phospho-app/gr00t-policy – Run with an API on Replicate

phospho-app / gr00t-policy

Fine-tune GR00T-N1 from Nvidia on a LeRobot dataset

Cold

Public
45 runs
A100 (80GB)
GitHub
Weights
Paper
License

Run with an API

Playground API Examples README Versions

Input

dataset_repo_id

*string

Shift + Return to add a new line

Hugging Face dataset ID to train on, LeRobot format > v2.0 expected, i.e. 'LegrandFrederic/dual-setup'

hugging_face_token

*secret

Hugging Face API token, needed to download your dataset and upload your model. Please use a fine grained token with Repository write access.

Including wandb_api_key and 4 more...

Run this model in Node.js with one line of code:

npx create-replicate --model=phospho-app/gr00t-policy

or set up a project from scratch

Install Replicate’s Node.js client library:

npm install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import and set up the client:

import Replicate from "replicate";

const replicate = new Replicate({
  auth: process.env.REPLICATE_API_TOKEN,
});

Run phospho-app/gr00t-policy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

const output = await replicate.run(
  "phospho-app/gr00t-policy:ad66b98718c35fbd76229858d59867f79227892631593aefc15f8ef6e136ca17",
  {
    input: {
      batch_size: 32,
      learning_rate: 0.0002,
      number_of_epochs_to_train_for: 15
    }
  }
);
console.log(output);

To learn more, take a look at the guide on getting started with Node.js.

Install Replicate’s Python client library:

pip install replicate

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Import the client:

import replicate

Run phospho-app/gr00t-policy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "phospho-app/gr00t-policy:ad66b98718c35fbd76229858d59867f79227892631593aefc15f8ef6e136ca17",
    input={
        "batch_size": 32,
        "learning_rate": 0.0002,
        "number_of_epochs_to_train_for": 15
    }
)
print(output)

To learn more, take a look at the guide on getting started with Python.

Set the REPLICATE_API_TOKEN environment variable:

export REPLICATE_API_TOKEN=<paste-your-token-here>

Find your API token in your account settings.

Run phospho-app/gr00t-policy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

curl -s -X POST \
  -H "Authorization: Bearer $REPLICATE_API_TOKEN" \
  -H "Content-Type: application/json" \
  -H "Prefer: wait" \
  -d $'{
    "version": "ad66b98718c35fbd76229858d59867f79227892631593aefc15f8ef6e136ca17",
    "input": {
      "batch_size": 32,
      "learning_rate": 0.0002,
      "number_of_epochs_to_train_for": 15
    }
  }' \
  https://api.replicate.com/v1/predictions

To learn more, take a look at Replicate’s HTTP API reference docs.

Output

No output yet! Press "Submit" to start a prediction.

Run time and cost

This model runs on Nvidia A100 (80GB) GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

This pipeline is designed to train a gr00t model from Nvidia on a LeRobot Dataset.

This run will push a model to Hugging Face for you to access. You will have the model id when the run completes.

Note that we expect your dataset to have 1 or 2 cameras, we also expect your robot to be 6 DoF, this will work with a regular so100.

If you are training specific configurations, this pipeline might fail.

With default parameters, training takes up to 3 hours (which costs roughly $10). Decrease the number of epochs for faster and cheaper training runs.

Reach out and join our Discord for more AI robotics: https://discord.com/invite/m8wzBGQA55