bacalhau
onnxruntime
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bacalhau | onnxruntime | |
---|---|---|
12 | 54 | |
606 | 12,656 | |
5.5% | 4.6% | |
9.8 | 10.0 | |
about 10 hours ago | 5 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
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.
[1] https://github.com/bacalhau-project/bacalhau
[2] https://github.com/duckdb/duckdb
- Bacalhau: Compute over Data framework for public, transparent, verifiable work
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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
- Waterlily.ai Launches to Make AI Art More Accessible and Equitable
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Building a Distributed World of WebAssembly with Bacalhau
Thank you so much for the feedback. Yeah, we REALLY do want to figure out a better naming/reference scheme. Do you have anything you've seen you really like?
Disclosure: I work on Bacalhau[1]
https://github.com/bacalhau-project/bacalhau
- What Is Bacalhau?
- GitHub
- The Bacalhau Vision – A Distributed Compute over Data Platform
onnxruntime
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Machine Learning with PHP
ONNX Runtime: ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
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AI Inference now available in Supabase Edge Functions
Embedding generation uses the ONNX runtime under the hood. This is a cross-platform inferencing library that supports multiple execution providers from CPU to specialized GPUs.
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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.
[1]: https://www.circle-lang.org/
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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
- Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
What are some alternatives?
duckdb-wasm - WebAssembly version of DuckDB
onnx - Open standard for machine learning interoperability
ch32v003fun - An open source software development stack for the CH32V003, a 10¢ 48 MHz RISC-V Microcontroller
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
Waterlily - A project bringing ethics back to AI
onnx-simplifier - Simplify your onnx model
web-llm - Bringing large-language models and chat to web browsers. Everything runs inside the browser with no server support.
ONNX-YOLOv7-Object-Detection - Python scripts performing object detection using the YOLOv7 model in ONNX.
JsCron - Javascript cron parser, schedule date generator
onnx-tensorflow - Tensorflow Backend for ONNX
twisted - Fetching riot games api data
MLflow - Open source platform for the machine learning lifecycle