6 रिपॉजिटरी
Awesome GitHub RepositoriesModel Deployment
Processes for preparing optimized models for production execution on target hardware.
Distinct from Deep Learning Optimization: Focuses on the end-to-end transition from model file to production execution, whereas Deep Learning Optimization focuses on the internal graph and computational refinements.
Explore 6 awesome GitHub repositories matching artificial intelligence & ml · Model Deployment. Refine with filters or upvote what's useful.
Awesome Model Deployment GitHub Repositories
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- nvidia/deeplearningexamples
NVIDIA/DeepLearningExamples
14,819GitHub पर देखेंThis project is a collection of optimized scripts, deployment patterns, and reference implementations designed for scaling and accelerating state-of-the-art AI models. It serves as a multi-domain model zoo and a distributed training framework, providing PyTorch reference implementations for training and deploying models on GPU-accelerated infrastructure. The repository distinguishes itself through an optimization suite focused on NVIDIA GPU hardware, utilizing automatic mixed precision and specialized math modes to increase training speed and throughput. It provides enterprise deployment patt
Implements processes for preparing and moving optimized models into production execution on target hardware.
Jupyter Notebookcomputer-visiondeep-learningdrug-discovery - apple/coremltools
apple/coremltools
5,333GitHub पर देखेंcoremltools is a conversion toolkit and translator designed to transform machine learning models from various frameworks into the Core ML format for execution on Apple hardware. It provides a suite of tools for migrating weights and architectures from external libraries into a deployable model format. The project includes an optimization tool and a programmatic interface for editing model graphs and modifying metadata to improve performance on target hardware. It also features a validation suite used to check model specifications and operation compatibility to ensure correct execution within
Prepares and validates optimized models for production execution on target hardware.
Pythoncoremlcoremltoolsmachine-learning - weiliu89/caffe
weiliu89/caffe
4,800GitHub पर देखेंCaffe एक हाई-परफॉरमेंस डीप लर्निंग फ्रेमवर्क और कन्वेन्शनल न्यूरल नेटवर्क लाइब्रेरी है जिसे न्यूरल नेटवर्क्स को ट्रेन और डिप्लॉय करने के लिए डिज़ाइन किया गया है। यह एक GPU-एक्सेलेरेटेड मशीन लर्निंग इंजन के रूप में कार्य करता है जिसका कोर C++ में लागू किया गया है ताकि हाई-थ्रूपुट टेंसर ऑपरेशन्स को सक्षम किया जा सके। यह प्रोजेक्ट एक डिक्लेरेटिव कॉन्फ़िगरेशन सिस्टम का उपयोग करता है जहाँ मॉडल आर्किटेक्चर और हाइपरपैरामीटर्स को बाहरी टेक्स्ट फाइलों में परिभाषित किया जाता है, जो नेटवर्क डिज़ाइन को निष्पादन कोड से अलग करता है। इसमें विभिन्न हार्डवेयर वातावरणों में कुशल डिप्लॉयमेंट के लिए प्रशिक्षित वेट्स और टोपोलॉजी को बाइनरी फाइलों में एक्सपोर्ट करने के लिए एक मॉडल सीरियलाइजेशन सिस्टम शामिल है। यह फ्रेमवर्क न्यूरल नेटवर्क आर्किटेक्चर डिज़ाइन, ग्रेडिएंट-आधारित ऑप्टिमाइज़ेशन के साथ सुपरवाइज्ड मॉडल ट्रेनिंग और इमेज क्लासिफिकेशन वर्कफ़्लो सहित व्यापक क्षमताएं प्रदान करता है। यह डेटासेट प्रीप्रोसेसिंग, न्यूरल फीचर एक्सट्रैक्शन और प्री-ट्रेंड मॉडल्स की फाइन-ट्यूनिंग के लिए टूल्स प्रदान करता है। C++ कोर को Python और MATLAB के लिए आधिकारिक बाइंडिंग्स के साथ एक मल्टी-लैंग्वेज इंटरफेस के माध्यम से एक्सेस किया जा सकता है।
Exports optimized network parameters into binary formats for production execution on CPU or GPU.
C++ - tingsongyu/pytorch-tutorial-2nd
TingsongYu/PyTorch-Tutorial-2nd
4,555GitHub पर देखेंThis project is a comprehensive instructional resource and course for building neural networks using PyTorch. It covers the fundamental building blocks of deep learning, including tensor manipulation, automatic differentiation, and the construction of modular neural network components. The repository serves as a technical guide for several specialized domains. It provides implementation details for computer vision tasks such as image classification, object detection, and semantic segmentation, as well as natural language processing workflows involving transformers, recurrent networks, and gen
Converts models via ONNX and TensorRT to implement acceleration and quantization for production environments.
Jupyter Notebookcomputer-visiondeepsortdiffusion-models - iree-org/iree
iree-org/iree
3,819GitHub पर देखेंIREE is an MLIR-based compiler toolchain and runtime designed to translate machine learning models from various frameworks into optimized binaries for execution across diverse hardware targets. It provides a unified pipeline to ingest models from PyTorch, TensorFlow, JAX, and ONNX, lowering them into a common intermediate representation for deployment on CPUs, GPUs, and bare-metal embedded systems. The project distinguishes itself through a bytecode virtual machine and a hardware abstraction layer that decouple high-level model logic from specific hardware instruction sets. It supports sophis
Translates models from various frameworks into optimized binaries for execution on diverse hardware targets.
C++compilercudajax - onnx/onnx-tensorrt
onnx/onnx-tensorrt
3,187GitHub पर देखेंThis project is a deep learning model compiler and parser that converts ONNX models into optimized TensorRT engines. It functions as a bridge that maps standardized ONNX operators to vendor-specific kernels to enable high-performance inference on NVIDIA GPUs. The system operates as a GPU inference optimizer, selecting hardware-specific kernels and tuning memory allocation to maximize throughput. It transforms neural network graphs into serialized binary execution plans to reduce runtime overhead. The toolset covers deep learning model deployment and edge AI performance tuning. It includes ca
Preparing machine learning models for production by ensuring they can be parsed and executed efficiently on target devices.
C++deep-learningnvidiaonnx
