RESTful API • JSON-RPC / HTTPS • HTTP • TCP
Linux • MacOS • Windows • WebAssembly
C 99 • Python 3 • JavaScript 🔜

Tired of slow REST backends or overly complex gRPC setups? UCall aims to be as approachable as FastAPI but significantly faster and more broadly applicable.

pip install fastapi uvicorn

pip install ucall

from fastapi import FastAPI

server = FastAPI()

@server.get('/sum')
def sum(a: int, b: int):
    return a + b

import uvicorn
uvicorn.run(...)    

from ucall.server import Server

server = Server()

@server.get('/sum')
def sum(a: int, b: int):
    return a + b

server.run()    

It takes over a millisecond to handle a trivial FastAPI call on a recent 8-core CPU. In that time, light could have traveled 300 km through optics to the neighboring city or country, in my case. How does UCall compare to FastAPI and gRPC?

How can a tiny pet-project with just a couple thousand lines of code compete with two of the most established networking libraries? UCall stands on the shoulders of Giants:

• io_uring for interrupt-less IO.

  • io_uring_prep_read_fixed on 5.1+.
  • io_uring_prep_accept_direct on 5.19+.
  • io_uring_register_files_sparse on 5.19+.
  • IORING_SETUP_COOP_TASKRUN optional on 5.19+.
  • IORING_SETUP_SINGLE_ISSUER optional on 6.0+.

• SIMD-accelerated parsers with manual memory control.

  • simdjson to parse JSON faster than gRPC can unpack ProtoBuf.
  • Turbo-Base64 to decode binary values from a Base64 form.
  • picohttpparser to navigate HTTP headers.

You have already seen the latency of the round trip..., the throughput in requests per second..., want to see the bandwidth? Try yourself!

@server
def echo(data: bytes):
    return data

FastAPI supports native types, while UCall also supports numpy.ndarray, PIL.Image and other custom types. This comes handy when you build real applications or want to deploy Multi-Modal AI, like we do with UForm.

from ucall.server import Server
import ufrom

server = Server()
model = uform.get_model('unum-cloud/uform-vl-multilingual')

@server
def vectorize(description: str, photo: PIL.Image.Image) -> numpy.ndarray:
    image = model.preprocess_image(photo)
    tokens = model.preprocess_text(description)
    joint_embedding = model.encode_multimodal(image=image, text=tokens)

    return joint_embedding.cpu().detach().numpy()

UCall offers a Python Client class and a CLI tool for easy interaction with UCall servers.

from ucall.client import Client

client = Client()
# Explicit JSON-RPC call:
response = client({
    'method': 'vectorize',
    'params': {
        'description': description,
        'image': image,
    },
    'jsonrpc': '2.0',
    'id': 100,
})
# Or the same with syntactic sugar:
response = client.vectorize(description=description, image=image) 

Aside from the Python Client, we provide an easy-to-use Command Line Interface, which comes with pip install ucall. It allow you to call a remote server, upload files, with direct support for images and NumPy arrays. Translating previous example into a Bash script, to call the server on the same machine:

ucall vectorize description='Product description' -i image=./local/path.png

To address a remote server:

ucall vectorize description='Product description' -i image=./local/path.png --uri 0.0.0.0 -p 8545

To print the docs, use ucall -h:

usage: ucall [-h] [--uri URI] [--port PORT] [-f [FILE ...]] [-i [IMAGE ...]] [--positional [POSITIONAL ...]] method [kwargs ...]

UCall Client CLI

positional arguments:
  method                method name
  kwargs                method arguments

options:
  -h, --help            show this help message and exit
  --uri URI             server uri
  --port PORT           server port
  -f [FILE ...], --file [FILE ...]
                        method positional arguments
  -i [IMAGE ...], --image [IMAGE ...]
                        method positional arguments
  --positional [POSITIONAL ...]
                        method positional arguments

You can also explicitly annotate types, to distinguish integers, floats, and strings, to avoid ambiguity.

ucall auth id=256
ucall auth id:int=256
ucall auth id:str=256

We will leave bandwidth measurements to enthusiasts, but will share some more numbers. The general logic is that you can't squeeze high performance from Free-Tier machines. Currently AWS provides following options: t2.micro and t4g.small, on older Intel and newer Graviton 2 chips. This library is so fast, that it doesn't need more than 1 core, so you can run a fast server even on a tiny Free-Tier server!

In this case, every server was bombarded by requests from 1 or a fleet of 32 other instances in the same availability zone. If you want to reproduce those benchmarks, check out the sum examples on GitHub.

UCall provides an ABI-stable C 99 interface. To use it with the CMake build system:

include(FetchContent)
FetchContent_Declare(
    ucall
    GIT_REPOSITORY https://github.com/unum-cloud/ucall
    GIT_SHALLOW TRUE
)
FetchContent_MakeAvailable(ucall)
include_directories(${ucall_SOURCE_DIR}/include)

The C usage example is mouthful compared to Python. We wanted to make it as lightweight as possible and to allow optional arguments without dynamic allocations and named lookups. So unlike the Python layer, we expect the user to manually extract the arguments from the call context with ucall_param_named_i64(), and its siblings.

#include <cstdio.h>
#include <ucall/ucall.h>

static void sum(ucall_call_t call, ucall_callback_tag_t) {
    int64_t a{}, b{};
    char printed_sum[256]{};
    bool got_a = ucall_param_named_i64(call, "a", 0, &a);
    bool got_b = ucall_param_named_i64(call, "b", 0, &b);
    if (!got_a || !got_b)
        return ucall_call_reply_error_invalid_params(call);

    int len = snprintf(printed_sum, 256, "%ll", a + b);
    ucall_call_reply_content(call, printed_sum, len);
}

int main(int argc, char** argv) {

    ucall_server_t server{};
    ucall_config_t config{};

    ucall_init(&config, &server);
    ucall_add_procedure(server, "sum", &sum);
    ucall_take_calls(server, 0);
    ucall_free(server);
    return 0;
}