Computing API

API Credentials

Obtaining API Credentials

Select API Credentials from the dropdown menu in the upper-right corner of the platform, and you will be redirected to the API credentials page.

Click Add Token, enter a custom token name, and the default validity period is 3650 days, which can be adjusted as needed.

After clicking OK, the newly generated token will appear in the list. Please note that the token is only generated once, so be sure to copy and save it properly! If the token is lost, you can create a new one or regenerate it.

Retrieving `region_id` and `clusterName`

region_id: Region ID
clusterName: Cluster name

Request

GET https://demo.cdc.datenfab.com/mdc/api/v1/regions

Response

Python Code:

import requests
headers = {
    "namespace": "$userid",
    "Authorization": "bearer $token"
}

url = "https://demo.cdc.datenfab.com/mdc/api/v1/regions"

response = requests.get(url, headers=headers)
print(response.content.decode())

Sample Response:

[{"id":"14c5a9e3-0d09-4aa0-99d3-c096b80cceaa","region":"北京一区","clusterName":"host-cluster","harborAddr":"images.neolink-ai.com","howlerID":"69e377a7-99d1-cca0-ef58-cfce14a336b2","createdAt":"2024-07-15T12:00:23.227+08:00","updatedAt":"2024-07-15T12:00:23.227+08:00"}]

Viewing GPU Specifications

Request

GET https://demo.cdc.datenfab.com/mdc/api/v1/skus/computings?region_id=$region_id

Response

Python Code：

import requests
headers = {
     "namespace" "$userid",
    "Authorization": "bearer $token"
}

url = "https://demo.cdc.datenfab.com/mdc/api/v1/skus/computings?region_id=14c5a9e3-0d09-4aa0-99d3-c096b80cceaa"
response = requests.get(url, headers=headers)

print(response.content.decode())

Sample Response:

[{"name":"H20","regionID":"14c5a9e3-0d09-4aa0-99d3-c096b80cceaa","cpuArch":"x86-64","cpuSeries":"Inter Xeon","cpucores":24,"memoryCount":192,"cpuVendor":"Inter","gpuSeries":"H20","gpuProduct":"H20","gpuResourceKey":"nvidia.com/gpu-h20","gpuCounts":1,"gpuMemory":96,"systemstorage":30,"datastorage":50,"skusID":"2","regionName":"北京一区","clusterName":"host-cluster","sellingType":"1","ratio":0.48,"discountID":"10000","discountStartTime":"2024-08-15T15:26:38+08:00","discountEndTime":"2024-10-01T00:05:00+08:00","price":10,"remainGPUCount":8},...]

Parameter Explanation:

Parameter Description name GPU model regionID Region ID gpuCounts Number of GPUs sellingType Billing type: 1 - Pay-as-you-go, 2 - Monthly, 3 - Weekly, 4 - Daily

Parameter	Description
name	GPU model
regionID	Region ID
gpuCounts	Number of GPUs
sellingType	Billing type: 1 - Pay-as-you-go, 2 - Monthly, 3 - Weekly, 4 - Daily

Creating a Computing Instance

Request

POST https://demo.cdc.datenfab.com/mdc/api/v1/clusters/$clusteName/instances

Body Parameters:

{
  "Cost": {
    "CostType": "",
    "CouponID": "",
    "DiskSKUID": "",
    "GPUSKUID": "",
    "CostTime": ""
  },
  "VolumeMount": [
    {
      "PathInContainer": "",
      "Size": ,
      "Type": ""
    }
  ],
  "namespace": "",
  "displayName": "",
  "image": "",
  "Region": "",
}

Parameter Explanation:

Name	Type	Required	Description
Cost	object	YES	GPU specification information
CostType	string	YES	Billing type: 1 - Pay-as-you-go, 2 - Monthly, etc.
CouponID	string	NO	Discount coupon ID (valid only for pay-as-you-go)
DiskSKUID	string	YES	Disk ID (currently only 1 available)
GPUSKUID	string	YES	GPU SKU ID (retrieved from GPU specifications)
CostTime	string	NO	Payment duration: e.g., 3 days: 3D, extend by +3D
VolumeMount	object	YES	Disk mount information
PathInContainer	string	NO	Default path /root/data
Size	integer	NO	Disk size in GB
Type	string	NO	Disk type (e.g., shared-filesystem)
namespace	string	YES	Namespace (same as user ID)
displayName	string	YES	Instance name
image	string	YES	Image name (official or private)
Region	string	NO	Default: Beijing Zone 1

Response

Python Code:

import requests
import json
headers = {
    "namespace" "$userid",
    "Authorization": "bearer $token"
}

url = "https://demo.cdc.datenfab.com/mdc/api/v1/clusters/host-cluster/instances"

body={
  "Cost": {
    "CostType": "1",
    "CouponID": "",
    "DiskSKUID": "1",
    "GPUSKUID": "66",
    "CostTime": ""
  },
  "VolumeMount": [
    {
      "PathInContainer": "/root/data",
      "Size": 50,
      "Type": "shared-filesystem"
    }
  ],
  "namespace": "xxx",
  "displayName": "test1",
  "image": "images.neolink-ai.com/matrixdc/tensorflow:2.15.0-python3.10-cuda12.1.0-cudnn8-devel-ubuntu22.04",
  "Region": ""
}
response = requests.post(url, json=body, headers=headers)

print(json.dumps(response.json(), indent=4))

Response:

{
    "name": "instance-ccffxxxx",
    "namespace": "xxx",
    "Region": "host-cluster",
    "displayName": "test1",
    "description": "",
    "annotations": {
        "demo.cdc.datenfab.com/cost-coupon-id": "",
        "demo.cdc.datenfab.com/cost-discount-id": "",
        "demo.cdc.datenfab.com/cost-disk-sku-id": "1",
        "demo.cdc.datenfab.com/cost-gpu-sku-id": "66",
        "demo.cdc.datenfab.com/cost-order-id": "94b9e467-9d34-45bf-8e8f-e04115cf58b4",
        "demo.cdc.datenfab.com/cost-time": "",
        "demo.cdc.datenfab.com/cost-type": "1",
        "demo.cdc.datenfab.com/description": "",
        "demo.cdc.datenfab.com/display-name": "test1",
        "demo.cdc.datenfab.com/gpu.machine": "4090",
        "demo.cdc.datenfab.com/gpu.resource.key": "nvidia.com/gpu-4090",
        "demo.cdc.datenfab.com/region": "host-cluster",
        "demo.cdc.datenfab.com/saveDataEnabled": "false",
        "demo.cdc.datenfab.com/unit-price": "250"
    },
    "uid": "8d40c2e6-a0b0-45c2-9b40-a0de065104f5",
    "image": "images.neolink-ai.com/matrixdc/tensorflow:2.15.0-python3.10-cuda12.1.0-cudnn8-devel-ubuntu22.04",
    "replicas": 1,
    "resources": {
        "cpu": "16",
        "memory": "96Gi",
        "GPUNum": "1",
        "GPUType": "4090"
    },
    "volumeMounts": [
        {
            "PathInContainer": "/",
            "Type": "local-filesystem",
            "Size": 30,
            "PVCName": "",
            "displayName": "\u7cfb\u7edf\u76d8",
            "filesystemType": "system"
        },
        {
            "PathInContainer": "/root/data",
            "Type": "shared-filesystem",
            "Size": 0,
            "PVCName": "volume-920020f7",
            "displayName": "\u78c1\u76d8-volume-920020f7",
            "filesystemType": "data"
        }
    ],
    "creationTimestamp": "2024-09-02T18:34:33+08:00",
    "updateTimestamp": "0001-01-01T00:00:00Z",
    "ScheduleStopTimestamp": "0001-01-01T00:00:00Z",
    "status": "Creating",
    "cost": {
        "CostType": "1",
        "CostTime": "",
        "DiscountID": "",
        "GPUSKUID": "66",
        "DiskSKUID": "1",
        "CouponID": ""
    }
}

Get Instance List

Request

GET https://demo.cdc.datenfab.com/mdc/api/v1/clusters/$clusterName/instances

Response

Python Code:

import requests
import json
headers = {
    "namespace" "$userid",
    "Authorization": "bearer $token"
}

url = "https://demo.cdc.datenfab.com/mdc/api/v1/clusters/host-cluster/instances"
response = requests.get(url, headers=headers)

data = response.json()

print(json.dumps(data, indent=4))

Response

{
  "hasNextPage": false,
  "nodes": [
    {
      "name": "instance-ccffxxxx",
      "namespace": "xxx",
      "Region": "host-cluster",
      "displayName": "test1",
      "description": "",
      "annotations": {
        "demo.cdc.datenfab.com/cost-coupon-id": "",
        "demo.cdc.datenfab.com/cost-discount-id": "",
        "demo.cdc.datenfab.com/cost-disk-sku-id": "1",
        "demo.cdc.datenfab.com/cost-gpu-sku-id": "66",
        "demo.cdc.datenfab.com/cost-order-id": "94b9e467-9d34-45bf-8e8f-e04115cf58b4",
        "demo.cdc.datenfab.com/cost-time": "",
        "demo.cdc.datenfab.com/cost-type": "1",
        "demo.cdc.datenfab.com/description": "",
        "demo.cdc.datenfab.com/display-name": "test1",
        "demo.cdc.datenfab.com/gpu.machine": "4090",
        "demo.cdc.datenfab.com/gpu.resource.key": "nvidia.com/gpu-4090",
        "demo.cdc.datenfab.com/region": "host-cluster",
        "demo.cdc.datenfab.com/saveDataEnabled": "false",
        "demo.cdc.datenfab.com/unit-price": "250"
      },
      "uid": "8d40c2e6-a0b0-45c2-9b40-a0de065104f5",
      "servers": {
        "Jupyterlab": "http://8d40c2e6-a0b0-45c2-9b40-a0de065104f5.gw.neolink-ai.com/jupyter?token=7d27443c5718e75bee2da9f92fe6a50c",
        "Tensorboard": "http://8d40c2e6-a0b0-45c2-9b40-a0de065104f5.gw.neolink-ai.com/monitor",
        "SSH": {
          "sshAddr": "ssh -p30099 root@8d40c2e6-a0b0-45c2-9b40-a0de065104f5.gws.demo.cdc.datenfab.com",
          "sshPassword": "kEyIX5R2dqJD"
        }
      },
      "image": "images.neolink-ai.com/matrixdc/tensorflow:2.15.0-python3.10-cuda12.1.0-cudnn8-devel-ubuntu22.04",
      "replicas": 1,
      "resources": {
        "cpu": "16",
        "memory": "96Gi",
        "GPUNum": "1",
        "GPUType": "4090"
      },
      "volumeMounts": [
        {
          "PathInContainer": "/",
          "Type": "local-filesystem",
          "Size": 30,
          "PVCName": "",
          "displayName": "\u7cfb\u7edf\u76d8",
          "filesystemType": "system"
        },
        {
          "PathInContainer": "/root/data",
          "Type": "shared-filesystem",
          "Size": 50,
          "PVCName": "volume-920020f7",
          "displayName": "\u78c1\u76d8-volume-920020f7",
          "filesystemType": "data"
        }
      ],
      "creationTimestamp": "2024-09-02T18:34:33+08:00",
      "updateTimestamp": "0001-01-01T00:00:00Z",
      "ScheduleStopTimestamp": "0001-01-01T00:00:00Z",
      "status": "Running",
      "cost": {
        "CostType": "1",
        "CostTime": "",
        "DiscountID": "",
        "GPUSKUID": "66",
        "DiskSKUID": "1",
        "CouponID": ""
      },
      "Pods": [
        {
          "Name": "instance-ccff84c2-dd6nm",
          "Containers": [
            {
              "Name": "worker"
            }
          ],
          "nodeScheduled": "gpu-4090-03",
          "ip": "172.31.213.79"
        }
      ]
    }
  ],
  "totalCount": 1
}

Get Instance List

Request

GET https://demo.cdc.datenfab.com/mdc/api/v1/clusters/$clusterName/instances/$instant_id

Response

Python Code:

import requests
import json
headers = {
    "namespace" :"$userid",
    "Authorization": "bearer $token"
}

url = "https://demo.cdc.datenfab.com/mdc/api/v1/clusters/host-cluster/instances/instance-4ca9xxxx"
response = requests.get(url, headers=headers)

data = response.json()

print(json.dumps(data, indent=4))

Response

{
    "name": "instance-4ca9xxxx",
    "namespace": "xxx",
    "Region": "host-cluster",
    "displayName": "test1",
    "description": "",
    "annotations": {
        "demo.cdc.datenfab.com/cost-coupon-id": "",
        "demo.cdc.datenfab.com/cost-discount-id": "",
        "demo.cdc.datenfab.com/cost-disk-sku-id": "1",
        "demo.cdc.datenfab.com/cost-gpu-sku-id": "66",
        "demo.cdc.datenfab.com/cost-order-id": "cf4c99ff-cf7f-4c86-8f15-87cf5fb2ba14",
        "demo.cdc.datenfab.com/cost-time": "",
        "demo.cdc.datenfab.com/cost-type": "1",
        "demo.cdc.datenfab.com/description": "",
        "demo.cdc.datenfab.com/display-name": "test1",
        "demo.cdc.datenfab.com/gpu.machine": "4090",
        "demo.cdc.datenfab.com/gpu.resource.key": "nvidia.com/gpu-4090",
        "demo.cdc.datenfab.com/region": "host-cluster",
        "demo.cdc.datenfab.com/unit-price": "250"
    },
    "uid": "a7d2cbab-591d-4482-95ea-2df994ed1b22",
    "servers": {
        "Jupyterlab": "http://a7d2cbab-591d-4482-95ea-2df994ed1b22.gw.neolink-ai.com/jupyter?token=23a7676a0d18451dee9c08c3649a8288",
        "Tensorboard": "http://a7d2cbab-591d-4482-95ea-2df994ed1b22.gw.neolink-ai.com/monitor",
        "SSH": {
            "sshAddr": "ssh -p30379 root@a7d2cbab-591d-4482-95ea-2df994ed1b22.gws.demo.cdc.datenfab.com",
            "sshPassword": "RuRbjZLWklIC"
        }
    },
    "image": "images.neolink-ai.com/matrixdc/tensorflow:2.15.0-python3.10-cuda12.1.0-cudnn8-devel-ubuntu22.04",
    "replicas": 1,
    "resources": {
        "cpu": "16",
        "memory": "96Gi",
        "GPUNum": "1",
        "GPUType": "4090"
    },
    "volumeMounts": [
        {
            "PathInContainer": "/",
            "Type": "local-filesystem",
            "Size": 30,
            "PVCName": "",
            "displayName": "\u7cfb\u7edf\u76d8",
            "filesystemType": "system"
        },
        {
            "PathInContainer": "/root/data",
            "Type": "shared-filesystem",
            "Size": 50,
            "PVCName": "volume-ed087526",
            "displayName": "\u78c1\u76d8-volume-ed087526",
            "filesystemType": "data"
        }
    ],
    "creationTimestamp": "2024-09-02T22:15:13+08:00",
    "updateTimestamp": "0001-01-01T00:00:00Z",
    "ScheduleStopTimestamp": "0001-01-01T00:00:00Z",
    "status": "Running",
    "cost": {
        "CostType": "1",
        "CostTime": "",
        "DiscountID": "",
        "GPUSKUID": "66",
        "DiskSKUID": "1",
        "CouponID": ""
    },
    "Pods": [
        {
            "Name": "instance-4ca97f58-6m9nv",
            "Containers": [
                {
                    "Name": "worker"
                }
            ],
            "nodeScheduled": "gpu-4090-03",
            "ip": "172.31.213.103"
        }
    ]
}

Stop Instance

Request

POST https://demo.cdc.datenfab.com/mdc/api/v1/clusters/$clusterName/instances/instance_name/power_off

Body Request Parameters:

body = {
    "gracePeriodSeconds": "",
    "needSnapshot": "",
    "imageName": "",
    "tag": ""
}

Description of the body parameter:

Name	Type	Required	Description
gracePeriodSeconds	string	NO	Grace period for terminating a pod in seconds. The system sends a stop command first, then waits for processes inside the container to finish before forcefully deleting it. Default is 10 seconds.
needSnapshot	string	NO	Whether to save the running container as an image upon shutdown. Default is False.
imageName	string	NO	Name of the saved image.
tag	string	NO	Version of the image.

Response

Python Code:

import requests
headers = {
    "namespace" :"$userid",
    "Authorization": "bearer $token"
}

url = "https://demo.cdc.datenfab.com/mdc/api/v1/clusters/host-cluster/instances/instance-3ba5xxxx/power_off"

body = {
    "gracePeriodSeconds": 10,
    "needSnapshot": True,
    "imageName": "image1",
    "tag": "v1.1.0"
}

response = requests.post(url,json=body,headers=headers)
print(response.content.decode())

Response

"ok"

Start Instance

Request

POST https://demo.cdc.datenfab.com/mdc/api/v1/clusters/$clusterName/instances/$instance_id/power_off

Response

import requests
headers = {
    "namespace" :"$userid",
    "Authorization": "bearer $token"
}

url = "https://demo.cdc.datenfab.com/mdc/api/v1/clusters/host-cluster/instances/instance-4ca9xxxx/power_on"

response = requests.post(url, headers=headers)
print(response.content.decode())

Delete Instance

Request

DELETE https://demo.cdc.datenfab.com/mdc/api/v1/clusters/$clusterName/instances?name=$instance_id

Response

Python Code:

import requests
headers = {
    "namespace" :"$userid",
    "Authorization": "bearer $token"
}

url = "https://demo.cdc.datenfab.com/mdc/api/v1/clusters/host-cluster/instances?name=instance-ccffxxxx"

response = requests.delete(url, headers=headers)

print(response.content.decode())

Response

"ok"

API Credentials​

Obtaining API Credentials​

Retrieving region_id and clusterName​

Request​

Response​

Viewing GPU Specifications​

Request​

Response​

Creating a Computing Instance​

Request​

Response​

Get Instance List​

Request​

Response​

Get Instance List​

Request​

Response​

Stop Instance​

Request​

Response​

Start Instance​

Request​

Response​

Delete Instance​

Request​

Response​

API Credentials

Obtaining API Credentials

Retrieving `region_id` and `clusterName`

Request

Response

Viewing GPU Specifications

Request

Response

Creating a Computing Instance

Request

Response

Get Instance List

Request

Response

Get Instance List

Request

Response

Stop Instance

Request

Response

Start Instance

Request

Response

Delete Instance

Request

Response