onnxruntime-rs
onnxruntime
| onnxruntime-rs | onnxruntime | |
|---|---|---|
| 1 | 54 | |
| 2 | 12,804 | |
| - | 2.7% | |
| 1.1 | 10.0 | |
| about 1 year ago | about 23 hours ago | |
| Rust | C++ | |
| Apache License 2.0 | MIT License |
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onnxruntime-rs
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onnxruntime
Having worked a lot with the original onnxruntime-rs crate I made a fork with a lot of changes (mainly focused on using NVIDIA and io-binding). It much more closely aligns with the onnxruntime c api: https://github.com/seddonm1/onnxruntime-rs
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?
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
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
torch2trt - An easy to use PyTorch to TensorRT converter