bacalhau
onnxruntime
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bacalhau | onnxruntime | |
---|---|---|
12 | 52 | |
586 | 12,386 | |
5.3% | 5.4% | |
9.8 | 10.0 | |
5 days ago | 3 days ago | |
Go | C++ | |
Apache License 2.0 | MIT License |
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.
bacalhau
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Deno Cron
This is really interesting - we’ve tried really hard to solve some of these with Bacalhau[1] - a much simpler distributed compute platform. Would love your feedback!
[1] https://github.com/bacalhau-project/bacalhau
Disclosure: I confounded Bacalhau
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Building a Distributed Data Warehouse Without Data Lakes
I think it's more generalized distributed compute, with OLAP being one of the use cases. There are a lot of videos on YouTube if you search "Bacalhau cluster" or similar, including what appears to be their legacy channel [0] (which I saw a year ago and found very impressive) and a newer one [1], but also conference talks from other channels.
I haven't been following the project, but I remember finding the original demo to be very impressive - just don't remember the details off-hand. Seems like a lot of work has taken place since then. Docs are here [2]
[0] https://www.youtube.com/@bacalhau3295
It's an interesting question!
The problem is that the data is spread everywhere - no choice about that. So with that in mind, how do you query that data? Today, the idea is that you HAVE to put it into a central location. With tools like Bacalhau[1] and DuckDB [2], you no longer have to - a single query can be sharded amongst all your data - EFFECTIVELY giving you a lot of what you want from a data lake.
It's not a replacement, but if you can do a few of these items WITHOUT moving the data, you will be able to see really significant cost and time savings.
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Ask HN: What tech is under the radar with all attention on ChatGPT etc.
Very selfishly - distributed compute. Not decentralized, distributed.
Compute and data are being created and run everywhere, we need platforms that understand how to use it and get insights without (or before) moving it.
Our contribution: https://github.com/bacalhau-project/bacalhau (think Kubernetes but built for the distributed world).
Disclosure: I co-founded the Bacalhau Project
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Waterlily.ai Launches to Make AI Art More Accessible and Equitable
Hi! I'm David Aronchick - co-founder for Bacalhau (the distributed compute framework that runs Waterlily.ai - https://github.com/bacalhau-project/bacalhau).
We'd love your thoughts on our project - if you have any questions, please ask away!
- The Bacalhau Vision – A Distributed Compute over Data Platform
onnxruntime
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Deep Learning in JavaScript
tfjs is dead, looking at the commit history. The standard now is to convert PyTorch to onnx, then use onnxruntime (https://github.com/microsoft/onnxruntime/tree/main/js/web) to run the model on the browsdr.
- FLaNK Stack 05 Feb 2024
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Vcc – The Vulkan Clang Compiler
- slang[2] has the potential, but the meta programming part is not as strong as C++, existing libraries cannot be used.
The above conclusion is drawn from my work https://github.com/microsoft/onnxruntime/tree/dev/opencl, purely nightmare to work with thoes drivers and jit compilers. Hopefully Vcc can take compute shader more seriously.
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Oracle-samples/sd4j: Stable Diffusion pipeline in Java using ONNX Runtime
I did. It depends what you want, for an overview of how ONNX Runtime works then Microsoft have a bunch of things on https://onnxruntime.ai, but the Java content is a bit lacking on there as I've not had time to write much. Eventually I'll probably write something similar to the C# SD tutorial they have on there but for the Java API.
For writing ONNX models from Java we added an ONNX export system to Tribuo in 2022 which can be used by anything on the JVM to export ONNX models in an easier way than writing a protobuf directly. Tribuo doesn't have full coverage of the ONNX spec, but we're happy to accept PRs to expand it, otherwise it'll fill out as we need it.
- Mamba-Chat: A Chat LLM based on State Space Models
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VectorDB: Vector Database Built by Kagi Search
What about models besides GPT? Most of the popular vector encoding models aren't using this architecture.
If you really didn't want PyTorch/Transformers, you could consider exporting your models to ONNX (https://github.com/microsoft/onnxruntime).
- Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
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PyTorch Primitives in WebGPU for the Browser
https://news.ycombinator.com/item?id=35696031 ... TIL about wonnx: https://github.com/webonnx/wonnx#in-the-browser-using-webgpu...
microsoft/onnxruntime: https://github.com/microsoft/onnxruntime
Apache/arrow has language-portable Tensors for cpp: https://arrow.apache.org/docs/cpp/api/tensor.html and rust: https://docs.rs/arrow/latest/arrow/tensor/struct.Tensor.html and Python: https://arrow.apache.org/docs/python/api/tables.html#tensors https://arrow.apache.org/docs/python/generated/pyarrow.Tenso...
Fwiw it looks like the llama.cpp Tensor is from ggml, for which there are CUDA and OpenCL implementations (but not yet ROCm, or a WebGPU shim for use with emscripten transpilation to WASM): https://github.com/ggerganov/llama.cpp/blob/master/ggml.h
Are the recommendable ways to cast e.g. arrow Tensors to pytorch/tensorflow?
FWIU, Rust has a better compilation to WASM; and that's probably faster than already-compiled-to-JS/ES TensorFlow + WebGPU.
What's a fair benchmark?
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How to create YOLOv8-based object detection web service using Python, Julia, Node.js, JavaScript, Go and Rust
Before continue, ensure that the ONNX runtime installed on your operating system, because the library that integrated to the Rust package may not work correctly. To install it, you can download the archive for your operating system from here, extract and copy contents of "lib" subfolder to the system libraries path of your operating system.
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Ask HN: What tech is under the radar with all attention on ChatGPT etc.
I can't seem to figure if the PR for the WebGPU backend for onnxruntime is supposed to land in a 1.14 release, a 1.15 release, has already landed, isn't yet scheduled to land, etc? https://github.com/microsoft/onnxruntime/pull/14579
https://github.com/microsoft/onnxruntime/releases I don't see it in any releases yet?
https://github.com/microsoft/onnxruntime/milestone/4 I don't see it in the upcoming milestone.
I don't see any examples or docs that go with it
https://github.com/microsoft/onnxruntime/wiki/Upcoming-Relea... This seems to be out of date
https://github.com/microsoft/onnxruntime/tree/rel-1.15.0 I do see the js/webgpu work merged into here so I guess it'll be released in 1.15.0
https://onnxruntime.ai/docs/reference/releases-servicing.htm...
> Official releases of ONNX Runtime are managed by the core ONNX Runtime team. A new release is published approximately every quarter, and the upcoming roadmap can be found here.
ONNX Runtime v1.14.0 was Feb 10th
What are some alternatives?
onnx - Open standard for machine learning interoperability
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
onnx-simplifier - Simplify your onnx model
ONNX-YOLOv7-Object-Detection - Python scripts performing object detection using the YOLOv7 model in ONNX.
onnx-tensorflow - Tensorflow Backend for ONNX
MLflow - Open source platform for the machine learning lifecycle
TensorRT - PyTorch/TorchScript/FX compiler for NVIDIA GPUs using TensorRT
FasterTransformer - Transformer related optimization, including BERT, GPT
tensorflow-directml - Fork of TensorFlow accelerated by DirectML
spark-nlp - State of the Art Natural Language Processing
torch2trt - An easy to use PyTorch to TensorRT converter
tch-rs - Rust bindings for the C++ api of PyTorch.