Osmosis CLI Training - Osmosis AI Docs

Use a training run to improve a base model with your rollout, grader, and platform dataset. Training configs live under configs/training/; push your code before submitting because the platform runs the selected workspace commit on managed training infrastructure.

Run an evaluation first to catch rollout, grader, model, and credential problems before committing training resources.

Workspace billing must be ready before submission. For self-serve workspaces, an owner or admin must add a valid payment method; invoiced workspaces must have no past-due invoice.

Quick Start

From inside your workspace directory:

osmosis dataset list
git push
osmosis train submit configs/training/my-rollout.toml

Then monitor or manage the run:

osmosis train list
osmosis train info <run-name>
osmosis train logs <run-name>
osmosis train stop <run-name>

Training Config

See Config Files for every field and its validation rules.

configs/training/my-rollout.toml

[experiment]
rollout = "my-rollout"                  # Directory under rollouts/
entrypoint = "main.py"                  # Entrypoint relative to that directory
model_path = "Qwen/Qwen3.6-35B-A3B"     # Base model available in the workspace
dataset = "my-platform-dataset"         # Name from `osmosis dataset list`
# branch = "my-feature"                 # Optional pushed branch
# commit_sha = "abc1234"                # Optional pinned commit

[training]
# Omit fields to use platform defaults.
# lr = 1e-6
# total_epochs = 1
# n_samples_per_prompt = 8
# rollout_batch_size = 32

[sampling]
# rollout_temperature = 1.0
# rollout_top_p = 1.0

[checkpoints]
# eval_interval = 10
# checkpoint_save_freq = 20

# [secrets]
# required = ["OPENAI_API_KEY"]

Submission validates the shared training config schema and rejects unknown fields and out-of-range values. It does not check the config against the selected model or backend, so an unsupported combination can surface during provisioning or execution.

Git Sync is the source of truth for your rollout code. The CLI reads config values from the local TOML file you pass, but rollout code comes from the synced workspace repository. Commit and push before submitting code changes. Set branch to use a pushed branch or commit_sha for a specific pushed revision; omit both to use the default branch.

How It Works

Resolve the workspace and code

The CLI reads the TOML from configs/training/ and resolves the workspace from Git origin. Omit branch and commit_sha to use the default branch’s latest synced commit. An explicit branch resolves its current head at submission and pins the resulting full SHA; commit_sha pins that commit directly.

Validate the request

The CLI validates the config structure and local path shape. The platform checks billing, the dataset and base model, secrets, and the shared config schema before scheduling. Provisioning then validates the selected commit’s entrypoint and build, and model- or backend-specific incompatibilities can surface during provisioning or execution; failures appear on the run.

Split the dataset

Osmosis shuffles the selected dataset and holds out about 20% of the rows for validation, rounded down and capped at 10,000 rows; the rest is used for training. The split is not configurable.

Run rollouts and update the model

The training service runs AgentWorkflow and Grader, routes policy calls through the active rollout context, and applies rewards to model updates. Failed rollout samples are excluded from training updates.

Record outputs

Metrics, rollout samples, logs, and any produced checkpoints appear on the run. A run is not guaranteed to produce a checkpoint if it ends before the first save or the selected training setup does not support one.

Commands

Command	Description
`osmosis train submit <config>.toml [--yes]`	Submit a config under `configs/training/`.
`osmosis train list [--limit N] [--all]`	List training runs for the current workspace.
`osmosis train info <run-name> [--output PATH]`	Show status, progress, checkpoints, and metrics.
`osmosis train logs <run-name>`	Show lifecycle logs for monitoring and diagnosis.
`osmosis train stop <run-name> [--yes]`	Request a graceful stop.

See the Command Reference for the full flag list.

Monitor a Run

train info reports current_step, total_steps, percent complete, and the latest reward while a run is active. Use train logs for scheduling, execution, and cleanup diagnostics. The Training Runs page documents the full status lifecycle, dashboard charts, rollout samples, checkpoints, and configuration view.

Next Steps

Evaluation

Validate the rollout and grader before training.

Config Files

Review every training field and constraint.

Training Runs

Monitor runs, samples, checkpoints, and logs in the Platform.

​Quick Start

​Training Config

​How It Works

​Commands

​Monitor a Run

​Next Steps

Evaluation

Config Files

Training Runs

Quick Start

Training Config

How It Works

Commands

Monitor a Run

Next Steps