Megatron-LM VS server

GPU cluster scaling has come a long way. Just checkout the scaling plot here: https://github.com/NVIDIA/Megatron-LM

Google's PaLM, Facebook's LLaMA, Nvidia's Megatron, I am missing some surely and Apple sure has something cooking as well but these are the big ones, of course none of them are publicly available, but research papers are reputable. All of the ones mentioned should beat GPT-3 although GPT-3.5 (chatGPT) should be bit better and ability to search (Bing) should level the playing field even further, but Google's PaLM with search functionality should be clearly ahead. This is why people are excited about GPT-4, GPT-3 was way ahead of anyone else when it came out but others were able to catch up since, we'll see if GPT-4 will be another bing jump among LLMs.

Described a collaboration involving NVIDIA Megatron-LM and Microsoft DeepSpeed to create an efficient, scalable, 3D parallel system capable of combining data, pipeline and tensor-slicing-based parallelism.

Microsoft and NVIDIA present the Megatron-Turing Natural Language Generation model (MT-NLG), powered by DeepSpeed and Megatron, the largest and robust monolithic transformer language model trained with 530 billion parameters.

Nvidia's own implementation of Transformers, i.e, Megatron on NVIDIA's Selene supercomputer (where GPT-3 is possible too) -https://github.com/NVIDIA/Megatron-LM

I am wondering what is the best / most cost-efficient way to serve llama V2. - llama.cpp (is it production ready or just for playing around?) ? - Triton inference server ? - HF text generation inference ?

I am looking for something like [Triton Inference Server](https://github.com/triton-inference-server/server) or [TFX Serving](https://www.tensorflow.org/tfx/guide/serving), but in Rust. I came across [Orkon](https://github.com/vertexclique/orkhon) which seems to be dormant and a bunch of examples off of the [Awesome-Rust-MachineLearning](https://github.com/vaaaaanquish/Awesome-Rust-MachineLearning)

You've already mentioned Seldon Core which is well worth looking at but if you're just after the raw multi-model serving aspect rather than a fully-fledged deployment framework you should maybe take a look at the individual inference servers: Triton Inference Server and MLServer both support multi-model serving for a wide variety of frameworks (and custom python models). MLServer might be a better option as it has an MLFlow runtime but only you will be able to decide that. There also might be other inference servers that do MMS that I'm not aware of.

[1] https://docs.nvidia.com/launchpad/ai/chatbot/latest/chatbot-triton-overview.html[2] https://github.com/triton-inference-server/server[3] https://neptune.ai/blog/deploying-ml-models-on-gpu-with-kyle-morris[4] https://thechief.io/c/editorial/comparison-cloud-gpu-providers/[5] https://geekflare.com/best-cloud-gpu-platforms/

"TensorFlow has the better deployment infrastructure"

Tensorflow Serving is nice in that it's so tightly integrated with Tensorflow. As usual that goes both ways. It's so tightly coupled to Tensorflow if the mlops side of the solution is using Tensorflow Serving you're going to get "trapped" in the Tensorflow ecosystem (essentially).

For pytorch models (and just about anything else) I've been really enjoying Nvidia Triton Server[0]. Of course it further entrenches Nvidia and CUDA in the space (although you can execute models CPU only) but for a deployment today and the foreseeable future you're almost certainly going to be using a CUDA stack anyway.

Triton Server is very impressive and I'm always surprised to see how relatively niche it is.

[0] - https://github.com/triton-inference-server/server

Inference servers essentially turn a model running on CPU and/or GPU hardware into a microservice.

Many of them support the kserve API standard[0] that supports everything from model loading/unloading to (of course) inference requests across models, versions, frameworks, etc.

So in the case of Triton[1] you can have any number of different TensorFlow/torch/tensorrt/onnx/etc models, versions, and variants. You can have one or more Triton instances running on hardware with access to local GPUs (for this example). Then you can put standard REST and or grpc load balancers (or whatever you want) in front of them, hit them via another API, whatever.

Now all your applications need to do to perform inference is do an HTTP POST (or use a client[2]) for model input, Triton runs it on a GPU (or CPU if you want), and you get back whatever the model output is.

Not a sales pitch for Triton but it (like some others) can also do things like dynamic batching with QoS parameters, automated model profiling and performance optimization[3], really granular control over resources, response caching, python middleware for application/biz logic, accelerated media processing with Nvidia DALI, all kinds of stuff.

[0] - https://github.com/kserve/kserve

[1] - https://github.com/triton-inference-server/server

[2] - https://github.com/triton-inference-server/client

[3] - https://github.com/triton-inference-server/model_analyzer

Replit built Ghostwriter on the open source scene based on Salesforce’s Codegen, using Nvidia’s FasterTransformer and Triton server for highly optimized decoders, and the knowledge distillation process of the CodeGen model from two billion parameters to a faster model of one billion parameters.

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.

GitHub