Search Results for "gpu-z" - Page 3
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Showing 226 open source projects for "gpu-z"
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Kata Containers
Build a standard implementation of lightweight Virtual Machines (VMs)
... of Intel Clear Containers with Hyper.sh RunV and scaled to include support for major architectures including AMD64, ARM, IBM p-series, and IBM z-series in addition to x86_64. Kata Containers also supports multiple hypervisors including QEMU, Cloud-Hypervisor, and Firecracker, and integrates with the containerd project among others. -
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Isaac ROS Visual SLAM
Visual SLAM/odometry package based on NVIDIA-accelerated cuVSLAM
Discover a faster, easier way to build advanced AI robotics applications with the NVIDIA Isaac™ ROS collection of accelerated computing packages and AI models, bringing NVIDIA acceleration to ROS developers everywhere. Isaac ROS Visual SLAM provides a high-performance, best-in-class ROS 2 package for VSLAM (visual simultaneous localization and mapping). This package uses one or more stereo cameras and optionally an IMU to estimate odometry as an input to navigation. It is GPU-accelerated... -
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OpenCost
Cost monitoring for Kubernetes workloads and cloud costs
OpenCost is a vendor-neutral open-source project for measuring and allocating cloud infrastructure and container costs in real-time. Built by Kubernetes experts and supported by Kubernetes practitioners, OpenCost shines a light into the black box of Kubernetes spending. Flexible, customizable cost allocation and cloud resource monitoring for accurate showback, chargeback, and ongoing reporting. Dynamic asset pricing, through integrations with AWS, Azure, and GCP billing APIs as well as... -
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ProbabilisticCircuits.jl
Probabilistic Circuits from the Juice library
This module provides a Julia implementation of Probabilistic Circuits (PCs), tools to learn structure and parameters of PCs from data, and tools to do tractable exact inference with them. Probabilistic Circuits provides a unifying framework for several family of tractable probabilistic models. PCs are represented as computational graphs that define a joint probability distribution as recursive mixtures (sum units) and factorizations (product units) of simpler distributions (input units).... -
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libctru
Homebrew development library for Nintendo 3DS/Horizon OS user mode
... by devkitPro) is the officially supported ARM cross-compiling toolchain, which provides the framework necessary to supply a usable POSIX-like environment, with working C and C++ standard libraries; as well as the tools required to compile homebrew in the 3DSX format, and assemble GPU shaders. The use of other ARM toolchains is severely discouraged. -
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DocArray
The data structure for multimodal data
... science powerhouse: greatly accelerate data scientists’ work on embedding, k-NN matching, querying, visualizing, evaluating via Torch/TensorFlow/ONNX/PaddlePaddle on CPU/GPU. Data in transit: optimized for network communication, ready-to-wire at anytime with fast and compressed serialization in Protobuf, bytes, base64, JSON, CSV, DataFrame. Perfect for streaming and out-of-memory data. One-stop k-NN: Unified and consistent API for mainstream vector databases. -
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Contour
Modern C++ Terminal Emulator
contour is a modern and actually fast, modal, virtual terminal emulator, for everyday use. It is aimed at power users with a modern feature mindset. Available on all 4 major platforms, Linux, OS/X, FreeBSD, Windows. GPU-accelerated rendering. Font ligatures support (such as in Fira Code). Unicode: Emoji support (-: 🌈 💝 😛 👪 - including ZWJ, VS15, VS16 emoji :-) Unicode: Grapheme cluster support. Bold and italic fonts. High-DPI support. Vertical Line Markers (quickly jump to markers in your... -
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Petastorm
Petastorm library enables single machine or distributed training
Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code. Petastorm is an open-source data access library developed at Uber ATG. This library enables single machine or distributed training and evaluation of deep learning models directly from datasets in Apache Parquet format. Petastorm supports popular... -
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PennyLane
A cross-platform Python library for differentiable programming
..., PyTorch, JAX, or TensorFlow, allowing hybrid CPU-GPU-QPU computations. The same quantum circuit model can be run on different devices. Install plugins to run your computational circuits on more devices, including Strawberry Fields, Amazon Braket, Qiskit and IBM Q, Google Cirq, Rigetti Forest, and the Microsoft QDK. -
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Lightly
A python library for self-supervised learning on images
A python library for self-supervised learning on images. We, at Lightly, are passionate engineers who want to make deep learning more efficient. That's why - together with our community - we want to popularize the use of self-supervised methods to understand and curate raw image data. Our solution can be applied before any data annotation step and the learned representations can be used to visualize and analyze datasets. This allows selecting the best core set of samples for model training... -
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Deep Java Library (DJL)
An engine-agnostic deep learning framework in Java
... favorite IDE to build, train, and deploy your models. DJL makes it easy to integrate these models with your Java applications. Because DJL is deep learning engine agnostic, you don't have to make a choice between engines when creating your projects. You can switch engines at any point. To ensure the best performance, DJL also provides automatic CPU/GPU choice based on hardware configuration. -
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BentoML
Unified Model Serving Framework
... to scale separately from the serving logic. Adaptive batching dynamically groups inference requests for optimal performance. Orchestrate distributed inference graph with multiple models via Yatai on Kubernetes. Easily configure CUDA dependencies for running inference with GPU. Automatically generate docker images for production deployment. -
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed... -
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Ray
A unified framework for scalable computing
Modern workloads like deep learning and hyperparameter tuning are compute-intensive and require distributed or parallel execution. Ray makes it effortless to parallelize single machine code — go from a single CPU to multi-core, multi-GPU or multi-node with minimal code changes. Accelerate your PyTorch and Tensorflow workload with a more resource-efficient and flexible distributed execution framework powered by Ray. Accelerate your hyperparameter search workloads with Ray Tune. Find the best... -
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oneDNN
oneAPI Deep Neural Network Library (oneDNN)
... architectures: Arm* 64-bit Architecture (AArch64), NVIDIA* GPU, OpenPOWER* Power ISA (PPC64), IBMz* (s390x), and RISC-V. oneDNN is intended for deep learning applications and framework developers interested in improving application performance on Intel CPUs and GPUs. Deep learning practitioners should use one of the applications enabled with oneDNN. -
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ArrayFire
ArrayFire, a general purpose GPU library
ArrayFire is a general-purpose tensor library that simplifies the process of software development for the parallel architectures found in CPUs, GPUs, and other hardware acceleration devices. The library serves users in every technical computing market. Data structures in ArrayFire are smartly managed to avoid costly memory transfers and to take advantage of each performance feature provided by the underlying hardware. The community of ArrayFire developers invites you to build with us if... -
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TensorFlow Model Garden
Models and examples built with TensorFlow
... are suitable. A flexible and lightweight library that users can easily use or fork when writing customized training loop code in TensorFlow 2.x. It seamlessly integrates with tf.distribute and supports running on different device types (CPU, GPU, and TPU). -
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Ebiten
A dead simple 2D game library for Go
...), and even on mobile (Android and iOS)! Plus, Ebiten is implemented in pure Go on Windows, so Windows developers do not need to install a C compiler. Nintendo Switch™ is also supported! While Ebiten's drawing API is very simple, Ebiten games run very fast with GPU power. Multiple images are integrated into a texture atlas internally, and drawing operations are automatically performed in batch when possible. -
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PyTorch Geometric
Geometric deep learning extension library for PyTorch
... of functionality of PyTorch Geometric to other packages, which needs to be additionally installed. These packages come with their own CPU and GPU kernel implementations based on C++/CUDA extensions. We do not recommend installation as root user on your system python. Please setup an Anaconda/Miniconda environment or create a Docker image. We provide pip wheels for all major OS/PyTorch/CUDA combinations. -
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Darknet YOLO
Real-Time Object Detection for Windows and Linux
This is YOLO-v3 and v2 for Windows and Linux. YOLO (You only look once) is a state-of-the-art, real-time object detection system of Darknet, an open source neural network framework in C. YOLO is extremely fast and accurate. It uses a single neural network to divide a full image into regions, and then predicts bounding boxes and probabilities for each region. This project is a fork of the original Darknet project. -
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Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
Implementation of 'lightweight' GAN proposed in ICLR 2021, in Pytorch. The main contribution of the paper is a skip-layer excitation in the generator, paired with autoencoding self-supervised learning in the discriminator. Quoting the one-line summary "converge on single gpu with few hours' training, on 1024 resolution sub-hundred images". Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before... -
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waifu2x ncnn Vulkan
waifu2x converter ncnn version, run fast GPU with vulkan
ncnn implementation of waifu2x converter. Runs fast on Intel/AMD/Nvidia/Apple-Silicon with Vulkan API. waifu2x-ncnn-vulkan uses ncnn project as the universal neural network inference framework. -
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Community Z Tools
Tool support for the Z formal notation
Community Z Tools Project (CZT): Tools for editing, typechecking and animating Z specifications and related notations. Includes a Java framework for building formal methods tools. NOTE: development of CZT has now moved to GitHub: https://github.com/community-z-users/czt -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the... -
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SageMaker MXNet Inference Toolkit
Toolkit for allowing inference and serving with MXNet in SageMaker
... 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. They've been tested for machine learning workloads on Amazon EC2, Amazon ECS and Amazon EKS services as well.