Alternatives

Products that do what Export YOLO/RF-DETR Models to ONNX, TensorRT, CoreML – 100% Local does

1IR
Initial release of the distributed TensorFlow runtime▲85
2016 · github.com
2CO
Cortex – Open-source alternative to SageMaker for model serving▲65
2020 · github.com
3FS
Fast Style Transfer in TensorFlow▲184
2016 · github.com
4TR
TensorFlow-Resources – Organized and Useful Resources about TensorFlow▲152
2018 · github.com
5DM
Deploy ML Models on a Budget▲117
2021 · github.com
6TJ
TensorSpace.js – Neural network 3D visualization framework▲167
2018 · github.com
7HH
Hottbox – Higher-Order Tensors Toolbox▲37
2018 · github.com
8LB
Lowdefy – Build internal tools with YAML on an open-source framework▲107
2021 · lowdefy.com
9BB
Bounding-box labeler tool to generate the training data for YOLO v2▲64
2018 · github.com
10TA
TDB: A Visual Debugger for TensorFlow▲144
2015 · github.com
11RJ
RemoteML – Job Directory for Remote Machine Learning Positions▲99
2018 · remoteml.com
12CM
Compile ML models into dependency-free source code for easy deployment▲37
I'm a machine learning engineer who always found it annoying to integrate ML models into phone apps, smartwatch apps, microcontroller firmware etc... Why do we need all these libraries and runtimes with all the overhead, compatibility issues and other headaches, when it's just some math to be executed? So I made a compiler that simply converts the model into plain source code with no dependencies, and it actually solved all my deployment problems. Now I'm curious if it can help anyone else too. Through the link you can submit your model file (Keras h5, onnx soon to be supported), and I'll…
2023 · waveworks.dk
13OS
Open sourcing nearby.lk data models library▲15
2016 · vpj.github.io
14MZ
Model Zoo: Deploy your machine learning model in a single line of code▲34
2020 · modelzoo.dev
15AV
Arraymancer v0.4, Nim tensor library now with OpenCL and Keras-like API▲11
2018 · github.com
16ZE
ZenML, extensible open-source MLOps framework for reproducible ML▲37
2020 · github.com
17TE
The easiest way to deploy TensorFlow – Inferrd v2.0▲15
2021 · inferrd.com
18TF
TensorFlow for .NET and Unity ML Agents▲10
2021 · losttech.software
19PE
Pollly – Editor for YAML localization and translation files▲17
2017 · pollly.orson.io
20XC
Xtensor: C++14 tensor expression library with numpy-style broadcasting▲7
2017 · github.com
21K2
KTransformers–236B Model and 1M Context LLM Inference on Local Machines▲20
Hey Hacker News! We are excited to share our open-source project, KTransformers, a flexible framework designed for cutting-edge LLM inference optimizations! Leveraging state-of-the-art kernels from llamafile and marlin, KTransformers seamlessly enhances the performance of HuggingFace Transformers, making it possible to operate large 236B MoE models or extremely long 1M context locally with promising speed. KTransformers is a Python-centric framework designed with extensibility at its core. By implementing and injecting an optimized module with a single line of code, users gain access to a…
2024 · github.com
22PA
Panini AI – A platform to serve ML/DL models at low latency▲29
2019 · panini.ai
23SP
Swift-powered AI apps on iOS: Real-time multimodal semantic search▲6
I was recently playing with Apple's CoreML and had several painful observations on tooling. It's not enough for a long read but should be for an HN post. In short, you can take a simple BERT-like encoder model in PyTorch, convert it into an f32 CoreML checkpoint, and run it on CPU or GPU, but not NPU. Let's unpack this. Having a simple and extensible format to exchange common ANN architectures is a big issue for anyone who uses more than one framework or programming language to run the same model. ONNX is the closest we have to that standard, but it's hard to call anything Protobuf-related…
2024 · github.com
24TO
TensorFlow on FreeBSD▲22
2016 · ecc-comp.blogspot.com

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