FetchML

A lightweight ML experiment platform with a tiny Zig CLI and a Go backend. Designed for homelabs and small teams.

Installation (recommended)

FetchML publishes pre-built release artifacts (CLI + Go services) on GitHub Releases.

If you prefer a one-shot check (recommended for most users), you can use:

./scripts/verify_release.sh --dir . --repo <org>/<repo>

1. Download the right archive for your platform

2. Verify checksums.txt signature (recommended)

The release includes a signed checksums.txt plus:

• checksums.txt.sig
• checksums.txt.cert

Verify the signature (keyless Sigstore) using cosign:

cosign verify-blob \
  --certificate checksums.txt.cert \
  --signature checksums.txt.sig \
  --certificate-identity-regexp "^https://github.com/jfraeysd/fetch_ml/.forgejo/workflows/release-mirror.yml@refs/tags/v.*$" \
  --certificate-oidc-issuer https://token.actions.githubusercontent.com \
  checksums.txt

3. Verify the SHA256 checksum against checksums.txt

4. Extract and install

Example (CLI on Linux x86_64):

# Download
curl -fsSLO https://github.com/jfraeysd/fetch_ml/releases/download/<tag>/ml-linux-x86_64.tar.gz
curl -fsSLO https://github.com/jfraeysd/fetch_ml/releases/download/<tag>/checksums.txt
curl -fsSLO https://github.com/jfraeysd/fetch_ml/releases/download/<tag>/checksums.txt.sig
curl -fsSLO https://github.com/jfraeysd/fetch_ml/releases/download/<tag>/checksums.txt.cert

# Verify
cosign verify-blob \
  --certificate checksums.txt.cert \
  --signature checksums.txt.sig \
  --certificate-identity-regexp "^https://github.com/jfraeysd/fetch_ml/.forgejo/workflows/release-mirror.yml@refs/tags/v.*$" \
  --certificate-oidc-issuer https://token.actions.githubusercontent.com \
  checksums.txt
sha256sum -c --ignore-missing checksums.txt

# Install
tar -xzf ml-linux-x86_64.tar.gz
chmod +x ml-linux-x86_64
sudo mv ml-linux-x86_64 /usr/local/bin/ml

ml --help

Quick start

# Clone and run (dev)
git clone <your-repo>
cd fetch_ml
make dev-up

# Or build the CLI locally
cd cli && make all
./zig-out/bin/ml --help

What you get

• Zig CLI (ml): Tiny, fast local client. Uses ~/.ml/config.toml and FETCH_ML_CLI_* env vars.
• Go backends: API server, worker, and a TUI for richer remote features.
• TUI over SSH: ml monitor launches the TUI on the server, keeping the local CLI minimal.
• CI/CD: Cross‑platform builds with zig build-exe and Go releases.

CLI usage

# Configure
cat > ~/.ml/config.toml <<EOF
worker_host = "127.0.0.1"
worker_user = "dev_user"
worker_base = "/tmp/ml-experiments"
worker_port = 22
api_key = "your-api-key"
EOF

# Core commands
ml status
ml queue my-job
ml cancel my-job
ml dataset list
ml monitor  # SSH to run TUI remotely

# Research features (see docs/src/research-features.md)
ml queue train.py --hypothesis "LR scaling..." --tags ablation
ml outcome set run_abc --outcome validates --summary "Accuracy +2%"
ml find --outcome validates --tag lr-test
ml compare run_abc run_def
ml privacy set run_abc --level team
ml export run_abc --anonymize
ml dataset verify /path/to/data

Phase 1 (V1) notes

• Task schema supports optional snapshot_id (opaque identifier) and dataset_specs (structured dataset inputs). If dataset_specs is present it takes precedence over legacy datasets / --datasets args.
• Snapshot restore (S1) stages verified snapshot_id into each task workspace and exposes it via FETCH_ML_SNAPSHOT_DIR and FETCH_ML_SNAPSHOT_ID. If snapshot_store.enabled: true in the worker config, the worker will pull <prefix>/<snapshot_id>.tar.gz from an S3-compatible store (e.g. MinIO), verify snapshot_sha256, and cache it under data_dir/snapshots/sha256/<snapshot_sha256>.
• Prewarm (best-effort) can fetch datasets for the next queued task while another task is running. Prewarm state is surfaced in ml status --json under the optional prewarm field.
• Env prewarm (best-effort) can build a warmed Podman image keyed by deps_manifest_sha256 and reuse it for later tasks.

Changelog

See CHANGELOG.md.

Build

Native C++ Libraries (Optional)

FetchML includes optional C++ native libraries for performance. See docs/src/native-libraries.md for detailed build instructions.

Quick start:

make native-build           # Build native libs
make native-smoke           # Run smoke test
go build -tags native_libs  # Enable native libraries

Standard Build

# CLI (Zig)
cd cli && make all      # release-small
make tiny              # extra-small
make fast              # release-fast

# Go backends
make cross-platform    # builds for Linux/macOS/Windows

Deploy

• Dev: docker-compose up -d
• Prod: Use the provided systemd units or containers on Rocky Linux.

Docs

See docs/ for detailed guides:

• docs/src/native-libraries.md – Native C++ libraries (build, test, deploy)
• docs/src/zig-cli.md – CLI reference
• docs/src/quick-start.md – Full setup guide
• docs/src/deployment.md – Production deployment
• docs/src/research-features.md – Research workflow features (narrative capture, outcomes, search)
• docs/src/privacy-security.md – Privacy levels, PII detection, anonymized export

CLI Architecture (2026-02)

The Zig CLI has been refactored for improved maintainability:

• Modular 3-layer architecture: core/ (foundation), local//server/ (mode-specific), commands/ (routers)
• Unified context: core.context.Context handles mode detection, output formatting, and dispatch
• Code reduction: experiment.zig reduced from 836 to 348 lines (58% reduction)
• Bug fixes: Resolved 15+ compilation errors across multiple commands

See cli/README.md for detailed architecture documentation.

Source code

The FetchML source code is intentionally not hosted on GitHub.

The canonical source repository is available at: <SOURCE_REPO_URL>.

License

FetchML is source-available for transparency and auditability. It is not open-source.

See LICENSE.