Synapse Plugin config.yaml Guide

The config.yaml file (or synapse.yaml) defines your plugin's metadata, actions, and runtime configuration.

Minimal Example

name: "My Plugin"
code: my-plugin
version: 1.0.0
category: custom

actions:
  train:
    entrypoint: plugin.train:TrainAction
    method: job
    description: "Train a model"

Complete Structure

# Basic metadata
name: "YOLOv8 Object Detection"
code: yolov8
version: 1.0.0
category: neural_net
description: "Train and run YOLOv8 models"
readme: README.md

# Package management
package_manager: pip  # or 'uv'
package_manager_options: []
wheels_dir: wheels

# Environment variables
env:
  DEBUG: "false"
  BATCH_SIZE: "32"

# Runtime environment (Ray)
runtime_env: {}

# Data type configuration
data_type: image
tasks:
  - image.object_detection
  - image.segmentation

# Actions
actions:
  train:
    entrypoint: plugin.train:TrainAction
    method: job
    description: "Train YOLO model"
  inference:
    entrypoint: plugin.inference:run
    method: task
    description: "Run inference"

Action Configuration

Field	Required	Description
entrypoint	Yes	Module path (module.path:ClassName or module.path.function)
method	No	Execution method: job, task, or serve (default: task)
description	No	Human-readable description
serve_options	No	Ray Serve deployment tuning (deployment actions only) — see below

Config Sync (Recommended)

Sync entrypoints, input/output types, and hyperparameters from code:

synapse plugin update-config

Note: update-config (and publish, which runs it implicitly) discovers actions by scanning every *.py under the project root and rewrites config.yaml in place. It ignores .synapseignore/.gitignore, so action classes in refs/, examples/, or a local .venv/ get pulled in and overwrite your real entrypoints. Publish from a clean staging copy (--path) to avoid this — see the publish command docs.

serve_options (deployment actions)

A deployment action passes serve_options straight to serve.deployment(). These control how the Ray Serve replica handles load — important for GPU model serving where each request can take many seconds.

deployment:
  entrypoint: plugin.deployment.InferenceDeployment
  method: job
  serve_options:
    max_ongoing_requests: 32     # requests admitted per replica before backpressure
    health_check_timeout_s: 180  # tolerate slow cold model loads
    health_check_period_s: 30
    # num_replicas, max_queued_requests, autoscaling_config, graceful_shutdown_* also supported

Option	Purpose
max_ongoing_requests	Concurrent requests admitted per replica. Keep this high (e.g. 32). A low value (especially 1) makes the replica return 503 "at capacity" under burst/retry traffic, which the SDK client retries — a storm that fails requests even when the work succeeds. Serialize GPU work with a lock in the handler instead.
health_check_timeout_s	Raise it (e.g. 180) so a one-time multi-GB cold model download doesn't get the replica killed as "unhealthy".
num_replicas	Number of replicas (default 1).

For the handler-side companion fixes (offloading blocking inference with asyncio.to_thread, the GPU lock), see the inference-action reference in the specialized-actions skill.

Execution Methods

Method	Use Case	Characteristics
job	Training, batch processing	Async, isolated, long-running (100s+)
task	Interactive operations	Sync, fast startup (<1s), serial per actor
serve	Model serving, inference	REST API endpoint, auto-scaling

Entrypoint Formats

Both formats are supported:

• Colon notation: plugin.train:TrainAction
• Dot notation: plugin.train.TrainAction

Additional Resources

For detailed configuration options:

• references/fields.md - All config.yaml fields
• references/smart-tool.md - Smart tool configuration