Hands-on tutorial for launching and deploying LLMs using Friendli Dedicated Endpoints with Weights & Biases artifacts through webhook automation.
This tutorial is designed to guide you through the process of easily deploying your models from the W&B Registry to Friendli Dedicated Endpoints in the W&B UI. Through a series of step-by-step instructions and hands-on examples, you’ll learn how to:
W&B users often rely on W&B Registry to manage the lifecycle of models – from tracking experiment artifacts to promoting the best-performing models for production use. As a W&B user, integrating Friendli Dedicated Endpoints directly into this workflow allows you to:
idempotencyKey
to ensure the reliability of automated workflows. Each deployment trigger via webhook automation is tracked with a unique idempotencyKey
, ensuring that operations like endpoint creation or updates are processed exactly once. It prevents duplicate or conflicting operations, giving you confidence in the consistency of your deployment.By the end of this tutorial, you’ll be equipped with the knowledge and skills necessary to seamlessly transfer your models from W&B Registry to Friendli Dedicated Endpoints for efficient deployment. So, let’s get started and explore the possibilities of Friendli Dedicated Endpoints!
Field | Description |
---|---|
wandbArtifactVersionName | Specific model artifact version from W&B. |
Field | Description |
---|---|
wandbArtifactVersionName | Specific model artifact version from W&B. |
Field | Description |
---|---|
wandbArtifactVersionName | Specific model artifact version from W&B. |
name | Name of the endpoint. |
projectId | Specific project ID of where the endpoint will be created. |
idempotencyKey | Unique value to track which webhook automation triggered an endpoint roll out. Use any unique value, but using the example value provided is recommended. |
accelerator | Hardware for the endpoint. |
autoscalingPolicy | Autoscaling settings for the endpoint. |
To gain more control over GPU resources for an endpoint, configure the accelerator
field by specifying the desired type and count. This is particularly useful for serving large models that require model or data parallelism.
Field | Description |
---|---|
accelerator.type | Specifies the instance type. |
accelerator.count | Specifies the number of instances. |
View more details about each field here.
Deploy your model artifact to Friendli Dedicated Endpoints by simply adding the alias set in Step 3 to a model artifact version!
After adding the alias, you can see the endpoint created in Friendli Dedicated Endpoints.
To roll out an endpoint to a new model artifact version, simply add the same alias to the new version you want to deploy. This updates the endpoint to use the new model artifact version. After assigning the alias, the endpoint will update to reflect the new version in Friendli Dedicated Endpoints.
Change model artifact from Version 0 to Version 1
idempotencyKey
is required to roll out an endpoint between different model artifact versions.Use the Friendli Dedicated Endpoints versioning feature to track the history of your model deployments and maintain a clear record of every update. By adding an alias to a model artifact version, you can deploy models and roll out updates across versions efficiently, without needing to create a new endpoint from scratch.
Versions
In the diagram,
v0
represents the first deployed version of the model when the endpoint was created.v1
is a newer model artifact version that the alias was reassigned to, triggering a rollout to update the endpoint accordingly.View more details about the versioning feature here.
If you have any feedback or issues about the integration with Friendli Dedicated Endpoints, please ask for support by sending an email to Support.