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  4. Petal Length Example

Petal Length Model

This example runs a petal length model on both the XGBoost and the ONNX runtimes. For more information about the Petal Length model, see the Iris flower data set.

​​ Prerequisites

Before continuing, make sure you have:

​​ Create two new Constellation projects

Generate two new Constellation projects named petal-length-xgboost and petal-length-onnx by running the create command. Then run list to review the details of your newly created projects:


$ npx wrangler constellation project create "petal-length-xgboost" XGBoost

$ npx wrangler constellation project create "petal-length-onnx" ONNX

$ npx wrangler constellation project list


┌──────────────────────────────────────┬──────────────────────┬─────────┐
│ id                                   │ name                 │ runtime │
├──────────────────────────────────────┼──────────────────────┼─────────┤
│ 2193053a-af0a-40a6-b757-00fa73908ef6 │ petal-length-xgboost │ XGBoost │
│ 1193053a-9f0a-30a6-a757-30fa73908ef2 │ petal-length-onnx    │ ONNX    │
└──────────────────────────────────────┴──────────────────────┴─────────┘

​​ Create a new Worker

Create a new Worker named petal-length-worker. You will install Wrangler, the developer platform CLI, for Constellation.


$ mkdir petal-length-worker

$ cd petal-length-worker

$ npm init -f

$ npm install wrangler --save-dev

$ npx wrangler init

Answer Wrangler’s configuration questions:


Would you like to use git to manage this Worker?: N
Would you like to use TypeScript? Y
Would you like to install the type definitions for Workers into your package.json?: Y
Would you like to create a Worker at src/index.ts?: Fetch handler
Would you like us to write your first test with Vitest?: N

​​ Bind your Constellation project to your Worker

In your petal-length-worker, find your wrangler.toml file.

Bindings allow your Workers to interact with resources on the Cloudflare developer platform, such as your Constellation project. Create a binding between your petal-length-xgboost and petal-length-onnx Constellation projects and your petal-length-worker Worker in your petal-length-worker Worker’s wrangler.toml configuration file.

Substitute the project_id with the project IDs you generated after running npx wrangler constellation project list in Create a new Constellation project:

wrangler.toml
# Top-level configuration

name = "petal-length-worker"

main = "src/index.ts"

compatibility_date = "2023-03-14"

node_compat = true

workers_dev = true



constellation = [
    {binding = 'XGBOOST_CLASSIFIER', project_id = '2193053a-af0a-40a6-b757-00fa73908ef6'},
    {binding = 'ONNX_CLASSIFIER', project_id = '1193053a-9f0a-30a6-a757-30fa73908ef2'}

]

​​ Install the client API library

In your petal-length-worker Worker, install the client API library:


$ npm install @cloudflare/constellation --save-dev

​​ Upload models

Upload the Petal Length models for XGBoost and ONNX in your petal-length-xgboost and petal-length-onnx Constellation projects:


$ wget https://pub-244e7ff663764dd99f3290aad8ea0ba7.r2.dev/petals.json

$ wget https://pub-244e7ff663764dd99f3290aad8ea0ba7.r2.dev/petals.onnx

$ npx wrangler constellation model upload "petal-length-xgboost" "petals" petals.json

$ npx wrangler constellation model upload "petal-length-onnx" "petals" petals.onnx

$ npx wrangler constellation model list "petal-length-xgboost"


┌──────────────────────────────────────┬──────────────────────────────────────┬──────────────┐
│ id                                   │ project_id                           │ name         │
├──────────────────────────────────────┼──────────────────────────────────────┼──────────────┤
│ 939ac893-5e55-32c0-0223-929edb231929 │ 2193053a-af0a-40a6-b757-00fa73908ef6 │ petals       │
└──────────────────────────────────────┴──────────────────────────────────────┴──────────────┘


$ npx wrangler constellation model list "petal-length-onnx"


┌──────────────────────────────────────┬──────────────────────────────────────┬──────────────┐
│ id                                   │ project_id                           │ name         │
├──────────────────────────────────────┼──────────────────────────────────────┼──────────────┤
│ 12312cda-5e55-33c0-8ffe-34r24aa76a39 │ 1193053a-9f0a-30a6-a757-30fa73908ef2 │ petals       │
└──────────────────────────────────────┴──────────────────────────────────────┴──────────────┘

Take note of the id fields as this will be the model IDs.

​​ Code

With your Worker configured, begin coding in your petal-length-worker’s index.ts file.

Replace 939ac893-5e55-32c0-0223-929edb231929 and 12312cda-5e55-33c0-8ffe-34r24aa76a39 with your actual model IDs.

src/index.ts
import { Tensor, InferenceSession, TensorType } from "@cloudflare/constellation";



export default {
    async fetch(request: Request, env: Env): Promise<Response> {
        if (request.method == "POST") {
            let payload: any = await request.json();
            const xgboostSession = new InferenceSession(
                env.XGBOOST_CLASSIFIER,
                "939ac893-5e55-32c0-0223-929edb231929"
            );
            const onnxSession = new InferenceSession(
                env.ONNX_CLASSIFIER,
                "12312cda-5e55-33c0-8ffe-34r24aa76a39"
            );


            const tensorInput = new Tensor(
                TensorType.Float32,
                Array.prototype.concat(...payload.data),
                { shape: [payload.batch_size, payload.feature_size] }
            );


            const onnxOutputTensor = Object.values(await onnxSession.run([tensorInput]))[0];
            const xgboostOutputTensor = Object.values(
                await xgboostSession.run({ input: tensorInput })
            )[0];


            return new Response(
                JSON.stringify({
                    xgboost_prob: xgboostOutputTensor.value,
                    onnx_prob: onnxOutputTensor.value,
                })
            );
        }
        return new Response(
            `try curl http://127.0.0.1:9000 -H "Content-Type: application/json" -d '{"data":[4.8, 3.0, 1.4, 0.1], "batch_size": 1, "feature_size": 4}'`
        );
    },

};



export interface Env {
    XGBOOST_CLASSIFIER: any;
    ONNX_CLASSIFIER: any;

}

​​ Test your project

​​ Run wrangler dev

Start a local server to test your petal-length-worker Worker by running wrangler dev:


$ npx wrangler dev --remote
⬣ Listening at http://0.0.0.0:8787

To test the models, run the following command:


$ curl http://127.0.0.1:9000 -H "Content-Type: application/json" -d '{"data":[4.8, 3.0, 1.4, 0.1], "batch_size": 1, "feature_size": 4}'
{"xgboost_prob":[0.35374999046325684],"onnx_prob":[0.35374999046325684]}

As you can see, you get the predicted values from the XGBoost and the ONNX Petal Length models.

​​ Deploy your project

When you are ready, deploy your Worker:


$ npx wrangler deploy