phospho-app/gr00t-policy:0212db68 – Run with an API on Replicate

Version

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phospho-app /gr00t-policy:0212db68

Playground API

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 3 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:0212db6881c7d02a5f6532165c8b8ff9d525fc6cf984bd1e47a6037962ff1722",
  {
    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:0212db6881c7d02a5f6532165c8b8ff9d525fc6cf984bd1e47a6037962ff1722",
    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": "0212db6881c7d02a5f6532165c8b8ff9d525fc6cf984bd1e47a6037962ff1722",
    "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.

You can run this model locally using Cog. First, install Cog:

brew install cog

If you don’t have Homebrew, there are other installation options available.

Run this to download the model and run it in your local environment:

cog predict r8.im/phospho-app/gr00t-policy@sha256:0212db6881c7d02a5f6532165c8b8ff9d525fc6cf984bd1e47a6037962ff1722 \
  -i 'batch_size=32' \
  -i 'learning_rate=0.0002' \
  -i 'number_of_epochs_to_train_for=15'

To learn more, take a look at the Cog documentation.

Run this to download the model and run it in your local environment:

docker run -d -p 5000:5000 --gpus=all r8.im/phospho-app/gr00t-policy@sha256:0212db6881c7d02a5f6532165c8b8ff9d525fc6cf984bd1e47a6037962ff1722
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "batch_size": 32,
      "learning_rate": 0.0002,
      "number_of_epochs_to_train_for": 15
    }
  }' \
  http://localhost:5000/predictions

To learn more, take a look at the Cog documentation.

Output

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