Search Results for "virtual windows machine"
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Showing 314 open source projects for "virtual windows machine"
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Crosvm
The Chrome OS Virtual Machine Monitor
crosvm (ChromeOS Virtual Machine Monitor) is a secure, lightweight virtual machine monitor built on top of the Linux KVM hypervisor. Developed for ChromeOS, it is designed to isolate and execute Linux and Android guests efficiently while maintaining strong security boundaries. Unlike general-purpose emulators like QEMU, crosvm avoids full hardware emulation and focuses on modern paravirtualized I/O using the virtio standard, reducing complexity and attack surface. ... -
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Slack Machine
A simple, yet powerful and extendable Slack bot
Slack Machine is a simple, yet powerful and extendable Slack bot framework. More than just a bot, Slack Machine is a framework that helps you develop your Slack workspace into a ChatOps powerhouse. Slack Machine is built with an intuitive plugin system that lets you build bots quickly but also allows for easy code organization. A plugin can look as simple as this: -
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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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Homemade Machine Learning
Python examples of popular machine learning algorithms
homemade-machine-learning is a repository by Oleksii Trekhleb containing Python implementations of classic machine-learning algorithms done “from scratch”, meaning you don’t rely heavily on high-level libraries but instead write the logic yourself to deepen understanding. Each algorithm is accompanied by mathematical explanations, visualizations (often via Jupyter notebooks), and interactive demos so you can tweak parameters, data, and observe outcomes in real time. The purpose is... -
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Docker-OSX
Run macOS VM in a Docker! Run near native OSX-KVM in Docker
Run Mac OS X in Docker with near-native performance! X11 Forwarding. iMessage security research! iPhone USB working! macOS in a Docker container. -
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Best-of Machine Learning with Python
A ranked list of awesome machine learning Python libraries
This curated list contains 900 awesome open-source projects with a total of 3.3M stars grouped into 34 categories. All projects are ranked by a project-quality score, which is calculated based on various metrics automatically collected from GitHub and different package managers. If you like to add or update projects, feel free to open an issue, submit a pull request, or directly edit the projects.yaml. Contributions are very welcome! General-purpose machine learning and deep learning... -
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Brownie
A Python-based development and testing framework for smart contracts
Brownie is a Python-based development and testing framework for smart contracts targeting the Ethereum Virtual Machine. Powerful debugging tools, including python-style tracebacks and custom error strings. The recommended way to install Brownie is via pipx. pipx installs Brownie into a virtual environment and makes it available directly from the command-line. Once installed, you will never have to activate a virtual environment prior to using Brownie. ... -
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Rasa
Open source machine learning framework to automate text conversations
...If you want to build it from the source, you have to install Poetry first. By default, Poetry will try to use the currently activated Python version to create the virtual environment for the current project automatically. -
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Otter-Grader
A Python and R autograding solution
...Otter supports local grading through parallel Docker containers, grading using the autograder platforms of 3rd party learning management systems (LMSs), the deployment of an Otter-managed grading virtual machine, and a client package that allows students to run public checks on their own machines. Otter is designed to grade Python scripts and Jupyter Notebooks, and is compatible with a few different LMSs, including Canvas and Gradescope. -
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Django friendly finite state machine
Django friendly finite state machine support
Django-fsm adds simple declarative state management for Django models. If you need parallel task execution, view, and background task code reuse over different flows - check my new project Django-view flow. Instead of adding a state field to a Django model and managing its values by hand, you use FSMField and mark model methods with the transition decorator. These methods could contain side effects of the state change. You may also take a look at the Django-fsm-admin project containing a... -
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Gradio
Create UIs for your machine learning model in Python in 3 minutes
Gradio is the fastest way to demo your machine learning model with a friendly web interface so that anyone can use it, anywhere! Gradio can be installed with pip. Creating a Gradio interface only requires adding a couple lines of code to your project. You can choose from a variety of interface types to interface your function. Gradio can be embedded in Python notebooks or presented as a webpage. A Gradio interface can automatically generate a public link you can share with colleagues that... -
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Flower
Flower: A Friendly Federated Learning Framework
A unified approach to federated learning, analytics, and evaluation. Federate any workload, any ML framework, and any programming language. Federated learning systems vary wildly from one use case to another. Flower allows for a wide range of different configurations depending on the needs of each individual use case. 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... -
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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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Poetry
Python packaging and dependency management made easy
Poetry is a comprehensive Python dependency management and packaging tool that streamlines project setup and maintenance. By consolidating various configuration files into a single pyproject.toml, it simplifies the management of dependencies, virtual environments, and package publishing. Poetry ensures consistency across development environments and facilitates the creation of reproducible builds, making it an essential tool for Python developers seeking efficiency and reliability. -
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Seldon Core
An MLOps framework to package, deploy, monitor and manage models
The de facto standard open-source platform for rapidly deploying machine learning models on Kubernetes. Seldon Core, our open-source framework, makes it easier and faster to deploy your machine learning models and experiments at scale on Kubernetes. Seldon Core serves models built in any open-source or commercial model building framework. You can make use of powerful Kubernetes features like custom resource definitions to manage model graphs. And then connect your continuous integration and... -
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TensorFlow
TensorFlow is an open source library for machine learning
Originally developed by Google for internal use, TensorFlow is an open source platform for machine learning. Available across all common operating systems (desktop, server and mobile), TensorFlow provides stable APIs for Python and C as well as APIs that are not guaranteed to be backwards compatible or are 3rd party for a variety of other languages. The platform can be easily deployed on multiple CPUs, GPUs and Google's proprietary chip, the tensor processing unit (TPU). TensorFlow... -
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Qiling
Qiling Advanced Binary Emulation Framework
...The API-rich Qiling Framework brings reverse and instrument binary to the next level quicker. Additionally, Qiling provides API access to register, memory, filesystem, operating system and debugger. Qiling also provides virtual machine-level API such as save and restore execution state. It combines binary instrumentation and binary emulation into one single framework. -
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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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Streamlit
The fastest way to build data apps in Python
...It’s all Python, open-source, and free! And once you’ve created an app you can use our cloud platform to deploy, manage, and share your app! Streamlit can also be installed in a virtual environment on Windows, Mac, and Linux. -
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DeepPavlov
A library for deep learning end-to-end dialog systems and chatbots
DeepPavlov makes it easy for beginners and experts to create dialogue systems. The best place to start is with user-friendly tutorials. They provide quick and convenient introduction on how to use DeepPavlov with complete, end-to-end examples. No installation needed. Guides explain the concepts and components of DeepPavlov. Follow step-by-step instructions to install, configure and extend DeepPavlov framework for your use case. DeepPavlov is an open-source framework for chatbots and virtual... -
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Kedro
A Python framework for creating reproducible, maintainable code
Kedro is an open sourced Python framework for creating maintainable and modular data science code. Provides the scaffolding to build more complex data and machine-learning pipelines. In addition, there's a focus on spending less time on the tedious "plumbing" required to maintain data science code; this means that you have more time to solve new problems. Standardises team workflows; the modular structure of Kedro facilitates a higher level of collaboration when teams solve problems... -
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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,... -
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LLM CLI
Access large language models from the command-line
A CLI utility and Python library for interacting with Large Language Models, both via remote APIs and models that can be installed and run on your own machine. -
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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... -
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lightning AI
The most intuitive, flexible, way for researchers to build models
Build in days not months with the most intuitive, flexible framework for building models and Lightning Apps (ie: ML workflow templates) which "glue" together your favorite ML lifecycle tools. Build models and build/publish end-to-end ML workflows that "glue" your favorite tools together. Models are “easy”, the “glue” work is hard. Lightning Apps are community-built templates that stitch together your favorite ML lifecycle tools into cohesive ML workflows that can run on your laptop or any...
