Get started with Path Foundation
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You can get started in 4 ways:
Run it locally
Download the model
from Hugging Face and run it
locally.
This is the recommended option, if you want to experiment with the model and
don't need to handle a high volume of data. Our GitHub repository includes a
notebook
that you can use to explore the model.
Deploy your own online service
Path Foundation can be deployed as a highly available and scalable HTTPS
endpoint on Vertex AI. The easiest way is
through
Model Garden.
This option is ideal for production-grade, online applications with low latency,
high scalability and availability requirements. Refer to
Vertex AI's service level agreement (SLA)
and pricing model for online
predictions.
Read the
API specification
to learn how to create online clients that interact with the service. A sample
notebook
is available to help you get started quickly.
For custom requirements, you can also adapt our
model serving implementation
and host it yourself on any API management system.
Launch a batch job
For larger dataset in a batch workflow, it's best to launch it as a
Vertex AI batch prediction job.
Note that Vertex AI's SLA and
pricing model are different for
batch prediction jobs.
Try out our online service
You can test out the online service before committing to deploying your own
using our
research endpoint.
This endpoint is for research purposes only.
You can reach out in several ways:
Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.
Last updated 2025-02-11 UTC.
[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Missing the information I need","missingTheInformationINeed","thumb-down"],["Too complicated / too many steps","tooComplicatedTooManySteps","thumb-down"],["Out of date","outOfDate","thumb-down"],["Samples / code issue","samplesCodeIssue","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-02-11 UTC."],[[["\u003cp\u003ePath Foundation can be downloaded and run locally for experimentation or deployed as an online service for high-volume data processing using Vertex AI.\u003c/p\u003e\n"],["\u003cp\u003eDevelopers can leverage Vertex AI batch prediction jobs for large datasets in batch workflows.\u003c/p\u003e\n"],["\u003cp\u003eA research endpoint is available for testing the online service before deploying a dedicated instance.\u003c/p\u003e\n"],["\u003cp\u003eUsers can interact with the project team through GitHub Discussions, Issues, or email for feedback or assistance.\u003c/p\u003e\n"]]],[],null,["# Get started with Path Foundation\n\nYou can get started in 4 ways:\n\nRun it locally\n--------------\n\nDownload the model\n[from Hugging Face](https://huggingface.co/google/path-foundation) and run it\nlocally.\n\nThis is the recommended option, if you want to experiment with the model and\ndon't need to handle a high volume of data. Our GitHub repository includes a\n[notebook](https://github.com/google-health/path-foundation/blob/master/notebooks/quick_start_with_hugging_face.ipynb)\nthat you can use to explore the model.\n\nDeploy your own online service\n------------------------------\n\nPath Foundation can be deployed as a highly available and scalable HTTPS\nendpoint on [Vertex AI](https://cloud.google.com/vertex-ai). The easiest way is\nthrough\n[Model Garden](https://console.cloud.google.com/vertex-ai/publishers/google/model-garden/path-foundation).\n\nThis option is ideal for production-grade, online applications with low latency,\nhigh scalability and availability requirements. Refer to\n[Vertex AI's service level agreement (SLA)](https://cloud.google.com/vertex-ai/sla)\nand [pricing model](https://cloud.google.com/vertex-ai/pricing) for online\npredictions.\n\nRead the\n[API specification](/health-ai-developer-foundations/path-foundation/serving-api)\nto learn how to create online clients that interact with the service. A sample\n[notebook](https://github.com/google-health/path-foundation/blob/master/notebooks/quick_start_with_model_garden.ipynb)\nis available to help you get started quickly.\n\nFor custom requirements, you can also adapt our\n[model serving implementation](https://github.com/google-health/path-foundation/tree/master/python/serving)\nand host it yourself on any API management system.\n\nLaunch a batch job\n------------------\n\nFor larger dataset in a batch workflow, it's best to launch it as a\n[Vertex AI batch prediction job](https://cloud.google.com/vertex-ai/docs/predictions/get-batch-predictions#request_a_batch_prediction).\nNote that [Vertex AI's SLA](https://cloud.google.com/vertex-ai/sla) and\n[pricing model](https://cloud.google.com/vertex-ai/pricing) are different for\nbatch prediction jobs.\n\nTry out our online service\n--------------------------\n\nYou can test out the online service before committing to deploying your own\nusing our\n[research endpoint](/health-ai-developer-foundations/model-serving/research-endpoints).\n\nThis endpoint is for research purposes only.\n\nContact\n-------\n\nYou can reach out in several ways:\n\n- Start or join a conversation on [GitHub Discussions](https://github.com/google-health/path-foundation/discussions).\n- File a Feature Request or Bug at [GitHub Issues](https://github.com/google-health/path-foundation/issues).\n- Send us feedback at `hai-def@google.com`."]]