TorchSharp
onnxruntime
TorchSharp | onnxruntime | |
---|---|---|
5 | 54 | |
1,254 | 12,894 | |
4.0% | 3.3% | |
9.5 | 10.0 | |
5 days ago | about 7 hours ago | |
C# | C++ | |
MIT License | MIT License |
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TorchSharp
- AI .NET
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Machine learning with NET
There exist TorchSharp that is a С# wrapper around the same libtorch.dll that is wrapped by Python code in PyTorch, https://github.com/dotnet/TorchSharp. Though I don't know how exhaustive it is.
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When the client's management is happy but their dev team is a pain
https://github.com/dotnet/TorchSharp here you go.
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Does anyone actually use ML.NET?
With TorchSharp, you have access to libtorch in .NET, the library that powers PyTorch. This is what's currently backing the Text Classification API
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.NET Core
In Artificial Intelligence Series-Overview
TorchSharp is a .NET library that provides access to libraries that support PyTorch. (Github https://github.com/xamarin/TorchSharp)
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?
TensorFlow.NET - .NET Standard bindings for Google's TensorFlow for developing, training and deploying Machine Learning models in C# and F#.
onnx - Open standard for machine learning interoperability
NumSharp - High Performance Computation for N-D Tensors in .NET, similar API to NumPy.
onnx-tensorrt - ONNX-TensorRT: TensorRT backend for ONNX
TensorFlowSharp - TensorFlow API for .NET languages
onnx-simplifier - Simplify your onnx model
DotMLBooks
ONNX-YOLOv7-Object-Detection - Python scripts performing object detection using the YOLOv7 model in ONNX.
nerfstudio - A collaboration friendly studio for NeRFs
onnx-tensorflow - Tensorflow Backend for ONNX
ParquetSharp.DataFrame - ParquetSharp.DataFrame is a .NET library for reading and writing Apache Parquet files into/from .NET DataFrames, using ParquetSharp
MLflow - Open source platform for the machine learning lifecycle