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RBLNServe (Model Server)

RBLN SDK offers a model serving framework called RBLNServe, which provides RBLN model inference services using RBLN NPUs through REST and gRPC protocols. With RBLNServe, users do not need to manually employ RBLN Runtime libraries. Instead, users can run inferences through web-based interfaces, simplifying integration with other web services.

Installation

Before we start, please make sure you have prepared the following prerequisites in your system:

  • Ubuntu 20.04 LTS (Debian bullseye) or higher
  • RBLN NPUs equipped (e.g., RBLN ATOM)
  • Python (supports 3.8 - 3.10)
  • RBLN SDK (Driver, Compiler)

Then, install RBLNServe with pip using the following command. This requires access rights to Rebellions' private PyPI server:

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$ pip install -i https://pypi.rbln.ai/simple rblnserve

Usages

Command Line Interface

After installation, you can use rblnserve CLI with the following options:

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$ rblnserve --help
Usage: rblnserve [OPTIONS]

Options:
  --host TEXT                     IPv4 address to bind
  --rest-port INTEGER             REST server port to listen
  --grpc-port INTEGER             GRPC server port to listen
  --config-file TEXT              path to model config yaml file
  --version                       print RBLNServe version 
  --install-completion [bash|zsh|fish|powershell|pwsh]
                                  Install completion for the specified shell.
  --show-completion [bash|zsh|fish|powershell|pwsh]
                                  Show completion for the specified shell, to
                                  copy it or customize the installation.
  --help                          Show this message and exit.

Serving models

Before running the RBLNServe, you need to prepare the compiled model (how to compile). Here is the example code:

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import torch
import torchvision
import rebel

# Prepare the compiled model ('resnet50.rbln' in this example)
model_name = "resnet50"
input_name = "input0"
input_shape = [1,3,224,224]

weights = torchvision.models.get_model_weights(model_name).DEFAULT
model = getattr(torchvision.models, model_name)(weights=weights).eval()
compiled_model = rebel.compile_from_torch(model, [(input_name, input_shape, torch.float32)])
compiled_model.save(model_name+".rbln")

Based on the compiled model /path/to/your/model/resnet50.rbln, you can create a configuration file model_config.yaml as below. You can add multiple model configurations to serve multiple models concurrently.

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models:
  - name: my-model
    path: "/path/to/your/model/resnet50.rbln"
    version: 0.1.0  #optional
    description: "ResNet50 Test"  #optional

Now, you can run the server with the following command:

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$ rblnserve --config-file=model_config.yaml

    ____________ _      _   _  _____                     
    | ___ \ ___ \ |    | \ | |/  ___|                    
    | |_/ / |_/ / |    |  \| |\ `--.  ___ _ ____   _____ 
    |    /| ___ \ |    | . ` | `--. \/ _ \ '__\ \ / / _ \
    | |\ \| |_/ / |____| |\  |/\__/ /  __/ |   \ V /  __/
    \_| \_\____/\_____/\_| \_/\____/ \___|_|    \_/ \___|

2023-08-16 09:46:58,809 INFO:     model has been loaded: name='my-model' path='/path/to/your/model/resnet50.rbln' version='0.1.0' description='ResNet50 Test'
2023-08-16 09:46:58,822 INFO:     Started server process [38167]
2023-08-16 09:46:58,822 INFO:     Waiting for application startup.
2023-08-16 09:46:58,825 INFO:     GRPC server listening on 0.0.0.0:8081
2023-08-16 09:46:58,825 INFO:     Application startup complete.
2023-08-16 09:46:58,825 INFO:     Uvicorn running on http://0.0.0.0:8080 (Press CTRL+C to quit)

Requesting inference via REST API

Before requesting inference, you need to prepare the json payload (payload specification). Here is the example code:

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import urllib.request
import json
import torchvision

model_name = "resnet50"
input_name = "input0"
input_shape = [1,3,224,224]
weights = torchvision.models.get_model_weights(model_name).DEFAULT
preprocess = weights.transforms()

# Prepare the json payload ('tabby.json' in this example)
img_url = "https://rbln-public.s3.ap-northeast-2.amazonaws.com/images/tabby.jpg"
response = urllib.request.urlopen(img_url)
with open("tabby.jpg", "wb") as f:
    f.write(response.read())

img = torchvision.io.image.read_image("tabby.jpg")
img = preprocess(img).unsqueeze(0).numpy()
input_data = img.flatten().tolist()
payload = {
    "inputs": [
        {
            "name": input_name,
            "shape": input_shape,
            "datatype": "FP32",
            "data": input_data
        }
    ]
}
with open("tabby.json", "w") as f:
    json.dump(payload, f)

Based on the json payload example /path/to/your/payload/tabby.json, you can send a request using one of the following methods:

Python requests 
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import requests
import json
url = "http://localhost:8080/v2/models/my-model/versions/0.1.0/infer"
with open("/path/to/your/payload/tabby.json", "r") as f:
    payload = json.load(f)
response = requests.post(url, json=payload)
print(response.json())
NodeJS axios 
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var axios = require("axios");
var fs = require("fs");

var payload = fs.readFileSync("/path/to/your/payload/tabby.json");
var config = {
    method: "post",
    url: "http://localhost:8080/v2/models/my-model/versions/0.1.0/infer",
    headers: {
        "Content-Type": "application/json"
    },
    data: payload
}

axios(config)
.then(function (response) {
    console.log(response.data);
})
.catch(function (error) {
    console.error(error);
})
curl 
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$ curl -X POST -H "Content-Type: application/json" -d "@/path/to/your/payload/tabby.json" http://localhost:8080/v2/models/my-model/versions/0.1.0/infer

Requesting inference via gRPC API

Before using gRPC API, you need to generate pb2 codes based on the KServe grpc proto file. Please refer the gRPC documentation to generate your own pb2 codes for Python or any other languages you want. Otherwise, you can directly use pb2 modules provided by rblnserve from rblnserve.api.grpc.predict_v2_pb2 and rblnserve.api.grpc.predict_v2_pb2_grpc.

Assuming you've already prepared the input tensors as described above, you can use one of the following methods (or any languages and libraries supporting gRPC) to send a request:

Python grpcio 
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import grpc

# import your generated pb2 files
import your.modules.generated_pb2 as pb2
from your.modules.generated_pb2_grpc import GRPCInferenceServiceStub

channel = grpc.insecure_channel("localhost:8081")  # replace with your rblnserve grpc endpoint
stub = GRPCInferenceServiceStub(channel)

# prepare your preprocessed inputs
input_data = ...

request = pb2.ModelInferRequest(
    model_name="my-model",
    model_version="0.1.0",
    inputs=[
        pb2.ModelInferRequest.InferInputTensor(
            name="input0",
            shape=[1, 3, 224, 224],
            datatype="FP32",
            contents=pb2.InferTensorContents(fp32_contents=input_data),
        )
    ],
)
response = stub.ModelInfer(request)
NodeJS grpc-js 
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const grpc = require("@grpc/grpc-js");
const protoLoader = require("@grpc/proto-loader");

const packageDefinition = protoLoader.loadSync("path_to_proto_file.proto", {
  keepCase: true,
  longs: String,
  enums: String,
  defaults: true,
  oneofs: true,
});

const protoDescriptor = grpc.loadPackageDefinition(packageDefinition);
const service =
  protoDescriptor.your.modules.generated_pb2_grpc.GRPCInferenceService;

const client = new service(
  "localhost:8081", // replace with your rblnserve grpc endpoint
  grpc.credentials.createInsecure()
);

// prepare your preprocessed inputs
const input_data = ...

const request = {
  modelName: "my-model",
  modelVersion: "0.1.0",
  inputs: [
    {
      name: "input0",
      shape: [1, 3, 224, 224],
      datatype: "FP32",
      contents: { fp32Contents: input_data },
    },
  ],
};

client.ModelInfer(request, (error, response) => {
  if (error) {
    console.error(error);
    return;
  }
  console.log(response);
});

API endpoints

The API endpoints served by RBLNServe are compliant with KServe Predict Protocol V2.

REST API

endpoint description
GET /v2 Returns server metadata (link)
GET /v2/health/live Returns server liveness (link)
GET /v2/health/ready Returns server readiness (link)
GET /v2/models/{MODEL_NAME} Returns a model metadata specified by MODEL_NAME (link)
GET /v2/models/{MODEL_NAME}/ready Returns a model readiness specified by MODEL_NAME (link)
POST /v2/models/{MODEL_NAME}/infer Returns inference results of a model specified by MODEL_NAME (link)
GET /v2/models/{MODEL_NAME}/versions/{MODEL_VERSION} Returns a model metadata specified by MODEL_NAME and MODEL_VERSION (link)
GET /v2/models/{MODEL_NAME}/versions/{MODEL_VERSION}/ready Returns a model readiness specified by MODEL_NAME and MODEL_VERSION (link)
POST /v2/models/{MODEL_NAME}/versions/{MODEL_VERSION}/infer Returns inference results of a model specified by MODEL_NAME and MODEL_VERSION (link)

You can also review and test REST APIs as described in the Swagger documentation page, which is accessible through http://localhost:8080/docs:

Image

gRPC API

rpc description
ServerMetadata Returns server metadata (link)
ServerLive Returns server liveness (link)
ServerReady Returns server readiness (link)
ModelMetadata Returns a model metadata specified by name and version (link)
ModelReady Returns a model readiness specified by name and version (link)
ModelInfer Returns inference results of a model specified by name and version (link)

Note that RBLNServe gRPC server supports server reflection, so you can inspect the service definition using tools such as Postman.