,6 +116,7 @@ def __init__(
         role: str,
         instance_count: Optional[Union[int, PipelineVariable]] = None,
         instance_type: Optional[Union[str, PipelineVariable]] = None,
+        keep_alive_period_in_seconds: Optional[Union[int, PipelineVariable]] = None,
         volume_size: Union[int, PipelineVariable] = 30,
         volume_kms_key: Optional[Union[str, PipelineVariable]] = None,
         max_run: Union[int, PipelineVariable] = 24 * 60 * 60,
@@ -167,6 +168,9 @@ def __init__(
             instance_type (str or PipelineVariable): Type of EC2 instance to use for training,
                 for example, ``'ml.c4.xlarge'``. Required if instance_groups is
                 not set.
+            keep_alive_period_in_seconds (int): The duration of time in seconds
+                to retain configured resources in a warm pool for subsequent
+                training jobs (default: None).
             volume_size (int or PipelineVariable): Size in GB of the storage volume to use for
                 storing input and output data during training (default: 30).
 
@@ -510,6 +514,7 @@ def __init__(
         self.role = role
         self.instance_count = instance_count
         self.instance_type = instance_type
+        self.keep_alive_period_in_seconds = keep_alive_period_in_seconds
         self.instance_groups = instance_groups
         self.volume_size = volume_size
         self.volume_kms_key = volume_kms_key
@@ -1578,6 +1583,11 @@ def _prepare_init_params_from_job_description(cls, job_details, model_channel_na
         if "EnableNetworkIsolation" in job_details:
             init_params["enable_network_isolation"] = job_details["EnableNetworkIsolation"]
 
+        if "KeepAlivePeriodInSeconds" in job_details["ResourceConfig"]:
+            init_params["keep_alive_period_in_seconds"] = job_details["ResourceConfig"][
+                "keepAlivePeriodInSeconds"
+            ]
+
         has_hps = "HyperParameters" in job_details
         init_params["hyperparameters"] = job_details["HyperParameters"] if has_hps else {}
 
@@ -2126,7 +2136,9 @@ def _is_local_channel(cls, input_uri):
         return isinstance(input_uri, string_types) and input_uri.startswith("file://")
 
     @classmethod
-    def update(cls, estimator, profiler_rule_configs=None, profiler_config=None):
+    def update(
+        cls, estimator, profiler_rule_configs=None, profiler_config=None, resource_config=None
+    ):
         """Update a running Amazon SageMaker training job.
 
         Args:
@@ -2135,18 +2147,23 @@ def update(cls, estimator, profiler_rule_configs=None, profiler_config=None):
                 updated in the training job. (default: None).
             profiler_config (dict): Configuration for how profiling information is emitted with
                 SageMaker Debugger. (default: None).
+            resource_config (dict): Configuration of the resources for the training job. You can
+                update the keep-alive period if the warm pool status is `Available`. No other fields
+                can be updated. (default: None).
 
         Returns:
             sagemaker.estimator._TrainingJob: Constructed object that captures
             all information about the updated training job.
         """
-        update_args = cls._get_update_args(estimator, profiler_rule_configs, profiler_config)
+        update_args = cls._get_update_args(
+            estimator, profiler_rule_configs, profiler_config, resource_config
+        )
         estimator.sagemaker_session.update_training_job(**update_args)
 
         return estimator.latest_training_job
 
     @classmethod
-    def _get_update_args(cls, estimator, profiler_rule_configs, profiler_config):
+    def _get_update_args(cls, estimator, profiler_rule_configs, profiler_config, resource_config):
         """Constructs a dict of arguments for updating an Amazon SageMaker training job.
 
         Args:
@@ -2156,13 +2173,17 @@ def _get_update_args(cls, estimator, profiler_rule_configs, profiler_config):
                 updated in the training job. (default: None).
             profiler_config (dict): Configuration for how profiling information is emitted with
                 SageMaker Debugger. (default: None).
+            resource_config (dict): Configuration of the resources for the training job. You can
+                update the keep-alive period if the warm pool status is `Available`. No other fields
+                can be updated. (default: None).
 
         Returns:
             Dict: dict for `sagemaker.session.Session.update_training_job` method
         """
         update_args = {"job_name": estimator.latest_training_job.name}
         update_args.update(build_dict("profiler_rule_configs", profiler_rule_configs))
         update_args.update(build_dict("profiler_config", profiler_config))
+        update_args.update(build_dict("resource_config", resource_config))
 
         return update_args
 
@@ -2218,6 +2239,7 @@ def __init__(
         role: str,
         instance_count: Optional[Union[int, PipelineVariable]] = None,
         instance_type: Optional[Union[str, PipelineVariable]] = None,
+        keep_alive_period_in_seconds: Optional[Union[int, PipelineVariable]] = None,
         volume_size: Union[int, PipelineVariable] = 30,
         volume_kms_key: Optional[Union[str, PipelineVariable]] = None,
         max_run: Union[int, PipelineVariable] = 24 * 60 * 60,
@@ -2270,6 +2292,9 @@ def __init__(
             instance_type (str or PipelineVariable): Type of EC2 instance to use for training,
                 for example, ``'ml.c4.xlarge'``. Required if instance_groups is
                 not set.
+            keep_alive_period_in_seconds (int): The duration of time in seconds
+                to retain configured resources in a warm pool for subsequent
+                training jobs (default: None).
             volume_size (int or PipelineVariable): Size in GB of the storage volume to use for
                 storing input and output data during training (default: 30).
 
@@ -2591,6 +2616,7 @@ def __init__(
             role,
             instance_count,
             instance_type,
+            keep_alive_period_in_seconds,
             volume_size,
             volume_kms_key,
             max_run,

src/sagemaker/job.py

@@ -78,6 +78,7 @@ def _load_config(inputs, estimator, expand_role=True, validate_uri=True):
             estimator.instance_groups,
             estimator.volume_size,
             estimator.volume_kms_key,
+            estimator.keep_alive_period_in_seconds,
         )
         stop_condition = _Job._prepare_stop_condition(estimator.max_run, estimator.max_wait)
         vpc_config = estimator.get_vpc_config()
@@ -281,14 +282,21 @@ def _prepare_output_config(s3_path, kms_key_id):
 
     @staticmethod
     def _prepare_resource_config(
-        instance_count, instance_type, instance_groups, volume_size, volume_kms_key
+        instance_count,
+        instance_type,
+        instance_groups,
+        volume_size,
+        volume_kms_key,
+        keep_alive_period_in_seconds,
     ):
         """Placeholder docstring"""
         resource_config = {
             "VolumeSizeInGB": volume_size,
         }
         if volume_kms_key is not None:
             resource_config["VolumeKmsKeyId"] = volume_kms_key
+        if keep_alive_period_in_seconds is not None:
+            resource_config["KeepAlivePeriodInSeconds"] = keep_alive_period_in_seconds
         if instance_groups is not None:
             if instance_count is not None or instance_type is not None:
                 raise ValueError(

src/sagemaker/session.py

@@ -821,6 +821,7 @@ def update_training_job(
         job_name,
         profiler_rule_configs=None,
         profiler_config=None,
+        resource_config=None,
     ):
         """Calls the UpdateTrainingJob API for the given job name and returns the response.
 
@@ -829,11 +830,15 @@ def update_training_job(
             profiler_rule_configs (list): List of profiler rule configurations. (default: ``None``).
             profiler_config(dict): Configuration for how profiling information is emitted with
                 SageMaker Profiler. (default: ``None``).
+            resource_config (dict): Configuration of the resources for the training job. You can
+                update the keep-alive period if the warm pool status is `Available`. No other fields
+                can be updated. (default: ``None``).
         """
         update_training_job_request = self._get_update_training_job_request(
             job_name=job_name,
             profiler_rule_configs=profiler_rule_configs,
             profiler_config=profiler_config,
+            resource_config=resource_config,
         )
         LOGGER.info("Updating training job with name %s", job_name)
         LOGGER.debug("Update request: %s", json.dumps(update_training_job_request, indent=4))
@@ -844,14 +849,18 @@ def _get_update_training_job_request(
         job_name,
         profiler_rule_configs=None,
         profiler_config=None,
+        resource_config=None,
     ):
-        """Constructs a request compatible for updateing an Amazon SageMaker training job.
+        """Constructs a request compatible for updating an Amazon SageMaker training job.
 
         Args:
             job_name (str): Name of the training job being updated.
             profiler_rule_configs (list): List of profiler rule configurations. (default: ``None``).
             profiler_config(dict): Configuration for how profiling information is emitted with
                 SageMaker Profiler. (default: ``None``).
+            resource_config (dict): Configuration of the resources for the training job. You can
+                update the keep-alive period if the warm pool status is `Available`. No other fields
+                can be updated. (default: ``None``).
 
         Returns:
             Dict: an update training request dict
@@ -866,6 +875,9 @@ def _get_update_training_job_request(
         if profiler_config is not None:
             update_training_job_request["ProfilerConfig"] = profiler_config
 
+        if resource_config is not None:
+            update_training_job_request["ResourceConfig"] = resource_config
+
         return update_training_job_request
 
     def process(

tests/integ/test_training_compiler.py

@@ -81,7 +81,6 @@ def skip_if_incompatible(gpu_instance_type, request):
         pytest.skip("no ml.p3 instances in this region")
 
 
-@pytest.mark.release
 @pytest.mark.parametrize(
     "gpu_instance_type,instance_count",
     [
@@ -130,6 +129,46 @@ def test_huggingface_pytorch(
         hf.fit(huggingface_dummy_dataset)
 
 
+@pytest.mark.release
+def test_huggingface_pytorch_release(
+    sagemaker_session,
+    gpu_instance_type,
+    huggingface_training_compiler_latest_version,
+    huggingface_training_compiler_pytorch_latest_version,
+    huggingface_dummy_dataset,
+):
+    """
+    Test the HuggingFace estimator with PyTorch
+    """
+    with timeout(minutes=TRAINING_DEFAULT_TIMEOUT_MINUTES):
+        data_path = os.path.join(DATA_DIR, "huggingface")
+
+        hf = HuggingFace(
+            py_version="py38",
+            entry_point=os.path.join(data_path, "run_glue.py"),
+            role="SageMakerRole",
+            transformers_version=huggingface_training_compiler_latest_version,
+            pytorch_version=huggingface_training_compiler_pytorch_latest_version,
+            instance_count=1,
+            instance_type=gpu_instance_type,
+            hyperparameters={
+                "model_name_or_path": "distilbert-base-cased",
+                "task_name": "wnli",
+                "do_train": True,
+                "do_eval": True,
+                "max_seq_length": 128,
+                "fp16": True,
+                "per_device_train_batch_size": 128,
+                "output_dir": "/opt/ml/model",
+            },
+            sagemaker_session=sagemaker_session,
+            disable_profiler=True,
+            compiler_config=HFTrainingCompilerConfig(),
+        )
+
+        hf.fit(huggingface_dummy_dataset)
+
+
 @pytest.mark.release
 def test_huggingface_tensorflow(
     sagemaker_session,

tests/unit/test_estimator.py

@@ -69,6 +69,7 @@
 BUCKET_NAME = "mybucket"
 INSTANCE_COUNT = 1
 INSTANCE_TYPE = "c4.4xlarge"
+KEEP_ALIVE_PERIOD_IN_SECONDS = 1800
 ACCELERATOR_TYPE = "ml.eia.medium"
 ROLE = "DummyRole"
 IMAGE_URI = "fakeimage"
@@ -351,6 +352,23 @@ def test_framework_with_heterogeneous_cluster(sagemaker_session):
     }
 
 
+def test_framework_with_keep_alive_period(sagemaker_session):
+    f = DummyFramework(
+        entry_point=SCRIPT_PATH,
+        role=ROLE,
+        sagemaker_session=sagemaker_session,
+        instance_groups=[
+            InstanceGroup("group1", "ml.c4.xlarge", 1),
+            InstanceGroup("group2", "ml.m4.xlarge", 2),
+        ],
+        keep_alive_period_in_seconds=KEEP_ALIVE_PERIOD_IN_SECONDS,
+    )
+    f.fit("s3://mydata")
+    sagemaker_session.train.assert_called_once()
+    _, args = sagemaker_session.train.call_args
+    assert args["resource_config"]["KeepAlivePeriodInSeconds"] == KEEP_ALIVE_PERIOD_IN_SECONDS
+
+
 def test_framework_with_debugger_and_built_in_rule(sagemaker_session):
     debugger_built_in_rule_with_custom_args = Rule.sagemaker(
         base_config=rule_configs.stalled_training_rule(),

tests/unit/test_job.py

@@ -29,6 +29,7 @@
 LOCAL_FILE_NAME = "file://local/file"
 INSTANCE_COUNT = 1
 INSTANCE_TYPE = "c4.4xlarge"
+KEEP_ALIVE_PERIOD = 1800
 INSTANCE_GROUP = InstanceGroup("group", "ml.c4.xlarge", 1)
 VOLUME_SIZE = 1
 MAX_RUNTIME = 1
@@ -599,7 +600,7 @@ def test_prepare_output_config_kms_key_none():
 
 def test_prepare_resource_config():
     resource_config = _Job._prepare_resource_config(
-        INSTANCE_COUNT, INSTANCE_TYPE, None, VOLUME_SIZE, None
+        INSTANCE_COUNT, INSTANCE_TYPE, None, VOLUME_SIZE, None, None
     )
 
     assert resource_config == {
@@ -609,9 +610,23 @@ def test_prepare_resource_config():
     }
 
 
+def test_prepare_resource_config_with_keep_alive_period():
+    resource_config = _Job._prepare_resource_config(
+        INSTANCE_COUNT, INSTANCE_TYPE, None, VOLUME_SIZE, VOLUME_KMS_KEY, KEEP_ALIVE_PERIOD
+    )
+
+    assert resource_config == {
+        "InstanceCount": INSTANCE_COUNT,
+        "InstanceType": INSTANCE_TYPE,
+        "VolumeSizeInGB": VOLUME_SIZE,
+        "VolumeKmsKeyId": VOLUME_KMS_KEY,
+        "KeepAlivePeriodInSeconds": KEEP_ALIVE_PERIOD,
+    }
+
+
 def test_prepare_resource_config_with_volume_kms():
     resource_config = _Job._prepare_resource_config(
-        INSTANCE_COUNT, INSTANCE_TYPE, None, VOLUME_SIZE, VOLUME_KMS_KEY
+        INSTANCE_COUNT, INSTANCE_TYPE, None, VOLUME_SIZE, VOLUME_KMS_KEY, None
     )
 
     assert resource_config == {
@@ -629,6 +644,7 @@ def test_prepare_resource_config_with_heterogeneous_cluster():
         [InstanceGroup("group1", "ml.c4.xlarge", 1), InstanceGroup("group2", "ml.m4.xlarge", 2)],
         VOLUME_SIZE,
         None,
+        None,
     )
 
     assert resource_config == {
@@ -648,6 +664,7 @@ def test_prepare_resource_config_with_instance_groups_instance_type_instance_cou
             [INSTANCE_GROUP],
             VOLUME_SIZE,
             None,
+            None,
         )
     assert "instance_count and instance_type cannot be set when instance_groups is set" in str(
         error
@@ -662,6 +679,7 @@ def test_prepare_resource_config_with_instance_groups_instance_type_instance_cou
             None,
             VOLUME_SIZE,
             None,
+            None,
         )
     assert "instance_count and instance_type must be set if instance_groups is not set" in str(
         error