curl --request POST \
  --url https://api.shadeform.ai/v1/templates/save \
  --header 'Content-Type: application/json' \
  --header 'X-API-KEY: <api-key>' \
  --data '
{
  "name": "My Template",
  "description": "A template for ML workloads",
  "public": true,
  "auto_delete": {
    "date_threshold": "2006-01-02T15:04:05-07:00",
    "spend_threshold": "3.14"
  },
  "alert": {
    "date_threshold": "2006-01-02T15:04:05-07:00",
    "spend_threshold": "3.14"
  },
  "volume_mount": {
    "auto": true
  },
  "tags": [
    "ml",
    "pytorch"
  ],
  "envs": [
    {
      "name": "HUGGING_FACE_HUB_TOKEN",
      "value": "hugging_face_api_token"
    }
  ],
  "networking": {
    "ufw_rules": [
      {
        "rule": "allow",
        "from_ip": "192.168.1.0/24",
        "to_ip": "10.0.0.0/8",
        "port": "80",
        "proto": "tcp"
      }
    ]
  }
}
'

import requests

url = "https://api.shadeform.ai/v1/templates/save"

payload = {
    "name": "My Template",
    "description": "A template for ML workloads",
    "public": True,
    "auto_delete": {
        "date_threshold": "2006-01-02T15:04:05-07:00",
        "spend_threshold": "3.14"
    },
    "alert": {
        "date_threshold": "2006-01-02T15:04:05-07:00",
        "spend_threshold": "3.14"
    },
    "volume_mount": { "auto": True },
    "tags": ["ml", "pytorch"],
    "envs": [
        {
            "name": "HUGGING_FACE_HUB_TOKEN",
            "value": "hugging_face_api_token"
        }
    ],
    "networking": { "ufw_rules": [
            {
                "rule": "allow",
                "from_ip": "192.168.1.0/24",
                "to_ip": "10.0.0.0/8",
                "port": "80",
                "proto": "tcp"
            }
        ] }
}
headers = {
    "X-API-KEY": "<api-key>",
    "Content-Type": "application/json"
}

response = requests.post(url, json=payload, headers=headers)

print(response.text)

const options = {
  method: 'POST',
  headers: {'X-API-KEY': '<api-key>', 'Content-Type': 'application/json'},
  body: JSON.stringify({
    name: 'My Template',
    description: 'A template for ML workloads',
    public: true,
    auto_delete: {date_threshold: '2006-01-02T15:04:05-07:00', spend_threshold: '3.14'},
    alert: {date_threshold: '2006-01-02T15:04:05-07:00', spend_threshold: '3.14'},
    volume_mount: {auto: true},
    tags: ['ml', 'pytorch'],
    envs: [{name: 'HUGGING_FACE_HUB_TOKEN', value: 'hugging_face_api_token'}],
    networking: {
      ufw_rules: [
        {
          rule: 'allow',
          from_ip: '192.168.1.0/24',
          to_ip: '10.0.0.0/8',
          port: '80',
          proto: 'tcp'
        }
      ]
    }
  })
};

fetch('https://api.shadeform.ai/v1/templates/save', 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.shadeform.ai/v1/templates/save",
  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([
    'name' => 'My Template',
    'description' => 'A template for ML workloads',
    'public' => true,
    'auto_delete' => [
        'date_threshold' => '2006-01-02T15:04:05-07:00',
        'spend_threshold' => '3.14'
    ],
    'alert' => [
        'date_threshold' => '2006-01-02T15:04:05-07:00',
        'spend_threshold' => '3.14'
    ],
    'volume_mount' => [
        'auto' => true
    ],
    'tags' => [
        'ml',
        'pytorch'
    ],
    'envs' => [
        [
                'name' => 'HUGGING_FACE_HUB_TOKEN',
                'value' => 'hugging_face_api_token'
        ]
    ],
    'networking' => [
        'ufw_rules' => [
                [
                                'rule' => 'allow',
                                'from_ip' => '192.168.1.0/24',
                                'to_ip' => '10.0.0.0/8',
                                'port' => '80',
                                'proto' => 'tcp'
                ]
        ]
    ]
  ]),
  CURLOPT_HTTPHEADER => [
    "Content-Type: application/json",
    "X-API-KEY: <api-key>"
  ],
]);

$response = curl_exec($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.shadeform.ai/v1/templates/save"

	payload := strings.NewReader("{\n  \"name\": \"My Template\",\n  \"description\": \"A template for ML workloads\",\n  \"public\": true,\n  \"auto_delete\": {\n    \"date_threshold\": \"2006-01-02T15:04:05-07:00\",\n    \"spend_threshold\": \"3.14\"\n  },\n  \"alert\": {\n    \"date_threshold\": \"2006-01-02T15:04:05-07:00\",\n    \"spend_threshold\": \"3.14\"\n  },\n  \"volume_mount\": {\n    \"auto\": true\n  },\n  \"tags\": [\n    \"ml\",\n    \"pytorch\"\n  ],\n  \"envs\": [\n    {\n      \"name\": \"HUGGING_FACE_HUB_TOKEN\",\n      \"value\": \"hugging_face_api_token\"\n    }\n  ],\n  \"networking\": {\n    \"ufw_rules\": [\n      {\n        \"rule\": \"allow\",\n        \"from_ip\": \"192.168.1.0/24\",\n        \"to_ip\": \"10.0.0.0/8\",\n        \"port\": \"80\",\n        \"proto\": \"tcp\"\n      }\n    ]\n  }\n}")

	req, _ := http.NewRequest("POST", url, payload)

	req.Header.Add("X-API-KEY", "<api-key>")
	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.shadeform.ai/v1/templates/save")
  .header("X-API-KEY", "<api-key>")
  .header("Content-Type", "application/json")
  .body("{\n  \"name\": \"My Template\",\n  \"description\": \"A template for ML workloads\",\n  \"public\": true,\n  \"auto_delete\": {\n    \"date_threshold\": \"2006-01-02T15:04:05-07:00\",\n    \"spend_threshold\": \"3.14\"\n  },\n  \"alert\": {\n    \"date_threshold\": \"2006-01-02T15:04:05-07:00\",\n    \"spend_threshold\": \"3.14\"\n  },\n  \"volume_mount\": {\n    \"auto\": true\n  },\n  \"tags\": [\n    \"ml\",\n    \"pytorch\"\n  ],\n  \"envs\": [\n    {\n      \"name\": \"HUGGING_FACE_HUB_TOKEN\",\n      \"value\": \"hugging_face_api_token\"\n    }\n  ],\n  \"networking\": {\n    \"ufw_rules\": [\n      {\n        \"rule\": \"allow\",\n        \"from_ip\": \"192.168.1.0/24\",\n        \"to_ip\": \"10.0.0.0/8\",\n        \"port\": \"80\",\n        \"proto\": \"tcp\"\n      }\n    ]\n  }\n}")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.shadeform.ai/v1/templates/save")

http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true

request = Net::HTTP::Post.new(url)
request["X-API-KEY"] = '<api-key>'
request["Content-Type"] = 'application/json'
request.body = "{\n  \"name\": \"My Template\",\n  \"description\": \"A template for ML workloads\",\n  \"public\": true,\n  \"auto_delete\": {\n    \"date_threshold\": \"2006-01-02T15:04:05-07:00\",\n    \"spend_threshold\": \"3.14\"\n  },\n  \"alert\": {\n    \"date_threshold\": \"2006-01-02T15:04:05-07:00\",\n    \"spend_threshold\": \"3.14\"\n  },\n  \"volume_mount\": {\n    \"auto\": true\n  },\n  \"tags\": [\n    \"ml\",\n    \"pytorch\"\n  ],\n  \"envs\": [\n    {\n      \"name\": \"HUGGING_FACE_HUB_TOKEN\",\n      \"value\": \"hugging_face_api_token\"\n    }\n  ],\n  \"networking\": {\n    \"ufw_rules\": [\n      {\n        \"rule\": \"allow\",\n        \"from_ip\": \"192.168.1.0/24\",\n        \"to_ip\": \"10.0.0.0/8\",\n        \"port\": \"80\",\n        \"proto\": \"tcp\"\n      }\n    ]\n  }\n}"

response = http.request(request)
puts response.read_body

{
  "id": "d290f1ee-6c54-4b01-90e6-d701748f0851"
}