Search Results for "tensorflow"
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Showing 268 open source projects for "tensorflow"
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TensorFlow
TensorFlow is an open source library for machine learning
Tensorflow can also be used for research and production with TensorFlow Extended. -
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TensorFlow Datasets
TFDS is a collection of datasets ready to use with TensorFlow,
TensorFlow Datasets is a collection of datasets ready to use, with TensorFlow or other Python ML frameworks, such as Jax. All datasets are exposed as tf.data. Datasets , enabling easy-to-use and high-performance input pipelines. To get started see the guide and our list of datasets. -
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TensorFlow Serving
Serving system for machine learning models
...In order to serve a Tensorflow model, simply export a SavedModel from your Tensorflow program. SavedModel is a language-neutral, recoverable, hermetic serialization format that enables higher-level systems and tools to produce, consume, and transform TensorFlow models. -
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TensorFlow Probability
Probabilistic reasoning and statistical analysis in TensorFlow
TensorFlow Probability is a library for probabilistic reasoning and statistical analysis. TensorFlow Probability (TFP) is a Python library built on TensorFlow that makes it easy to combine probabilistic models and deep learning on modern hardware (TPU, GPU). It's for data scientists, statisticians, ML researchers, and practitioners who want to encode domain knowledge to understand data and make predictions. -
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TensorFlow Privacy
Library for training machine learning models with privacy for data
Library for training machine learning models with privacy for training data. This repository contains the source code for TensorFlow Privacy, a Python library that includes implementations of TensorFlow optimizers for training machine learning models with differential privacy. The library comes with tutorials and analysis tools for computing the privacy guarantees provided. -
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SageMaker TensorFlow
SageMaker specific extensions to TensorFlow
This package contains SageMaker-specific extensions to TensorFlow, including the PipeModeDataset class, that allows SageMaker Pipe Mode channels to be read using TensorFlow Datasets. This package supports Python 3.7-3.9 and TensorFlow versions 1.7 and higher, including 2.0-2.9.1. For TensorFlow 1.x support, see the master branch. sagemaker-tensorflow releases for all supported versions are available on PyPI. -
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TensorFlow.js
TensorFlow.js is a library for machine learning in JavaScript
TensorFlow.js is a library for machine learning in JavaScript. Develop ML models in JavaScript, and use ML directly in the browser or in Node.js. Use off-the-shelf JavaScript models or convert Python TensorFlow models to run in the browser or under Node.js. Retrain pre-existing ML models using your own data. Build and train models directly in JavaScript using flexible and intuitive APIs. Tensors are the core datastructure of TensorFlow.js They are a generalization of vectors and matrices to potentially higher dimensions. Built on top of TensorFlow.js, the ml5.js library provides access to machine learning algorithms and models in the browser with a concise, approachable API. ... -
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TensorFlow Model Garden
Models and examples built with TensorFlow
The TensorFlow Model Garden is a repository with a number of different implementations of state-of-the-art (SOTA) models and modeling solutions for TensorFlow users. We aim to demonstrate the best practices for modeling so that TensorFlow users can take full advantage of TensorFlow for their research and product development. To improve the transparency and reproducibility of our models, training logs on TensorBoard.dev are also provided for models to the extent possible though not all models are suitable. ... -
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SageMaker TensorFlow Training Toolkit
Toolkit for running TensorFlow training scripts on SageMaker
Toolkit for running TensorFlow training scripts on SageMaker. SageMaker TensorFlow Training Toolkit is an open-source library for using TensorFlow to train models on Amazon SageMaker. To use your TensorFlow Serving model on SageMaker, you first need to create a SageMaker Model. After creating a SageMaker Model, you can use it to create SageMaker Batch Transform Jobs for offline inference, or create SageMaker Endpoints for real-time inference. -
The most advanced C and C++ source code analyzerTrustInSoft Analyzer is a C and C++ source code analyzer powered by formal methods, mathematical & logical reasonings that allow for exhaustive analysis of source code. This analysis can be run without false positives or false negatives, so that every real bug in the code is found. Developers receive several benefits: a user-friendly graphical interface that directs developers to the root cause of bugs, and instant utility to expand the coverage of their existing tests. Unlike traditional source code analysis tools, TrustInSoft’s solution is not only the most comprehensive approach on the market but is also progressive, instantly deployable by developers, even if they lack experience with formal methods, from exhaustive analysis up to a functional proof that the software developed meets specifications.
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TensorFlow Model Optimization Toolkit
A toolkit to optimize ML models for deployment for Keras & TensorFlow
...Choose the model and optimization tool depending on your task. In many cases, pre-optimized models can improve the efficiency of your application. Try the post-training tools to optimize an already-trained TensorFlow model. Use training-time optimization tools and learn about the techniques. -
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GPflow
Gaussian processes in TensorFlow
GPflow is a package for building Gaussian process models in Python. It implements modern Gaussian process inference for composable kernels and likelihoods. GPflow builds on TensorFlow 2.4+ and TensorFlow Probability for running computations, which allows fast execution on GPUs. -
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Kubeflow
Machine Learning Toolkit for Kubernetes
...With Kubeflow you can deploy best-of-breed open-source systems for ML to diverse infrastructures. You can also take advantage of a number of great features, such as services for managing Jupyter notebooks and support for a TensorFlow Serving container. Wherever you may be running Kubernetes, you can run Kubeflow as well. -
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Netron
Visualizer for neural network, deep learning, machine learning models
Netron is a viewer for neural network, deep learning and machine learning models. Netron supports ONNX, Keras, TensorFlow Lite, Caffe, Darknet, Core ML, MNN, MXNet, ncnn, PaddlePaddle, Caffe2, Barracuda, Tengine, TNN, RKNN, MindSpore Lite, and UFF. Netron has experimental support for TensorFlow, PyTorch, TorchScript, OpenVINO, Torch, Arm NN, BigDL, Chainer, CNTK, Deeplearning4j, MediaPipe, ML.NET, scikit-learn, TensorFlow.js. There is an extense variety of sample model files to download or open using the browser version. ... -
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Kubeflow Training Operator
Distributed ML Training and Fine-Tuning on Kubernetes
Kubeflow Training Operator is a Kubernetes-native project for fine-tuning and scalable distributed training of machine learning (ML) models created with various ML frameworks such as PyTorch, TensorFlow, XGBoost, MPI, Paddle, and others. -
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talos
Hyperparameter Optimization for TensorFlow, Keras and PyTorch
Talos radically changes the ordinary Keras, TensorFlow (tf.keras), and PyTorch workflow by fully automating hyperparameter tuning and model evaluation. Talos exposes Keras and TensorFlow (tf.keras) and PyTorch functionality entirely and there is no new syntax or templates to learn. Talos is made for data scientists and data engineers that want to remain in complete control of their TensorFlow (tf.keras) and PyTorch models, but are tired of mindless parameter hopping and confusing optimization solutions that add complexity instead of reducing it. ... -
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AWS Deep Learning Containers
A set of Docker images for training and serving models in TensorFlow
AWS Deep Learning Containers (DLCs) are a set of Docker images for training and serving models in TensorFlow, TensorFlow 2, PyTorch, and MXNet. Deep Learning Containers provide optimized environments with TensorFlow and MXNet, Nvidia CUDA (for GPU instances), and Intel MKL (for CPU instances) libraries and are available in the Amazon Elastic Container Registry (Amazon ECR). The AWS DLCs are used in Amazon SageMaker as the default vehicles for your SageMaker jobs such as training, inference, transforms etc. ... -
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frugally-deep
A lightweight header-only library for using Keras (TensorFlow) models
Use Keras models in C++ with ease. A lightweight header-only library for using Keras (TensorFlow) models in C++. Works out-of-the-box also when compiled into a 32-bit executable. (Of course, 64 bit is fine too.) Avoids temporarily allocating (potentially large chunks of) additional RAM during convolutions (by not materializing the im2col input matrix). Utterly ignores even the most powerful GPU in your system and uses only one CPU core per prediction. -
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Tensorforce
A TensorFlow library for applied reinforcement learning
Tensorforce is an open-source deep reinforcement learning framework built on TensorFlow, emphasizing modularized design and straightforward usability for applied research and practice. -
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Foolbox
Python toolbox to create adversarial examples
...It is built on top of EagerPy and works natively with models in PyTorch, TensorFlow, and JAX. -
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OpenVINO
OpenVINO™ Toolkit repository
...It supports pre-trained models from the Open Model Zoo, along with 100+ open source and public models in popular formats such as TensorFlow, ONNX, PaddlePaddle, MXNet, Caffe, Kaldi. -
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TensorLy
Tensor Learning in Python
...It allows to easily perform tensor decomposition, tensor learning and tensor algebra. Its backend system allows to seamlessly perform computation with NumPy, PyTorch, JAX, TensorFlow, CuPy or Paddle, and run methods at scale on CPU or GPU. -
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ktrain
ktrain is a Python library that makes deep learning AI more accessible
...If you need a newer version of transformers, it is usually safe for you to upgrade transformers, as long as you do it after installing ktrain. As of v0.30.x, TensorFlow installation is optional and only required if training neural networks. -
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Model Explorer
A modern model graph visualizer and debugger
Model Explorer is a visual tool for exploring, debugging, and optimizing ML models deployed on edge devices. Developed by Google AI Edge, it offers a browser-based interface to inspect layer-wise performance, memory usage, and inference timing of TensorFlow Lite and other supported models. It’s a powerful utility for developers optimizing models for constrained environments. -
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Frigate
NVR with realtime local object detection for IP cameras
Frigate - NVR With Realtime Object Detection for IP Cameras A complete and local NVR designed for Home Assistant with AI object detection. Uses OpenCV and Tensorflow to perform realtime object detection locally for IP cameras. Use of a Google Coral Accelerator is optional, but highly recommended. The Coral will outperform even the best CPUs and can process 100+ FPS with very little overhead. -
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TensorFlow Addons
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
TensorFlow Addons is a repository of contributions that conform to well-established API patterns but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast-moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the community). ...