Search Results for "machine learning python" - Page 3
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Showing 142 open source projects for "machine learning python"
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CacheGuard Gateway
Free UTM appliance: firewall, VPN, WAF and antivirus in one ISO.
Securing your network should not require an enterprise budget. CacheGuard is a free open-source network security appliance for startups and growing businesses that need serious protection without the complexity. Install CacheGuard-OS on any x86 machine or VM and get a complete security gateway in under an hour. No plug-ins, no compatibility issues. Everything works out of the box. CacheGuard-OS is not an app, it IS the OS. A fully custom network appliance operating system built from... -
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MailCleaner
Anti Spam SMTP Gateway
MailCleaner Open Source Edition is now discontinued but will continue under the spamtagger project https://github.com/SpamTagger [antispam] MailCleaner is an anti-spam / anti-virus filter SMTP gateway with user and admin web interfaces, quarantine, multi-domains, multi-templates, multi-languages. Using Bayes, RBLs, Spamassassin, MailScanner, ClamAV. Based on Debian. Enterprise ready. MailCleaner is an anti spam gateway installed between your mail infrastructure and the Internet. It... -
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FreeAskInternet
FreeAskInternet is a completely free running search aggregator
FreeAskInternet is a self-hosted search and answer application that combines web results with large language models. A local SearXNG instance sends queries to multiple search engines and returns a broader collection of sources. The backend crawls selected pages, supplies their content to a configured model, and streams a synthesized response to the chat interface. It supports several hosted model services as well as custom local backends such as Ollama and llama.cpp. The web interface is... -
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AzkaOs
Open source operating system based flutter and kernel zircon
AzkaOS is a personal and experimental operating system project aiming to create a lightweight and customizable platform written primarily in Python and C/C++. It combines ideas from traditional desktop OSes with a scripting-heavy approach, offering a hybrid system that prioritizes ease of customization and user scripting. Though still in its early stages, AzkaOS aims to support desktop environments, package management, and basic system utilities while enabling users to control behavior... -
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bKappa
Cross-platform incremental backup
...If you are looking for that, BorgBackup and restic are good options as of 2024. Warning! Old 1.x versions of bKappa are insecure. Do not use them. Because backup metadata is serialized with the "pickle" Python module (https://docs.python.org/2.7/library/pickle.html), a malicious backup can take over your machine. -
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OnnxStream
Lightweight inference library for ONNX files, written in C++
The challenge is to run Stable Diffusion 1.5, which includes a large transformer model with almost 1 billion parameters, on a Raspberry Pi Zero 2, which is a microcomputer with 512MB of RAM, without adding more swap space and without offloading intermediate results on disk. The recommended minimum RAM/VRAM for Stable Diffusion 1.5 is typically 8GB. Generally, major machine learning frameworks and libraries are focused on minimizing inference latency and/or maximizing throughput, all of which at the cost of RAM usage. So I decided to write a super small and hackable inference library specifically focused on minimizing memory consumption: OnnxStream. OnnxStream is based on the idea of decoupling the inference engine from the component responsible for providing the model weights, which is a class derived from WeightsProvider. ... -
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Coq Jupyter
Jupyter kernel for Coq
Make sure that CoqIDE (8.6 or newer) is installed and coqidetop or coqidetop.opt (coqtop for Coq versions before 8.9.0) is in your PATH. Also, make sure the python command is recognized on your machine. If not you can set up an alias for it e.g. python-is-python3 on Ubuntu. There are a number of convenience improvements over standard Jupyter notebook behavior that are implemented to support Coq-specific use cases. -
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CLIP-as-service
Embed images and sentences into fixed-length vectors
CLIP-as-service is a low-latency high-scalability service for embedding images and text. It can be easily integrated as a microservice into neural search solutions. Serve CLIP models with TensorRT, ONNX runtime and PyTorch w/o JIT with 800QPS[*]. Non-blocking duplex streaming on requests and responses, designed for large data and long-running tasks. Horizontally scale up and down multiple CLIP models on single GPU, with automatic load balancing. Easy-to-use. No learning curve, minimalist... -
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Raspberry_Quemu_forWindows
This is QEMU virtual machine with Raspbian OS for students KNUCA
Це віртуальна машина QEMU із зібраною операційною системою Raspbian для емуляції Raspberry Pi на Windows 7, 8, 10. Використовується для занять з робототехніки в Київському національному університеті будівництва і архітектури. pi/raspberry -
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NuPIC
Numenta platform for intelligent computing
The Numenta Platform for Intelligent Computing (NuPIC) is a machine intelligence platform that implements the HTM learning algorithms. HTM is a detailed computational theory of the neocortex. At the core of HTM are time-based continuous learning algorithms that store and recall spatial and temporal patterns. NuPIC is suited to a variety of problems, particularly anomaly detection and prediction of streaming data sources. -
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SageMaker Experiments Python SDK
Experiment tracking and metric logging for Amazon SageMaker notebooks
Experiment tracking in SageMaker Training Jobs, Processing Jobs, and Notebooks. SageMaker Experiments is an AWS service for tracking machine learning Experiments. The SageMaker Experiments Python SDK is a high-level interface to this service that helps you track Experiment information using Python. Experiment tracking powers the machine learning integrated development environment Amazon SageMaker Studio. Experiment: A collection of related Trials. Add Trials to an Experiment that you wish to compare together. ... -
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Spektral
Graph Neural Networks with Keras and Tensorflow 2
...Spektral implements some of the most popular layers for graph deep learning. Spektral also includes lots of utilities for representing, manipulating, and transforming graphs in your graph deep learning projects. Spektral is compatible with Python 3.6 and above, and is tested on the latest versions of Ubuntu, MacOS, and Windows. Other Linux distros should work as well. The 1.0 release of Spektral is an important milestone for the library and brings many new features and improvements. -
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TensorFlow Documentation
TensorFlow documentation
An end-to-end platform for machine learning. TensorFlow makes it easy to create ML models that can run in any environment. Learn how to use the intuitive APIs through interactive code samples. -
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gpustat
A simple command-line utility for querying and monitoring GPU status
...Because it is easy to install via pip and requires minimal configuration, gpustat is widely used in machine learning environments, research clusters, and shared GPU servers. The tool also supports watch mode for continuous monitoring and JSON output for integration into automation pipelines. Overall, gpustat focuses on speed, clarity, and scriptability, making it especially useful for engineers who need quick GPU visibility without heavy monitoring stacks. -
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scanless
Online port scan scraper
scanless is a Python command-line utility and library for running port scans through third-party online scanning services. It is built for users who want quick visibility into exposed ports without running a local scanner directly from their own machine. The tool can scan an IP address or domain, choose a specific supported scanner, select a random scanner, or run all available scanners. -
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KubeOperator
An open source, lightweight Kubernetes distribution
...Support online environment and offline environment deployment. Provides a visual web UI. Supports cluster planning, deployment and operations. Easily run workloads like machine learning, high-performance computing, and more. Quickly deploy and manage applications in K8S. Only two steps to complete the KubeOperator installation and deployment. -
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crunch++ Debian
LightWeight Debian OS
Here I have new Theme Debian Called Crunch++ , and the it has basic facilities, but have different way of operation like right click for choosing the application, which you want to run, or to make Machine shut down, reboot ... etc. It is OVF Version, means you can download and run it on VMware and Virtual Machine Software. In this there is not Dock so you need to Install any dock software, I will prefer plank dock. username - hemant password - <spacebar> one time this os can be used for experiment purpose, and learning purpose. ... -
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dispy
Distributed and Parallel Computing with/for Python.
dispy is a generic and comprehensive, yet easy to use framework for creating and using compute clusters to execute computations in parallel across multiple processors in a single machine (SMP), among many machines in a cluster, grid or cloud. dispy is well suited for data parallel (SIMD) paradigm where a computation (Python function or standalone program) is evaluated with different (large) datasets independently. dispy supports public / private / hybrid cloud computing, fog / edge computing. -
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Face Mask Detection
Face Mask Detection system based on computer vision and deep learning
Face Mask Detection system based on computer vision and deep learning using OpenCV and Tensorflow/Keras. Face Mask Detection System built with OpenCV, Keras/TensorFlow using Deep Learning and Computer Vision concepts in order to detect face masks in static images as well as in real-time video streams. Amid the ongoing COVID-19 pandemic, there are no efficient face mask detection applications which are now in high demand for transportation means, densely populated areas, residential... -
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elastic-jupyter-operator
Cloud-native way to provide elastic Jupyter Notebooks on Kubernetes
Jupyter is a free, open-source, interactive web tool known as a computational notebook, which researchers can use to combine software code, computational output, explanatory text, and multimedia resources in a single document. For data scientists and machine learning engineers, Jupyter has emerged as a de facto standard. At the same time, there has been growing criticism that the way notebooks are being used leads to low resource utilization. GPU and other hardware resources will be bound to the specified notebooks even if the data scientists do not need them currently. This project proposes some Kubernetes CRDs to solve these problems. -
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Zenoss Community Edition
Zenoss - Intelligent IT Operations Management
Zenoss provides software-defined IT operations for the world’s largest organizations. We deliver the ultimate level of IT service health with simplicity by providing the most granular and intelligent IT service modeling possible, at any scale, and sharing these unique insights with other IT operations management (ITOM) tools to make them more efficient. Zenoss Community Edition is not a “demo” or trial version of Zenoss Enterprise or Zenoss Cloud! Before You install Zenoss Community... -
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Sourcetrail
Free and open-source interactive source explorer
Sourcetrail is a free and open-source cross-platform source explorer that helps you get productive on unfamiliar source code. Building Sourcetrail requires several dependencies to be in place on your machine. However, our CMake based setup allows disabling indexing support for specific languages which reduces the number of dependencies to a minimum. Sourcetrail is an interactive source explorer that simplifies navigation in existing source code by indexing your code and gathering data about... -
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DeepSpeech
Open source embedded speech-to-text engine
DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. A pre-trained English model is available for use and can be downloaded following the instructions in the usage docs. If you want to use the pre-trained English model for performing speech-to-text, you can download it (along with other important inference material) from the DeepSpeech releases page. -
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BerryNet
Deep learning gateway on Raspberry Pi and other edge devices
This project turns edge devices such as Raspberry Pi into an intelligent gateway with deep learning running on it. No internet connection is required, everything is done locally on the edge device itself. Further, multiple edge devices can create a distributed AIoT network. At DT42, we believe that bringing deep learning to edge devices is the trend towards the future. It not only saves costs of data transmission and storage but also makes devices able to respond according to the events... -
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Keras TCN
Keras Temporal Convolutional Network
TCNs exhibit longer memory than recurrent architectures with the same capacity. Performs better than LSTM/GRU on a vast range of tasks (Seq. MNIST, Adding Problem, Copy Memory, Word-level PTB...). Parallelism (convolutional layers), flexible receptive field size (possible to specify how far the model can see), stable gradients (backpropagation through time, vanishing gradients). The usual way is to import the TCN layer and use it inside a Keras model. The receptive field is defined as the...
