Tutorial on using TorchServe on Vertex AI (#2667)

* Tutorial on using TorchServe on Vertex AI

This new tutorial is based on the Vertex AI documentation.
The PR provides a similar doc as a tutorial hosted locally.
Moving this tutorial to recipes folder as it is more of a recipe.

---------

Signed-off-by: Sahdev Zala <spzala@us.ibm.com>
Co-authored-by: Ankith Gunapal <agunapal@ischool.Berkeley.edu>
Co-authored-by: sekyonda <127536312+sekyondaMeta@users.noreply.github.com>
Co-authored-by: Svetlana Karslioglu <svekars@meta.com>

Author: 4 people

index.rst

@@ -286,7 +286,7 @@ What's new in PyTorch tutorials?
    :header: Introduction to ONNX Registry
    :card_description: Demonstrate end-to-end how to address unsupported operators by using ONNX Registry.
    :image: _static/img/thumbnails/cropped/Exporting-PyTorch-Models-to-ONNX-Graphs.png
-   :link: advanced/onnx_registry_tutorial.html 
+   :link: advanced/onnx_registry_tutorial.html
    :tags: Production,ONNX,Backends
 
 .. Reinforcement Learning
@@ -1043,6 +1043,7 @@ Additional Resources
    intermediate/scaled_dot_product_attention_tutorial
    beginner/knowledge_distillation_tutorial
 
+
 .. toctree::
    :maxdepth: 2
    :includehidden:

recipes_source/recipes_index.rst

@@ -324,6 +324,15 @@ Recipes are bite-sized, actionable examples of how to use specific PyTorch featu
    :link: ../recipes/DCP_tutorial.html
    :tags: Distributed-Training
 
+.. TorchServe
+
+.. customcarditem::
+   :header: Deploying a PyTorch Stable Diffusion model as a Vertex AI Endpoint
+   :card_description: Learn how to deploy model in Vertex AI with TorchServe
+   :image: ../_static/img/thumbnails/cropped/generic-pytorch-logo.png
+   :link: ../recipes/torchserve_vertexai_tutorial.html
+   :tags: Production
+
 .. End of tutorial card section
 
 .. raw:: html

recipes_source/torchserve_vertexai_tutorial.rst

@@ -0,0 +1,144 @@
+Deploying a PyTorch Stable Diffusion model as a Vertex AI Endpoint
+==================================================================
+
+Deploying large models, like Stable Diffusion, can be challenging and time-consuming.
+
+In this recipe, we will show how you can streamline the deployment of a PyTorch Stable Diffusion
+model by leveraging Vertex AI.
+
+PyTorch is the framework used by Stability AI on Stable
+Diffusion v1.5.  Vertex AI is a fully-managed machine learning platform with tools and
+infrastructure designed to help ML practitioners accelerate and scale ML in production with
+the benefit of open-source frameworks like PyTorch.
+
+In four steps you can deploy a PyTorch Stable Diffusion model (v1.5).
+
+Deploying your Stable Diffusion model on a Vertex AI Endpoint can be done in four steps:
+
+* Create a custom TorchServe handler.
+
+* Upload model artifacts to Google Cloud Storage (GCS).
+
+* Create a Vertex AI model with the model artifacts and a prebuilt PyTorch container image.
+
+* Deploy the Vertex AI model onto an endpoint.
+
+Let’s have a look at each step in more detail. You can follow and implement the steps using the
+`Notebook example <https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/vertex_endpoints/torchserve/dreambooth_stablediffusion.ipynb>`__.
+
+NOTE: Please keep in mind that this recipe requires a billable Vertex AI as explained in more details in the notebook example.
+
+Create a custom TorchServe handler
+----------------------------------
+
+TorchServe is an easy and flexible tool for serving PyTorch models. The model deployed to Vertex AI
+uses TorchServe to handle requests and return responses from the model.
+You must create a custom TorchServe handler to include in the model artifacts uploaded to Vertex AI. Include the handler file in the
+directory with the other model artifacts, like this: `model_artifacts/handler.py`.
+
+After creating the handler file, you must package the handler as a model archiver (MAR) file.
+The output file must be named `model.mar`.
+
+
+.. code:: shell
+
+    !torch-model-archiver \
+    -f \
+    --model-name <your_model_name> \
+    --version 1.0 \
+     --handler model_artifacts/handler.py \
+    --export-path model_artifacts
+
+Upload model artifacts to Google Cloud Storage (GCS)
+----------------------------------------------------
+
+In this step we are uploading
+`model artifacts <https://github.com/pytorch/serve/tree/master/model-archiver#artifact-details>`__
+to GCS, like the model file or handler. The advantage of storing your artifacts on GCS is that you can
+track the artifacts in a central bucket.
+
+
+.. code:: shell
+
+    BUCKET_NAME = "your-bucket-name-unique"  # @param {type:"string"}
+    BUCKET_URI = f"gs://{BUCKET_NAME}/"
+
+    # Will copy the artifacts into the bucket
+    !gsutil cp -r model_artifacts $BUCKET_URI
+
+Create a Vertex AI model with the model artifacts and a prebuilt PyTorch container image
+----------------------------------------------------------------------------------------
+
+Once you've uploaded the model artifacts into a GCS bucket, you can upload your PyTorch model to
+`Vertex AI Model Registry <https://cloud.google.com/vertex-ai/docs/model-registry/introduction>`__.
+From the Vertex AI Model Registry, you have an overview of your models
+so you can better organize, track, and train new versions. For this you can use the
+`Vertex AI SDK <https://cloud.google.com/vertex-ai/docs/python-sdk/use-vertex-ai-python-sdk>`__
+and this
+`pre-built PyTorch container <https://cloud.google.com/blog/products/ai-machine-learning/prebuilt-containers-with-pytorch-and-vertex-ai>`__.
+
+
+.. code:: shell
+
+    from google.cloud import aiplatform as vertexai
+    PYTORCH_PREDICTION_IMAGE_URI = (
+        "us-docker.pkg.dev/vertex-ai/prediction/pytorch-gpu.1-12:latest"
+    )
+    MODEL_DISPLAY_NAME = "stable_diffusion_1_5-unique"
+    MODEL_DESCRIPTION = "stable_diffusion_1_5 container"
+
+    vertexai.init(project='your_project', location='us-central1', staging_bucket=BUCKET_NAME)
+
+    model = aiplatform.Model.upload(
+        display_name=MODEL_DISPLAY_NAME,
+        description=MODEL_DESCRIPTION,
+        serving_container_image_uri=PYTORCH_PREDICTION_IMAGE_URI,
+        artifact_uri=BUCKET_URI,
+    )
+
+Deploy the Vertex AI model onto an endpoint
+-------------------------------------------
+
+Once the model has been uploaded to Vertex AI Model Registry you can then take it and deploy
+it to an Vertex AI Endpoint. For this you can use the Console or the Vertex AI SDK. In this
+example you will deploy the model on a NVIDIA Tesla P100 GPU and n1-standard-8 machine. You can
+specify your machine type.
+
+
+.. code:: shell
+
+    endpoint = aiplatform.Endpoint.create(display_name=ENDPOINT_DISPLAY_NAME)
+
+    model.deploy(
+        endpoint=endpoint,
+        deployed_model_display_name=MODEL_DISPLAY_NAME,
+        machine_type="n1-standard-8",
+        accelerator_type="NVIDIA_TESLA_P100",
+        accelerator_count=1,
+        traffic_percentage=100,
+        deploy_request_timeout=1200,
+        sync=True,
+    )
+
+If you follow this
+`notebook <https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/vertex_endpoints/torchserve/dreambooth_stablediffusion.ipynb>`__
+you can also get online predictions using the Vertex AI SDK as shown in the following snippet.
+
+
+.. code:: shell
+
+    instances = [{"prompt": "An examplePup dog with a baseball jersey."}]
+    response = endpoint.predict(instances=instances)
+
+    with open("img.jpg", "wb") as g:
+        g.write(base64.b64decode(response.predictions[0]))
+
+    display.Image("img.jpg")
+
+Create a Vertex AI model with the model artifacts and a prebuilt PyTorch container image
+
+More resources
+--------------
+
+This tutorial was created using the vendor documentation. To refer to the original documentation on the vendor site, please see
+`torchserve example <https://cloud.google.com/blog/products/ai-machine-learning/get-your-genai-model-going-in-four-easy-steps>`__.