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
261 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 6 more...

wandb_api_key

secret

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

hf_model_name

string

Shift + Return to add a new line

Hugging Face model name to upload the trained model to (optional). If not provided, a random name will be generated.

Default: ""

batch_size

integer

(minimum: 1, maximum: 80)

Batch size for training

Default: 64

number_of_epochs_to_train_for

integer

(minimum: 1, maximum: 50)

Number of epochs to train for

Default: 20

learning_rate

number

(minimum: 0.00001, maximum: 0.01)

Learning rate for training

Default: 0.0002

modal_token_id

secret

Modal token ID, if you don't know what this is, leave it empty

modal_token_secret

secret

Modal token secret, if you don't know what this is, leave it empty

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:d02d5ccfaa9170ed101eda9db5ffc5c793ca6733c09e1e73bb60742291ce5217",
  {
    input: {
      batch_size: 32,
      hf_model_name: "",
      learning_rate: 0.0002,
      dataset_repo_id: "00ri/so100_battery",
      hugging_face_token: "",
      number_of_epochs_to_train_for: 10
    }
  }
);

// To access the file URL:
console.log(output.url()); //=> "http://example.com"

// To write the file to disk:
fs.writeFile("my-image.png", 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:d02d5ccfaa9170ed101eda9db5ffc5c793ca6733c09e1e73bb60742291ce5217",
    input={
        "batch_size": 32,
        "hf_model_name": "",
        "learning_rate": 0.0002,
        "dataset_repo_id": "00ri/so100_battery",
        "hugging_face_token": "",
        "number_of_epochs_to_train_for": 10
    }
)
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": "d02d5ccfaa9170ed101eda9db5ffc5c793ca6733c09e1e73bb60742291ce5217",
    "input": {
      "batch_size": 32,
      "hf_model_name": "",
      "learning_rate": 0.0002,
      "dataset_repo_id": "00ri/so100_battery",
      "hugging_face_token": "",
      "number_of_epochs_to_train_for": 10
    }
  }' \
  https://api.replicate.com/v1/predictions

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

Output

Rendering markdown...

{
  "completed_at": "2025-03-29T14:22:07.720515Z",
  "created_at": "2025-03-29T13:08:07.205000Z",
  "data_removed": false,
  "error": null,
  "id": "8t7sg6tbmnrj20cnw82be5gbzr",
  "input": {
    "batch_size": 32,
    "learning_rate": 0.0002,
    "dataset_repo_id": "00ri/so100_battery",
    "hugging_face_token": "[REDACTED]",
    "hugging_face_model_name": "00ri/so100_battery-ovrr0igj39",
    "number_of_epochs_to_train_for": 10
  },
  "logs": "Your model will be uploaded to: https://huggingface.co/00ri/so100_battery-ovrr0igj39\nValidating Hugging Face dataset: 00ri/so100_battery\nWeights & Biases enabled: False\nExisting dataset branches and tags: ['main', 'v2.1']\nUsing branch v2.1\nDownloading dataset\nGenerating modality.json file\nWill train for 7583 steps, which is 10 epochs\nStarting training with command: python /tmp/Isaac-GR00T/scripts/gr00t_finetune.py --dataset-path /tmp/outputs/data --num-gpus 1 --output-dir /tmp/outputs/train --max-steps 7583 --save-steps 500 --data-config so100_wc --learning_rate 0.0002 --report_to tensorboard --video_backend torchvision_av\n0%|          | 0/7583 [00:00<?, ?it/s]The input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in torch.bfloat16.\n1%|          | 38/7583 [00:19<50:47,  2.48it/s]\n2%|▏         | 114/7583 [00:49<50:46,  2.45it/s]\n3%|▎         | 190/7583 [01:20<49:36,  2.48it/s]\n4%|▎         | 266/7583 [01:51<48:59,  2.49it/s]\n5%|▍         | 342/7583 [02:21<48:35,  2.48it/s]\n6%|▌         | 418/7583 [02:52<48:17,  2.47it/s]\n7%|▋         | 493/7583 [03:23<48:13,  2.45it/s]\n8%|▊         | 569/7583 [04:15<46:49,  2.50it/s]\n9%|▊         | 645/7583 [04:46<46:59,  2.46it/s]\n10%|▉         | 721/7583 [05:16<45:41,  2.50it/s]\n11%|█         | 797/7583 [05:47<46:11,  2.45it/s]\n12%|█▏        | 873/7583 [06:18<45:03,  2.48it/s]\n13%|█▎        | 948/7583 [06:48<44:19,  2.50it/s]\n14%|█▎        | 1024/7583 [07:39<43:59,  2.48it/s]\n15%|█▍        | 1100/7583 [08:10<43:21,  2.49it/s]\n16%|█▌        | 1176/7583 [08:40<42:43,  2.50it/s]\n17%|█▋        | 1252/7583 [09:11<42:35,  2.48it/s]\n18%|█▊        | 1328/7583 [09:42<41:57,  2.49it/s]\n19%|█▊        | 1403/7583 [10:12<41:56, 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Skipping to prevent empty commit.\nUploading checkpoint file /tmp/outputs/train/checkpoint-7583/model-00001-of-00002.safetensors to branch checkpoint-7583 as model-00001-of-00002.safetensors\nNo files have been modified since last commit. Skipping to prevent empty commit.\nUploading checkpoint file /tmp/outputs/train/checkpoint-7583/model-00002-of-00002.safetensors to branch checkpoint-7583 as model-00002-of-00002.safetensors\nNo files have been modified since last commit. Skipping to prevent empty commit.\nUploading checkpoint file /tmp/outputs/train/checkpoint-7583/model.safetensors.index.json to branch checkpoint-7583 as model.safetensors.index.json\nNo files have been modified since last commit. Skipping to prevent empty commit.\nUploading checkpoint file /tmp/outputs/train/checkpoint-7583/optimizer.pt to branch checkpoint-7583 as optimizer.pt\nNo files have been modified since last commit. 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Skipping to prevent empty commit.\nUploading main file: /tmp/outputs/train/model-00001-of-00002.safetensors\nNo files have been modified since last commit. Skipping to prevent empty commit.\nUploading main file: /tmp/outputs/train/model-00002-of-00002.safetensors\nNo files have been modified since last commit. Skipping to prevent empty commit.\nUploading main file: /tmp/outputs/train/model.safetensors.index.json\nNo files have been modified since last commit. Skipping to prevent empty commit.\nUploading main file: /tmp/outputs/train/training_args.bin\nNo files have been modified since last commit. Skipping to prevent empty commit.\nUploading config file: /tmp/outputs/train/experiment_cfg/metadata.json as experiment_cfg/metadata.json\nModel successfully uploaded to https://huggingface.co/00ri/so100_battery-ovrr0igj39\nTraining completed successfully\nWandb run URL: None\nFind your trained model on Hugging Face: https://huggingface.co/00ri/so100_battery-ovrr0igj39",
  "metrics": {
    "predict_time": 4345.957984516,
    "total_time": 4440.515515
  },
  "output": "https://replicate.delivery/yhqm/EeA0vq3JepvcOE1s6cKKcFk0TplWSX8cybeTiuMhmO5fAt1RB/README.md",
  "started_at": "2025-03-29T13:09:41.762531Z",
  "status": "succeeded",
  "urls": {
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    "get": "https://api.replicate.com/v1/predictions/8t7sg6tbmnrj20cnw82be5gbzr",
    "cancel": "https://api.replicate.com/v1/predictions/8t7sg6tbmnrj20cnw82be5gbzr/cancel"
  },
  "version": "ad66b98718c35fbd76229858d59867f79227892631593aefc15f8ef6e136ca17"
}

Generated in

72 minutes 26 seconds

Tweak it ShareReport

Your model will be uploaded to: https://huggingface.co/00ri/so100_battery-ovrr0igj39
Validating Hugging Face dataset: 00ri/so100_battery
Weights & Biases enabled: False
Existing dataset branches and tags: ['main', 'v2.1']
Using branch v2.1
Downloading dataset
Generating modality.json file
Will train for 7583 steps, which is 10 epochs
Starting training with command: python /tmp/Isaac-GR00T/scripts/gr00t_finetune.py --dataset-path /tmp/outputs/data --num-gpus 1 --output-dir /tmp/outputs/train --max-steps 7583 --save-steps 500 --data-config so100_wc --learning_rate 0.0002 --report_to tensorboard --video_backend torchvision_av
0%|          | 0/7583 [00:00<?, ?it/s]The input hidden states seems to be silently casted in float32, this might be related to the fact you have upcasted embedding or layer norm layers in float32. We will cast back the input in torch.bfloat16.
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Uploading config file: /tmp/outputs/train/experiment_cfg/metadata.json as experiment_cfg/metadata.json
Model successfully uploaded to https://huggingface.co/00ri/so100_battery-ovrr0igj39
Training completed successfully
Wandb run URL: None
Find your trained model on Hugging Face: https://huggingface.co/00ri/so100_battery-ovrr0igj39

This output was created using a different version of the model, phospho-app/gr00t-policy:ad66b987.

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