Merge branch 'master' into master

Author: rvasahu-amazon

doc/frameworks/pytorch/using_pytorch.rst

@@ -132,14 +132,49 @@ Using third-party libraries
 ---------------------------
 
 When running your training script on SageMaker, it will have access to some pre-installed third-party libraries including ``torch``, ``torchvision``, and ``numpy``.
-For more information on the runtime environment, including specific package versions, see `SageMaker PyTorch Docker containers <https://github.com/aws/deep-learning-containers/tree/master/pytorch>`_.
 
 If there are other packages you want to use with your script, you can include a ``requirements.txt`` file in the same directory as your training script to install other dependencies at runtime. Both ``requirements.txt`` and your training script should be put in the same folder. You must specify this folder in ``source_dir`` argument when creating PyTorch estimator.
 
 The function of installing packages using ``requirements.txt`` is supported for all PyTorch versions during training. When serving a PyTorch model, support for this function varies with PyTorch versions. For PyTorch 1.3.1 or newer, ``requirements.txt`` must be under folder ``code``. The SageMaker PyTorch Estimator will automatically save ``code`` in ``model.tar.gz`` after training (assuming you set up your script and ``requirements.txt`` correctly as stipulated in the previous paragraph). In the case of bringing your own trained model for deployment, you must save ``requirements.txt`` under folder ``code`` in ``model.tar.gz`` yourself or specify it through ``dependencies``. For PyTorch 1.2.0, ``requirements.txt`` is not supported for inference. For PyTorch 0.4.0 to 1.1.0, ``requirements.txt`` must be in ``source_dir``.
 
 A ``requirements.txt`` file is a text file that contains a list of items that are installed by using ``pip install``. You can also specify the version of an item to install. For information about the format of a ``requirements.txt`` file, see `Requirements Files <https://pip.pypa.io/en/stable/user_guide/#requirements-files>`__ in the pip documentation.
 
+If you were to use your own custom Docker Image, the `SageMaker Python SDK` and the `SageMaker Training Toolkit <https://github.com/aws/sagemaker-training-toolkit/>`__ need to be installed.
+
+To do so, you can add the following lines to your ``requirements.txt`` file:
+
+.. code:: text
+
+    sagemaker
+    sagemaker-training
+
+Deep Learning Framework-Specific SageMaker Toolkits and Containers
+------------------------------------------------------------------
+
+Framework-specific Toolkits exist. You might want to use them in your applications for framework-specific features.
+
+For Training Toolkits, see:
+
+- `SageMaker PyTorch Training Toolkit <https://github.com/aws/sagemaker-pytorch-training-toolkit>`__
+- `SageMaker MXNet Training Toolkit <https://github.com/aws/sagemaker-mxnet-training-toolkit>`__
+- `SageMaker TensorFlow Training Toolkit <https://github.com/aws/sagemaker-tensorflow-training-toolkit>`__
+
+For Inference Toolkits, see:
+
+- `SageMaker PyTorch Inference Toolkit <https://github.com/aws/sagemaker-pytorch-inference-toolkit>`__
+- `SageMaker MXNet Inference Toolkit <https://github.com/aws/sagemaker-mxnet-training-toolkit>`__
+- `SageMaker TensorFlow Inference Toolkit <https://github.com/aws/sagemaker-tensorflow-inference-toolkit>`__
+- `SageMaker HuggingFace Inference Toolkit <https://github.com/aws/sagemaker-huggingface-inference-toolkit>`__
+
+Moreover, for more information on the container runtime environment, including specific framework versions and configurations,
+see `AWS Deep Learning Containers <https://github.com/aws/deep-learning-containers/>`_. More specifically, see:
+
+- `Images for PyTorch <https://github.com/aws/deep-learning-containers/tree/master/pytorch>`__
+- `Images for MXNet <https://github.com/aws/deep-learning-containers/tree/master/mxnet>`__
+- `Images for TensorFlow <https://github.com/aws/deep-learning-containers/tree/master/tensorflow>`__
+- `Images for HuggingFace <https://github.com/aws/deep-learning-containers/tree/master/huggingface>`__
+
+
 Create an Estimator
 ===================
 

inference-experience-dev-tester.sh

@@ -8,8 +8,8 @@ testpaths = tests
 test=pytest
 
 [metadata]
-description-file = README.rst
-license_file = LICENSE.txt
+description_file = README.rst
+license_files = LICENSE.txt
 
 [wheel]
 universal = 1

setup.cfg

@@ -265,7 +265,11 @@ def register(
         )
 
     def prepare_container_def(
-        self, instance_type=None, accelerator_type=None, serverless_inference_config=None
+        self,
+        instance_type=None,
+        accelerator_type=None,
+        serverless_inference_config=None,
+        accept_eula=None,
     ):
         """Return a container definition with framework configuration set in model environment.
 
@@ -278,6 +282,11 @@ def prepare_container_def(
             serverless_inference_config (sagemaker.serverless.ServerlessInferenceConfig):
                 Specifies configuration related to serverless endpoint. Instance type is
                 not provided in serverless inference. So this is used to find image URIs.
+            accept_eula (bool): For models that require a Model Access Config, specify True or
+                False to indicate whether model terms of use have been accepted.
+                The `accept_eula` value must be explicitly defined as `True` in order to
+                accept the end-user license agreement (EULA) that some
+                models require. (Default: None).
 
         Returns:
             dict[str, str]: A container definition object usable with the
@@ -307,7 +316,12 @@ def prepare_container_def(
             deploy_env[MODEL_SERVER_WORKERS_PARAM_NAME.upper()] = to_string(
                 self.model_server_workers
             )
-        return sagemaker.container_def(deploy_image, self.model_data, deploy_env)
+        return sagemaker.container_def(
+            deploy_image,
+            self.model_data,
+            deploy_env,
+            accept_eula=accept_eula,
+        )
 
     def serving_image_uri(
         self, region_name, instance_type, accelerator_type=None, serverless_inference_config=None

src/sagemaker/chainer/model.py

@@ -733,6 +733,7 @@ def prepare_container_def(
         instance_type=None,
         accelerator_type=None,
         serverless_inference_config=None,
+        accept_eula=None,
     ):  # pylint: disable=unused-argument
         """A container definition with framework configuration set in model environment variables.
 

src/sagemaker/djl_inference/model.py

@@ -327,9 +327,6 @@ def validate_mp_config(config):
         ValueError: If any of the keys have incorrect values.
     """
 
-    if "partitions" not in config:
-        raise ValueError("'partitions' is a required parameter.")
-
     def validate_positive(key):
         try:
             if not isinstance(config[key], int) or config[key] < 1:

src/sagemaker/fw_utils.py

@@ -465,6 +465,7 @@ def prepare_container_def(
         accelerator_type=None,
         serverless_inference_config=None,
         inference_tool=None,
+        accept_eula=None,
     ):
         """A container definition with framework configuration set in model environment variables.
 
@@ -479,6 +480,11 @@ def prepare_container_def(
                 not provided in serverless inference. So this is used to find image URIs.
             inference_tool (str): the tool that will be used to aid in the inference.
                 Valid values: "neuron, neuronx, None" (default: None).
+            accept_eula (bool): For models that require a Model Access Config, specify True or
+                False to indicate whether model terms of use have been accepted.
+                The `accept_eula` value must be explicitly defined as `True` in order to
+                accept the end-user license agreement (EULA) that some
+                models require. (Default: None).
 
         Returns:
             dict[str, str]: A container definition object usable with the
@@ -510,7 +516,10 @@ def prepare_container_def(
                 self.model_server_workers
             )
         return sagemaker.container_def(
-            deploy_image, self.repacked_model_data or self.model_data, deploy_env
+            deploy_image,
+            self.repacked_model_data or self.model_data,
+            deploy_env,
+            accept_eula=accept_eula,
         )
 
     def serving_image_uri(

src/sagemaker/huggingface/model.py

@@ -3,123 +3,67 @@
     "versions": {
         "0.25.0": {
             "registries": {
-                "af-south-1": "626614931356",
-                "il-central-1": "780543022126",
-                "ap-east-1": "871362719292",
                 "ap-northeast-1": "763104351884",
-                "ap-northeast-2": "763104351884",
-                "ap-northeast-3": "364406365360",
                 "ap-south-1": "763104351884",
                 "ap-southeast-1": "763104351884",
                 "ap-southeast-2": "763104351884",
-                "ap-southeast-3": "907027046896",
-                "ca-central-1": "763104351884",
-                "cn-north-1": "727897471807",
-                "cn-northwest-1": "727897471807",
                 "eu-central-1": "763104351884",
-                "eu-north-1": "763104351884",
                 "eu-west-1": "763104351884",
-                "eu-west-2": "763104351884",
                 "eu-west-3": "763104351884",
-                "eu-south-1": "692866216735",
-                "me-south-1": "217643126080",
                 "sa-east-1": "763104351884",
                 "us-east-1": "763104351884",
                 "us-east-2": "763104351884",
-                "us-west-1": "763104351884",
                 "us-west-2": "763104351884"
             },
             "repository": "djl-inference",
             "tag_prefix": "0.25.0-neuronx-sdk2.15.0"
         },
         "0.24.0": {
             "registries": {
-                "af-south-1": "626614931356",
-                "il-central-1": "780543022126",
-                "ap-east-1": "871362719292",
                 "ap-northeast-1": "763104351884",
-                "ap-northeast-2": "763104351884",
-                "ap-northeast-3": "364406365360",
                 "ap-south-1": "763104351884",
                 "ap-southeast-1": "763104351884",
                 "ap-southeast-2": "763104351884",
-                "ap-southeast-3": "907027046896",
-                "ca-central-1": "763104351884",
-                "cn-north-1": "727897471807",
-                "cn-northwest-1": "727897471807",
                 "eu-central-1": "763104351884",
-                "eu-north-1": "763104351884",
                 "eu-west-1": "763104351884",
-                "eu-west-2": "763104351884",
                 "eu-west-3": "763104351884",
-                "eu-south-1": "692866216735",
-                "me-south-1": "217643126080",
                 "sa-east-1": "763104351884",
                 "us-east-1": "763104351884",
                 "us-east-2": "763104351884",
-                "us-west-1": "763104351884",
                 "us-west-2": "763104351884"
             },
             "repository": "djl-inference",
             "tag_prefix": "0.24.0-neuronx-sdk2.14.1"
         },
         "0.23.0": {
             "registries": {
-                "af-south-1": "626614931356",
-                "il-central-1": "780543022126",
-                "ap-east-1": "871362719292",
                 "ap-northeast-1": "763104351884",
-                "ap-northeast-2": "763104351884",
-                "ap-northeast-3": "364406365360",
                 "ap-south-1": "763104351884",
                 "ap-southeast-1": "763104351884",
                 "ap-southeast-2": "763104351884",
-                "ap-southeast-3": "907027046896",
-                "ca-central-1": "763104351884",
-                "cn-north-1": "727897471807",
-                "cn-northwest-1": "727897471807",
                 "eu-central-1": "763104351884",
-                "eu-north-1": "763104351884",
                 "eu-west-1": "763104351884",
-                "eu-west-2": "763104351884",
                 "eu-west-3": "763104351884",
-                "eu-south-1": "692866216735",
-                "me-south-1": "217643126080",
                 "sa-east-1": "763104351884",
                 "us-east-1": "763104351884",
                 "us-east-2": "763104351884",
-                "us-west-1": "763104351884",
                 "us-west-2": "763104351884"
             },
             "repository": "djl-inference",
             "tag_prefix": "0.23.0-neuronx-sdk2.12.0"
         },
         "0.22.1": {
             "registries": {
-                "af-south-1": "626614931356",
-                "il-central-1": "780543022126",
-                "ap-east-1": "871362719292",
                 "ap-northeast-1": "763104351884",
-                "ap-northeast-2": "763104351884",
-                "ap-northeast-3": "364406365360",
                 "ap-south-1": "763104351884",
                 "ap-southeast-1": "763104351884",
                 "ap-southeast-2": "763104351884",
-                "ap-southeast-3": "907027046896",
-                "ca-central-1": "763104351884",
-                "cn-north-1": "727897471807",
-                "cn-northwest-1": "727897471807",
                 "eu-central-1": "763104351884",
-                "eu-north-1": "763104351884",
                 "eu-west-1": "763104351884",
-                "eu-west-2": "763104351884",
                 "eu-west-3": "763104351884",
-                "eu-south-1": "692866216735",
-                "me-south-1": "217643126080",
                 "sa-east-1": "763104351884",
                 "us-east-1": "763104351884",
                 "us-east-2": "763104351884",
-                "us-west-1": "763104351884",
                 "us-west-2": "763104351884"
             },
             "repository": "djl-inference",

src/sagemaker/image_uri_config/djl-neuronx.json

@@ -13,6 +13,7 @@
 """This module stores constants related to SageMaker JumpStart."""
 from __future__ import absolute_import
 import logging
+import os
 from typing import Dict, Set, Type
 import boto3
 from sagemaker.base_deserializers import BaseDeserializer, JSONDeserializer
@@ -33,6 +34,8 @@
 from sagemaker.session import Session
 
 
+ENV_VARIABLE_DISABLE_JUMPSTART_LOGGING = "DISABLE_JUMPSTART_LOGGING"
+
 JUMPSTART_LAUNCHED_REGIONS: Set[JumpStartLaunchedRegionInfo] = set(
     [
         JumpStartLaunchedRegionInfo(
@@ -209,6 +212,19 @@
 
 JUMPSTART_LOGGER = logging.getLogger("sagemaker.jumpstart")
 
+# disable logging if env var is set
+JUMPSTART_LOGGER.addHandler(
+    type(
+        "",
+        (logging.StreamHandler,),
+        {
+            "emit": lambda self, *args, **kwargs: logging.StreamHandler.emit(self, *args, **kwargs)
+            if not os.environ.get(ENV_VARIABLE_DISABLE_JUMPSTART_LOGGING)
+            else None
+        },
+    )()
+)
+
 try:
     DEFAULT_JUMPSTART_SAGEMAKER_SESSION = Session(
         boto3.Session(region_name=JUMPSTART_DEFAULT_REGION_NAME)