#grpc-client #model #http #another #server #framework #jams

jams-client

A HTTP and gRPC client library for interacting with J.A.M.S - Just Another Model Server

2 releases

new 0.2.2 Oct 27, 2024
0.2.1 Oct 23, 2024
0.2.0 Oct 22, 2024
0.1.0 Oct 17, 2024

#433 in HTTP server

Download history 106/week @ 2024-10-11 322/week @ 2024-10-18 149/week @ 2024-10-25

577 downloads per month

Apache-2.0

30KB
652 lines

JAMS-CLIENT

A HTTP and gRPC client library for interacting with J.A.M.S - Just Another Model Server written in Rust 🦀

Installation

Add the following to cargo.toml

jams-client = "0.1"

Usage

Start J.A.M.S by following the instructions here

Both HTTP and gRPC have the same API with the only difference in client creation

use jams_client::*;

// Create client
let client = http::ApiClient::new(get_url()).unwrap();

// For GRPC client
// let client = grpc::ApiClient::new(get_url()).unwrap();

// Predict
let model_name = "titanic_model".to_string();
let model_input = serde_json::json!(
        {
            "pclass": ["1", "3"],
            "sex": ["male", "female"],
            "age": [22.0, 23.79929292929293],
            "sibsp": ["0", "1", ],
            "parch": ["0", "0"],
            "fare": [151.55, 14.4542],
            "embarked": ["S", "C"],
            "class": ["First", "Third"],
            "who": ["man", "woman"],
            "adult_male": ["True", "False"],
            "deck": ["Unknown", "Unknown"],
            "embark_town": ["Southampton", "Cherbourg"],
            "alone": ["True", "False"]
        }
)
.to_string();
let resp = client.predict(model_name, model_input).await;
let predictions = resp.unwrap().to_vec() // use values


// Health Check
let resp = client.health_check().await;

// Get Models
let result = client.get_models().await;

// Add model - <MODEL_FRAMEWORK>-<MODEL_NAME>
let resp = client.add_model("pytorch-my_awesome_californiahousing_model".to_string()).await

// Update Model
let resp = client.update_model("titanic_model".to_string()).await;

// Delete Model
client.delete_model("my_awesome_penguin_model".to_string()).await.unwrap();

Dependencies

~7–18MB
~253K SLoC