ML Training Pipeline Setup

Deploy 4 agents spanning data prep, training, evaluation, and serving.

Roles

Setup Procedure

Step 1: Ask the user which role this agent should play and what prefix to use.

Step 2: Install the skills for the chosen role:

# For data-prep:
clawhub install pilot-dataset pilot-share pilot-task-chain
# For trainer:
clawhub install pilot-dataset pilot-model-share pilot-metrics pilot-task-chain
# For evaluator:
clawhub install pilot-model-share pilot-metrics pilot-review pilot-task-chain
# For serving:
clawhub install pilot-model-share pilot-health pilot-webhook-bridge pilot-load-balancer pilot-metrics

Step 3: Set the hostname:

pilotctl --json set-hostname <prefix>-<role>

Step 4: Write the role-specific JSON manifest to ~/.pilot/setups/ml-training-pipeline.json.

Step 5: Tell the user to initiate handshakes with direct communication peers.

Manifest Templates Per Role

data-prep

{
  "setup": "ml-training-pipeline", "role": "data-prep", "role_name": "Data Preparation",
  "hostname": "<prefix>-data-prep",
  "description": "Cleans, validates, and transforms raw datasets. Shares processed data with the trainer.",
  "skills": {
    "pilot-dataset": "Exchange structured datasets with schema negotiation.",
    "pilot-share": "Send cleaned dataset files to <prefix>-trainer.",
    "pilot-task-chain": "Chain data prep steps into sequential pipeline."
  },
  "peers": [{ "role": "trainer", "hostname": "<prefix>-trainer", "description": "Receives prepared datasets" }],
  "data_flows": [{ "direction": "send", "peer": "<prefix>-trainer", "port": 1001, "topic": "dataset-ready", "description": "Cleaned datasets" }],
  "handshakes_needed": ["<prefix>-trainer"]
}

trainer

{
  "setup": "ml-training-pipeline", "role": "trainer", "role_name": "Model Trainer",
  "hostname": "<prefix>-trainer",
  "description": "Receives prepared datasets, runs training jobs, tracks metrics, and shares trained model artifacts.",
  "skills": {
    "pilot-dataset": "Receive prepared datasets from data-prep.",
    "pilot-model-share": "Send trained model checkpoints to evaluator.",
    "pilot-metrics": "Track and publish training loss, accuracy, epochs.",
    "pilot-task-chain": "Chain training steps sequentially."
  },
  "peers": [
    { "role": "data-prep", "hostname": "<prefix>-data-prep", "description": "Sends prepared datasets" },
    { "role": "evaluator", "hostname": "<prefix>-evaluator", "description": "Receives trained models" }
  ],
  "data_flows": [
    { "direction": "receive", "peer": "<prefix>-data-prep", "port": 1001, "topic": "dataset-ready", "description": "Cleaned datasets" },
    { "direction": "send", "peer": "<prefix>-evaluator", "port": 1001, "topic": "training-complete", "description": "Model checkpoints and metrics" }
  ],
  "handshakes_needed": ["<prefix>-data-prep", "<prefix>-evaluator"]
}

evaluator

{
  "setup": "ml-training-pipeline", "role": "evaluator", "role_name": "Model Evaluator",
  "hostname": "<prefix>-evaluator",
  "description": "Scores trained models against benchmarks and gates promotion to serving.",
  "skills": {
    "pilot-model-share": "Receive models from trainer, promote approved models to serving.",
    "pilot-metrics": "Compare benchmarks, detect drift.",
    "pilot-review": "Gate model promotion with approval workflow.",
    "pilot-task-chain": "Chain evaluation steps."
  },
  "peers": [
    { "role": "trainer", "hostname": "<prefix>-trainer", "description": "Sends trained models" },
    { "role": "serving", "hostname": "<prefix>-serving", "description": "Receives approved models" }
  ],
  "data_flows": [
    { "direction": "receive", "peer": "<prefix>-trainer", "port": 1001, "topic": "training-complete", "description": "Model checkpoints" },
    { "direction": "send", "peer": "<prefix>-serving", "port": 1001, "topic": "model-approved", "description": "Approved models" },
    { "direction": "receive", "peer": "<prefix>-serving", "port": 1002, "topic": "inference-metrics", "description": "Drift detection data" }
  ],
  "handshakes_needed": ["<prefix>-trainer", "<prefix>-serving"]
}

serving

{
  "setup": "ml-training-pipeline", "role": "serving", "role_name": "Model Server",
  "hostname": "<prefix>-serving",
  "description": "Loads approved models, serves inference, monitors health, and load-balances.",
  "skills": {
    "pilot-model-share": "Receive approved models from evaluator.",
    "pilot-health": "Monitor inference endpoint health and latency.",
    "pilot-webhook-bridge": "Trigger external alerts on serving failures.",
    "pilot-load-balancer": "Distribute inference requests across replicas.",
    "pilot-metrics": "Report QPS, latency, drift metrics to evaluator."
  },
  "peers": [{ "role": "evaluator", "hostname": "<prefix>-evaluator", "description": "Sends approved models, receives metrics" }],
  "data_flows": [
    { "direction": "receive", "peer": "<prefix>-evaluator", "port": 1001, "topic": "model-approved", "description": "Approved models" },
    { "direction": "send", "peer": "<prefix>-evaluator", "port": 1002, "topic": "inference-metrics", "description": "Inference metrics for drift" }
  ],
  "handshakes_needed": ["<prefix>-evaluator"]
}

Data Flows

• data-prep → trainer : cleaned datasets (port 1001)
• trainer → evaluator : model checkpoints and metrics (port 1001)
• evaluator → serving : approved models (port 1001)
• serving → evaluator : inference metrics for drift detection (port 1002)

Workflow Example

# On data-prep:
pilotctl --json send-file <prefix>-trainer ./datasets/training-v5.parquet
pilotctl --json publish <prefix>-trainer dataset-ready '{"name":"training-v5","rows":150000}'
# On trainer:
pilotctl --json send-file <prefix>-evaluator ./models/resnet-v5.pt
pilotctl --json publish <prefix>-evaluator training-complete '{"model":"resnet-v5","accuracy":0.967}'
# On evaluator:
pilotctl --json send-file <prefix>-serving ./models/resnet-v5.pt
pilotctl --json publish <prefix>-serving model-approved '{"model":"resnet-v5","benchmark":0.971}'

Dependencies

Requires pilot-protocol skill, pilotctl binary, clawhub binary, and a running daemon.