jittor
tvm
| jittor | tvm | |
|---|---|---|
| 4 | 16 | |
| 2,998 | 11,186 | |
| - | 1.3% | |
| 7.6 | 9.9 | |
| 9 days ago | 7 days 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.
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jittor
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VSL; Vlang's Scientific Library
Would it make sense to have a backend support for OpenXLA, Apache TVM, Jittor or other similar to get free GPU, TPU and other accelerators for free ?
- Jittor: High-performance deep learning framework based on JIT and meta-operators
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Tinygrad: A simple and powerful neural network framework
Very similar idea as Jittor, convolution definitely can be break down: https://github.com/Jittor/jittor/blob/master/python/jittor/n...
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How do I deal with ML models taking soooo long to train, when I have to optimize results?
-I've found JIT quite useful: https://github.com/Jittor/jittor
tvm
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Show HN: I built a free in-browser Llama 3 chatbot powered by WebGPU
Yes. Web-llm is a wrapper of tvmjs: https://github.com/apache/tvm
Just wrappers all the way down
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Making AMD GPUs competitive for LLM inference
Yes, this is coming! Myself and others at OctoML and in the TVM community are actively working on multi-gpu support in the compiler and runtime. Here are some of the merged and active PRs on the multi-GPU (multi-device) roadmap:
Support in TVM’s graph IR (Relax) - https://github.com/apache/tvm/pull/15447
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VSL; Vlang's Scientific Library
Would it make sense to have a backend support for OpenXLA, Apache TVM, Jittor or other similar to get free GPU, TPU and other accelerators for free ?
- Apache TVM
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MLC LLM - "MLC LLM is a universal solution that allows any language model to be deployed natively on a diverse set of hardware backends and native applications, plus a productive framework for everyone to further optimize model performance for their own use cases."
I have tried the iPhone app. It's fast. They're using Apache TVM which should allow better use of native accelerators on different devices. Like using metal on Apple and Vulcan or CUDA or whatever instead of just running the thing on the CPU like llama.cpp.
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ONNX Runtime merges WebGPU back end
I was going to answer the same, I find the approach of machine learning compilers that directly compile models to host and device code better than having to bring a huge runtime. There are exciting projects in this area like TVM Unity, IREE [2], or torch.export [3]
[1] https://github.com/apache/tvm/tree/unity
[2] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...
[3] https://pytorch.org/get-started/pytorch-2.0/#inference-and-e...
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Esp32 tensorflow lite
Apache TVM home page: https://tvm.apache.org/
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Decompiling x86 Deep Neural Network Executables
It's pretty clear its referring to the output of Apache TVM and Meta's Glow
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Run Stable Diffusion on Your M1 Mac’s GPU
As mentioned in sibling comments, Torch is indeed the glue in this implementation. Other glues are TVM[0] and ONNX[1]
These just cover the neural net though, and there is lots of surrounding code and pre-/post-processing that isn't covered by these systems.
For models on Replicate, we use Docker, packaged with Cog for this stuff.[2] Unfortunately Docker doesn't run natively on Mac, so if we want to use the Mac's GPU, we can't use Docker.
I wish there was a good container system for Mac. Even better if it were something that spanned both Mac and Linux. (Not as far-fetched as it seems... I used to work at Docker and spent a bit of time looking into this...)
[0] https://tvm.apache.org/
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How to get started with machine learning.
Or use TVM, the idea is to compile your model into code that you can load at runtime. Similar to onnxruntime, it only does DNN inference; so you need domain-specific code.
What are some alternatives?
Res2Net-PretrainedModels - (ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"
TensorRT - NVIDIA® TensorRT™ is an SDK for high-performance deep learning inference on NVIDIA GPUs. This repository contains the open source components of TensorRT.
shumai - Fast Differentiable Tensor Library in JavaScript and TypeScript with Bun + Flashlight
mlc-llm - Enable everyone to develop, optimize and deploy AI models natively on everyone's devices.
vsl - V library to develop Artificial Intelligence and High-Performance Scientific Computations
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
StylizedNeRF - [CVPR 2022] Code for StylizedNeRF: Consistent 3D Scene Stylization as Stylized NeRF via 2D-3D mutual learning
stable-diffusion - This version of CompVis/stable-diffusion features an interactive command-line script that combines text2img and img2img functionality in a "dream bot" style interface, a WebGUI, and multiple features and other enhancements. [Moved to: https://github.com/invoke-ai/InvokeAI]
nnabla - Neural Network Libraries
nebuly - The user analytics platform for LLMs
loop_tool - A thin, highly portable toolkit for efficiently compiling dense loop-based computation.
stable-diffusion