Search Results for "virtual windows machine" - Page 5
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Showing 2000 open source projects for "virtual windows machine"
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MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
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AWS Neuron
Powering Amazon custom machine learning chips
AWS Neuron is a software development kit (SDK) for running machine learning inference using AWS Inferentia chips. It consists of a compiler, run-time, and profiling tools that enable developers to run high-performance and low latency inference using AWS Inferentia-based Amazon EC2 Inf1 instances. Using Neuron developers can easily train their machine learning models on any popular framework such as TensorFlow, PyTorch, and MXNet, and run it optimally on Amazon EC2 Inf1 instances. You can... -
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Fishing Funds
Fund, big market, stock, virtual currency status bar display for apps
Display real-time trends of Chinese funds in the menubar. Fund, big market, stock, virtual currency status bar displays small applications, developed based on Electron, supports MacOS, Windows, Linux clients, data sources come from Tiantian Fund, Ant Fund, Love Fund, Tencent Securities, Sina Fund, etc. This project refers to electron-react-boilerplate-menubar, which is developed based on Electron React Boilerplate and menubar. -
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Valet Linux
A fork of Laravel Valet to work in Linux.
Valet Linux is a Laravel development environment for Linux minimalists. No Vagrant, no /etc/hosts file. You can even share your sites publicly using local tunnels. Yeah, we like it too. Valet Linux configures your system to always run Nginx in the background when your machine starts. Then, using DnsMasq, Valet proxies all requests on the *.test domain to point to sites installed on your local machine. In other words, a blazing-fast Laravel development environment that uses roughly 7MB of... -
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Solidity
The Smart Contract Programming Language
Solidity is a statically-typed curly-braces programming language designed for developing smart contracts that run on Ethereum. Solidity 0.8.4 adds custom structured errors, allows more flexible configuration of the SMTChecker and fixes a bug in the Solidity ABI decoder v2. Read more about the bug here. With v0.8.4, errors can be defined inside and outside of contracts (including interfaces and libraries). This provides a convenient and gas-efficient way to explain to users why an operation... -
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Google Translate PHP
Free Google Translate API PHP Package
A simple and effective PHP library for translating text using Google Translate without needing an API key. It allows developers to integrate real-time translation features into their applications with minimal setup and supports multiple languages, leveraging Google Translate’s unofficial endpoint. -
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DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
DeepCTR-Torch is an easy-to-use, Modular and Extendible package of deep-learning-based CTR models along with lots of core components layers that can be used to build your own custom model easily.It is compatible with PyTorch.You can use any complex model with model.fit() and model.predict(). With the great success of deep learning, DNN-based techniques have been widely used in CTR estimation tasks. The data in the CTR estimation task usually includes high sparse,high cardinality categorical... -
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Karpathy-Inspired Claude Code Guidelines
A single CLAUDE.md file to improve Claude Code behavior
Karpathy-Inspired Claude Code Guidelines is a curated learning and experimentation repository inspired by the work and teaching philosophy of Andrej Karpathy, designed to help learners build practical competence in deep learning, neural networks, and AI infrastructure. The project organizes a progressive path through exercises, notebooks, code examples, and practical mini-projects that echo Karpathy’s approach to “learning by doing,” where students build core concepts from first principles... -
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Chicory
Native JVM WebAssembly runtime
Chicory is a WebAssembly runtime designed specifically for the Java Virtual Machine, enabling developers to run Wasm modules entirely within Java applications without requiring native dependencies or JNI. Implemented in pure Java, it provides a portable and secure execution environment that can run anywhere the JVM is available, including cloud, desktop, and embedded systems. The runtime loads WebAssembly binaries and exposes their functions as callable Java APIs, allowing seamless integration with existing Java codebases. ... -
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Lux
The Lux Programming Language
Lux is a new programming language in the making. It's meant to be a functional, statically-typed Lisp that will run on several platforms, such as the Java Virtual Machine and JavaScript, Python, Lua, or Ruby interpreters. Lux is in the beta stage. The JVM compiler is pretty stable and the standard library has grown to a respectable size. Also, new experimental support for JavaScript, Python, Lua, and Ruby has been added. Read carefully before using this project, as the license disallows commercial use, and has other conditions which may be undesirable for some. ... -
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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... -
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XGBoost
Scalable and Flexible Gradient Boosting
XGBoost is an optimized distributed gradient boosting library, designed to be scalable, flexible, portable and highly efficient. It supports regression, classification, ranking and user defined objectives, and runs on all major operating systems and cloud platforms. XGBoost works by implementing machine learning algorithms under the Gradient Boosting framework. It also offers parallel tree boosting (GBDT, GBRT or GBM) that can quickly and accurately solve many data science problems.... -
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The Julia Programming Language
High-level, high-performance dynamic language for technical computing
Julia is a fast, open source high-performance dynamic language for technical computing. It can be used for data visualization and plotting, deep learning, machine learning, scientific computing, parallel computing and so much more. Having a high level syntax, Julia is easy to use for programmers of every level and background. Julia has more than 2,800 community-registered packages including various mathematical libraries, data manipulation tools, and packages for general purpose... -
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Final Cut
A text-based widget toolkit
...It provides (as an overlying layer) virtual windows for the realization of window movements. The update methods only transfer differences to the virtual terminal or physical screen. -
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AutoKeras
AutoML library for deep learning
AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. AutoKeras only support Python 3. If you followed previous steps to use virtualenv to install tensorflow, you can just activate the virtualenv. Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0. AutoKeras supports several tasks with extremely simple interface. AutoKeras would search for the... -
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Metaflow
A framework for real-life data science
Metaflow is a human-friendly Python library that helps scientists and engineers build and manage real-life data science projects. Metaflow was originally developed at Netflix to boost productivity of data scientists who work on a wide variety of projects from classical statistics to state-of-the-art deep learning. -
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Apache Spark
A unified analytics engine for large-scale data processing
Apache Spark is a unified engine for large-scale data processing, offering APIs for batch jobs, streaming, machine learning, and graph computation. It builds on resilient distributed datasets (RDDs) and the newer DataFrame/Dataset abstractions to provide fault-tolerant, in-memory computation across clusters. Spark’s execution engine handles scheduling, shuffles, caching, and data locality so users can focus on transformations rather than infrastructure plumbing. With Spark Streaming... -
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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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CosmoScout VR
Virtual universe which lets you explore & analyze planetary datasets
A virtual universe which lets you explore, analyze and present huge planetary datasets and large simulation data in real-time. CosmoScout VR is a modular virtual universe developed at the German Aerospace Center (DLR). It lets you explore, analyze and present huge planetary data sets and large simulation data in real-time. The software can be built on Linux (gcc or clang) and Windows (msvc). -
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dlib
Toolkit for making machine learning and data analysis applications
Dlib is a modern C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems. It is used in both industry and academia in a wide range of domains including robotics, embedded devices, mobile phones, and large high performance computing environments. Dlib's open source licensing allows you to use it in any application, free of charge. -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and... -
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kube-vip
Kubernetes Control Plane Virtual IP and Load-Balancer
kube-vip provides Kubernetes clusters with a virtual IP and load balancer for both the control plane (for building a highly-available cluster) and Kubernetes Services of type LoadBalancer without relying on any external hardware or software. ARP is a layer 2 protocol that is used to inform the network of the location of a new address. When a new IP address is configured to a device, there needs to be a mechanism to inform the network of which piece of hardware is hosting this new address.... -
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Netis Cloud Probe
A Software Probe for network packet capturing and forwarding in Cloud
Netis Cloud Probe (Packet Agent, name used before)is an open source project to deal with such a situation: it captures packets on Machine A but has to use them on Machine B. This case is very common when you try to monitor network traffic in the LAN. -
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PackageCompiler
Compile your Julia Package
Julia is, in general, a "just-barely-ahead-of-time" compiled language. When you call a function for the first time, Julia compiles it for precisely the types of arguments given. This can take some time. All subsequent calls within that same session use this fast compiled function, but if you restart Julia you lose all the compiled work. PackageCompiler allows you to do this work upfront — further ahead of time — and store the results for a lower latency startup. You can save loaded packages... -
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Dominator
Zero-cost ultra-high-performance declarative DOM library using FRP
Dominator is a high-performance declarative DOM library for Rust that focuses on building web applications with minimal overhead by directly manipulating real DOM nodes instead of relying on a virtual DOM. It uses a functional reactive programming model based on signals, allowing UI components to automatically update in response to state changes in an efficient and predictable manner. The library is designed to be “zero-cost,” meaning that abstractions compile down to highly optimized code... -
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SDGym
Benchmarking synthetic data generation methods
...Select any of the publicly available datasets from the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device.
