serve VS deepsparse

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

Interesting company. Yannic Kilcher interviewed Nir Shavit last year and they went into some depth: https://www.youtube.com/watch?v=0PAiQ1jTN5k DeepSparse is on GitHub: https://github.com/neuralmagic/deepsparse

sparsity https://github.com/neuralmagic/deepsparse

For 1), what is the easiest way to speed up inference (assume only PyTorch and primarily GPU but also some CPU)? I have been using ONNX and Torchscript but there is a bit of a learning curve and sometimes it can be tricky to get the model to actually work. Is there anything else worth trying? I am enthused by things like TorchDynamo (although I have not tested it extensively) due to its apparent ease of use. I also saw the post yesterday about Kernl using (OpenAI) Triton kernels to speed up transformer models which also looks interesting. Are things like SageMaker Neo or NeuralMagic worth trying? My only reservation with some of these is they still seem to be pretty model/architecture specific. I am a little reluctant to put much time into these unless I know others have had some success first.

You should look into deepsparse, they are working on delivering GPU level performance on consumer CPUs with some great results: https://github.com/neuralmagic/deepsparse. There is a great interview with the founder, Nir Shavit here: https://piped.kavin.rocks/watch?v=0PAiQ1jTN5k

Utilizing the oBERT research we published at Neural Magic and some further iteration, we’ve enabled an increase in NLP performance of 175X while retaining 99% accuracy on the question-answering task in MLPerf. A combination of distillation, layer dropping, quantization, and unstructured pruning with oBERT enabled these large performance gains through the DeepSparse Engine. All of our contributions and research are open-sourced or free to use. Read through the oBERT paper on arxiv, try out the research in SparseML, and dive into the writeup to learn more about how we achieved these impressive results and utilize them for your own use cases!

Open-source projects leveraged by nebullvm include OpenVINO, TensorRT, Intel Neural Compressor, SparseML and DeepSparse, Apache TVM, ONNX Runtime, TFlite and XLA. A huge thank you to the open-source community for developing and maintaining these amazing projects.

BERT-Large (345 million parameters) is now faster than the much smaller DistilBERT (66 million parameters) all while retaining the accuracy of the much larger BERT-Large model! We made this possible with Intel Labs by applying cutting-edge sparsification and quantization research from their Prune Once For All paper and utilizing it in the DeepSparse engine. It makes BERT-Large 12x smaller while delivering 8x latency speedup on commodity CPUs. We open-sourced the research in SparseML; run through the overview here and give it a try!

And benchmark/deploy with 8X better performance in DeepSparse!

Hi _Arsenie, this runs the deepsparse.server command for multiple models. and btw, we recently updated the READMEs for the Deepsparse Engine https://github.com/neuralmagic/deepsparse