Create Your First Environment

Next, you can create a environment by specifying the "authentication ID", "image ID" and "instance node name". You can configure additional resources such as CPU, memory, and storage, After the request is submitted, the user must wait for some time, and the instance creation progress can be monitored through a separate query API.

Example Request:

Replace {{API_TOKEN}} with your actual token.

For the "payment_type" parameter, there are three options:

• 1 represents USD

• 2 represents NMT

• 3 represents CNY

Curl

curl --location 'https://api.netmind.ai/v1/rentgpu/containers' \
--header 'Content-Type: application/json' \
--header 'Authorization: Bearer {{API_TOKEN}}' \
--data '
{
  "name": "demo_test",
  "auth_id": 0,
  "gpu": 1,
  "node_name": "...",
  "image_template_id": 1,
  "payment_type": 0
}'

Python

import requests
import json

url = "https://api.netmind.ai/v1/rentgpu/containers"

payload = json.dumps({
  "name": "demo_test",
  "auth_id": 0,
  "gpu": 1,
  "node_name": "...",
  "image_template_id": 0,
  "payment_type": 0
})
headers = {
  'Content-Type': 'application/json',
  'Authorization': 'Bearer {{API_TOKEN}}'
}

response = requests.request("POST", url, headers=headers, data=payload)

print(response.text)

Example Response:

{
    "name": "demo_test",
    "auth_id": 0,
    "gpu": 1,
    "node_name": "...",
    "type": 0,
    "image_template_id": 1,
    "enable_vscode": false,
    "enable_jupyter": false,
    "cpu": 20,
    "disk_size": 300,
    "memory": 40,
    "gpu_name": "NVIDIA_GeForce_RTX_3090",
    "id": 0,
    "ssh": null,
    "status": "PENDING",
    "exposed_service": null,
    "vscode_service": null,
    "jupyter_service": null
}