Get inference application template

Retrieves detailed information about a specific machine learning application template from the catalog. The response includes the application’s metadata, documentation, tags, and a complete set of components with configuration options, compatible flavors, and deployment capabilities — all necessary for building and customizing an AI application.

GET /cloud/v3/inference/applications/catalog/{application_name}

Get inference application template

cURL

curl --request GET \
  --url https://api.gcore.com/cloud/v3/inference/applications/catalog/{application_name} \
  --header 'Authorization: <api-key>'

import requests

url = "https://api.gcore.com/cloud/v3/inference/applications/catalog/{application_name}"

headers = {"Authorization": "<api-key>"}

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

print(response.text)

const options = {method: 'GET', headers: {Authorization: '<api-key>'}};

fetch('https://api.gcore.com/cloud/v3/inference/applications/catalog/{application_name}', 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.gcore.com/cloud/v3/inference/applications/catalog/{application_name}",
  CURLOPT_RETURNTRANSFER => true,
  CURLOPT_ENCODING => "",
  CURLOPT_MAXREDIRS => 10,
  CURLOPT_TIMEOUT => 30,
  CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
  CURLOPT_CUSTOMREQUEST => "GET",
  CURLOPT_HTTPHEADER => [
    "Authorization: <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"
	"net/http"
	"io"
)

func main() {

	url := "https://api.gcore.com/cloud/v3/inference/applications/catalog/{application_name}"

	req, _ := http.NewRequest("GET", url, nil)

	req.Header.Add("Authorization", "<api-key>")

	res, _ := http.DefaultClient.Do(req)

	defer res.Body.Close()
	body, _ := io.ReadAll(res.Body)

	fmt.Println(string(body))

}

HttpResponse<String> response = Unirest.get("https://api.gcore.com/cloud/v3/inference/applications/catalog/{application_name}")
  .header("Authorization", "<api-key>")
  .asString();

require 'uri'
require 'net/http'

url = URI("https://api.gcore.com/cloud/v3/inference/applications/catalog/{application_name}")

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

request = Net::HTTP::Get.new(url)
request["Authorization"] = '<api-key>'

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

200

{
  "components": {
    "model": {
      "description": "Llama 3.2 1B is a multilingual large language model developed by Meta.",
      "display_name": "Llama 3.2 1B",
      "exposable": true,
      "license_url": "https://example.com/license",
      "parameters": {
        "prefix_cache": {
          "default_value": "1",
          "description": "Controls automatic prefix caching; set to 1 to speed up inference for repetitive prompts, or 0 to disable for diverse inputs.",
          "display_name": "prefix_cache",
          "examples": [
            "0",
            "1"
          ]
        }
      },
      "readme": "Detailed documentation",
      "required": true,
      "suitable_flavors": [
        {
          "name": "inference-16vcpu-232gib-1xh100-80gb"
        }
      ]
    }
  },
  "cover_url": "https://cdn.example.com",
  "description": "This is a demo application",
  "display_name": "Llama-3.2-1B-Instruct",
  "name": "demo-app",
  "readme": "Detailed documentation",
  "tags": {
    "category": "Model",
    "model_docs_endpoint": "/docs",
    "model_inference_engine": "vllm",
    "model_owner": "Meta",
    "model_type": "Text Generation"
  }
}

Authorizations

Authorization · string · header · required

API key for authentication. Make sure to include the word apikey, followed by a single space and then your token. Example: apikey 1234$abcdef

Path Parameters

application_name · string · required

Name of application in catalog

Pattern: ^[a-z0-9-.]{1,63}$

Response

200 - application/json

OK

components · Components · object · required

Configurable components of the application

Show child attributes

Example:

{
  "model": {
    "description": "Llama 3.2 1B is a multilingual large language model developed by Meta.",
    "display_name": "Llama 3.2 1B",
    "exposable": true,
    "license_url": "https://example.com/license",
    "parameters": {
      "prefix_cache": {
        "default_value": "1",
        "description": "Controls automatic prefix caching; set to 1 to speed up inference for repetitive prompts, or 0 to disable for diverse inputs.",
        "display_name": "prefix_cache",
        "examples": ["0", "1"]
      }
    },
    "readme": "Detailed documentation",
    "required": true,
    "suitable_flavors": [
      {
        "name": "inference-16vcpu-232gib-1xh100-80gb"
      }
    ]
  }
}

cover_url · string · required

URL to the application's cover image

Example:

"https://cdn.example.com"

description · string · required

Brief overview of the application

Example:

"This is a demo application"

display_name · string · required

Human-readable name of the application

Example:

"Llama-3.2-1B-Instruct"

name · string · required

Unique application identifier in the catalog

Example:

"demo-app"

readme · string · required

Detailed documentation or instructions

Example:

"Detailed documentation"

tags · Tags · object · required

Categorization key-value pairs

Show child attributes

Example:

{
  "category": "Model",
  "model_docs_endpoint": "/docs",
  "model_inference_engine": "vllm",
  "model_owner": "Meta",
  "model_type": "Text Generation"
}