jfraeysd/fetch_ml
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fetch_ml/cli/README.md
Jeremie Fraeys 2c596038b5
refactor(cli): update build system and config for local mode 
- Update Makefile with build-sqlite target matching rsync pattern
- Fix build.zig to handle SQLite assets and dataset_hash linking
- Add SQLite asset detection mirroring rsync binary detection
- Update CLI README with local mode documentation
- Restructure rsync assets into rsync/ subdirectory
- Remove obsolete files (fix_arraylist.sh, old rsync_placeholder.bin)
- Add build_rsync.sh script to fetch/build rsync from source
2026-02-20 15:50:52 -05:00

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ML CLI

Fast CLI tool for managing ML experiments. Supports both local mode (SQLite) and server mode (WebSocket).

Quick Start

# 1. Build
zig build

# 2. Initialize local tracking (creates fetch_ml.db)
./zig-out/bin/ml init

# 3. Create experiment and run locally
./zig-out/bin/ml experiment create --name "baseline"
./zig-out/bin/ml run start --experiment <id> --name "run-1"
./zig-out/bin/ml experiment log --run <id> --name loss --value 0.5
./zig-out/bin/ml run finish --run <id>

Commands

Local Mode Commands (SQLite)

  • ml init - Initialize local experiment tracking database
  • ml experiment create --name <name> - Create experiment locally
  • ml experiment list - List experiments from SQLite
  • ml experiment log --run <id> --name <key> --value <val> - Log metrics
  • ml run start --experiment <id> [--name <name>] - Start a run
  • ml run finish --run <id> - Mark run as finished
  • ml run fail --run <id> - Mark run as failed
  • ml run list - List all runs

Server Mode Commands (WebSocket)

  • ml sync <path> - Sync project to server
  • ml queue <job1> [job2 ...] [--commit <id>] [--priority N] [--note <text>] - Queue jobs
  • ml status - Check system/queue status
  • ml validate <commit_id> [--json] [--task <task_id>] - Validate provenance
  • ml cancel <job> - Cancel a running/queued job

Shared Commands (Auto-detect Mode)

  • ml experiment log|show|list|delete - Works in both local and server mode
  • ml monitor - Launch TUI (local SQLite or remote SSH)

Notes:

  • Commands auto-detect mode from config (sqlite:// vs wss://)
  • --json mode is designed to be pipe-friendly

Configuration

Local Mode (SQLite)

# .fetchml/config.toml or ~/.ml/config.toml
tracking_uri = "sqlite://./fetch_ml.db"
artifact_path = "./experiments/"
sync_uri = ""  # Optional: server to sync with

Server Mode (WebSocket)

# ~/.ml/config.toml
worker_host = "worker.local"
worker_user = "mluser"
worker_base = "/data/ml-experiments"
worker_port = 22
api_key = "your-api-key"

Building

Development

cd cli
zig build

Production (requires SQLite in assets/)

cd cli
make build-sqlite    # Fetch SQLite amalgamation
zig build prod       # Build with embedded SQLite

Install

# Install to system
make install

# Or copy binary manually
cp zig-out/bin/ml /usr/local/bin/

Need Help?

  • ml --help - Show command help
  • ml <command> --help - Show command-specific help