Rayform
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Getting started

Quickstart

Upload one recording, wait for it to process, and load the result in Python.

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Rayform is in private preview. APIs, formats and the SDK described here may change before general availability.

This guide takes one recording from your machine to a PyTorch batch. You need Python 3.10 or later and a Rayform API key from the private preview.

1. Install the SDK

Shell
pip install rayform

The core package depends only on NumPy and PyArrow. Add extras when you need them:

Shell
pip install "rayform[torch]"        # PyTorch datasets
pip install "rayform[lerobot]"      # LeRobot export helpers
pip install "rayform[rerun]"        # open segments in the Rerun viewer

2. Authenticate

Create a key in the console and export it. The SDK and the CLI both read RAYFORM_API_KEY.

Shell
export RAYFORM_API_KEY="rf_live_..."
rayform whoami
Text
org_7Hq2 · Acme Robotics · key rf_live_...9c2f (read, write)

3. Upload a recording

Any MCAP file or ROS 2 bag with at least one camera works. Include joint states if a robot is in the loop. site groups recordings made in the same place, so repeat visits are registered into one coordinate frame.

Python
import rayform

client = rayform.Client()
session = client.sessions.upload(
    "recordings/kitchen_2026-10-14.mcap",
    site="lab-2",
    wait=True,          # block until processing finishes
)
print(session.id, session.status)
Text
ses_01JA8K2M9Q processed

Uploads are resumable: if the connection drops, run the same call again and it continues from the last confirmed part.

4. Look at what came back

Python
for seg in session.segments():
    print(seg.id, seg.duration_s, [e.type for e in seg.events()])
Text
seg_4f0a 7.45 ['grasp', 'place']
seg_4f0b 5.10 ['open_drawer']
seg_4f0c 9.80 ['grasp', 'handover', 'place']

Open one in the Rerun viewer to check the reconstruction against the raw camera frames:

Python
rayform.rerun.show(session.segments()[0])

5. Freeze a slice and train

Python
from torch.utils.data import DataLoader

slice_ = client.slices.create(segments=session.segments(), name="quickstart")

dataset = rayform.torch.EpisodeDataset(
    slice_,
    obs=["cam0/rgb", "cam0/depth", "joints/position"],
    action="joints/position_delta",
    fps=15,
)
batch = next(iter(DataLoader(dataset, batch_size=8)))
print({k: tuple(v.shape) for k, v in batch.items()})
Text
{'cam0/rgb': (8, 3, 480, 640), 'cam0/depth': (8, 1, 480, 640), 'joints/position': (8, 7), 'action': (8, 7)}

Next steps

Last updated October 18, 2026