Merge branch 'master' into PT113Release

Author: Qingzi-Lan

.flake8

@@ -3,3 +3,4 @@ application_import_names = sagemaker, tests
 import-order-style = google
 per-file-ignores =
     tests/unit/test_tuner.py: F405
+    src/sagemaker/config/config_schema.py: E501

CHANGELOG.md

@@ -1,5 +1,74 @@
 # Changelog
 
+## v2.150.0 (2023-04-26)
+
+### Features
+
+ * Introduce TensorBoard app class
+
+### Bug Fixes and Other Changes
+
+ * Update data wrangler images
+
+## v2.149.0 (2023-04-25)
+
+### Features
+
+ * Support TF2.12 SageMaker DLC
+
+### Bug Fixes and Other Changes
+
+ * update the doc for Join function
+ * change s3UploadMode of sagemaker clarify processing output for computer vision jobs.
+
+### Documentation Changes
+
+ * Add Remote Function updates
+
+## v2.148.0 (2023-04-20)
+
+### Features
+
+ * [huggingface] Add `torch.distributed` support for Trainium and `torchrun`
+ * Add PyTorch 2.0 to SDK
+
+### Bug Fixes and Other Changes
+
+ * updating batch transform job in monitoring schedule
+
+## v2.147.0 (2023-04-18)
+
+### Features
+
+ * support different types of deletion mode
+
+## v2.146.1 (2023-04-17)
+
+### Bug Fixes and Other Changes
+
+ * skip failing tests temporarily
+ * Added ml.p4d and ml.p4de as supported instances for DeepSpeed
+
+### Documentation Changes
+
+ * Add Model Registry Model Collection
+
+## v2.146.0 (2023-04-13)
+
+### Features
+
+ * Add support for JSON model inputs for Clarify Processor
+
+### Bug Fixes and Other Changes
+
+ * Feature/list collection
+ * improve reliability of Run integration test
+ * Add a comment that smdataparallel lib excludes tf 2.12 support
+
+### Documentation Changes
+
+ * Update reference to load run method in documentation
+
 ## v2.145.0 (2023-04-06)
 
 ### Features

README.rst

@@ -133,6 +133,8 @@ To run the integration tests, the following prerequisites must be met
 1. AWS account credentials are available in the environment for the boto3 client to use.
 2. The AWS account has an IAM role named :code:`SageMakerRole`.
    It should have the AmazonSageMakerFullAccess policy attached as well as a policy with `the necessary permissions to use Elastic Inference <https://docs.aws.amazon.com/sagemaker/latest/dg/ei-setup.html>`__.
+3. To run remote_function tests, dummy ecr repo should be created. It can be created by running -
+    :code:`aws ecr create-repository --repository-name remote-function-dummy-container`
 
 We recommend selectively running just those integration tests you'd like to run. You can filter by individual test function names with:
 

VERSION

@@ -1 +1 @@
-2.145.1.dev0
+2.150.1.dev0

doc/api/inference/model_collection.rst

@@ -0,0 +1,8 @@
+Model Collection
+----------------
+
+.. automodule:: sagemaker.collection
+    :members:
+    :undoc-members:
+    :show-inheritance:
+

doc/experiments/sagemaker.experiments.rst

@@ -7,7 +7,7 @@ Run
 .. autoclass:: sagemaker.experiments.Run
     :members:
 
-.. automethod:: sagemaker.experiments.load_run
+.. automethod:: sagemaker.experiments.run.load_run
 
 .. automethod:: sagemaker.experiments.list_runs
 

doc/overview.rst

@@ -2021,6 +2021,32 @@ set default values for. For the full schema, see ``sagemaker.config.config_schem
         Tags:
         - Key: 'tag_key'
           Value: 'tag_value'
+      PythonSDK:
+        Modules:
+          RemoteFunction:
+            Dependencies: 'path/to/requirements.txt'
+            EnableInterContainerTrafficEncryption: true
+            EnvironmentVariables: {'EnvVarKey': 'EnvVarValue'}
+            ImageUri: '555555555555.dkr.ecr.us-west-2.amazonaws.com/my-image:latest'
+            IncludeLocalWorkDir: true
+            InstanceType: 'ml.m5.large'
+            JobCondaEnvironment: 'your_conda_env'
+            PreExecutionCommands:
+              - 'command_1'
+              - 'command_2'
+            PreExecutionScript: 'path/to/script.sh'
+            RoleArn: 'arn:aws:iam::555555555555:role/MyRole'
+            S3KmsKeyId: 'yourkmskeyid'
+            S3RootUri: 's3://my-bucket/my-project'
+            VpcConfig:
+              SecurityGroupIds:
+                - 'sg123'
+              Subnets:
+                - 'subnet-1234'
+            Tags:
+              - Key: 'tag_key'
+                Value: 'tag_value'
+            VolumeKmsKeyId: 'yourkmskeyid'
 
 Configuration file locations
 ============================
@@ -2170,8 +2196,8 @@ types support setting defaults with a configuration file.
 -  Tags
 -  Enable inter-container traffic encryption
 
-List of APIs supported
-----------------------
+List of APIs and SDK capabilities supported
+-------------------------------------------
 
 Default values for the supported parameters of these APIs apply to
 all create and update calls for that API. For example, if a supported
@@ -2202,6 +2228,10 @@ configuration file.
 Hyperparameter Tuning Job: Supported indirectly via ``TrainingJob`` API. While this API is not directly supported, it includes the training job definition as a parameter.
 If you provide defaults for this parameter as part of the ``TrainingJob`` API, these defaults are also used for Hyperparameter Tuning Job.
 
+The following goups of SDK capabilities support defaults with a configuration file.
+
+-  Remote Function ``@remote decorator``, ``RemoteExecutor```
+
 Configuration file resolution
 =============================
 
@@ -2413,6 +2443,45 @@ specifically the contents of ``'body': b'{...}`` .
    'query_string': ..., 'method': 'POST', 'headers': {...}, 'body': b'{...}', 'url': 'https://api.sagemaker.us-west-2.amazonaws.com/',
    'context': {...}}
 
+
+************************************************************
+Run Machine Learning code on SageMaker using remote function
+************************************************************
+
+You can integrate your local machine language (ML) code to run in a Amazon SageMaker Training job by wrapping
+your code inside a @remote decorator as shown in the following code example.
+
+.. code-block:: python
+
+    from sagemaker.remote_function import remote
+    import numpy as np
+
+    @remote(instance_type="ml.m5.large")
+    def matrix_multiply(a, b):
+        return np.matmul(a, b)
+
+    a = np.array([[1, 0],
+                 [0, 1]])
+    b = np.array([1, 2])
+
+    assert (matrix_multiply(a, b) == np.array([1,2])).all()
+
+The SageMaker Python SDK will automatically translate your existing workspace environment and any associated data
+processing code and datasets into a SageMaker Training job that runs on the SageMaker Training platform.
+You can also activate a persistent cache feature, which will further reduce job start up latency by caching previously
+downloaded dependency packages. This reduction in job latency is greater than the reduction in latency from using
+SageMaker managed warm pools alone. The following sections show you how to wrap your local ML code and tailor your
+experience for your use case including customizing your environment and integrating with SageMaker Experiments.
+
+See the `Run your local code as a SageMaker Training job <https://docs.aws.amazon.com/sagemaker/latest/dg/train-remote-decorator.html>`__ for detailed developer guide.
+
+Follow is the API specification for methods and classes related to remote function feature.
+
+.. toctree::
+    :maxdepth: 1
+
+    remote_function/sagemaker.remote_function.rst
+
 ***
 FAQ
 ***

doc/remote_function/sagemaker.remote_function.rst

@@ -0,0 +1,26 @@
+Remote function classes and methods specification
+=================================================
+
+
+@remote decorator
+-----------------
+
+.. automethod:: sagemaker.remote_function.client.remote
+
+
+RemoteExecutor
+--------------
+
+.. autoclass:: sagemaker.remote_function.RemoteExecutor
+    :members:
+
+
+Future
+------
+
+.. autoclass:: sagemaker.remote_function.client.Future
+    :members:
+
+.. automethod:: sagemaker.remote_function.client.list_futures
+
+.. automethod:: sagemaker.remote_function.client.get_future

requirements/extras/test_requirements.txt

@@ -21,3 +21,4 @@ sagemaker-experiments==0.1.35
 Jinja2==3.0.3
 pandas>=1.3.5,<1.5
 scikit-learn==1.0.2
+cloudpickle==2.2.1

setup.py

@@ -49,6 +49,7 @@ def read_requirements(filename):
 required_packages = [
     "attrs>=20.3.0,<23",
     "boto3>=1.26.28,<2.0",
+    "cloudpickle==2.2.1",
     "google-pasta",
     "numpy>=1.9.0,<2.0",
     "protobuf>=3.1,<4.0",
@@ -62,6 +63,7 @@ def read_requirements(filename):
     "PyYAML==5.4.1",
     "jsonschema",
     "platformdirs",
+    "tblib==1.7.0",
 ]
 
 # Specific use case dependencies