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Top 23 Tensor Open-Source Projects
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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einops
Flexible and powerful tensor operations for readable and reliable code (for pytorch, jax, TF and others)
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burn
Burn is a new comprehensive dynamic Deep Learning Framework built using Rust with extreme flexibility, compute efficiency and portability as its primary goals.
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mars
Mars is a tensor-based unified framework for large-scale data computation which scales numpy, pandas, scikit-learn and Python functions.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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DataFrame
C++ DataFrame for statistical, Financial, and ML analysis -- in modern C++ using native types and contiguous memory storage
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awesome-tensor-compilers
A list of awesome compiler projects and papers for tensor computation and deep learning.
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Arraymancer
A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
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opt_einsum
⚡️Optimizing einsum functions in NumPy, Tensorflow, Dask, and more with contraction order optimization.
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TensorFlow, developed by Google, and PyTorch, developed by Facebook, are two of the most popular frameworks for building and training complex machine learning models. TensorFlow is known for its flexibility and robust scalability, making it suitable for both research prototypes and production deployments. PyTorch is praised for its ease of use, simplicity, and dynamic computational graph that allows for more intuitive coding of complex AI models. Both frameworks support a wide range of AI models, from simple linear regression to complex deep neural networks.
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
Project mention: Einops: Flexible and powerful tensor operations for readable and reliable code | news.ycombinator.com | 2023-12-12
[package] name = "resnet_burn" version = "0.1.0" edition = "2021" [dependencies] burn = { git = "https://github.com/tracel-ai/burn.git", rev = "75cb5b6d5633c1c6092cf5046419da75e7f74b11", features = ["ndarray"] } burn-import = { git = "https://github.com/tracel-ai/burn.git", rev = "75cb5b6d5633c1c6092cf5046419da75e7f74b11" } image = { version = "0.24.7", features = ["png", "jpeg"] }
Does nx not work for you? https://github.com/elixir-nx/nx/tree/main/nx#readme
Project mention: New multithreaded version of C++ DataFrame was released | news.ycombinator.com | 2024-02-13
> So long as Pytorch only practically works with Nvidia GPUs, everything else is little more than a rounding error.
This is changing.
https://github.com/merrymercy/awesome-tensor-compilers
There are more and better projects that can compile an existing PyTorch codebase into a more optimized format for a range of devices. Triton (which is part of PyTorch) TVM and the MLIR based efforts (like torch-MLIR or IREE) are big ones, but there are smaller fish like GGML and Tinygrad, or more narrowly focused projects like Meta's AITemplate (which works on AMD datacenter GPUs).
Hardware is in a strange place now... It feels like everyone but Cerebras and AMD/Intel was squeezed out, but with all the money pouring in, I think this is temporary.
Project mention: 80% faster, 50% less memory, 0% loss of accuracy Llama finetuning | news.ycombinator.com | 2023-12-01Good point - the main issue is we encountered this exact issue with our old package Hyperlearn (https://github.com/danielhanchen/hyperlearn).
I OSSed all the code to the community - I'm actually an extremely open person and I love contributing to the OSS community.
The issue was the package got gobbled up by other startups and big tech companies with no credit - I didn't want any cash from it, but it stung and hurt really bad hearing other startups and companies claim it was them who made it faster, whilst it was actually my work. It hurt really bad - as an OSS person, I don't want money, but just some recognition for the work.
I also used to accept and help everyone with their writing their startup's software, but I never got paid or even any thanks - sadly I didn't expect the world to be such a hostile place.
So after a sad awakening, I decided with my brother instead of OSSing everything, we would first OSS something which is still very good - 5X faster training is already very reasonable.
I'm all open to other suggestions on how we should approach this though! There are no evil intentions - in fact I insisted we OSS EVERYTHING even the 30x faster algos, but after a level headed discussion with my brother - we still have to pay life expenses no?
If you have other ways we can go about this - I'm all ears!! We're literally making stuff up as we go along!
It is a small DSL written using macros at https://github.com/mratsim/Arraymancer/blob/master/src/array....
Nim has pretty great meta-programming capabilities and arraymancer employs some cool features like emitting cuda-kernels on the fly using standard templates depending on backend !
I agree! Much of this work was done as part of the overarching TACO project (https://github.com/tensor-compiler/taco), in an attempt to distribute sparse tensor computations (https://rohany.github.io/publications/sc2022-spdistal.pdf). MLIR recently (~mid 2022) began implementing the ideas from TACO into a "sparse tensor" dialect, so perhaps some of these ideas could make it into there. I'm working with MLIR these days, and if I could re-do the project now I would probably utilize and targetb the MLIR linalg infrastructure!
Project mention: Beginner projects/resources to learn about ML Compilers | /r/Compilers | 2023-04-27For tensor layout, this is the standard for all libraries: https://github.com/dmlc/dlpack/blob/main/include/dlpack/dlpack.h
Project mention: ExecuTorch: Enabling On-Device interference for embedded devices | news.ycombinator.com | 2023-10-17Yes ExecuTorch is currently targeted at Edge devices. The runtime is written in C++ with 50KB binary size (without kernels) and should run in most of platforms. You are right that we have not integrated to Nvidia backend yet. Have you tried torch.compile() in PyTorch 2.0? It would do the Nvidia optimization for you without Torchscript. If you have specific binary size or edge specific request, feel free to file issues in https://github.com/pytorch/executorch/issues
Project mention: Neuromorphic learning, working memory, and metaplasticity in nanowire networks | news.ycombinator.com | 2023-04-24This gives you a ludicrous advantage over current neural net accelerators. Specifically 3-5 orders is magnitude in energy and time, as demonstrated in the BranScaleS system https://www.humanbrainproject.eu/en/science-development/focu...
Unfortunately, that doesn't solve the problem of learning. Just because you can build efficient neuromorphic systems doesn't mean that we know how to train them. Briefly put, the problem is that a physical system has physical constraints. You can't just read the global state in NWN and use gradient descent as we would in deep learning. Rather, we have to somehow use local signals to approximate local behaviour that's helpful on a global scale. That's why they use Hebbian learning in the paper (what fires together, wires together), but it's tricky to get right and I haven't personally seen examples that scale to systems/problems of "interesting" sizes. This is basically the frontier of the field: we need local, but generalizable, learning rules that are stable across time and compose freely into higher-order systems.
Regarding educational material, I'm afraid I haven't seen great entries for learning about SNNs in full generality. I co-author a simulator (https://github.com/norse/norse/) based on PyTorch with a few notebook tutorials (https://github.com/norse/notebooks) that may be helpful.
I'm actually working on some open resources/course material for neuromorphic computing. So if you have any wishes/ideas, please do reach out. Like, what would a newcomer be looking for specifically?
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
Tensor related posts
- Arraymancer – Deep Learning Nim Library
- Go, Python, Rust, and production AI applications
- Making AMD GPUs competitive for LLM inference
- Candle: Torch Replacement in Rust
- MatX: Faster Chips for LLMs
- Run Llama2-70B in Web Browser with WebGPU Acceleration
- Ask HN: How to get good as a self taught ML engineer?
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Index
What are some of the best open-source Tensor projects? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | Pytorch | 77,783 |
| 2 | tvm | 11,156 |
| 3 | einops | 7,897 |
| 4 | cupy | 7,753 |
| 5 | burn | 7,020 |
| 6 | MegEngine | 4,713 |
| 7 | mars | 2,675 |
| 8 | nx | 2,460 |
| 9 | DataFrame | 2,258 |
| 10 | awesome-tensor-compilers | 2,167 |
| 11 | dfdx | 1,600 |
| 12 | hyperlearn | 1,510 |
| 13 | Arraymancer | 1,304 |
| 14 | taco | 1,203 |
| 15 | egison | 900 |
| 16 | dlpack | 849 |
| 17 | opt_einsum | 803 |
| 18 | executorch | 710 |
| 19 | norse | 611 |
| 20 | Tullio.jl | 581 |
| 21 | DiffSharp | 573 |
| 22 | Grassmann.jl | 449 |
| 23 | TensorOperations.jl | 414 |
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