Merge branch 'main' into pt111-example-update

advanced_functionality/pytorch_deploy_large_GPT_model/GPT-J-6B-model-parallel-inference-DJL.ipynb

@@ -114,8 +114,7 @@
     "\n",
     "\n",
     "# # Build the docker image locally with the image name and then push it to ECR\n",
-    "image_id=$(docker images -q | head -n1)\n",
-    "docker tag $image_id ${fullname}\n",
+    "docker tag deepjavalibrary/djl-serving:0.18.0-deepspeed ${fullname}\n",
     "\n",
     "docker push $fullname"
    ]

ground_truth_labeling_jobs/ground_truth_streaming_labeling_jobs/ground_truth_create_streaming_labeling_job.ipynb

@@ -8,6 +8,9 @@
     "\n",
     "You can use a streaming labeling job to perpetually send new data objects to Amazon SageMaker Ground Truth to be labeled. Ground Truth streaming labeling jobs remain active until they are manually stopped or have been idle for more than 10 days. You can intermittently send new data objects to workers while the labeling job is active. \n",
     "\n",
+    "#### Note: Streaming Labeling Jobs are currently not supported on the Ground Truth Console. The way to work with this is launching the streaming job via api and viewing statistics on the Ground Truth labeling job.\n",
+    "\n",
+    "\n",
     "Use this notebook to create a Ground Truth streaming labeling job using any of the [built-in task types](https://docs.aws.amazon.com/sagemaker/latest/dg/sms-task-types.html). You can make necessary parameter changes for the custom workflow. You can either configure the notebook to create a labeling job using your own input data, or run the notebook on *default* mode and use provided, image input data. **To use your own input data, set `DEFAULT` to `False`**."
    ]
   },

index.rst

@@ -39,6 +39,7 @@ We recommend the following notebooks as a broad introduction to the capabilities
    :maxdepth: 1
    :caption: Prepare data
 
+   sagemaker-datawrangler/index
    sagemaker_processing/spark_distributed_data_processing/sagemaker-spark-processing_outputs
    sagemaker_processing/basic_sagemaker_data_processing/basic_sagemaker_processing_outputs
 
@@ -210,10 +211,16 @@ More examples
    sagemaker-clarify/index
    scientific_details_of_algorithms/index
    aws_marketplace/index
+
 
 
 .. toctree::
    :maxdepth: 1
    :caption: Community examples
 
-   contrib/index
+   contrib/index
+
+
+
+
+

sagemaker-datawrangler/README.md

@@ -0,0 +1,41 @@
+![Amazon SageMaker Data Wrangler](https://github.com/aws/amazon-sagemaker-examples/raw/main/_static/sagemaker-banner.png)
+
+# Amazon SageMaker Data Wrangler Examples
+
+Example flows that demonstrate how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler.
+
+## :books: Background
+
+[Amazon SageMaker Data Wrangler](https://aws.amazon.com/sagemaker/data-wrangler/) reduces the time it takes to aggregate and prepare data for ML. From a single interface in SageMaker Studio, you can import data from Amazon S3, Amazon Athena, Amazon Redshift, AWS Lake Formation, and Amazon SageMaker Feature Store, and in just a few clicks SageMaker Data Wrangler will automatically load, aggregate, and display the raw data. It will then make conversion recommendations based on the source data, transform the data into new features, validate the features, and provide visualizations with recommendations on how to remove common sources of error such as incorrect labels. Once your data is prepared, you can build fully automated ML workflows with Amazon SageMaker Pipelines or import that data into Amazon SageMaker Feature Store.
+
+
+
+The [SageMaker example notebooks](https://sagemaker-examples.readthedocs.io/en/latest/) are Jupyter notebooks that demonstrate the usage of Amazon SageMaker.
+
+## :hammer_and_wrench: Setup
+
+Amazon SageMaker Data Wrangler is a feature in Amazon SageMaker Studio. Use this section to learn how to access and get started using Data Wrangler. Do the following:
+
+* Complete each step in [Prerequisites](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-getting-started.html#data-wrangler-getting-started-prerequisite).
+
+* Follow the procedure in [Access Data Wrangler](https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-getting-started.html#data-wrangler-getting-started-access) to start using Data Wrangler.
+
+
+
+
+## :notebook: Examples
+
+### **[Tabular DataFlow](tabular-dataflow/README.md)**
+
+This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Tabular dataset.
+
+### **[Timeseries DataFlow](timeseries-dataflow/readme.md)**
+
+This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Timeseries dataset.
+
+### **[Joined DataFlow](joined-dataflow/readme.md)**
+
+This example provide quick walkthrough of how to aggregate and prepare data for Machine Learning using Amazon SageMaker Data Wrangler for Joined dataset.
+
+
+

sagemaker-datawrangler/import-flow.md

@@ -0,0 +1,11 @@
+## Import Flow
+
+Each of the example has a flow file available which you can directly import to expedite the process or validate the flow.
+
+Here are the steps to import the flow
+
+* Download the flow file
+
+* In Sagemaker Studio, drag and drop the flow file or use the upload button to browse the flow and upload
+
+![uploadflow](/uploadflow.png)

sagemaker-datawrangler/index.rst

@@ -0,0 +1,69 @@
+
+
+Amazon SageMaker Data Wrangler
+=======================================
+
+These example flows demonstrates how to aggregate and prepare data for
+Machine Learning using Amazon SageMaker Data Wrangler.
+
+
+------------------
+
+`Amazon SageMaker Data
+Wrangler <https://aws.amazon.com/sagemaker/data-wrangler/>`__ reduces
+the time it takes to aggregate and prepare data for ML. From a single
+interface in SageMaker Studio, you can import data from Amazon S3,
+Amazon Athena, Amazon Redshift, AWS Lake Formation, and Amazon SageMaker
+Feature Store, and in just a few clicks SageMaker Data Wrangler will
+automatically load, aggregate, and display the raw data. It will then
+make conversion recommendations based on the source data, transform the
+data into new features, validate the features, and provide
+visualizations with recommendations on how to remove common sources of
+error such as incorrect labels. Once your data is prepared, you can
+build fully automated ML workflows with Amazon SageMaker Pipelines or
+import that data into Amazon SageMaker Feature Store.
+
+The `SageMaker example
+notebooks <https://sagemaker-examples.readthedocs.io/en/latest/>`__ are
+Jupyter notebooks that demonstrate the usage of Amazon SageMaker.
+
+Setup
+-------------------------
+
+Amazon SageMaker Data Wrangler is a feature in Amazon SageMaker Studio.
+Use this section to learn how to access and get started using Data
+Wrangler. Do the following:
+
+-  Complete each step in
+   `Prerequisites <https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-getting-started.html#data-wrangler-getting-started-prerequisite>`__.
+
+-  Follow the procedure in `Access Data
+   Wrangler <https://docs.aws.amazon.com/sagemaker/latest/dg/data-wrangler-getting-started.html#data-wrangler-getting-started-access>`__
+   to start using Data Wrangler.
+
+Examples
+-------------------
+
+Tabular Dataflow
+---------------------------
+
+.. toctree::
+   :maxdepth: 1
+
+   tabular-dataflow/index
+
+Timeseries Dataflow
+----------------------------
+
+.. toctree::
+   :maxdepth: 1
+
+   timeseries-dataflow/index
+
+Joined Dataflow
+----------------------------
+
+.. toctree::
+   :maxdepth: 1
+
+   joined-dataflow/index