Simulusion CLI#

The project exposes a small command-line interface for common developer tasks:

  • Run the CLI with: python -m simulusion.cli <command>; after installing the package an installed entrypoint is available as simulusion <command>. Tests and plugin integrations may invoke the top-level Click app (simulusion.cli.app) or the console-script callable (simulusion.cli.run).

  • The CLI uses Click directly and lazy imports to keep startup fast; some commands require optional dependencies (listed below).

Commands#

  • version - Print the installed simulusion package version (or “unknown” if the package cannot be imported).

  • envs list - List built-in environment backends and example environment ids. This reads the environment catalog from simulusion.catalog if available.

  • datasets list [PATH] - List dataset entries under PATH. If PATH is not provided the command uses SIMULUSION_HOME or defaults to ~/.simulusion.

  • datasets convert <src> [--dest-format video] [--out-dir DIR] - Convert a simple dataset file into another format. The initial implementation supports converting HDF5 (.h5) or NumPy (.npz/.npy) datasets into MP4 video files (one file per episode) when --dest-format video is used. Output files are written to the specified --out-dir or ./converted_datasets by default.

  • collect --env <ENV_ID> [--steps N] [--out PATH] [--random-policy] - Run a (random) policy for a number of environment steps and save interactions to a compressed .npz with keys observations, actions, rewards, dones.

  • datasets import lerobot <source> <out_dir> - Convert a local LeRobot dataset folder into an episode folder, the format every training task reads. Options: --camera, --all-cameras, --episodes 0-9. See Your Own Data: Episodes and LeRobot.

  • datasets info <path> - Summarise an episode folder: episodes, frames, action space, cameras, observations, source.

  • train [task] [--config FILE] [--print-config] [key=value ...] - Train on the shared Trainer from a run config. task is any registered training task (simulusion train --help-tasks lists them), optional when the config names one; --config takes a YAML file or the name of a shipped config in simulusion/configs/runs/; every field can be overridden with dotted key=value pairs. See One Trainer for Every Model.

  • train <model> --legacy [extra args...] [--inproc] - Launch a family’s own, older training entrypoint (python -m simulusion.training.train_<name>), forwarding the extra args; --inproc calls its main() in-process instead of spawning a subprocess. Commands written for those entrypoints (a --config file without task:, or flat overrides such as total_epochs=100) are recognised and still run there.

  • eval --model <NAME> --checkpoint <PATH> [options] - Evaluate a trained world model. Generative models (diamond) are scored with FID, FVD, LPIPS, and PSNR, comparing real trajectories (collected from the environment) against generated ones. jepa has no rollout to score, so it runs the paper’s frozen-encoder linear probe instead and takes --root-path rather than --game; options that belong to the other model are rejected rather than ignored. See Evaluation Guide for details and interpretation.

    Key options:

    • --model, -m — model type (currently diamond)

    • --checkpoint, -c — path to checkpoint

    • --game, -g — environment name

    • --num-videos — number of trajectories (default 256)

    • --metrics — comma-separated metrics, e.g. fid,fvd,lpips,psnr

    • --record PATH — save real and generated videos

    • --output, -o — save results JSON

  • play --model <NAME> --checkpoint <PATH> [options] - Interactively play inside a trained world model. Two modes toggled by TAB: REAL (env stepping) and DREAM (model imagination). Press arrow keys / WASD to override the agent’s actions.

    Key options:

    • --model, -m — model type (currently diamond)

    • --checkpoint, -c — path to checkpoint

    • --game, -g — environment name

    • --deterministic / --stochastic — action selection (default deterministic)

    • --record PATH — save gameplay video

    • --record-fps — video framerate (default 20)

  • models list - Print the known training entrypoints and (when available) exported model names from simulusion.models.

Environment / optional dependencies#

  • SIMULUSION_HOME - Directory used by datasets list when no path is provided.

  • The following commands require optional packages which may not be installed in all environments:

    • collect: requires gym/gymnasium and numpy.

    • datasets convert: requires h5py, numpy and video helpers used by the repository.

Notes and examples#

  • Example: show version

simulusion version

  • Example: list environments

simulusion envs list

  • Example: list datasets in default location

simulusion datasets list

  • Example: convert a local HDF5 dataset to MP4 files

simulusion datasets convert data/my_dataset.h5 --out-dir /tmp/videos

  • Example: collect 1000 steps from Pong and save as pong.npz

simulusion collect --env ALE/Pong-v5 --steps 1000 --out pong.npz

  • Example: train Genie on a folder of clips with the shared Trainer, in bf16

simulusion train genie data.path=data/clips trainer.precision=bf16 trainer.max_steps=20000

  • Example: repeat a finished run exactly

simulusion train --config runs/genie_20261020-120000/config.yaml

  • Example: import a LeRobot dataset and train Dreamer’s world model on it

simulusion datasets import lerobot datasets/pusht data/pusht
simulusion train --config dreamer-wm data.path=data/pusht

  • Example: run IRIS training with a library YAML config and a dot-list override

simulusion train iris --config simulusion/configs/experiments/iris.yaml total_epochs=100

  • Example: inspect a composed JEPA config without starting training

simulusion train jepa --config simulusion/configs/experiments/jepa.yaml optimization.epochs=50 --print-config

  • Example: launch a DIAMOND preset from the unified training CLI

simulusion train diamond --config simulusion/configs/experiments/diamond.yaml preset=small seed=3

  • Example: evaluate a DIAMOND checkpoint

simulusion eval --model diamond --checkpoint checkpoints/diamond/checkpoint.pt --game Breakout-v5

  • Example: linear-probe an I-JEPA checkpoint

simulusion eval --model jepa --checkpoint results/jepa/jepa_run-latest.pth.tar \
    --root-path /data/imagenet --model-name vit_base --output probe.json

  • Example: interactively play inside a DIAMOND world model

simulusion play --model diamond --checkpoint checkpoints/diamond/checkpoint.pt --game Breakout-v5 --record gameplay.mp4

Maintaining this page#

If you add or rename CLI commands in simulusion.cli, update this page with the new usage, examples, and any additional optional dependencies.