# Pilot Ml Training Pipeline Setup > Deploy an end-to-end ML training pipeline with 4 agents. Use this skill when: 1. User wants to set up a machine learning training pipeline 2. User is configuring a data prep, training, evaluation, or serving agent 3. User asks about ML model lifecycle management across agents Do NOT use this skill when: - User wants to share a single model file (use pilot-model-share instead) - User wants to transfer a dataset (use pilot-dataset instead) - Skill: `teoslayer/pilot-ml-training-pipeline-setup` (Agent Skill, multi-file: 2 files) - Install (CLI): `npx skillmds@latest add teoslayer/pilot-ml-training-pipeline-setup` - Raw SKILL.md: https://api.skillmd.com/api/skills/teoslayer/pilot-ml-training-pipeline-setup/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: DevOps & Infra - License: AGPL-3.0 - Author: TeoSlayer (https://skillmd.com/u/teoslayer) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/teoslayer/pilot-ml-training-pipeline-setup --- # ML Training Pipeline Setup Deploy 4 agents spanning data prep, training, evaluation, and serving. ## Roles | Role | Hostname | Skills | Purpose | |------|----------|--------|---------| | data-prep | `-data-prep` | pilot-dataset, pilot-share, pilot-task-chain | Cleans and transforms datasets | | trainer | `-trainer` | pilot-dataset, pilot-model-share, pilot-metrics, pilot-task-chain | Trains models, tracks metrics | | evaluator | `-evaluator` | pilot-model-share, pilot-metrics, pilot-review, pilot-task-chain | Evaluates and gates promotion | | serving | `-serving` | pilot-model-share, pilot-health, pilot-webhook-bridge, pilot-load-balancer, pilot-metrics | Serves inference requests | ## 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: ```bash # 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: ```bash pilotctl --json set-hostname - ``` **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 ```json { "setup": "ml-training-pipeline", "role": "data-prep", "role_name": "Data Preparation", "hostname": "-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 -trainer.", "pilot-task-chain": "Chain data prep steps into sequential pipeline." }, "peers": [{ "role": "trainer", "hostname": "-trainer", "description": "Receives prepared datasets" }], "data_flows": [{ "direction": "send", "peer": "-trainer", "port": 1001, "topic": "dataset-ready", "description": "Cleaned datasets" }], "handshakes_needed": ["-trainer"] } ``` ### trainer ```json { "setup": "ml-training-pipeline", "role": "trainer", "role_name": "Model Trainer", "hostname": "-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": "-data-prep", "description": "Sends prepared datasets" }, { "role": "evaluator", "hostname": "-evaluator", "description": "Receives trained models" } ], "data_flows": [ { "direction": "receive", "peer": "-data-prep", "port": 1001, "topic": "dataset-ready", "description": "Cleaned datasets" }, { "direction": "send", "peer": "-evaluator", "port": 1001, "topic": "training-complete", "description": "Model checkpoints and metrics" } ], "handshakes_needed": ["-data-prep", "-evaluator"] } ``` ### evaluator ```json { "setup": "ml-training-pipeline", "role": "evaluator", "role_name": "Model Evaluator", "hostname": "-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": "-trainer", "description": "Sends trained models" }, { "role": "serving", "hostname": "-serving", "description": "Receives approved models" } ], "data_flows": [ { "direction": "receive", "peer": "-trainer", "port": 1001, "topic": "training-complete", "description": "Model checkpoints" }, { "direction": "send", "peer": "-serving", "port": 1001, "topic": "model-approved", "description": "Approved models" }, { "direction": "receive", "peer": "-serving", "port": 1002, "topic": "inference-metrics", "description": "Drift detection data" } ], "handshakes_needed": ["-trainer", "-serving"] } ``` ### serving ```json { "setup": "ml-training-pipeline", "role": "serving", "role_name": "Model Server", "hostname": "-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": "-evaluator", "description": "Sends approved models, receives metrics" }], "data_flows": [ { "direction": "receive", "peer": "-evaluator", "port": 1001, "topic": "model-approved", "description": "Approved models" }, { "direction": "send", "peer": "-evaluator", "port": 1002, "topic": "inference-metrics", "description": "Inference metrics for drift" } ], "handshakes_needed": ["-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 ```bash # On data-prep: pilotctl --json send-file -trainer ./datasets/training-v5.parquet pilotctl --json publish -trainer dataset-ready '{"name":"training-v5","rows":150000}' # On trainer: pilotctl --json send-file -evaluator ./models/resnet-v5.pt pilotctl --json publish -evaluator training-complete '{"model":"resnet-v5","accuracy":0.967}' # On evaluator: pilotctl --json send-file -serving ./models/resnet-v5.pt pilotctl --json publish -serving model-approved '{"model":"resnet-v5","benchmark":0.971}' ``` ## Dependencies Requires `pilot-protocol` skill, `pilotctl` binary, `clawhub` binary, and a running daemon.