momentum-cli

The Momentum customer CLI + experiment-logging SDK: authenticate and bulk-upload field data straight to your workspace's storage bucket, and report training/eval runs from your own compute into your workspace's experiment tracker.

PyPI distribution: ydderd-momentum-cli · Homebrew formula: momentum-cli · command: momentum. (The clean momentum-cli PyPI name was taken, so the distribution carries the ydderd- prefix; the import package momentum_cli, the momentum command, and the brew name are unaffected.)

Why this is a separate package

The CLI talks to the Momentum API purely over HTTP (and to R2 over S3). It shares no Python code with the backend, so it ships with a tiny dependency set — httpx + boto3 — instead of the full server stack (torch, opencv, fastapi, …). That keeps the install small and avoids shipping the backend's AGPL detector to customers.

Install

brew install ydderd/momentum/momentum-cli
# or:
pipx install ydderd-momentum-cli
momentum --help

Usage

momentum auth login                       # opens a browser; a workspace admin approves
momentum auth whoami                      # confirm tenant
momentum upload ./your-data --scan        # bulk upload + trigger ingest
momentum datasets list                    # list datasets (id, name, kind) to pick a target
momentum datasets assign <session_id> --dataset "Eval set" --split eval  # route an upload into a dataset + split
momentum ingest status                    # ingest ledger stats
momentum eval submit --model hf://lab/pi05-fast --benchmark bench_roboarena@3  # open an eval run, print URL
momentum trials log --policy hf://lab/pi05-fast --n 20 --successes 14 --calibration-set pcsk-…
momentum trials log --csv trials.csv      # bulk floor tallies (per-row partial success)
momentum secrets set lab-bucket           # store a secret (value from stdin); prints its creds_ref
momentum secrets list                     # secret names + configured (never values)

upload always writes to your workspace's one raw prefix — there's no target to choose. Whether what you uploaded is raw drone video (needs extraction) or already-extracted frames is classified server-side once it lands, not by the client beforehand.

For headless/CI use, skip the browser with a token minted by a workspace admin: momentum auth login --token <fw_cli_…>.

Config is stored at ~/.momentum/config.json (an existing ~/.flywheel/config.json is copied over once on first use). Auth precedence: MOMENTUM_CLI_TOKEN env > config file.

Experiment-logging SDK

Training and eval runs executed on your own compute (Modal, Brev, a lab box) report themselves into your workspace's experiment tracker — W&B-style, and safe to leave in production training code (a logging failure never raises into the train):

import momentum_cli as momentum

run = momentum.init(name="my_sft_run", tags=["sft"], config={"iters": 800, "lr": 2e-4},
                    provider="modal")
run.log({"train/loss": 0.42}, step=100)
run.finish(status="succeeded", checkpoint_ref="r2://bucket/ckpt", cost_usd=295.26)

# later — scoring results and billed cost arrive after the train, so annotation
# works on finished runs:
momentum.annotate(run.id, results={"auroc": {"value": 0.61, "ci": [0.55, 0.67]}})

Eval runs (policy context — the CI-integration path)

An eval process (a lab rig, Modal, the robot) evaluates model × benchmark@version and streams its rollouts back. Same never-raise/heartbeat/reattach posture as training runs; rollouts buffer and flush in batches, each with a client-generated id so a re-sent batch is idempotent:

ev = momentum.eval_run(benchmark="bench_roboarena@3", model="hf://lab/pi05-fast", seeds=3)
ev.log_rollout(scenario="scn_pick", seed=0, status="success",
               scorer={"success": True, "task_progress": 1.0}, latency_p50=61.0)
ev.log_rollout(scenario="scn_pick", seed=1, status="fail", scorer={"success": False})
ev.finish()                                   # flushes any buffered rollouts first
ev.annotate(results={"headline": {"value": 0.5, "ci": [0.31, 0.69]}})   # post-hoc scoring

eval_run() prints the run URL on create; eval_run(run_id=…) (or MOMENTUM_EVAL_RUN_ID) reattaches after a preemption. Runs land in the UI under Eval runs.

Real trials (floor tallies → calibration audit)

Report real-robot trials of a policy; landing trials that ground a calibration set recomputes that world model's τ/ρ trust:

momentum.real_trials.log(policy="hf://lab/pi05-fast", scenario="scn_pick",
                         n=20, successes=14, operator="alice", calibration_set="pcsk-…")

report = momentum.real_trials.log_csv("trials.csv")   # a path or raw CSV text; per-row partial success
print(report["accepted"], report["rejected"])

Secrets & referenced episodes

Register an episode that lives in your own bucket by first storing its credentials in the tenant secret store (Fernet-encrypted at rest; the value is never returned by a read), then passing the returned creds_ref:

ref = momentum.secrets.set("lab-bucket", '{"access_key": "…", "secret_key": "…"}')  # → "secret://lab-bucket"
momentum.episodes.register("s3://lab-corpus/session_042", creds_ref=ref)
momentum.secrets.list()      # {name: {configured: bool}}, incl. provider keys under provider:<name>

Auth: MOMENTUM_API_KEY env (a fw_cli_… token — inject it as a secret in your training environment), falling back to the token saved by momentum auth login. MOMENTUM_API_URL overrides the API endpoint. with momentum.init(...) as run: (and momentum.eval_run(...)) marks the run failed (with the exception) if the block raises. Runs land in the workspace UI under Experiments / Eval runs.

Release/consumption mechanics (PyPI, git-ref installs, versioning): see PUBLISHING.md.

Developer notes

These knobs exist for Momentum developers and are intentionally hidden from customer-facing help and docs:

• --api-url <url> on momentum auth login — persist a non-production API base URL to the config (e.g. a local API). Hidden via argparse.SUPPRESS.
• MOMENTUM_API_URL env — override the API base per-invocation. Takes precedence over the config file.

Precedence for the API base URL: MOMENTUM_API_URL env > api_url in config > default (https://flywheeling.fly.dev/api — swap to a custom domain once one is live).

Point the CLI at a local backend during development:

MOMENTUM_API_URL=http://localhost:8000 momentum auth whoami
# or persist it:
momentum auth login --token <fw_cli_…> --api-url http://localhost:8000

Local development

cd cli
uv sync
uv run momentum --help
uv run pytest

Releasing (PyPI + Homebrew)

PyPI is the source of truth; the Homebrew formula wraps the published PyPI sdist.

1. Publish to PyPI — via GitHub Actions (Trusted Publishing, no token)

The .github/workflows/publish-cli.yml workflow builds and publishes over OIDC. Cut a release by pushing a namespaced tag from the monorepo default branch:

git tag cli-v0.1.0 && git push origin cli-v0.1.0

The PyPI project is ydderd-momentum-cli, published from ydderd/momentum via the pypi environment. (First publish activates the "pending" Trusted Publisher and creates the project.)

2. Update the Homebrew tap formula

After the PyPI release exists, point release.sh at your tap checkout — with SKIP_PUBLISH=1 it skips the upload and only fetches the published sdist's url/sha256, rewrites the formula, and regenerates its Python resource blocks:

SKIP_PUBLISH=1 \
FORMULA_PATH=/path/to/homebrew-momentum/Formula/momentum-cli.rb \
  cli/scripts/release.sh

Then commit + push the tap. Customers install with:

brew install ydderd/momentum/momentum-cli

release.sh can also publish to PyPI itself (UV_PUBLISH_TOKEN=pypi-… cli/scripts/release.sh) if you prefer a token-based local release over the GitHub Action.

Bumping a release: change version in pyproject.toml, push a new cli-v* tag, then re-run step 2.