serving VS serve

Yet another TEDIOUS BATTLE: Python vs. C++/C stack.

This project gained popularity due to the HIGH DEMAND for running large models with 1B+ parameters, like `llama`. Python dominates the interface and training ecosystem, but prior to llama.cpp, non-ML professionals showed little interest in a fast C++ interface library. While existing solutions like tensorflow-serving [1] in C++ were sufficiently fast with GPU support, llama.cpp took the initiative to optimize for CPU and trim unnecessary code, essentially code-golfing and sacrificing some algorithm correctness for improved performance, which isn't favored by "ML research".

NOTE: In my opinion, a true pioneer was DarkNet, which implemented the YOLO model series and significantly outperformed others [2]. Same trick basically like llama.cpp

[1] https://github.com/tensorflow/serving

I mean, probably - it's written in C++ https://github.com/tensorflow/serving

We’re looking into that solution at the moment, the issue I’m referring to is related to this https://github.com/tensorflow/serving/issues/1948 we’ll know if the plug-in approach works for our uses soon but haven’t started looking into implementing it yet

Which ML framework do you use? Tensorflow has https://github.com/tensorflow/serving. You could also use the Rust bindings to load a saved model and expose it using one of the Rust HTTP servers. It doesn't matter whether you trained your model in Python as long as you export its saved model.

Most likely it's a model server running something like https://github.com/tensorflow/serving and if there isn't a lot of load, the resource could kill some of its tasks. I wouldn't imagine it's sitting around pondering deep thoughts.

https://github.com/tensorflow/serving

https://thenewstack.io/tutorial-deploying-tensorflow-models-...

GitHub

You serve models via https://www.tensorflow.org/tfx/guide/serving which is written entirely in C++ (https://github.com/tensorflow/serving/tree/master/tensorflow_serving/model_servers), no Python on the serving path or in the shipped product.

What's wrong with just using Torchserve[1]? We've been using it to serve embedding models in production.

[1] https://pytorch.org/serve/

Looks like you are already up to speed loading LLaMa models which is great. Assuming this is Hugging Face PyTorch checkpoint, I think it should be possible to spin up a TorchServe instance which has in-built support for API access and HF Transformers. Since scale and latency aren’t a big concern for you, this should be good enough start.

You could split the service in two, flask for the web part and a service to serve the model, I haven't used it but there is https://pytorch.org/serve/

I'm attempting to set up inference for a torchserve container, and it's really tough to figure out what's not allowing me to connect to my network with the ports that I'm trying to expose. I'm using Google Kubernetes Engine and Helm via tweaking one of the tutorials at [torchserve](github.com/pytorch/serve). Specifically, it's the GKE tutorial [here](https://github.com/pytorch/serve/tree/master/kubernetes).

I did a Space to showcase a bit the speedups we can have in a end-to-end case with TorchServe to deploy the model on a cloud instance (AWS EC2 g4dn, using one T4 GPU): https://huggingface.co/spaces/fxmarty/bettertransformer-demo

For 2), I am aware of a few options. Triton inference server is an obvious one as is the ‘transformer-deploy’ version from LDS. My only reservation here is that they require the model compilation or are architecture specific. I am aware of others like Bento, Ray serving and TorchServe. Ideally I would have something that allows any (PyTorch model) to be used without the extra compilation effort (or at least optionally) and has some convenience things like ease of use, easy to deploy, easy to host multiple models and can perform some dynamic batching. Anyway, I am really interested to hear people's experience here as I know there are now quite a few options! Any help is appreciated! Disclaimer - I have no affiliation or are connected in any way with the libraries or companies listed here. These are just the ones I know of. Thanks in advance.

If you built the model using pytorch or tensorflow, I'd suggest using torchserve or TF serving to serve the model as its own "microservice," then query it from your django app. Among other things, it will make retraining and updating your model a lot easier.

GitHub