Open Source Python Networking Software
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Python Networking Software
View 337 business solutionsBrowse free open source Python Networking Software and projects below. Use the toggles on the left to filter open source Python Networking Software by OS, license, language, programming language, and project status.
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Gen AI apps are built with MongoDB AtlasMongoDB Atlas provides built-in vector search and a flexible document model so developers can build, scale, and run gen AI apps without stitching together multiple databases. From LLM integration to semantic search, Atlas simplifies your AI architecture—and it’s free to get started.
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Secure remote access solution to your private network, in the cloud or on-prem.OpenVPN is here to bring simple, flexible, and cost-effective secure remote access to companies of all sizes, regardless of where their resources are located.
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Age and Gender Estimation
Keras implementation of a CNN network for age and gender estimation
Keras implementation of a CNN network for age and gender estimation. This is a Keras implementation of a CNN for estimating age and gender from a face image [1, 2]. In training, the IMDB-WIKI dataset is used. Because the face images in the UTKFace dataset is tightly cropped (there is no margin around the face region), faces should also be cropped in demo.py if weights trained by the UTKFace dataset is used. Please set the margin argument to 0 for tight cropping. You can evaluate a trained model on the APPA-REAL (validation) dataset. We pose the age regression problem as a deep classification problem followed by a softmax expected value refinement and show improvements over direct regression training of CNNs. Our proposed method, Deep EXpectation (DEX) of apparent age, first detects the face in the test image and then extracts the CNN predictions from an ensemble of 20 networks on the cropped face. -
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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 maximum number of steps back in time from current sample at time T, that a filter from (block, layer, stack, TCN) can hit (effective history) + 1. The receptive field of the TCN can be calculated. Once keras-tcn is installed as a package, you can take a glimpse of what is possible to do with TCNs. Some tasks examples are available in the repository for this purpose. -
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FastoCloud PRO
IPTV/NVR/CCTV/Video cloud https://fastocloud.com
IPTV/Video cloud Features: Cross-platform (Linux, MacOSX, FreeBSD, Raspbian/Armbian) GPU/CPU Encode/Decode/Post Processing Stream statistics CCTV Adaptive hls streams Load balancing Temporary urls HLS push EPG scanning Subtitles to text conversions AD insertion Logo overlay Video effects Relays Timeshifts Catchups Playlists Restream/Transcode from online streaming services like Youtube, Twitch Mozaic Many Outputs Physical Inputs Streaming Protocols File Formats Presets Vods/Series server-side support Pay per view channels Channels on demand HTTP Live Streaming (HLS) server-side support Public API, client server communication via JSON RPC Protocol gzip compression Deep learning video analysis Supported deep learning frameworks: Tensorflow NCSDK Caffe ML Hardware: -
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Astrape
Optical-packet node transceiver frequency allocation
In an optical network scenario which consists of multiple nodes (whiteboxes) at its edges and ROADMs in-between, the coherent transceiver average laser configuration time is improved. The process is evaluated according to a testbed setup. This is facilitated in the appropriate lab equipment (or via simulation when required). For that purpose, a software agent (Netconf server) residing at the whiteboxes, is developed receiving input from the Software-Defined Networking (SDN) packet controller (PacketCTL - a Netconf client). Then, configuration of the local transceiver laser frequencies of the controlled pluggable devices takes place, for facilitating the connectivity in-between the ROADM network. Also, the agent records and reports back telemetry data (feedback) which is used by the PacketCTL's resource-allocating mechanism to improve efficiency within the network topology. -
No-Nonsense Code-to-Cloud Security for Devs | AikidoAikido provides a unified security platform for developers, combining 12 powerful scans like SAST, DAST, and CSPM. AI-driven AutoFix and AutoTriage streamline vulnerability management, while runtime protection blocks attacks.
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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 design on client and server. Intuitive and consistent API for image and sentence embedding. Async client support. Easily switch between gRPC, HTTP, WebSocket protocols with TLS and compression. Smooth integration with neural search ecosystem including Jina and DocArray. Build cross-modal and multi-modal solutions in no time. -
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CommunityBackup
...a work in progress.
Setup files are quite old. -
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Deep Learning with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow
Introduction to Deep Neural Networks with Keras and Tensorflow. To date tensorflow comes in two different packages, namely tensorflow and tensorflow-gpu, whether you want to install the framework with CPU-only or GPU support, respectively. NVIDIA Drivers and CuDNN must be installed and configured before hand. Please refer to the official Tensorflow documentation for further details. Since version 0.9 Theano introduced the libgpuarray in the stable release (it was previously only available in the development version). The goal of libgpuarray is (from the documentation) make a common GPU ndarray (n dimensions array) that can be reused by all projects that is as future proof as possible, while keeping it easy to use for simple need/quick test. The easiest way to get (most) these is to use an all-in-one installer such as Anaconda from Continuum. These are available for multiple architectures. -
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Imagen - Pytorch
Implementation of Imagen, Google's Text-to-Image Neural Network
Implementation of Imagen, Google's Text-to-Image Neural Network that beats DALL-E2, in Pytorch. It is the new SOTA for text-to-image synthesis. Architecturally, it is actually much simpler than DALL-E2. It consists of a cascading DDPM conditioned on text embeddings from a large pre-trained T5 model (attention network). It also contains dynamic clipping for improved classifier-free guidance, noise level conditioning, and a memory-efficient unit design. It appears neither CLIP nor prior network is needed after all. And so research continues. For simpler training, you can directly supply text strings instead of precomputing text encodings. (Although for scaling purposes, you will definitely want to precompute the textual embeddings + mask) -
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WHEREAMI is an intelligent and self-learning network detection and configuration utility. With this capability, a user can just walk to a network range and WHEREAMI will self determine and connect to that network with relevant policies.
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Photo and Video Editing APIs and SDKsUnlock Picsart's full editing suite by embedding our Editor SDK directly into your platform. Offer your users the power of a full design suite without leaving your site.
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META is a decision making software which aims are to track computer attackers, computer attacks and to help investigators finding useful elements.
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PyG
Graph Neural Network Library for PyTorch
PyG (PyTorch Geometric) is a library built upon PyTorch to easily write and train Graph Neural Networks (GNNs) for a wide range of applications related to structured data. It consists of various methods for deep learning on graphs and other irregular structures, also known as geometric deep learning, from a variety of published papers. In addition, it consists of easy-to-use mini-batch loaders for operating on many small and single giant graphs, multi GPU-support, DataPipe support, distributed graph learning via Quiver, a large number of common benchmark datasets (based on simple interfaces to create your own), the GraphGym experiment manager, and helpful transforms, both for learning on arbitrary graphs as well as on 3D meshes or point clouds. All it takes is 10-20 lines of code to get started with training a GNN model (see the next section for a quick tour). -
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Spektral
Graph Neural Networks with Keras and Tensorflow 2
Spektral is a Python library for graph deep learning, based on the Keras API and TensorFlow 2. The main goal of this project is to provide a simple but flexible framework for creating graph neural networks (GNNs). You can use Spektral for classifying the users of a social network, predicting molecular properties, generating new graphs with GANs, clustering nodes, predicting links, and any other task where data is described by graphs. 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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howmanypeoplearearound
Count the number of people around you by monitoring wifi signals
howmanypeoplearearound calculates the number of people in the vicinity using the approximate number of smartphones as a proxy (since ~70% of people have smartphones nowadays). A cellphone is determined to be in proximity to the computer based on sniffing WiFi probe requests. Possible uses of howmanypeoplearearound include, monitoring foot traffic in your house with Raspberry Pis, seeing if your roommates are home, etc. There are a number of possible USB WiFi adapters that support monitor mode. Namely you want to find a USB adapter with one of the following chipsets: Atheros AR9271, Ralink RT3070, Ralink RT3572, or Ralink RT5572. You will be prompted for the WiFi adapter to use for scanning. Make sure to use an adapter that supports "monitor" mode. You can modify the scan time, designate the adapter, or modify the output using some command-line options. -
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minder
Monitoring your infrastructure for free.
This software presents a flexible and configurable proposal for monitoring and management of real and virtual HPC infrastructures, compatible with paradigm of cloud computing. We help you to answer: 1) What is the performance of my resources? 2) What equipment and resources do we have already? 3) What do we need to upgrade or repair? 4) What can we consolidate to reduce complexity or reduce energy use? 5) What resources would be better reused somewhere else? Status: PreAlpha, so any help shall be welcome. Made for LINUX. GNU General Public License version 3.0 (GPLv3)
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