Custom Models

Deploy model to Ray object store

Changed on

Pre-load model weights into the Ray object store for fast access by plugins.

This operation:

  1. Downloads the model archive from S3
  2. Deserializes weights based on format (safetensors, pytorch, etc.)
  3. Stores weights in Ray object store for zero-copy sharing

After deployment, plugins can load the model instantly using:

from engine.models.loader import load_namespace_model
weights = load_namespace_model("model_id")

Note: This is optional - models are also loaded on-demand when plugins first request them. Use this endpoint to pre-warm the cache.

post/v1/namespaces/{namespace_id}/models/{model_id}/deploy

Request

  • Base URL: https://api.mixpeek.com
  • URL: https://api.mixpeek.com/v1/namespaces/{namespace_id}/models/{model_id}/deploy
  • Auth: HTTP bearer

Path parameters

namespace_idstring required
model_idstring required

Response

Successful Response

successboolean required

Whether deployment succeeded

model_idstring required

Model identifier

namespace_idstring required

Namespace ID

deployment_statusstring required

Deployment status (deployed, failed)

cachedboolean

Whether model is cached in object store

messagestring required

Status message

Changes

  • 61a175e8eb9aRevision changes
    • warning

      removed the optional property from the response with the status

    • info

      added the non-success response with the status

    • info

      added the non-success response with the status

    • info

      added the optional property to the response with the status

    • info

      added the required property to the response with the status

    • info

      added the required property to the response with the status

  • b75474e978ffRevision changes
    • info

      added the media type application/json for the response with the status

    • info

      added the media type application/json for the response with the status