Search Results for "network"
Sort By:
Showing 305 open source projects for "network"
View related business solutions-
Ship Agents FasterGemini Enterprise Agent Platform lets you rapidly build, scale, govern and optimize production-ready agents grounded in your organization's data. The platform enables developers to build custom or pre-built agents for virtually any use case. New customers get $300 in free credits.
-
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.
-
1
spaCy
Industrial-strength Natural Language Processing (NLP)
...Since its inception it was designed to be used for real world applications-- for building real products and gathering real insights. It comes with pretrained statistical models and word vectors, convolutional neural network models, easy deep learning integration and so much more. spaCy is the fastest syntactic parser in the world according to independent benchmarks, with an accuracy within 1% of the best available. It's blazing fast, easy to install and comes with a simple and productive API. -
2
Stock prediction deep neural learning
Predicting stock prices using a TensorFlow LSTM
...To identify patterns and trends in stock prices, deep learning techniques can be used for machine learning. Long short-term memory (LSTM) is a type of recurrent neural network (RNN) that is specifically designed for sequence modeling and prediction. -
3
Cobbler
Cobbler is a versatile Linux deployment server
Cobbler is a Linux installation server that allows for rapid setup of network installation environments. It glues together and automates many associated Linux tasks so you do not have to hop between many various commands and applications when deploying new systems, and, in some cases, changing existing ones. Cobbler can help with provisioning, managing DNS and DHCP, package updates, power management, configuration management orchestration, and much more. -
4
Stanza
Stanford NLP Python library for many human languages
...It contains tools, which can be used in a pipeline, to convert a string containing human language text into lists of sentences and words, to generate base forms of those words, their parts of speech and morphological features, to give a syntactic structure dependency parse, and to recognize named entities. The toolkit is designed to be parallel among more than 70 languages, using the Universal Dependencies formalism. Stanza is built with highly accurate neural network components that also enable efficient training and evaluation with your own annotated data. -
Paessler: Easy to Use With Enterprise Power. Free TrialYou shouldn't have to choose between a monitoring tool that's easy to use and one that's powerful enough for a complex environment. PRTG's low-code interface lets any admin build dashboards, set alerts and monitor devices without scripting, while custom sensors and full API access are there when your team needs deeper control. One platform, no compromise. Download a free 30-day trial now.
-
5
NetworkX
Network analysis in Python
NetworkX is a Python package for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. Data structures for graphs, digraphs, and multigraphs. Many standard graph algorithms. Network structure and analysis measures. Generators for classic graphs, random graphs, and synthetic networks. Nodes can be "anything" (e.g., text, images, XML records). Edges can hold arbitrary data (e.g., weights, time-series). Open source 3-clause BSD license. Well tested with over 90% code coverage. Additional benefits from Python include fast prototyping, easy to teach, and multi-platform. ... -
6
Secdev Scapy
Scapy: the Python-based interactive packet manipulation program
...Scapy can easily be used as an interactive shell to interact with the network. Scapy works without any external Python modules on Linux and BSD like operating systems. -
7
Grab Framework Project
Web Scraping Framework
...Each handler is able to spawn new network requests. Network requests are processed concurrently with a pool of asynchronous web sockets. Grab provides interface called Spider to develop multithreaded web-site scrapers. -
8
Kubeasz
Install K8S cluster anintroduce the principle of component interaction
Use Ansible script to install K8S cluster, introduce the principle of component interaction, convenient and direct, not affected by domestic network environment. The project is committed to providing tools for rapid deployment of high-availability k8sclusters, and also strives to become a k8sa reference book for practice and use; ansible-playbook to automate deployment and utilization based on binary methods; to provide one-click installation scripts, and to install each component according to step-by-step execution. -
9
AUTOMATIC1111 Stable Diffusion web UI
Stable Diffusion web UI
AUTOMATIC1111's stable-diffusion-webui is a powerful, user-friendly web interface built on the Gradio library that allows users to easily interact with Stable Diffusion models for AI-powered image generation. Supporting both text-to-image (txt2img) and image-to-image (img2img) generation, this open-source UI offers a rich feature set including inpainting, outpainting, attention control, and multiple advanced upscaling options. With a flexible installation process across Windows, Linux, and... -
Train ML Models With SQL You Already KnowBuild and deploy ML models using familiar SQL. Automate data prep with built-in Gemini. Query 1 TB and store 10 GB free monthly.
-
10
NNCF
Neural Network Compression Framework for enhanced OpenVINO
NNCF (Neural Network Compression Framework) is an optimization toolkit for deep learning models, designed to apply quantization, pruning, and other techniques to improve inference efficiency. -
11
Python Outlier Detection
A Python toolbox for scalable outlier detection
...PyOD includes more than 30 detection algorithms, from classical LOF (SIGMOD 2000) to the latest COPOD (ICDM 2020) and SUOD (MLSys 2021). Since 2017, PyOD [AZNL19] has been successfully used in numerous academic researches and commercial products [AZHC+21, AZNHL19]. PyOD has multiple neural network-based models, e.g., AutoEncoders, which are implemented in both PyTorch and Tensorflow. PyOD contains multiple models that also exist in scikit-learn. It is possible to train and predict with a large number of detection models in PyOD by leveraging SUOD framework. A benchmark is supplied for select algorithms to provide an overview of the implemented models. ... -
12
DeepXDE
A library for scientific machine learning & physics-informed learning
DeepXDE is a library for scientific machine learning and physics-informed learning. DeepXDE includes the following algorithms. Physics-informed neural network (PINN). Solving different problems. Solving forward/inverse ordinary/partial differential equations (ODEs/PDEs) [SIAM Rev.] Solving forward/inverse integro-differential equations (IDEs) [SIAM Rev.] fPINN: solving forward/inverse fractional PDEs (fPDEs) [SIAM J. Sci. Comput.] NN-arbitrary polynomial chaos (NN-aPC): solving forward/inverse stochastic PDEs (sPDEs) [J. ... -
13
Penzai
A JAX research toolkit to build, edit, & visualize neural networks
Penzai, developed by Google DeepMind, is a JAX-based library for representing, visualizing, and manipulating neural network models as functional pytree data structures. It is designed to make machine learning research more interpretable and interactive, particularly for tasks like model surgery, ablation studies, architecture debugging, and interpretability research. Unlike conventional neural network libraries, Penzai exposes the full internal structure of models, enabling fine-grained inspection and modification after training. ... -
14
Flax
Flax is a neural network library for JAX
Flax is a flexible neural-network library for JAX that embraces functional programming while offering ergonomic module abstractions. Its design separates pure computation from state by threading parameter collections and RNGs explicitly, enabling reproducibility, transformation, and easy experimentation with JAX transforms like jit, pmap, and vmap. Modules define parameterized computations, but initialization and application remain side-effect free, which pairs naturally with JAX’s staging and compilation model. ... -
15
AIMET
AIMET is a library that provides advanced quantization and compression
...With this goal, QuIC presents the AI Model Efficiency Toolkit (AIMET) - a library that provides advanced quantization and compression techniques for trained neural network models. AIMET enables neural networks to run more efficiently on fixed-point AI hardware accelerators. Quantized inference is significantly faster than floating point inference. For example, models that we’ve run on the Qualcomm® Hexagon™ DSP rather than on the Qualcomm® Kryo™ CPU have resulted in a 5x to 15x speedup. Plus, an 8-bit model also has a 4x smaller memory footprint relative to a 32-bit model. ... -
16
Haiku
JAX-based neural network library
Haiku is a library built on top of JAX designed to provide simple, composable abstractions for machine learning research. Haiku is a simple neural network library for JAX that enables users to use familiar object-oriented programming models while allowing full access to JAX’s pure function transformations. Haiku is designed to make the common things we do such as managing model parameters and other model state simpler and similar in spirit to the Sonnet library that has been widely used across DeepMind. ... -
17
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,... -
18
FairChem
FAIR Chemistry's library of machine learning methods for chemistry
...The centerpiece models (e.g., UMA variants) plug directly into the ASE ecosystem via a FAIRChem calculator, so users can run relaxations, molecular dynamics, spin-state energetics, and surface catalysis workflows with the same pretrained network by switching a task flag. Tasks span heterogeneous domains—catalysis (OC20-style), inorganic materials (OMat), molecules (OMol), MOFs (ODAC), and molecular crystals (OMC)—allowing one model family to serve many simulations. The README provides quick paths for pulling models (e.g., via Hugging Face access), then running energy/force predictions on GPU or CPU. -
19
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. -
20
Kubespray
Deploy a Production Ready Kubernetes Cluster
Can be deployed on AWS, GCE, Azure, OpenStack, vSphere, Equinix Metal (bare metal), Oracle Cloud Infrastructure (Experimental), or Baremetal. Highly available cluster. Composable (Choice of the network plugin for instance). Supports most popular Linux distributions. Continuous integration tests. The list of available docker versions is 18.09, 19.03, and 20.10. The recommended docker version is 20.10. The kubelet might break on docker's non-standard version numbering (it no longer uses semantic versioning). To ensure auto-updates don't break your cluster look into e.g. yum version lock plugin or apt pin). ... -
21
Lightweight' GAN
Implementation of 'lightweight' GAN, proposed in ICLR 2021
...Augmentation is essential for Lightweight GAN to work effectively in a low data setting. You can test and see how your images will be augmented before they pass into a neural network (if you use augmentation). The general recommendation is to use suitable augs for your data and as many as possible, then after some time of training disable the most destructive (for image) augs. You can turn on automatic mixed precision with one flag --amp. You should expect it to be 33% faster and save up to 40% memory. Aim is an open-source experiment tracker that logs your training runs, and enables a beautiful UI to compare them. -
22
ai-notebooks
Some ipython notebooks implementing AI algorithms
...Projects explore problems such as MNIST learning, GANs, VAEs, model compression, and knowledge distillation. Other notebooks examine reinforcement learning through PPO, SAC, TD3, VPG, and MuZero experiments. Transformer, recurrent-network, counterfactual-regret, and normalizing-flow examples broaden the collection. The notebooks are designed to be read directly on GitHub, making the repository useful for studying algorithms through working code. -
23
Opacus
Training PyTorch models with differential privacy
...Vectorized per-sample gradient computation that is 10x faster than micro batching. Supports most types of PyTorch models and can be used with minimal modification to the original neural network. Open source, modular API for differential privacy research. Everyone is welcome to contribute. ML practitioners will find this to be a gentle introduction to training a model with differential privacy as it requires minimal code changes. Differential Privacy researchers will find this easy to experiment and tinker with, allowing them to focus on what matters. -
24
DeepCTR-Torch
Easy-to-use,Modular and Extendible package of deep-learning models
...Factorization-Machine and it’s variants are widely used to learn the low-order feature interaction. High-order Extractor learns feature combination through complex neural network functions like MLP, Cross Net, etc. -
25
DeepCTR
Package of deep-learning based CTR models
DeepCTR is a Easy-to-use,Modular and Extendible package of deep-learning based CTR models along with lots of core components layers which can be used to easily build custom models. You can use any complex model with model.fit(), and model.predict(). Provide tf.keras.Model like interface for quick experiment. Provide tensorflow estimator interface for large scale data and distributed training. It is compatible with both tf 1.x and tf 2.x. With the great success of deep learning,DNN-based...
