curl --request POST \
--url https://api.example.com/training/start \
--header 'Content-Type: application/json' \
--data '
{
"dataset_name": "<string>",
"model_name": "<string>",
"private_mode": false,
"training_params": {
"batch_size": 75,
"save_freq": 5000,
"steps": 500000
},
"user_hf_token": "<string>",
"wandb_api_key": "<string>"
}
'import requests
url = "https://api.example.com/training/start"
payload = {
"dataset_name": "<string>",
"model_name": "<string>",
"private_mode": False,
"training_params": {
"batch_size": 75,
"save_freq": 5000,
"steps": 500000
},
"user_hf_token": "<string>",
"wandb_api_key": "<string>"
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
dataset_name: '<string>',
model_name: '<string>',
private_mode: false,
training_params: {batch_size: 75, save_freq: 5000, steps: 500000},
user_hf_token: '<string>',
wandb_api_key: '<string>'
})
};
fetch('https://api.example.com/training/start', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.example.com/training/start",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'dataset_name' => '<string>',
'model_name' => '<string>',
'private_mode' => false,
'training_params' => [
'batch_size' => 75,
'save_freq' => 5000,
'steps' => 500000
],
'user_hf_token' => '<string>',
'wandb_api_key' => '<string>'
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.example.com/training/start"
payload := strings.NewReader("{\n \"dataset_name\": \"<string>\",\n \"model_name\": \"<string>\",\n \"private_mode\": false,\n \"training_params\": {\n \"batch_size\": 75,\n \"save_freq\": 5000,\n \"steps\": 500000\n },\n \"user_hf_token\": \"<string>\",\n \"wandb_api_key\": \"<string>\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.example.com/training/start")
.header("Content-Type", "application/json")
.body("{\n \"dataset_name\": \"<string>\",\n \"model_name\": \"<string>\",\n \"private_mode\": false,\n \"training_params\": {\n \"batch_size\": 75,\n \"save_freq\": 5000,\n \"steps\": 500000\n },\n \"user_hf_token\": \"<string>\",\n \"wandb_api_key\": \"<string>\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/training/start")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n \"dataset_name\": \"<string>\",\n \"model_name\": \"<string>\",\n \"private_mode\": false,\n \"training_params\": {\n \"batch_size\": 75,\n \"save_freq\": 5000,\n \"steps\": 500000\n },\n \"user_hf_token\": \"<string>\",\n \"wandb_api_key\": \"<string>\"\n}"
response = http.request(request)
puts response.read_body{
"training_id": 123,
"message": "<string>",
"model_url": "<string>",
"status": "ok"
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}Start training a model
Start training an ACT or gr00t model on the specified dataset. This will upload a trained model to the Hugging Face Hub using the main branch of the specified dataset.
curl --request POST \
--url https://api.example.com/training/start \
--header 'Content-Type: application/json' \
--data '
{
"dataset_name": "<string>",
"model_name": "<string>",
"private_mode": false,
"training_params": {
"batch_size": 75,
"save_freq": 5000,
"steps": 500000
},
"user_hf_token": "<string>",
"wandb_api_key": "<string>"
}
'import requests
url = "https://api.example.com/training/start"
payload = {
"dataset_name": "<string>",
"model_name": "<string>",
"private_mode": False,
"training_params": {
"batch_size": 75,
"save_freq": 5000,
"steps": 500000
},
"user_hf_token": "<string>",
"wandb_api_key": "<string>"
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
dataset_name: '<string>',
model_name: '<string>',
private_mode: false,
training_params: {batch_size: 75, save_freq: 5000, steps: 500000},
user_hf_token: '<string>',
wandb_api_key: '<string>'
})
};
fetch('https://api.example.com/training/start', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://api.example.com/training/start",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'dataset_name' => '<string>',
'model_name' => '<string>',
'private_mode' => false,
'training_params' => [
'batch_size' => 75,
'save_freq' => 5000,
'steps' => 500000
],
'user_hf_token' => '<string>',
'wandb_api_key' => '<string>'
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://api.example.com/training/start"
payload := strings.NewReader("{\n \"dataset_name\": \"<string>\",\n \"model_name\": \"<string>\",\n \"private_mode\": false,\n \"training_params\": {\n \"batch_size\": 75,\n \"save_freq\": 5000,\n \"steps\": 500000\n },\n \"user_hf_token\": \"<string>\",\n \"wandb_api_key\": \"<string>\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://api.example.com/training/start")
.header("Content-Type", "application/json")
.body("{\n \"dataset_name\": \"<string>\",\n \"model_name\": \"<string>\",\n \"private_mode\": false,\n \"training_params\": {\n \"batch_size\": 75,\n \"save_freq\": 5000,\n \"steps\": 500000\n },\n \"user_hf_token\": \"<string>\",\n \"wandb_api_key\": \"<string>\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.example.com/training/start")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n \"dataset_name\": \"<string>\",\n \"model_name\": \"<string>\",\n \"private_mode\": false,\n \"training_params\": {\n \"batch_size\": 75,\n \"save_freq\": 5000,\n \"steps\": 500000\n },\n \"user_hf_token\": \"<string>\",\n \"wandb_api_key\": \"<string>\"\n}"
response = http.request(request)
puts response.read_body{
"training_id": 123,
"message": "<string>",
"model_url": "<string>",
"status": "ok"
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}Body
Pydantic model for training request validation. This version consolidates all model name and parameter logic into a single validator to prevent redundant operations and fix the duplicate suffix bug.
Dataset repository ID on Hugging Face, should be a public dataset
Type of model to train, supports 'ACT', 'gr00t', and 'pi0'
ACT, ACT_BBOX, gr00t, pi0, custom Name of the trained model to upload to Hugging Face, should be in the format phospho-app/<model_name> or <model_name>
Whether to use private training (PRO users only)
Training parameters are left to None by default and are set depending on the dataset in the training pipeline.
- TrainingParamsAct
- TrainingParamsActWithBbox
- TrainingParamsGr00T
- TrainingParamsPi0
Show child attributes
Show child attributes
User's personal HF token for private training
WandB API key for tracking training, you can find it at https://wandb.ai/authorize
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