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server | TensorRT | |
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24 | 22 | |
7,314 | 9,065 | |
5.4% | 4.0% | |
9.5 | 5.0 | |
1 day ago | 10 days ago | |
Python | C++ | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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server
- FLaNK Weekly 08 Jan 2024
- Is there any open source app to load a model and expose API like OpenAI?
- "A matching Triton is not available"
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best way to serve llama V2 (llama.cpp VS triton VS HF text generation inference)
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 ?
- Triton Inference Server - Backend
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Single RTX 3080 or two RTX 3060s for deep learning inference?
For inference of CNNs, memory should really not be an issue. If it is a software engineering problem, not a hardware issue. FP16 or Int8 for weights is fine and weight size won’t increase due to the high resolution. And during inference memory used for hidden layer tensors can be reused as soon as the last consumer layer has been processed. You likely using something that is designed for training for inference and that blows up the memory requirement, or if you are using TensorRT or something like that, you need to be careful to avoid that every tasks loads their own copy of the library code into the GPU. Maybe look at https://github.com/triton-inference-server/server
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Machine Learning Inference Server in Rust?
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)
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Multi-model serving options
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.
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I mean,.. we COULD just make our own lol
[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/
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Why TensorFlow for Python is dying a slow death
"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
TensorRT
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AMD MI300X 30% higher performance than Nvidia H100, even with optimized stack
> It's not rocket science to implement matrix multiplication in any GPU.
You're right, it's harder. Saying this as someone who's done more work on the former than the latter. (I have, with a team, built a rocket engine. And not your school or backyard project size, but nozzle bigger than your face kind. I've also written CUDA kernels and boy is there a big learning curve to the latter that you gotta fundamentally rethink how you view a problem. It's unquestionable why CUDA devs are paid so much. Really it's only questionable why they aren't paid more)
I know it is easy to think this problem is easy, it really looks that way. But there's an incredible amount of optimization that goes into all of this and that's what's really hard. You aren't going to get away with just N for loops for a tensor rank N. You got to chop the data up, be intelligent about it, manage memory, how you load memory, handle many data types, take into consideration different results for different FMA operations, and a whole lot more. There's a whole lot of non-obvious things that result in high optimization (maybe obvious __after__ the fact, but that's not truthfully "obvious"). The thing is, the space is so well researched and implemented that you can't get away with naive implementations, you have to be on the bleeding edge.
Then you have to do that and make it reasonably usable for the programmer too, abstracting away all of that. Cuda also has a huge head start and momentum is not a force to be reckoned with (pun intended).
Look at TensorRT[0]. The software isn't even complete and it still isn't going to cover all neural networks on all GPUs. I've had stuff work on a V100 and H100 but not an A100, then later get fixed. They even have the "Apple Advantage" in that they have control of the hardware. I'm not certain AMD will have the same advantage. We talk a lot about the difficulties of being first mover, but I think we can also recognize that momentum is an advantage of being first mover. And it isn't one to scoff at.
[0] https://github.com/NVIDIA/TensorRT
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Getting SDXL-turbo running with tensorRT
(python demo_txt2img.py "a beautiful photograph of Mt. Fuji during cherry blossom"). https://github.com/NVIDIA/TensorRT/tree/release/8.6/demo/Diffusion
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Show HN: Ollama for Linux – Run LLMs on Linux with GPU Acceleration
- https://github.com/NVIDIA/TensorRT
TVM and other compiler-based approaches seem to really perform really well and make supporting different backends really easy. A good friend who's been in this space for a while told me llama.cpp is sort of a "hand crafted" version of what these compilers could output, which I think speaks to the craftmanship Georgi and the ggml team have put into llama.cpp, but also the opportunity to "compile" versions of llama.cpp for other model architectures or platforms.
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Nvidia Introduces TensorRT-LLM for Accelerating LLM Inference on H100/A100 GPUs
https://github.com/NVIDIA/TensorRT/issues/982
Maybe? Looks like tensorRT does work, but I couldn't find much.
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Train Your AI Model Once and Deploy on Any Cloud
highly optimized transformer-based encoder and decoder component, supported on pytorch, tensorflow and triton
TensorRT, custom ml framework/ inference runtime from nvidia, https://developer.nvidia.com/tensorrt, but you have to port your models
- A1111 just added support for TensorRT for webui as an extension!
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WIP - TensorRT accelerated stable diffusion img2img from mobile camera over webrtc + whisper speech to text. Interdimensional cable is here! Code: https://github.com/venetanji/videosd
It uses the nvidia demo code from: https://github.com/NVIDIA/TensorRT/tree/main/demo/Diffusion
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[P] Get 2x Faster Transcriptions with OpenAI Whisper Large on Kernl
The traditional way to deploy a model is to export it to Onnx, then to TensorRT plan format. Each step requires its own tooling, its own mental model, and may raise some issues. The most annoying thing is that you need Microsoft or Nvidia support to get the best performances, and sometimes model support takes time. For instance, T5, a model released in 2019, is not yet correctly supported on TensorRT, in particular K/V cache is missing (soon it will be according to TensorRT maintainers, but I wrote the very same thing almost 1 year ago and then 4 months ago so… I don’t know).
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Speeding up T5
I've tried to speed it up with TensorRT and followed this example: https://github.com/NVIDIA/TensorRT/blob/main/demo/HuggingFace/notebooks/t5.ipynb - it does give considerable speedup for batch-size=1 but it does not work with bigger batch sizes, which is useless as I can simply increase the batch-size of HuggingFace model.
- demoDiffusion on TensorRT - supports 3090, 4090, and A100
What are some alternatives?
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
FasterTransformer - Transformer related optimization, including BERT, GPT
ROCm - AMD ROCm™ Software - GitHub Home [Moved to: https://github.com/ROCm/ROCm]
pinferencia - Python + Inference - Model Deployment library in Python. Simplest model inference server ever.
vllm - A high-throughput and memory-efficient inference and serving engine for LLMs
Triton - Triton is a dynamic binary analysis library. Build your own program analysis tools, automate your reverse engineering, perform software verification or just emulate code.
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
Megatron-LM - Ongoing research training transformer models at scale
stable-diffusion-webui - Stable Diffusion web UI