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

phospho-app / act-policy

Let's you train an ACT policy from a LeRobot dataset

Cold

Public
1 run
T4

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'

hf_token

*string

Shift + Return to add a new line

Hugging Face API token (used to upload your trained model to your HF profile), find yours here: https://huggingface.co/settings/token

hf_model_name

string

Shift + Return to add a new line

Hugging Face model name, the name of the model to be created, i.e. 'trained-act-replicate'

Default: "trained-act-replicate"

wandb_api_key

string

Shift + Return to add a new line

Weights & Biases API key (optional, to track the online training), find yours here: https://wandb.ai/authorize

Default: ""

wandb_project

string

Shift + Return to add a new line

Weights & Biases project name

Default: "ACT-Replicate"

job_name

string

Shift + Return to add a new line

Name for this training job

Default: "replicate_training"

steps

integer

(minimum: 1, maximum: 200000)

Number of steps to train for

Default: 100000

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

npx create-replicate --model=phospho-app/act-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/act-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/act-policy:c4380deba6ec891be6998949d504b7b013b1736e495642ec945d873a1495a12a",
  {
    input: {
      steps: 100000,
      job_name: "replicate_training",
      hf_model_name: "trained-act-replicate",
      wandb_api_key: "",
      wandb_project: "ACT-Replicate"
    }
  }
);
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/act-policy using Replicate’s API. Check out the model's schema for an overview of inputs and outputs.

output = replicate.run(
    "phospho-app/act-policy:c4380deba6ec891be6998949d504b7b013b1736e495642ec945d873a1495a12a",
    input={
        "steps": 100000,
        "job_name": "replicate_training",
        "hf_model_name": "trained-act-replicate",
        "wandb_api_key": "",
        "wandb_project": "ACT-Replicate"
    }
)
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/act-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": "c4380deba6ec891be6998949d504b7b013b1736e495642ec945d873a1495a12a",
    "input": {
      "steps": 100000,
      "job_name": "replicate_training",
      "hf_model_name": "trained-act-replicate",
      "wandb_api_key": "",
      "wandb_project": "ACT-Replicate"
    }
  }' \
  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/act-policy@sha256:c4380deba6ec891be6998949d504b7b013b1736e495642ec945d873a1495a12a \
  -i 'steps=100000' \
  -i 'job_name="replicate_training"' \
  -i 'hf_model_name="trained-act-replicate"' \
  -i 'wandb_api_key=""' \
  -i 'wandb_project="ACT-Replicate"'

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/act-policy@sha256:c4380deba6ec891be6998949d504b7b013b1736e495642ec945d873a1495a12a
curl -s -X POST \
  -H "Content-Type: application/json" \
  -d $'{
    "input": {
      "steps": 100000,
      "job_name": "replicate_training",
      "hf_model_name": "trained-act-replicate",
      "wandb_api_key": "",
      "wandb_project": "ACT-Replicate"
    }
  }' \
  http://localhost:5000/predictions

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

Output

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

Run time and cost

This model runs on Nvidia T4 GPU hardware. We don't yet have enough runs of this model to provide performance information.

Readme

ACT policy - phospho easy training pipeline

This setup allows you to train an ACT policy, using LeRobot for training.

🚀 What This Pipeline Does

Trains ACT models using datasets formatted for LeRobot (v2.0+).
Automatically validates dataset availability and access permissions.
Uploads trained models to your Hugging Face profile.
Supports Weights & Biases for tracking training progress.
Runs training efficiently on GPUs with configurable steps and job names.

🔗 Useful Links

phospho Documentation: https://docs.phospho.ai
Get Your Robot: https://robots.phospho.ai

🛠 Setup & Usage

To train your model, provide the following inputs:

Dataset Repo ID (Hugging Face dataset formatted for LeRobot).
Hugging Face Token (for uploading trained models).
Model Name (for storing results in Hugging Face).
Weights & Biases API Key (optional, for experiment tracking).
Training Steps The default is 100K steps, you can leave it like so or lower to 60/80K for quicker and cheaper training.

The pipeline ensures all necessary dependencies are installed, including LeRobot and related libraries, before executing the training process. Once trained, the model will be available on Hugging Face for further use, you will also be able to download a .zip archive of your model.