model_analyzer
nebuly
| model_analyzer | nebuly | |
|---|---|---|
| 2 | 105 | |
| 376 | 8,363 | |
| 4.5% | 0.1% | |
| 8.2 | 8.4 | |
| 1 day ago | 6 months ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
model_analyzer
- [P] Benchmarking some PyTorch Inference Servers
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Show HN: Software for Remote GPU-over-IP
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
nebuly
- Nebuly – The LLM Analytics Platform
- Ask HN: Any tools or frameworks to monitor the usage of OpenAI API keys?
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What are you building with LLMs? I'm writing an article about what people are building with LLMs
Hi everyone. I’m the creator of ChatLLaMA https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama, an opensource framework to train LLMs with limited resources and create There’s been amazing usage of LLMs in these days, from chatbots to retrieve about company’s product information, to cooking assistants for traditional dishes, and much more. And you? What you building or would love to build with LLMs? Let me know and I’ll share the article about your stories soon. https://qpvirevo4tz.typeform.com/to/T3PruEuE Cheers
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Show HN: ChatLLaMA – A ChatGPT style chatbot for Facebook's LLaMA
How does it differentiate from the original ChatLLaMA? https://github.com/nebuly-ai/nebullvm/tree/main/apps/acceler...
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🤖🌟 Unlock the Power of Personal AI: Introducing ChatLLaMA, Your Custom Personal Assistant! 🚀💬
Was this made with the ChatLLaMA library? https://github.com/nebuly-ai/nebullvm/tree/main/apps/accelerate/chatllama
- Meta LLM LLaMA leaked, all over the internet as we speak
- Meta LLM LLAMA leaked, it's all over the internet as we speak.
- Meta LLM LLAMMA leaked, it's all over the internet as we speak.
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Plug and play modules to optimize the performances of your AI systems
Some of the available modules include:
Speedster: Automatically apply the best set of SOTA optimization techniques to achieve the maximum inference speed-up on your hardware. https://github.com/nebuly-ai/nebullvm/blob/main/apps/acceler...
Nos: Automatically maximize the utilization of GPU resources in a Kubernetes cluster through real-time dynamic partitioning and elastic quotas. https://github.com/nebuly-ai/nos
ChatLLaMA: Build faster and cheaper ChatGPT-like training process based on LLaMA architectures. https://github.com/nebuly-ai/nebullvm/tree/main/apps/acceler...
OpenAlphaTensor: Increase the computational performances of an AI model with custom-generated matrix multiplication algorithm fine-tuned for your specific hardware. https://github.com/nebuly-ai/nebullvm/tree/main/apps/acceler...
Forward-Forward: The Forward Forward algorithm is a method for training deep neural networks that replaces the backpropagation forward and backward passes with two forward passes. https://github.com/nebuly-ai/nebullvm/tree/main/apps/acceler...
- Open source implementation for LLaMA-based ChatGPT
What are some alternatives?
kserve - Standardized Serverless ML Inference Platform on Kubernetes
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
DeepSpeed - DeepSpeed is a deep learning optimization library that makes distributed training and inference easy, efficient, and effective.
AITemplate - AITemplate is a Python framework which renders neural network into high performance CUDA/HIP C++ code. Specialized for FP16 TensorCore (NVIDIA GPU) and MatrixCore (AMD GPU) inference.
text-generation-webui - A Gradio web UI for Large Language Models. Supports transformers, GPTQ, AWQ, EXL2, llama.cpp (GGUF), Llama models.
alpaca-lora - Instruct-tune LLaMA on consumer hardware
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
deepsparse - Sparsity-aware deep learning inference runtime for CPUs
openvino - OpenVINO™ is an open-source toolkit for optimizing and deploying AI inference
dlcompiler-comparison - The quantitative performance comparison among DL compilers on CNN models.
llama - Inference code for Llama models
tflite-micro - Infrastructure to enable deployment of ML models to low-power resource-constrained embedded targets (including microcontrollers and digital signal processors).