mosec 0.6.1

Model Serving made Efficient in the Cloud

Model Serving made Efficient in the Cloud.

Introduction

Mosec is a high-performance and flexible model serving framework for building ML model-enabled backend and microservices. It bridges the gap between any machine learning models you just trained and the efficient online service API.

• Highly performant: web layer and task coordination built with Rust 🦀, which offers blazing speed in addition to efficient CPU utilization powered by async I/O
• Ease of use: user interface purely in Python 🐍, by which users can serve their models in an ML framework-agnostic manner using the same code as they do for offline testing
• Dynamic batching: aggregate requests from different users for batched inference and distribute results back
• Pipelined stages: spawn multiple processes for pipelined stages to handle CPU/GPU/IO mixed workloads
• Cloud friendly: designed to run in the cloud, with the model warmup, graceful shutdown, and Prometheus monitoring metrics, easily managed by Kubernetes or any container orchestration systems
• Do one thing well: focus on the online serving part, users can pay attention to the model optimization and business logic

Installation

Mosec requires Python 3.7 or above. Install the latest PyPI package with:

pip install -U mosec

Usage

We demonstrate how Mosec can help you easily host a pre-trained stable diffusion model as a service. You need to install diffusers and transformers as prerequisites:

pip install --upgrade diffusers[torch] transformers

Write the server

Firstly, we import the libraries and set up a basic logger to better observe what happens.

from io import BytesIO
from typing import List

import torch  # type: ignore
from diffusers import StableDiffusionPipeline  # type: ignore

from mosec import Server, Worker, get_logger
from mosec.mixin import MsgpackMixin

logger = get_logger()

Then, we build an API for clients to query a text prompt and obtain an image based on the stable-diffusion-v1-5 model in just 3 steps.

1. Define your service as a class which inherits mosec.Worker. Here we also inherit MsgpackMixin to employ the msgpack serialization format^(a).

2. Inside the __init__ method, initialize your model and put it onto the corresponding device. Optionally you can assign self.example with some data to warm up^(b) the model. Note that the data should be compatible with your handler's input format, which we detail next.

3. Override the forward method to write your service handler^(c), with the signature forward(self, data: Any | List[Any]) -> Any | List[Any]. Receiving/returning a single item or a tuple depends on whether dynamic batching^(d) is configured.

class StableDiffusion(MsgpackMixin, Worker):
    def __init__(self):
        self.pipe = StableDiffusionPipeline.from_pretrained(
            "runwayml/stable-diffusion-v1-5", torch_dtype=torch.float16
        )
        device = "cuda" if torch.cuda.is_available() else "cpu"
        self.pipe = self.pipe.to(device)
        self.example = ["useless example prompt"] * 4  # warmup (bs=4)

    def forward(self, data: List[str]) -> List[memoryview]:
        logger.debug("generate images for %s", data)
        res = self.pipe(data)
        logger.debug("NSFW: %s", res[1])
        images = []
        for img in res[0]:
            dummy_file = BytesIO()
            img.save(dummy_file, format="JPEG")
            images.append(dummy_file.getbuffer())
        return images

Note

(a) In this example we return an image in the binary format, which JSON does not support (unless encoded with base64 that makes it longer). Hence, msgpack suits our need better. If we do not inherit MsgpackMixin, JSON will be used by default. In other words, the protocol of the service request/response can either be msgpack or JSON.

(b) Warm-up usually helps to allocate GPU memory in advance. If the warm-up example is specified, the service will only be ready after the example is forwarded through the handler. However, if no example is given, the first request's latency is expected to be longer. The example should be set as a single item or a tuple depending on what forward expects to receive. Moreover, in the case where you want to warm up with multiple different examples, you may set multi_examples (demo here).

(c) This example shows a single-stage service, where the StableDiffusion worker directly takes in client's prompt request and responds the image. Thus the forward can be considered as a complete service handler. However, we can also design a multi-stage service with workers doing different jobs (e.g., downloading images, forward model, post-processing) in a pipeline. In this case, the whole pipeline is considered as the service handler, with the first worker taking in the request and the last worker sending out the response. The data flow between workers is done by inter-process communication.

(d) Since dynamic batching is enabled in this example, the forward method will wishfully receive a list of string, e.g., ['a cute cat playing with a red ball', 'a man sitting in front of a computer', ...], aggregated from different clients for batch inference, improving the system throughput.

Finally, we append the worker to the server to construct a single-stage workflow (multiple stages can be pipelined to further boost the throughput, see this example), and specify the number of processes we want it to run in parallel (num=1), and the maximum batch size (max_batch_size=4, the maximum number of requests dynamic batching will accumulate before timeout; timeout is defined with the flag --wait in milliseconds, meaning the longest time Mosec waits until sending the batch to the Worker).

if __name__ == "__main__":
    server = Server()
    # 1) `num` specify the number of processes that will be spawned to run in parallel.
    # 2) By configuring the `max_batch_size` with the value > 1, the input data in your
    # `forward` function will be a list (batch); otherwise, it's a single item.
    server.append_worker(StableDiffusion, num=1, max_batch_size=4, max_wait_time=10)
    server.run()

Run the server

The above snippets are merged in our example file. You may directly run at the project root level. We first have a look at the command line arguments (explanations here):

python examples/stable_diffusion/server.py --help

Then let's start the server with debug logs:

python examples/stable_diffusion/server.py --debug

And in another terminal, test it:

python examples/stable_diffusion/client.py --prompt "a cute cat playing with a red ball" --output cat.jpg --port 8000

You will get an image named "cat.jpg" in the current directory.

You can check the metrics:

curl http://127.0.0.1:8000/metrics

That's it! You have just hosted your stable-diffusion model as a service! 😉

Examples

More ready-to-use examples can be found in the Example section. It includes:

• Multi-stage workflow demo: a simple echo demo even without any ML model.
• Shared memory IPC: inter-process communication with shared memory.
• Customized GPU allocation: deploy multiple replicas, each using different GPUs.
• Customized metrics: record your own metrics for monitoring.
• Jax jitted inference: just-in-time compilation speeds up the inference.
• PyTorch deep learning models:
  • sentiment analysis: infer the sentiment of a sentence.
  • image recognition: categorize a given image.
  • stable diffusion: generate images based on texts, with msgpack serialization.

Configuration

• Dynamic batching
  • max_batch_size is configured when you append_worker (make sure inference with the max value won't cause the out-of-memory in GPU).
  • --wait (default=10ms) is configured through CLI arguments (this usually should <= one batch inference duration).
  • If enabled, it will collect a batch either when it reaches the max_batch_size or the wait time.
• Check the arguments doc.

Deployment

• This may require some shared memory, remember to set the --shm-size flag if you are using docker.
• This service doesn't require Gunicorn or NGINX, but you can certainly use the ingress controller. BTW, it should be the PID 1 process in the container since it controls multiple processes.
• Remember to collect the metrics.
  • mosec_service_batch_size_bucket shows the batch size distribution.
  • mosec_service_batch_duration_second_bucket shows the duration of dynamic batching for each connection in each stage (starts from receiving the first task).
  • mosec_service_process_duration_second_bucket shows the duration of processing for each connection in each stage (including the IPC time but excluding the mosec_service_batch_duration_second_bucket).
  • mosec_service_remaining_task shows the number of currently processing tasks.
  • mosec_service_throughput shows the service throughput.
• Stop the service with SIGINT or SIGTERM since it has the graceful shutdown logic.

Adopters

Here are some of the companies and individual users that are using Mosec:

• MOSS: An open sourced conversational language model like ChatGPT.
• TensorChord: A platform for building and deploying AI models.

Contributing

We welcome any kind of contribution. Please give us feedback by raising issues or discussing on Discord. You could also directly contribute your code and pull request!

To start develop, you can use envd to create an isolated and clean Python & Rust environment. Check the envd-docs or build.envd for more information.

Qualitative Comparison^*

	Batcher	Pipeline	Parallel	I/O Format(1)	Framework(2)	Backend	Activity
TF Serving	✅	✅	✅	Limited(a)	Heavily TF	C++
Triton	✅	✅	✅	Limited	Multiple	C++
MMS	✅	❌	✅	Limited	Heavily MX	Java
BentoML	✅	❌	❌	Limited(b)	Multiple	Python
Streamer	✅	❌	✅	Customizable	Agnostic	Python
Flask(3)	❌	❌	❌	Customizable	Agnostic	Python
Mosec	✅	✅	✅	Customizable	Agnostic	Rust

^{*As accessed on 08 Oct 2021. By no means is this comparison showing that other frameworks are inferior, but rather it is used to illustrate the trade-off. The information is not guaranteed to be absolutely accurate. Please let us know if you find anything that may be incorrect.}

^{(1): Data format of the service's request and response. "Limited" in the sense that the framework has pre-defined requirements on the format. (2): Supported machine learning frameworks. "Heavily" means the serving framework is designed towards a specific ML framework. Thus it is hard, if not impossible, to adapt to others. "Multiple" means the serving framework provides adaptation to several existing ML frameworks. "Agnostic" means the serving framework does not necessarily care about the ML framework. Hence it supports all ML frameworks (in Python). (3): Flask is a representative of general purpose web frameworks to host ML models.}