Release Notes - 0.6.0

FuriosaAI SDK 0.6.0 is a major release. It includes 234 PRs on performance improvements, added functionalities, and bug fixes, as well as approximately 900 commits.

How to upgrade

If you are using the APT repositories, you easily upgrade with the instructions below: More detailed instructions can be found at Driver, Firmware, and Runtime Installation.

apt-get update && \
apt-get install furiosa-driver-pdma furiosa-libnpu-warboy furiosa-libnux

pip uninstall furiosa-sdk-quantizer furiosa-sdk-runtime furiosa-sdk-validator && \
pip install --upgrade furiosa-sdk

Major changes

The kernel driver (furiosa-driver-pdma) has been upgraded to 1.2.2, and the user-level driver (furiosa-libnpu-warboy) has been upgraded to 0.5.2, thereby providing more stable and higher NPU performance. Other major changes include the following:

Compiler

Addition of NPU accelerated operators (see List of Supported Operators for NPU Acceleration for full list of accelerated operators)
- Space-to-depth (CRD mode)
- Transpose
- Slice (height axis only)
- Concat (height axis only)
- Grouped Convolution (if groups <= 128)
Improvements to significantly reduce frequency of CPU tasks in models with operators that require large memory usage (reduced execution time)

Quantizer

Improve model quantization process to ensure idempotency
Remove PyTorch reliance
Improve code quality by removing multiple Pylint warnings
Upgrade multiple library dependencies (e.g. Numpy -> 1.21.5, Pyyaml -> 6.0.0)

Python SDK

Python SDK project structure change
- furiosa-sdk-runtime -> furiosa-sdk
- furiosa-sdk-quantizer -> furiosa-quantizer
- furiosa-sdk-validator -> furiosa-litmus
Validator, a package that checks for model compatibility with Furiosa SDK, is renamed to litmus. Installation instruction has also been updated accordingly.

See furiosa litmus (Checking for model compatibility) for more detailed usage instructions.

$ pip install 'furiosa-sdk[litmus]'

Furiosa Serving: Addition of FastAPI-based advanced serving library

furiosa-serving is based on FastAPI. It allows you to easily add Python-based business logic or image pre/postprocessing code, before or after executing model inference API.

You can install using the following instructions.

$ pip install 'furiosa-sdk[serving]'

The usage example is shown below. You can find more detailed instructions at furiosa-serving Github.

from typing import Dict

from fastapi import File, UploadFile
from furiosa.server.utils.thread import synchronous
from furiosa.serving import ServeAPI, ServeModel
import numpy as np


serve = ServeAPI()


model: ServeModel = synchronous(serve.model)(
    'imagenet',
    location='./examples/assets/models/image_classification.onnx'
)

@model.post("/models/imagenet/infer")
async def infer(image: UploadFile = File(...)) -> Dict:
    # Convert image to Numpy array with your preprocess() function
    tensors: List[np.ndarray] = preprocess(image)

    # Infer from ServeModel
    result: List[np.ndarray] = await model.predict(tensors)

    # Classify model from numpy array with your postprocess() function
    response: Dict = postprocess(result)

    return response