feat: Add Model Garden deploy SDK documentation and use cases.

PiperOrigin-RevId: 751151322

Author: vertex-sdk-bot

vertexai/model_garden/README.md

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+# Vertex Model Garden SDK for Python
+
+The Vertex Model Garden SDK helps developers use [Model Garden](https://cloud.google.com/vertex-ai/generative-ai/docs/model-garden/explore-models) open models to build AI-powered features and applications.
+The SDKs support use cases like the following:
+
+- Deploy an open model
+- Export open model weights
+
+## Installation
+
+To install the
+[google-cloud-aiplatform](https://pypi.org/project/google-cloud-aiplatform/)
+Python package, run the following command:
+
+```shell
+pip3 install --upgrade --user "google-cloud-aiplatform>=1.84"
+```
+
+## Usage
+
+For detailed instructions, see [deploy an open model](https://cloud.google.com/vertex-ai/generative-ai/docs/model-garden/use-models#deploy_an_open_model) and [deploy notebook tutorial](https://github.com/GoogleCloudPlatform/vertex-ai-samples/blob/main/notebooks/community/model_garden/model_garden_deployment_tutorial.ipynb).
+
+## Quick Start: Default Deployment
+
+This is the simplest way to deploy a model. If you provide just a model name, the SDK will use the default deployment configuration.
+
+```python
+from vertexai.preview import model_garden
+
+model = model_garden.OpenModel("google/paligemma@paligemma-224-float32")
+endpoint = model.deploy()
+```
+
+**Use case:** Fast prototyping, first-time users evaluating model outputs.
+
+## List Deployable Models
+
+You can list all models that are currently deployable via Model Garden:
+
+```python
+from vertexai.preview import model_garden
+
+models = model_garden.list_deployable_models()
+```
+
+To filter only Hugging Face models or by keyword:
+
+```python
+models = model_garden.list_deployable_models(list_hf_models=True, model_filter="stable-diffusion")
+```
+
+**Use case:** Discover available models before deciding which one to deploy.
+
+## Hugging Face Model Deployment
+
+Deploy a model directly from Hugging Face using the model ID.
+
+```python
+model = model_garden.OpenModel("Qwen/Qwen2-1.5B-Instruct")
+endpoint = model.deploy()
+```
+
+**Use case:** Leverage community or third-party models without custom container setup. If the model is gated, you may need to provide a Hugging Face access token:
+
+```python
+endpoint = model.deploy(hugging_face_access_token="your_hf_token")
+```
+
+**Use case:** Deploy gated Hugging Face models requiring authentication.
+
+## List Deployment Configurations
+
+You can inspect available deployment configurations for a model:
+
+```python
+model = model_garden.OpenModel("google/paligemma@paligemma-224-float32")
+deploy_options = model.list_deploy_options()
+```
+
+**Use case:** Evaluate compatible machine specs and containers before deployment.
+
+## Customize Deployment: Machine and Resource Configuration
+
+Specify exact hardware resources and endpoint/model names.
+
+```python
+endpoint = model.deploy(
+ machine_type="g2-standard-4",
+ accelerator_type="NVIDIA_L4",
+ accelerator_count=1,
+ min_replica_count=1,
+ max_replica_count=1,
+ endpoint_display_name="paligemma-endpoint",
+ model_display_name="paligemma-model"
+)
+```
+
+**Use case:** Production configuration, performance tuning, scaling.
+
+## EULA Acceptance
+
+Some models require acceptance of a license agreement. Pass `eula=True` if prompted.
+
+```python
+model = model_garden.OpenModel("google/gemma2@gemma-2-27b-it")
+endpoint = model.deploy(eula=True)
+```
+
+**Use case:** First-time deployment of EULA-protected models.
+
+## Spot VM Deployment
+
+Schedule workloads on Spot VMs for lower cost.
+
+```python
+endpoint = model.deploy(spot=True)
+```
+
+**Use case:** Cost-sensitive development and batch workloads.
+
+## Fast Tryout Deployment
+
+Enable experimental fast-deploy path for popular models.
+
+```python
+endpoint = model.deploy(fast_tryout_enabled=True)
+```
+
+**Use case:** Interactive experimentation without full production setup.
+
+## Dedicated Endpoints
+
+Create a dedicated DNS-isolated endpoint.
+
+```python
+endpoint = model.deploy(use_dedicated_endpoint=True)
+```
+
+**Use case:** Traffic isolation for enterprise or regulated workloads.
+
+## Reservation Affinity
+
+Use shared or specific Compute Engine reservations.
+
+```python
+endpoint = model.deploy(
+ reservation_affinity_type="SPECIFIC_RESERVATION",
+ reservation_affinity_key="compute.googleapis.com/reservation-name",
+ reservation_affinity_values="projects/YOUR_PROJECT/zones/YOUR_ZONE/reservations/YOUR_RESERVATION"
+)
+```
+
+**Use case:** Optimized resource usage with pre-reserved capacity.
+
+## Custom Container Image
+
+Override the default container with a custom image.
+
+```python
+endpoint = model.deploy(
+ serving_container_image_uri="us-docker.pkg.dev/vertex-ai/custom-container:latest"
+)
+```
+
+**Use case:** Use of custom inference servers or fine-tuned environments.
+
+## Advanced Full Container Configuration
+
+Further customize startup probes, health checks, shared memory, and gRPC ports.
+
+```python
+endpoint = model.deploy(
+ serving_container_image_uri="us-docker.pkg.dev/vertex-ai/custom-container:latest",
+ container_command=["python3"],
+ container_args=["serve.py"],
+ container_ports=[8888],
+ container_env_vars={"ENV": "prod"},
+ container_predict_route="/predict",
+ container_health_route="/health",
+ serving_container_shared_memory_size_mb=512,
+ serving_container_grpc_ports=[9000],
+ serving_container_startup_probe_exec=["/bin/check-start.sh"],
+ serving_container_health_probe_exec=["/bin/health-check.sh"]
+)
+```
+
+**Use case:** Production-grade deployments requiring deep customization of runtime behavior and monitoring.
+
+## Documentation
+
+You can find complete documentation for the Vertex AI SDKs and Model Garden in the Google Cloud [documentation](https://cloud.google.com/vertex-ai/docs/generative-ai/learn/overview)
+
+## Contributing
+
+See [Contributing](https://github.com/googleapis/python-aiplatform/blob/main/CONTRIBUTING.rst) for more information on contributing to the Vertex AI Python SDK.
+
+## License
+
+The contents of this repository are licensed under the [Apache License, version 2.0](http://www.apache.org/licenses/LICENSE-2.0).