PaddlePaddle
onnx
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PaddlePaddle | onnx | |
---|---|---|
6 | 38 | |
21,616 | 16,858 | |
1.0% | 2.4% | |
10.0 | 9.5 | |
4 days ago | 3 days ago | |
C++ | Python | |
Apache License 2.0 | Apache License 2.0 |
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.
PaddlePaddle
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List of AI-Models
Click to Learn more...
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Ask HN: Are there any notable Chinese FLOSS projects?
PaddlePaddle?
https://github.com/PaddlePaddle/Paddle
Also, Baidu have quite a few OSS projects out there in general.
https://github.com/baidu
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Volcano vs Yunikorn vs Knative
Volcano is a batch scheduler on top of Kube-batch targetting spark-operator, plain old MPI, chinesium paddlepaddle, and Kromwell HPC.
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Baidu AI Researchers Introduce SE-MoE That Proposes Elastic MoE Training With 2D Prefetch And Fusion Communication Over Hierarchical Storage
Continue reading | Check out the paper, and Github
- I have issue with only __habs for half datatype? Please help!
- Alternatives to google collab?
onnx
- Onyx, a new programming language powered by WebAssembly
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From Lab to Live: Implementing Open-Source AI Models for Real-Time Unsupervised Anomaly Detection in Images
Once your model has been trained and validated using Anomalib, the next step is to prepare it for real-time implementation. This is where ONNX (Open Neural Network Exchange) or OpenVINO (Open Visual Inference and Neural network Optimization) comes into play.
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Object detection with ONNX, Pipeless and a YOLO model
ONNX is an open format from the Linux Foundation to represent machine learning models. It is becoming extensively adopted by the Machine Learning community and is compatible with most of the machine learning frameworks like PyTorch, TensorFlow, etc. Converting a model between any of those formats and ONNX is really simple and can be done in most cases with a single command.
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38TB of data accidentally exposed by Microsoft AI researchers
ONNX[0], model-as-protosbufs, continuing to gain adoption will hopefully solve this issue.
[0] https://github.com/onnx/onnx
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Reddit’s LLM text model for Ads Safety
Running inference for large models on CPU is not a new problem and fortunately there has been great development in many different optimization frameworks for speeding up matrix and tensor computations on CPU. We explored multiple optimization frameworks and methods to improve latency, namely TorchScript, BetterTransformer and ONNX.
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Operationalize TensorFlow Models With ML.NET
ONNX is a format for representing machine learning models in a portable way. Additionally, ONNX models can be easily optimized and thus become smaller and faster.
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Onnx Runtime: “Cross-Platform Accelerated Machine Learning”
I would say onnx.ai [0] provides more information about ONNX for those who aren’t working with ML/DL.
[0] https://onnx.ai
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Does ONNX Runtime not support Double/float64?
It's not clear why you thing this sub is appropriate for some third party system with a Python interface. Why don't you try their discussion group: https://github.com/onnx/onnx/discussions
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Async behaviour in python web frameworks
This kind of indirection through standardisation is pretty common to make compatibility between different kinds of software components easier. Some other good examples are the LSP project from Microsoft and ONNX to represent machine learning models. The first provides a standard so that IDEs don't have to re-invent the weel for every programming language. The latter decouples training frameworks from inference frameworks. Going back to WSGI, you can find a pretty extensive rationale for the WSGI standard here if interested.
- Pickle safety in Python
What are some alternatives?
tensorflow - An Open Source Machine Learning Framework for Everyone
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
PyTorch-NLP - Basic Utilities for PyTorch Natural Language Processing (NLP)
stable-diffusion-webui - Stable Diffusion web UI
Keras - Deep Learning for humans
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/Sygil-Dev/sygil-webui]
xgboost - Scalable, Portable and Distributed Gradient Boosting (GBDT, GBRT or GBM) Library, for Python, R, Java, Scala, C++ and more. Runs on single machine, Hadoop, Spark, Dask, Flink and DataFlow
sentence-transformers - Multilingual Sentence & Image Embeddings with BERT
MLflow - Open source platform for the machine learning lifecycle
stable-diffusion - A latent text-to-image diffusion model
gym - A toolkit for developing and comparing reinforcement learning algorithms.
stable-diffusion-webui - Stable Diffusion web UI [Moved to: https://github.com/sd-webui/stable-diffusion-webui]