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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).

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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.

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Global options

OptionEnv varDescription
--server, -sTRAINING_SERVER_URLOrchestrator URL (defaults to the production server)
--token, -tINNATE_SERVICE_KEYService key — required
--issuerINNATE_AUTH_ISSUER_URLOIDC issuer; the token is exchanged for a JWT here

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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>

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Commands

CommandDescription
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_IDPrint a run’s current status
watch [SKILL_DIR] RUN_IDPoll a run until it completes (--interval seconds, default 20)
runs [SKILL_DIR]List all runs for the skill
skillsList all your skills
download [SKILL_DIR] RUN_IDDownload 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_IDSet the trained checkpoint + stats file into metadata.json so the robot loads it

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Launching a run

run takes a server-side preset, a fully custom configuration, or a preset with overrides:
OptionDescription
--presetServer-side preset (e.g. act-default) — pre-fills everything below
--repoGitHub owner/repo of a custom ACT fork to train with
--refBranch, tag, or commit in that repo
--command, -cTraining command parts (repeatable)
--env, -eEnvironment variables as KEY=VALUE (repeatable)
--gpu-typeGPU type (e.g. H100)
--min-gpus / --max-gpusGPU count bounds
--hoursEstimated duration
--budgetMax total cost in USD
--checkpoint-patterns, -pGlobs 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

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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