Search Results for "machine" - Page 2
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Showing 1802 open source projects for "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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tvm
Open deep learning compiler stack for cpu, gpu, etc.
Apache TVM is an open source machine learning compiler framework for CPUs, GPUs, and machine learning accelerators. It aims to enable machine learning engineers to optimize and run computations efficiently on any hardware backend. The vision of the Apache TVM Project is to host a diverse community of experts and practitioners in machine learning, compilers, and systems architecture to build an accessible, extensible, and automated open-source framework that optimizes current and emerging machine learning models for any hardware platform. ... -
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LXD
Powerful system container and virtual machine manager
LXD is a next-generation system container and virtual machine manager. It offers a unified user experience around full Linux systems running inside containers or virtual machines. LXD is image based and provides images for a wide number of Linux distributions. It provides flexibility and scalability for various use cases, with support for different storage backends and network types and the option to install on hardware ranging from an individual laptop or cloud instance to a full server rack. ... -
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Lima
Linux virtual machines, with a focus on running containers
Lima launches Linux virtual machines with automatic file sharing and port forwarding (similar to WSL2). -
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The LLVM Compiler Infrastructure
Collection of modular and reusable compiler and toolchain technologies
The LLVM Project is a collection of modular and reusable compiler and toolchain technologies. Despite its name, LLVM has little to do with traditional virtual machines. LLVM began as a research project at the University of Illinois, with the goal of providing a modern, SSA-based compilation strategy capable of supporting both static and dynamic compilation of arbitrary programming languages. Since then, LLVM has grown to be an umbrella project consisting of a number of subprojects, many of... -
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cAdvisor
Analyzes resource usage and performance characteristics
...You can run a single cAdvisor to monitor the whole machine. -
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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 best detailed configuration for you. ... -
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Rasa
Open source machine learning framework to automate text conversations
Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual assistants on Facebook Messenger, Slack, Google Hangouts, Webex Teams, Microsoft Bot Framework, Rocket.Chat, Mattermost, Telegram, and Twilio or on your own custom conversational channels. Rasa helps you build contextual assistants capable of having layered conversations with lots of back-and-forths. -
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MLJ
A Julia machine learning framework
MLJ (Machine Learning in Julia) is a toolbox written in Julia providing a common interface and meta-algorithms for selecting, tuning, evaluating, composing and comparing about 200 machine learning models written in Julia and other languages. The functionality of MLJ is distributed over several repositories illustrated in the dependency chart below. -
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DevPod
Codespaces but open-source, client-only and unopinionated
...Each developer environment runs in a container and is specified through a devcontainer.json. Through DevPod providers, these environments can be created on any backend, such as the local computer, a Kubernetes cluster, any reachable remote machine, or in a VM in the cloud. You can think of DevPod as the glue that connects your local IDE to a machine that you want to develop. So depending on the requirements of your project, you can either create a workspace locally on the computer, on a beefy cloud machine with many GPUs, or a spare remote computer. Within DevPod, every workspace is managed the same way, which also makes it easy to switch between workspaces that might be hosted somewhere else. -
PRTG Catches Network Issues Before They Cause DowntimeReactive troubleshooting usually means hearing about a problem from frustrated users, not your monitoring tool. PRTG sets threshold-based alerts across devices, servers and applications, notifying your team by email, SMS or push the moment a metric crosses a set limit. That means catching a failing disk or overloaded server before it becomes an outage and getting time back from firefighting. Start a free trial and set your first alerts today.
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DVC
Data Version Control | Git for Data & Models
DVC is built to make ML models shareable and reproducible. It is designed to handle large files, data sets, machine learning models, and metrics as well as code. Version control machine learning models, data sets and intermediate files. DVC connects them with code and uses Amazon S3, Microsoft Azure Blob Storage, Google Drive, Google Cloud Storage, Aliyun OSS, SSH/SFTP, HDFS, HTTP, network-attached storage, or disc to store file contents. Version control machine learning models, data sets, and intermediate files. ... -
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VirtualBuddy
Virtualize macOS 12 and later on Apple Silicon
VirtualBuddy can virtualize macOS 12 and later on Apple Silicon, with the goal of offering features that are useful to developers who need to test their apps on multiple versions of macOS, especially betas. -
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Quickemu
Quickly create and run optimized Windows, macOS and Linux
Quickly create and run optimized Windows, macOS, and Linux virtual machines. Quickemu is a wrapper for the excellent QEMU that automatically "does the right thing" when creating virtual machines. No requirement for exhaustive configuration options. You decide what operating system you want to run and Quickemu takes care of the rest. The original objective of the project was to enable quick testing of Linux distributions where the virtual machines and their configuration can be stored... -
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Windows (Dockur)
Windows inside a Docker container
This project makes it surprisingly easy to run a Windows environment inside a Docker container by using a QEMU-based virtual machine under the hood. It provides a turnkey image and a simple set of environment variables so you can select Windows editions, control disk persistence, and access the VM via a web-based VNC console or similar remote viewers. Because the VM is wrapped in Docker, you can treat Windows as a disposable, repeatable service: create, snapshot with volumes, tear down, and rebuild with consistent results. ... -
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GHDL
VHDL 2008/93/87 simulator
...GHDL runs on GNU/Linux, Windows and macOS; on x86, x86_64, armv6/armv7/aarch32, aarch64 and ppc64. You can freely download nightly assets, use OCI images (aka Docker/Podman containers), or try building it on your own machine. -
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Trellis
WordPress LEMP stack with PHP 8.1, Composer, WP-CLI
Trellis uses Vagrant to automatically create a self-contained virtual machine. Stop cluttering up your host machine with software like MAMP and use the same software you would in production. You’ll get a complete WordPress server running all the software you need to be configured according to the best practices. All of this is powered by Ansible for configuration management. You don’t have to use brittle and confusing Bash scripts or worry about commands you found to copy and paste. ... -
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Aglais XQVM
A rust implementation of the Quip Network's quantum virtual machine
...It integrates with the broader Quip ecosystem, allowing it to function as part of a larger protocol stack that includes blockchain infrastructure and node management tools. The virtual machine abstraction enables developers to define and execute computational logic in a controlled environment, potentially bridging classical and quantum paradigms. Its design suggests use cases in advanced computation, cryptography, or experimental distributed systems. -
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Deepchecks
Test Suites for validating ML models & data
Deepchecks is the leading tool for testing and for validating your machine learning models and data, and it enables doing so with minimal effort. Deepchecks accompany you through various validation and testing needs such as verifying your data’s integrity, inspecting its distributions, validating data splits, evaluating your model and comparing between different models. While you’re in the research phase, and want to validate your data, find potential methodological problems, and/or validate your model and evaluate it. ... -
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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. -
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Vagrant
Vagrant is a tool for building and distributing environments
...Download the open-source Vagrant binary and run it locally or within your environments. Vagrant is designed for everyone as the simplest and fastest way to create a virtualized environment. Single workflow to build and manage virtual machine environments. Declarative configuration file describes all the requirements and builds them through a consistent workflow. Mirror production environments by providing the same operating system, packages, users, and configurations, all while giving users the flexibility to use their favorite editor, IDE, and browser. Share files and folders between a local machine and the Vagrant box. ... -
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Sov.ai
A curated list of practical financial machine learning tools and apps
Financial Machine Learning is a curated directory of practical tools, repositories, datasets, papers, and educational resources for quantitative finance. It organizes material across trading, forecasting, portfolio construction, risk, alternative data, and financial machine learning techniques. Dedicated sections cover supervised and unsupervised learning, deep learning, reinforcement learning, natural language processing, and time-series analysis. -
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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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xqvm-py
A python implementation of the Quip Network's quantum virtual machine
xq-py is a Python implementation of the Quip Network’s quantum virtual machine, offering a more accessible and flexible alternative to the Rust-based version for experimentation and rapid development. It is designed to provide similar functionality to xq-rs while prioritizing ease of use, readability, and integration with Python-based data science and research tools. The project enables developers to simulate or prototype quantum-inspired computations without needing to work at a lower systems level. ... -
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Flower
Flower: A Friendly Federated Learning Framework
...Flower originated from a research project at the University of Oxford, so it was built with AI research in mind. Many components can be extended and overridden to build new state-of-the-art systems. Different machine learning frameworks have different strengths. Flower can be used with any machine learning framework, for example, PyTorch, TensorFlow, Hugging Face Transformers, PyTorch Lightning, scikit-learn, JAX, TFLite, MONAI, fastai, MLX, XGBoost, Pandas for federated analytics, or even raw NumPy for users who enjoy computing gradients by hand. -
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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. -
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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. ...
