Create Rl Training Job
curl --request POST \
--url https://api.training.wandb.ai/v1/preview/training-jobs \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"model_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"trajectory_groups": [
{
"trajectories": [
{
"exchanges": {},
"messages_and_choices": [
{
"content": "<string>",
"role": "developer",
"name": "<string>"
}
],
"tools": [
{
"function": {
"name": "<string>",
"description": "<string>",
"parameters": {},
"strict": true
},
"type": "function"
}
],
"additional_histories": [
{
"messages_and_choices": [
{
"content": "<string>",
"role": "developer",
"name": "<string>"
}
],
"tools": [
{
"function": {
"name": "<string>",
"description": "<string>",
"parameters": {},
"strict": true
},
"type": "function"
}
]
}
],
"reward": 0,
"initial_policy_version": 123,
"final_policy_version": 123,
"metrics": {},
"metadata": {},
"logs": [
"<string>"
],
"start_time": "2023-11-07T05:31:56Z"
}
],
"exceptions": [
{
"type": "<string>",
"message": "<string>",
"traceback": "<string>"
}
],
"metadata": {},
"metrics": {},
"logs": [
"<string>"
]
}
],
"experimental_config": {
"advantage_balance": 123,
"allow_training_without_logprobs": true,
"epsilon": 123,
"epsilon_high": 123,
"kimi_k2_tau": 123,
"kl_penalty_coef": 123,
"kl_penalty_reference_step": 123,
"kl_penalty_step_lag": 123,
"kl_ref_adapter_path": "<string>",
"learning_rate": 123,
"logprob_calculation_chunk_size": 123,
"mask_prob_ratio": true,
"max_negative_advantage_importance_sampling_weight": 123,
"normalize_advantages": true,
"num_trajectories_learning_rate_multiplier_power": 123,
"packed_sequence_length": 123,
"plot_tensors": true,
"ppo": true,
"precalculate_logprobs": true,
"scale_learning_rate_by_reward_std_dev": true,
"scale_rewards": true,
"truncated_importance_sampling": 123
}
}
'import requests
url = "https://api.training.wandb.ai/v1/preview/training-jobs"
payload = {
"model_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"trajectory_groups": [
{
"trajectories": [
{
"exchanges": {},
"messages_and_choices": [
{
"content": "<string>",
"role": "developer",
"name": "<string>"
}
],
"tools": [
{
"function": {
"name": "<string>",
"description": "<string>",
"parameters": {},
"strict": True
},
"type": "function"
}
],
"additional_histories": [
{
"messages_and_choices": [
{
"content": "<string>",
"role": "developer",
"name": "<string>"
}
],
"tools": [
{
"function": {
"name": "<string>",
"description": "<string>",
"parameters": {},
"strict": True
},
"type": "function"
}
]
}
],
"reward": 0,
"initial_policy_version": 123,
"final_policy_version": 123,
"metrics": {},
"metadata": {},
"logs": ["<string>"],
"start_time": "2023-11-07T05:31:56Z"
}
],
"exceptions": [
{
"type": "<string>",
"message": "<string>",
"traceback": "<string>"
}
],
"metadata": {},
"metrics": {},
"logs": ["<string>"]
}
],
"experimental_config": {
"advantage_balance": 123,
"allow_training_without_logprobs": True,
"epsilon": 123,
"epsilon_high": 123,
"kimi_k2_tau": 123,
"kl_penalty_coef": 123,
"kl_penalty_reference_step": 123,
"kl_penalty_step_lag": 123,
"kl_ref_adapter_path": "<string>",
"learning_rate": 123,
"logprob_calculation_chunk_size": 123,
"mask_prob_ratio": True,
"max_negative_advantage_importance_sampling_weight": 123,
"normalize_advantages": True,
"num_trajectories_learning_rate_multiplier_power": 123,
"packed_sequence_length": 123,
"plot_tensors": True,
"ppo": True,
"precalculate_logprobs": True,
"scale_learning_rate_by_reward_std_dev": True,
"scale_rewards": True,
"truncated_importance_sampling": 123
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
model_id: '3c90c3cc-0d44-4b50-8888-8dd25736052a',
trajectory_groups: [
{
trajectories: [
{
exchanges: {},
messages_and_choices: [{content: '<string>', role: 'developer', name: '<string>'}],
tools: [
{
function: {name: '<string>', description: '<string>', parameters: {}, strict: true},
type: 'function'
}
],
additional_histories: [
{
messages_and_choices: [{content: '<string>', role: 'developer', name: '<string>'}],
tools: [
{
function: {name: '<string>', description: '<string>', parameters: {}, strict: true},
type: 'function'
}
]
}
],
reward: 0,
initial_policy_version: 123,
final_policy_version: 123,
metrics: {},
metadata: {},
logs: ['<string>'],
start_time: '2023-11-07T05:31:56Z'
}
],
exceptions: [{type: '<string>', message: '<string>', traceback: '<string>'}],
metadata: {},
metrics: {},
logs: ['<string>']
}
],
experimental_config: {
advantage_balance: 123,
allow_training_without_logprobs: true,
epsilon: 123,
epsilon_high: 123,
kimi_k2_tau: 123,
kl_penalty_coef: 123,
kl_penalty_reference_step: 123,
kl_penalty_step_lag: 123,
kl_ref_adapter_path: '<string>',
learning_rate: 123,
logprob_calculation_chunk_size: 123,
mask_prob_ratio: true,
max_negative_advantage_importance_sampling_weight: 123,
normalize_advantages: true,
num_trajectories_learning_rate_multiplier_power: 123,
packed_sequence_length: 123,
plot_tensors: true,
ppo: true,
precalculate_logprobs: true,
scale_learning_rate_by_reward_std_dev: true,
scale_rewards: true,
truncated_importance_sampling: 123
}
})
};
fetch('https://api.training.wandb.ai/v1/preview/training-jobs', 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.training.wandb.ai/v1/preview/training-jobs",
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([
'model_id' => '3c90c3cc-0d44-4b50-8888-8dd25736052a',
'trajectory_groups' => [
[
'trajectories' => [
[
'exchanges' => [
],
'messages_and_choices' => [
[
'content' => '<string>',
'role' => 'developer',
'name' => '<string>'
]
],
'tools' => [
[
'function' => [
'name' => '<string>',
'description' => '<string>',
'parameters' => [
],
'strict' => true
],
'type' => 'function'
]
],
'additional_histories' => [
[
'messages_and_choices' => [
[
'content' => '<string>',
'role' => 'developer',
'name' => '<string>'
]
],
'tools' => [
[
'function' => [
'name' => '<string>',
'description' => '<string>',
'parameters' => [
],
'strict' => true
],
'type' => 'function'
]
]
]
],
'reward' => 0,
'initial_policy_version' => 123,
'final_policy_version' => 123,
'metrics' => [
],
'metadata' => [
],
'logs' => [
'<string>'
],
'start_time' => '2023-11-07T05:31:56Z'
]
],
'exceptions' => [
[
'type' => '<string>',
'message' => '<string>',
'traceback' => '<string>'
]
],
'metadata' => [
],
'metrics' => [
],
'logs' => [
'<string>'
]
]
],
'experimental_config' => [
'advantage_balance' => 123,
'allow_training_without_logprobs' => true,
'epsilon' => 123,
'epsilon_high' => 123,
'kimi_k2_tau' => 123,
'kl_penalty_coef' => 123,
'kl_penalty_reference_step' => 123,
'kl_penalty_step_lag' => 123,
'kl_ref_adapter_path' => '<string>',
'learning_rate' => 123,
'logprob_calculation_chunk_size' => 123,
'mask_prob_ratio' => true,
'max_negative_advantage_importance_sampling_weight' => 123,
'normalize_advantages' => true,
'num_trajectories_learning_rate_multiplier_power' => 123,
'packed_sequence_length' => 123,
'plot_tensors' => true,
'ppo' => true,
'precalculate_logprobs' => true,
'scale_learning_rate_by_reward_std_dev' => true,
'scale_rewards' => true,
'truncated_importance_sampling' => 123
]
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"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.training.wandb.ai/v1/preview/training-jobs"
payload := strings.NewReader("{\n \"model_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trajectory_groups\": [\n {\n \"trajectories\": [\n {\n \"exchanges\": {},\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ],\n \"additional_histories\": [\n {\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ]\n }\n ],\n \"reward\": 0,\n \"initial_policy_version\": 123,\n \"final_policy_version\": 123,\n \"metrics\": {},\n \"metadata\": {},\n \"logs\": [\n \"<string>\"\n ],\n \"start_time\": \"2023-11-07T05:31:56Z\"\n }\n ],\n \"exceptions\": [\n {\n \"type\": \"<string>\",\n \"message\": \"<string>\",\n \"traceback\": \"<string>\"\n }\n ],\n \"metadata\": {},\n \"metrics\": {},\n \"logs\": [\n \"<string>\"\n ]\n }\n ],\n \"experimental_config\": {\n \"advantage_balance\": 123,\n \"allow_training_without_logprobs\": true,\n \"epsilon\": 123,\n \"epsilon_high\": 123,\n \"kimi_k2_tau\": 123,\n \"kl_penalty_coef\": 123,\n \"kl_penalty_reference_step\": 123,\n \"kl_penalty_step_lag\": 123,\n \"kl_ref_adapter_path\": \"<string>\",\n \"learning_rate\": 123,\n \"logprob_calculation_chunk_size\": 123,\n \"mask_prob_ratio\": true,\n \"max_negative_advantage_importance_sampling_weight\": 123,\n \"normalize_advantages\": true,\n \"num_trajectories_learning_rate_multiplier_power\": 123,\n \"packed_sequence_length\": 123,\n \"plot_tensors\": true,\n \"ppo\": true,\n \"precalculate_logprobs\": true,\n \"scale_learning_rate_by_reward_std_dev\": true,\n \"scale_rewards\": true,\n \"truncated_importance_sampling\": 123\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
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.training.wandb.ai/v1/preview/training-jobs")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"model_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trajectory_groups\": [\n {\n \"trajectories\": [\n {\n \"exchanges\": {},\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ],\n \"additional_histories\": [\n {\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ]\n }\n ],\n \"reward\": 0,\n \"initial_policy_version\": 123,\n \"final_policy_version\": 123,\n \"metrics\": {},\n \"metadata\": {},\n \"logs\": [\n \"<string>\"\n ],\n \"start_time\": \"2023-11-07T05:31:56Z\"\n }\n ],\n \"exceptions\": [\n {\n \"type\": \"<string>\",\n \"message\": \"<string>\",\n \"traceback\": \"<string>\"\n }\n ],\n \"metadata\": {},\n \"metrics\": {},\n \"logs\": [\n \"<string>\"\n ]\n }\n ],\n \"experimental_config\": {\n \"advantage_balance\": 123,\n \"allow_training_without_logprobs\": true,\n \"epsilon\": 123,\n \"epsilon_high\": 123,\n \"kimi_k2_tau\": 123,\n \"kl_penalty_coef\": 123,\n \"kl_penalty_reference_step\": 123,\n \"kl_penalty_step_lag\": 123,\n \"kl_ref_adapter_path\": \"<string>\",\n \"learning_rate\": 123,\n \"logprob_calculation_chunk_size\": 123,\n \"mask_prob_ratio\": true,\n \"max_negative_advantage_importance_sampling_weight\": 123,\n \"normalize_advantages\": true,\n \"num_trajectories_learning_rate_multiplier_power\": 123,\n \"packed_sequence_length\": 123,\n \"plot_tensors\": true,\n \"ppo\": true,\n \"precalculate_logprobs\": true,\n \"scale_learning_rate_by_reward_std_dev\": true,\n \"scale_rewards\": true,\n \"truncated_importance_sampling\": 123\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.training.wandb.ai/v1/preview/training-jobs")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"model_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trajectory_groups\": [\n {\n \"trajectories\": [\n {\n \"exchanges\": {},\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ],\n \"additional_histories\": [\n {\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ]\n }\n ],\n \"reward\": 0,\n \"initial_policy_version\": 123,\n \"final_policy_version\": 123,\n \"metrics\": {},\n \"metadata\": {},\n \"logs\": [\n \"<string>\"\n ],\n \"start_time\": \"2023-11-07T05:31:56Z\"\n }\n ],\n \"exceptions\": [\n {\n \"type\": \"<string>\",\n \"message\": \"<string>\",\n \"traceback\": \"<string>\"\n }\n ],\n \"metadata\": {},\n \"metrics\": {},\n \"logs\": [\n \"<string>\"\n ]\n }\n ],\n \"experimental_config\": {\n \"advantage_balance\": 123,\n \"allow_training_without_logprobs\": true,\n \"epsilon\": 123,\n \"epsilon_high\": 123,\n \"kimi_k2_tau\": 123,\n \"kl_penalty_coef\": 123,\n \"kl_penalty_reference_step\": 123,\n \"kl_penalty_step_lag\": 123,\n \"kl_ref_adapter_path\": \"<string>\",\n \"learning_rate\": 123,\n \"logprob_calculation_chunk_size\": 123,\n \"mask_prob_ratio\": true,\n \"max_negative_advantage_importance_sampling_weight\": 123,\n \"normalize_advantages\": true,\n \"num_trajectories_learning_rate_multiplier_power\": 123,\n \"packed_sequence_length\": 123,\n \"plot_tensors\": true,\n \"ppo\": true,\n \"precalculate_logprobs\": true,\n \"scale_learning_rate_by_reward_std_dev\": true,\n \"scale_rewards\": true,\n \"truncated_importance_sampling\": 123\n }\n}"
response = http.request(request)
puts response.read_body{
"id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"model_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"progress": 123,
"error_message": "<string>",
"training_start": "2023-11-07T05:31:56Z",
"training_end": "2023-11-07T05:31:56Z",
"created_at": "2023-11-07T05:31:56Z",
"updated_at": "2023-11-07T05:31:56Z"
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}Create RL Training Job
Create a new RL (Reinforcement Learning) training job.
POST
/
v1
/
preview
/
training-jobs
Create Rl Training Job
curl --request POST \
--url https://api.training.wandb.ai/v1/preview/training-jobs \
--header 'Authorization: Bearer <token>' \
--header 'Content-Type: application/json' \
--data '
{
"model_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"trajectory_groups": [
{
"trajectories": [
{
"exchanges": {},
"messages_and_choices": [
{
"content": "<string>",
"role": "developer",
"name": "<string>"
}
],
"tools": [
{
"function": {
"name": "<string>",
"description": "<string>",
"parameters": {},
"strict": true
},
"type": "function"
}
],
"additional_histories": [
{
"messages_and_choices": [
{
"content": "<string>",
"role": "developer",
"name": "<string>"
}
],
"tools": [
{
"function": {
"name": "<string>",
"description": "<string>",
"parameters": {},
"strict": true
},
"type": "function"
}
]
}
],
"reward": 0,
"initial_policy_version": 123,
"final_policy_version": 123,
"metrics": {},
"metadata": {},
"logs": [
"<string>"
],
"start_time": "2023-11-07T05:31:56Z"
}
],
"exceptions": [
{
"type": "<string>",
"message": "<string>",
"traceback": "<string>"
}
],
"metadata": {},
"metrics": {},
"logs": [
"<string>"
]
}
],
"experimental_config": {
"advantage_balance": 123,
"allow_training_without_logprobs": true,
"epsilon": 123,
"epsilon_high": 123,
"kimi_k2_tau": 123,
"kl_penalty_coef": 123,
"kl_penalty_reference_step": 123,
"kl_penalty_step_lag": 123,
"kl_ref_adapter_path": "<string>",
"learning_rate": 123,
"logprob_calculation_chunk_size": 123,
"mask_prob_ratio": true,
"max_negative_advantage_importance_sampling_weight": 123,
"normalize_advantages": true,
"num_trajectories_learning_rate_multiplier_power": 123,
"packed_sequence_length": 123,
"plot_tensors": true,
"ppo": true,
"precalculate_logprobs": true,
"scale_learning_rate_by_reward_std_dev": true,
"scale_rewards": true,
"truncated_importance_sampling": 123
}
}
'import requests
url = "https://api.training.wandb.ai/v1/preview/training-jobs"
payload = {
"model_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"trajectory_groups": [
{
"trajectories": [
{
"exchanges": {},
"messages_and_choices": [
{
"content": "<string>",
"role": "developer",
"name": "<string>"
}
],
"tools": [
{
"function": {
"name": "<string>",
"description": "<string>",
"parameters": {},
"strict": True
},
"type": "function"
}
],
"additional_histories": [
{
"messages_and_choices": [
{
"content": "<string>",
"role": "developer",
"name": "<string>"
}
],
"tools": [
{
"function": {
"name": "<string>",
"description": "<string>",
"parameters": {},
"strict": True
},
"type": "function"
}
]
}
],
"reward": 0,
"initial_policy_version": 123,
"final_policy_version": 123,
"metrics": {},
"metadata": {},
"logs": ["<string>"],
"start_time": "2023-11-07T05:31:56Z"
}
],
"exceptions": [
{
"type": "<string>",
"message": "<string>",
"traceback": "<string>"
}
],
"metadata": {},
"metrics": {},
"logs": ["<string>"]
}
],
"experimental_config": {
"advantage_balance": 123,
"allow_training_without_logprobs": True,
"epsilon": 123,
"epsilon_high": 123,
"kimi_k2_tau": 123,
"kl_penalty_coef": 123,
"kl_penalty_reference_step": 123,
"kl_penalty_step_lag": 123,
"kl_ref_adapter_path": "<string>",
"learning_rate": 123,
"logprob_calculation_chunk_size": 123,
"mask_prob_ratio": True,
"max_negative_advantage_importance_sampling_weight": 123,
"normalize_advantages": True,
"num_trajectories_learning_rate_multiplier_power": 123,
"packed_sequence_length": 123,
"plot_tensors": True,
"ppo": True,
"precalculate_logprobs": True,
"scale_learning_rate_by_reward_std_dev": True,
"scale_rewards": True,
"truncated_importance_sampling": 123
}
}
headers = {
"Authorization": "Bearer <token>",
"Content-Type": "application/json"
}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {Authorization: 'Bearer <token>', 'Content-Type': 'application/json'},
body: JSON.stringify({
model_id: '3c90c3cc-0d44-4b50-8888-8dd25736052a',
trajectory_groups: [
{
trajectories: [
{
exchanges: {},
messages_and_choices: [{content: '<string>', role: 'developer', name: '<string>'}],
tools: [
{
function: {name: '<string>', description: '<string>', parameters: {}, strict: true},
type: 'function'
}
],
additional_histories: [
{
messages_and_choices: [{content: '<string>', role: 'developer', name: '<string>'}],
tools: [
{
function: {name: '<string>', description: '<string>', parameters: {}, strict: true},
type: 'function'
}
]
}
],
reward: 0,
initial_policy_version: 123,
final_policy_version: 123,
metrics: {},
metadata: {},
logs: ['<string>'],
start_time: '2023-11-07T05:31:56Z'
}
],
exceptions: [{type: '<string>', message: '<string>', traceback: '<string>'}],
metadata: {},
metrics: {},
logs: ['<string>']
}
],
experimental_config: {
advantage_balance: 123,
allow_training_without_logprobs: true,
epsilon: 123,
epsilon_high: 123,
kimi_k2_tau: 123,
kl_penalty_coef: 123,
kl_penalty_reference_step: 123,
kl_penalty_step_lag: 123,
kl_ref_adapter_path: '<string>',
learning_rate: 123,
logprob_calculation_chunk_size: 123,
mask_prob_ratio: true,
max_negative_advantage_importance_sampling_weight: 123,
normalize_advantages: true,
num_trajectories_learning_rate_multiplier_power: 123,
packed_sequence_length: 123,
plot_tensors: true,
ppo: true,
precalculate_logprobs: true,
scale_learning_rate_by_reward_std_dev: true,
scale_rewards: true,
truncated_importance_sampling: 123
}
})
};
fetch('https://api.training.wandb.ai/v1/preview/training-jobs', 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.training.wandb.ai/v1/preview/training-jobs",
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([
'model_id' => '3c90c3cc-0d44-4b50-8888-8dd25736052a',
'trajectory_groups' => [
[
'trajectories' => [
[
'exchanges' => [
],
'messages_and_choices' => [
[
'content' => '<string>',
'role' => 'developer',
'name' => '<string>'
]
],
'tools' => [
[
'function' => [
'name' => '<string>',
'description' => '<string>',
'parameters' => [
],
'strict' => true
],
'type' => 'function'
]
],
'additional_histories' => [
[
'messages_and_choices' => [
[
'content' => '<string>',
'role' => 'developer',
'name' => '<string>'
]
],
'tools' => [
[
'function' => [
'name' => '<string>',
'description' => '<string>',
'parameters' => [
],
'strict' => true
],
'type' => 'function'
]
]
]
],
'reward' => 0,
'initial_policy_version' => 123,
'final_policy_version' => 123,
'metrics' => [
],
'metadata' => [
],
'logs' => [
'<string>'
],
'start_time' => '2023-11-07T05:31:56Z'
]
],
'exceptions' => [
[
'type' => '<string>',
'message' => '<string>',
'traceback' => '<string>'
]
],
'metadata' => [
],
'metrics' => [
],
'logs' => [
'<string>'
]
]
],
'experimental_config' => [
'advantage_balance' => 123,
'allow_training_without_logprobs' => true,
'epsilon' => 123,
'epsilon_high' => 123,
'kimi_k2_tau' => 123,
'kl_penalty_coef' => 123,
'kl_penalty_reference_step' => 123,
'kl_penalty_step_lag' => 123,
'kl_ref_adapter_path' => '<string>',
'learning_rate' => 123,
'logprob_calculation_chunk_size' => 123,
'mask_prob_ratio' => true,
'max_negative_advantage_importance_sampling_weight' => 123,
'normalize_advantages' => true,
'num_trajectories_learning_rate_multiplier_power' => 123,
'packed_sequence_length' => 123,
'plot_tensors' => true,
'ppo' => true,
'precalculate_logprobs' => true,
'scale_learning_rate_by_reward_std_dev' => true,
'scale_rewards' => true,
'truncated_importance_sampling' => 123
]
]),
CURLOPT_HTTPHEADER => [
"Authorization: Bearer <token>",
"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.training.wandb.ai/v1/preview/training-jobs"
payload := strings.NewReader("{\n \"model_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trajectory_groups\": [\n {\n \"trajectories\": [\n {\n \"exchanges\": {},\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ],\n \"additional_histories\": [\n {\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ]\n }\n ],\n \"reward\": 0,\n \"initial_policy_version\": 123,\n \"final_policy_version\": 123,\n \"metrics\": {},\n \"metadata\": {},\n \"logs\": [\n \"<string>\"\n ],\n \"start_time\": \"2023-11-07T05:31:56Z\"\n }\n ],\n \"exceptions\": [\n {\n \"type\": \"<string>\",\n \"message\": \"<string>\",\n \"traceback\": \"<string>\"\n }\n ],\n \"metadata\": {},\n \"metrics\": {},\n \"logs\": [\n \"<string>\"\n ]\n }\n ],\n \"experimental_config\": {\n \"advantage_balance\": 123,\n \"allow_training_without_logprobs\": true,\n \"epsilon\": 123,\n \"epsilon_high\": 123,\n \"kimi_k2_tau\": 123,\n \"kl_penalty_coef\": 123,\n \"kl_penalty_reference_step\": 123,\n \"kl_penalty_step_lag\": 123,\n \"kl_ref_adapter_path\": \"<string>\",\n \"learning_rate\": 123,\n \"logprob_calculation_chunk_size\": 123,\n \"mask_prob_ratio\": true,\n \"max_negative_advantage_importance_sampling_weight\": 123,\n \"normalize_advantages\": true,\n \"num_trajectories_learning_rate_multiplier_power\": 123,\n \"packed_sequence_length\": 123,\n \"plot_tensors\": true,\n \"ppo\": true,\n \"precalculate_logprobs\": true,\n \"scale_learning_rate_by_reward_std_dev\": true,\n \"scale_rewards\": true,\n \"truncated_importance_sampling\": 123\n }\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Authorization", "Bearer <token>")
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.training.wandb.ai/v1/preview/training-jobs")
.header("Authorization", "Bearer <token>")
.header("Content-Type", "application/json")
.body("{\n \"model_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trajectory_groups\": [\n {\n \"trajectories\": [\n {\n \"exchanges\": {},\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ],\n \"additional_histories\": [\n {\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ]\n }\n ],\n \"reward\": 0,\n \"initial_policy_version\": 123,\n \"final_policy_version\": 123,\n \"metrics\": {},\n \"metadata\": {},\n \"logs\": [\n \"<string>\"\n ],\n \"start_time\": \"2023-11-07T05:31:56Z\"\n }\n ],\n \"exceptions\": [\n {\n \"type\": \"<string>\",\n \"message\": \"<string>\",\n \"traceback\": \"<string>\"\n }\n ],\n \"metadata\": {},\n \"metrics\": {},\n \"logs\": [\n \"<string>\"\n ]\n }\n ],\n \"experimental_config\": {\n \"advantage_balance\": 123,\n \"allow_training_without_logprobs\": true,\n \"epsilon\": 123,\n \"epsilon_high\": 123,\n \"kimi_k2_tau\": 123,\n \"kl_penalty_coef\": 123,\n \"kl_penalty_reference_step\": 123,\n \"kl_penalty_step_lag\": 123,\n \"kl_ref_adapter_path\": \"<string>\",\n \"learning_rate\": 123,\n \"logprob_calculation_chunk_size\": 123,\n \"mask_prob_ratio\": true,\n \"max_negative_advantage_importance_sampling_weight\": 123,\n \"normalize_advantages\": true,\n \"num_trajectories_learning_rate_multiplier_power\": 123,\n \"packed_sequence_length\": 123,\n \"plot_tensors\": true,\n \"ppo\": true,\n \"precalculate_logprobs\": true,\n \"scale_learning_rate_by_reward_std_dev\": true,\n \"scale_rewards\": true,\n \"truncated_importance_sampling\": 123\n }\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://api.training.wandb.ai/v1/preview/training-jobs")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Authorization"] = 'Bearer <token>'
request["Content-Type"] = 'application/json'
request.body = "{\n \"model_id\": \"3c90c3cc-0d44-4b50-8888-8dd25736052a\",\n \"trajectory_groups\": [\n {\n \"trajectories\": [\n {\n \"exchanges\": {},\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ],\n \"additional_histories\": [\n {\n \"messages_and_choices\": [\n {\n \"content\": \"<string>\",\n \"role\": \"developer\",\n \"name\": \"<string>\"\n }\n ],\n \"tools\": [\n {\n \"function\": {\n \"name\": \"<string>\",\n \"description\": \"<string>\",\n \"parameters\": {},\n \"strict\": true\n },\n \"type\": \"function\"\n }\n ]\n }\n ],\n \"reward\": 0,\n \"initial_policy_version\": 123,\n \"final_policy_version\": 123,\n \"metrics\": {},\n \"metadata\": {},\n \"logs\": [\n \"<string>\"\n ],\n \"start_time\": \"2023-11-07T05:31:56Z\"\n }\n ],\n \"exceptions\": [\n {\n \"type\": \"<string>\",\n \"message\": \"<string>\",\n \"traceback\": \"<string>\"\n }\n ],\n \"metadata\": {},\n \"metrics\": {},\n \"logs\": [\n \"<string>\"\n ]\n }\n ],\n \"experimental_config\": {\n \"advantage_balance\": 123,\n \"allow_training_without_logprobs\": true,\n \"epsilon\": 123,\n \"epsilon_high\": 123,\n \"kimi_k2_tau\": 123,\n \"kl_penalty_coef\": 123,\n \"kl_penalty_reference_step\": 123,\n \"kl_penalty_step_lag\": 123,\n \"kl_ref_adapter_path\": \"<string>\",\n \"learning_rate\": 123,\n \"logprob_calculation_chunk_size\": 123,\n \"mask_prob_ratio\": true,\n \"max_negative_advantage_importance_sampling_weight\": 123,\n \"normalize_advantages\": true,\n \"num_trajectories_learning_rate_multiplier_power\": 123,\n \"packed_sequence_length\": 123,\n \"plot_tensors\": true,\n \"ppo\": true,\n \"precalculate_logprobs\": true,\n \"scale_learning_rate_by_reward_std_dev\": true,\n \"scale_rewards\": true,\n \"truncated_importance_sampling\": 123\n }\n}"
response = http.request(request)
puts response.read_body{
"id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"model_id": "3c90c3cc-0d44-4b50-8888-8dd25736052a",
"progress": 123,
"error_message": "<string>",
"training_start": "2023-11-07T05:31:56Z",
"training_end": "2023-11-07T05:31:56Z",
"created_at": "2023-11-07T05:31:56Z",
"updated_at": "2023-11-07T05:31:56Z"
}{
"detail": [
{
"loc": [
"<string>"
],
"msg": "<string>",
"type": "<string>"
}
]
}Authorizations
Bearer authentication header of the form Bearer <token>, where <token> is your auth token.
Body
application/json
Response
Successful Response
Schema for TrainingJob response.
Training job status enumeration.
Available options:
PENDING, RUNNING, COMPLETED, FAILED, CANCELLED Was this page helpful?
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