Search Results for "compute" - Page 11
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Showing 646 open source projects for "compute"
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Singularity
Open source container platform designed to be simple, fast, and secure
Singularity is an open-source container platform designed to be simple, fast, and secure. Many container platforms are available, but Singularity is designed for ease of use on shared systems and in high-performance computing (HPC) environments. -
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FreeRTOS AWS Reference Integrations
IoT operating system for microcontrollers.
...A microcontroller contains a simple, resource-constrained processor that can be found in many devices, including appliances, sensors, fitness trackers, industrial automation, and automobiles. Many of these small devices can benefit from connecting to the cloud or locally to other devices, but have limited compute power and memory capacity. -
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Procgen
Procedurally-Generated Game-Like Gym-Environments
Procgen (short for Procedural Generation Benchmark) is a suite of 16 procedurally generated, game-like reinforcement learning environments designed to evaluate generalization and sample efficiency in RL agents. Unlike fixed, deterministic environments, Procgen generates new levels (layouts, obstacles, visual variation) each episode, making it impossible for an agent to simply memorize trajectories. The environments are designed to run very quickly (thousands of steps per second on a single... -
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...NESSIE is an electronic structure code that uses a real-space FEM discretization and domain-decomposition (DD) to perform all-electron ground-state DFT and real-time excited-state TDDFT calculations. The code is written to take advantage of multilevel parallelisms of the FEAST eigenvalue solver to target systems containing many distributed-memory compute nodes. The real space mesh is generated using the software tetgen.
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xcks
Calculates or verifies XCKS and ZXCKS checksums' files
...It is able to read and write XCKS and ZXCKS checksums' files. xcks comes with libxcks which can be used independently of xcks. libxcks is a C++17 library. It can compute checkums, read and write XCKS and ZXCKS checksums' files. xcks is licensed under GPLv3+. libxcks is licensed under LGPLv3+. -
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MACE
Deep learning inference framework optimized for mobile platforms
Mobile AI Compute Engine (or MACE for short) is a deep learning inference framework optimized for mobile heterogeneous computing on Android, iOS, Linux and Windows devices. Runtime is optimized with NEON, OpenCL and Hexagon, and Winograd algorithm is introduced to speed up convolution operations. The initialization is also optimized to be faster. -
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git-sizer
Compute various size metrics for a Git repository
git-sizer computes various size metrics for a local Git repository, flagging those that might cause you problems or inconvenience. Is the repository too big overall? Ideally, Git repositories should be under 1 GiB, and (without special handling) they start to get unwieldy over 5 GiB. Big repositories take a long time to clone and repack, and take a lot of disk space. Avoid storing generated files (e.g., compiler output, JAR files) in Git. It would be better to regenerate them when necessary,... -
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Intel compute-runtime
Intel® Graphics Compute Runtime
The Intel(R) Graphics Compute Runtime for oneAPI Level Zero and OpenCL(TM) Driver is an open source project providing compute API support (Level Zero, OpenCL) for Intel graphics hardware architectures (HD Graphics, Xe). -
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YeastpRofileSpearman
Software to calculate profiles around TSS and TTS of yeast mRNAs
...The data of TSS and TTS counts used to compute the cross-correlation profiles were taken from the work of Pelechano et al. (1). The input file must be a BedGraph formatted file based on yeast genome sequence release R64. The output file is a text tab-delimited table reporting Rs values for the various shifts of variable coordinates with respect to TSS or TTS. 1) V. Pelechano et al., Nature, 497 (2013) 127-13. -
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Trax
Deep learning with clear code and speed
Trax is an end-to-end library for deep learning that focuses on clear code and speed. It is actively used and maintained in the Google Brain team. Run a pre-trained Transformer, create a translator in a few lines of code. Features and resources, API docs, where to talk to us, how to open an issue and more. Walkthrough, how Trax works, how to make new models and train on your own data. Trax includes basic models (like ResNet, LSTM, Transformer) and RL algorithms (like REINFORCE, A2C, PPO). It... -
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gpt-2-simple
Python package to easily retrain OpenAI's GPT-2 text-generating model
...For finetuning, it is strongly recommended to use a GPU, although you can generate using a CPU (albeit much more slowly). If you are training in the cloud, using a Colaboratory notebook or a Google Compute Engine VM w/ the TensorFlow Deep Learning image is strongly recommended. (as the GPT-2 model is hosted on GCP) You can use gpt-2-simple to retrain a model using a GPU for free in this Colaboratory notebook, which also demos additional features of the package. Note: Development on gpt-2-simple has mostly been superceded by aitextgen, which has similar AI text generation capabilities with more efficient training time. -
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Vivid 3D
Vivid is a modern C++ 3D engine using OpenGL4+
Vivid is a modern C++ 3D engine using OpenGL4+. It is written using Visual C++ 2022, and relies on several open source projects to achieve it's goal of making it easy and run to make modern games with it. -
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Terraform Inventory
Terraform State, Ansible dynamic inventory
This is a little Go app that generates a dynamic Ansible inventory from a Terraform state file. It allows one to spawn a bunch of instances with Terraform, then (re-)provision them with Ansible. This library is stable, but I've been neglecting it somewhat on account of no longer using Ansible at work. On OSX, install it with Homebrew. Alternatively, you can download a release suitable for your platform and unzip it. Make sure the terraform-inventory binary is executable, and you're ready to... -
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Program to compute the observer position in astro-navigation calculating the true position of observed bodies.
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TimeSformer
The official pytorch implementation of our paper
...TimeSformer was influential in showing that pure transformer architectures—without convolutional backbones—can perform strongly on video classification tasks. Its flexible attention design allows experimenting with different factoring (spatial-then-temporal, joint, etc.) to trade off compute, memory, and accuracy. -
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lomb
Lomb_Scargle periodogram
...Researchers may want to use it without having to install and learn a scripting language such as R, Julia, or Python. In all of these languages, Lomb-Scargle periodograms modules are indeed available. Here, I provide executables for Mac, Linux, and Windows designed to compute the Lomb-Scargle periodogram for data stored in a comma separated file (.csv, as can be created e.g. in Microsoft Excel©. Written in Nim. -
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QuCAT
Quantum Circuit Analyzer Tool
...This open source python library provides standard analysis tools for superconducting electronic circuits, built around Josephson junctions. QuCAT features an intuitive graphical or programmatical interface to create circuits, the ability to compute their Hamiltonian, and a set of complimentary functionalities such as calculating dissipation rates or visualizing current flows in the circuit. QuCAT currently supports quantization in the basis of normal modes. -
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TensorFlow MacOS
TensorFlow for macOS 11.0+ accelerated using Apple's ML Compute
This repository provided a pre-release of TensorFlow and TensorFlow Addons tailored for macOS 11+ with native hardware acceleration via Apple’s ML Compute, supporting both Apple Silicon (M1) and Intel Macs. It shipped ready-made Python 3.8 wheels and install scripts so developers could quickly get an accelerated stack running without building from source. As TensorFlow added a Metal PluggableDevice path, the project directed users toward using Apple’s tensorflow-metal to get GPU acceleration on Mac directly through Metal. ... -
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XLM (Cross-lingual Language Model)
PyTorch original implementation of Cross-lingual Language Model
...The repository provides preprocessing pipelines, training code, and fine-tuning scripts so you can reproduce benchmark results or adapt models to your own multilingual corpora. Pretrained checkpoints cover dozens of languages and multiple model sizes, balancing quality and compute needs. -
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Swift for TensorFlow
Swift for TensorFlow
...By embedding machine learning functionality into the Swift compiler and language design, the project enables developers to write high-performance machine learning models while maintaining the readability and safety of modern programming practices. Swift for TensorFlow also introduces tools that allow developers to compute gradients automatically, which is essential for training neural networks through gradient-based optimization. -
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SPIRE
A tool to compute projections of membrane structures
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constructs-go
Golang bindings for constructs
...A construct can represent a single AWS resource, such as an Amazon Simple Storage Service (Amazon S3) bucket, or it can be a higher-level abstraction consisting of multiple AWS related resources. Examples of such components include a worker queue with its associated compute capacity, or a scheduled job with monitoring resources and a dashboard. The AWS CDK includes a collection of constructs called the AWS Construct Library, containing constructs for every AWS service. -
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TTS
Deep learning for text to speech
...Notebooks for extensive model benchmarking. Modular (but not too much) code base enabling easy testing for new ideas. Text2Spec models (Tacotron, Tacotron2, Glow-TTS, SpeedySpeech). Speaker Encoder to compute speaker embeddings efficiently. Vocoder models (MelGAN, Multiband-MelGAN, GAN-TTS, ParallelWaveGAN, WaveGrad, WaveRNN). If you are only interested in synthesizing speech with the released TTS models, installing from PyPI is the easiest option. -
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parallel_hmmer
Lets HMMER programs use databases presplit to compute nodes.
Modified HMMER that can utilize databases split across compute nodes, as opposed to using one large database on the master node and moving all of the data across the network on each run. Also implements a web server interface which is aware of these changes. -
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RT Schedule
A realtime-schedule generator for educational purposes
RT Schedule is a realtime-schedule generator to create and present simple and idealised RT schedules from task lists. The purpose is to compute the basic properties of a task set and create a schedule using the rate monotonic (RM), earliest deadline first (EDF) and least laxity first (LLF) algorithms. The program uses simple, idealised systems, since students must be able to schedule them manually. RT Schedule computes the data and generates clear graphs for lecture notes and exams.
