Training from the CLI

The CLI is the power-user / headless path. For the standard record → train → deploy workflow, the app and web app training UI is the polished route. Reach for the CLI when you’re scripting training, working over SSH, or need to point a run at a custom ACT fork (the researcher feature).

The training_client CLI ships with Innate OS and drives the same cloud training orchestrator the robot’s training node uses. Run it on the robot over SSH (or inside the Docker container).

Setup

Source the ROS workspace so the module is importable:

source ~/innate-os/ros2_ws/install/setup.zsh

Authentication is by service key (--server defaults to Innate’s orchestrator). The robot’s training node already uses the same key; to run the CLI yourself, set it in your shell:

export INNATE_SERVICE_KEY="<your-service-key>"
# optional overrides: TRAINING_SERVER_URL, INNATE_AUTH_ISSUER_URL

Then invoke commands with python -m training_client.cli <command> (or the installed innate-training <command> shorthand). Every command takes a SKILL_DIR — a skill’s dataset directory (under ~/innate-os/workspace/custom_skills/<skill>), defaulting to the current directory. The skill’s cloud ID is read from SKILL_DIR/metadata.json, which submit writes.

Global options

Option	Env var	Description
`--server`, `-s`	`TRAINING_SERVER_URL`	Orchestrator URL (defaults to the production server)
`--token`, `-t`	`INNATE_SERVICE_KEY`	Service key — required
`--issuer`	`INNATE_AUTH_ISSUER_URL`	OIDC issuer; the token is exchanged for a JWT here

Typical workflow

# 1. Register the skill with the orchestrator (writes the skill ID into metadata.json)
python -m training_client.cli submit ./my-skill

# 2. Upload its dataset
python -m training_client.cli upload ./my-skill

# 3. Launch a training run with a server-side preset
python -m training_client.cli run ./my-skill --preset act-default

# 4. Watch it until it finishes
python -m training_client.cli watch ./my-skill <run-id>

# 5. Download results, then activate the trained checkpoint so the robot loads it
python -m training_client.cli download ./my-skill <run-id>
python -m training_client.cli activate ./my-skill <run-id>

Commands

Command	Description
`submit [SKILL_DIR]`	Create (or reuse) a cloud skill from the dataset dir; writes `training_skill_id` to `metadata.json`
`upload [SKILL_DIR]`	Upload the skill’s data files
`run [SKILL_DIR]`	Launch a training run (options below)
`status [SKILL_DIR] RUN_ID`	Print a run’s current status
`watch [SKILL_DIR] RUN_ID`	Poll a run until it completes (`--interval` seconds, default 20)
`runs [SKILL_DIR]`	List all runs for the skill
`skills`	List all your skills
`download [SKILL_DIR] RUN_ID`	Download a completed run’s results (`--dest` for the output dir)
`fetch-data [SKILL_DIR]`	Download the skill’s input training data (`--dest`)
`activate [SKILL_DIR] RUN_ID`	Set the trained checkpoint + stats file into `metadata.json` so the robot loads it

Launching a run

run takes a server-side preset, a fully custom configuration, or a preset with overrides:

Option	Description
`--preset`	Server-side preset (e.g. `act-default`) — pre-fills everything below
`--repo`	GitHub `owner/repo` of a custom ACT fork to train with
`--ref`	Branch, tag, or commit in that repo
`--command`, `-c`	Training command parts (repeatable)
`--env`, `-e`	Environment variables as `KEY=VALUE` (repeatable)
`--gpu-type`	GPU type (e.g. `H100`)
`--min-gpus` / `--max-gpus`	GPU count bounds
`--hours`	Estimated duration
`--budget`	Max total cost in USD
`--checkpoint-patterns`, `-p`	Globs for which output files to upload (e.g. `checkpoints/*/.pt`)

A preset is the quickest path; override individual values as needed:

python -m training_client.cli run ./my-skill --preset act-default --max-gpus 4 --budget 500

Custom ACT fork

The headline power-user feature: point a run at your own fork of the ACT training code — change the architecture or loss, then train against it with no server-side changes.

python -m training_client.cli run ./my-skill \
    --repo innate-inc/ACT-test \
    --ref lambda_refactor \
    -c "pip install -r requirements.txt; python3 -m act_test.train_dist --data-dir /data/dataset" \
    --hours 8 --gpu-type H100 --min-gpus 1 --max-gpus 2 \
    --budget 200

Get Started

MARS Hardware

Simulator

Controller App

Web App

Agent SDK

Hacking on MARS

Manipulation

ROS2 Core

Troubleshooting & Support

Hackathon

Training from the CLI

Setup

Global options

Typical workflow

Commands

Launching a run

Custom ACT fork

​Setup

​Global options

​Typical workflow

​Commands

​Launching a run

​Custom ACT fork

Setup

Global options

Typical workflow

Commands

Launching a run

Custom ACT fork