Search Results for "model-builder" - Page 3
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Showing 88 open source projects for "model-builder"
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Host LLMs in Production With On-Demand GPUsDeploy your model, get an endpoint, pay only for compute time. No GPU provisioning or infrastructure management required.
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$300 Free Credits to Build on Google CloudPut your $300 in credit toward real workloads, then keep building with free monthly usage for 20+ products. No commitment and no charge until you upgrade.
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1
pipeless
A computer vision framework to create and deploy apps in minutes
...You provide some functions that are executed for new video frames and Pipeless takes care of everything else. You can easily use industry-standard models, such as YOLO, or load your custom model in one of the supported inference runtimes. Pipeless ships some of the most popular inference runtimes, such as the ONNX Runtime, allowing you to run inference with high performance on CPU or GPU out-of-the-box. You can deploy your Pipeless application with a single command to edge and IoT devices or the cloud. -
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towhee
Framework that is dedicated to making neural data processing
...From images to text to 3D molecular structures, Towhee supports data transformation for nearly 20 different unstructured data modalities. We provide end-to-end pipeline optimizations, covering everything from data decoding/encoding, to model inference, making your pipeline execution 10x faster. Towhee provides out-of-the-box integration with your favorite libraries, tools, and frameworks, making development quick and easy. Towhee includes a pythonic method-chaining API for describing custom data processing pipelines. We also support schemas, making processing unstructured data as easy as handling tabular data. -
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Django Notifications
GitHub notifications alike app for Django
...To generate a notification anywhere in your code, simply import the notify signal and send it with your actor, recipient, and verb. Generating notifications is probably best done in a separate signal. Using django-model-utils, we get the ability to add queryset methods to not only the manager, but to all querysets that will be used, including related objects. To ensure users always have the most up-to-date notifications, django-notifications includes a simple javascript API for updating specific fields within a django template. Customize the display of notifications using javascript callbacks. -
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Horovod
Distributed training framework for TensorFlow, Keras, PyTorch, etc.
Horovod was originally developed by Uber to make distributed deep learning fast and easy to use, bringing model training time down from days and weeks to hours and minutes. With Horovod, an existing training script can be scaled up to run on hundreds of GPUs in just a few lines of Python code. Horovod can be installed on-premise or run out-of-the-box in cloud platforms, including AWS, Azure, and Databricks. Horovod can additionally run on top of Apache Spark, making it possible to unify data processing and model training into a single pipeline. ... -
Build Agents and Models on One PlatformGemini Enterprise Agent Platform is Google Cloud's comprehensive platform for developers to build, scale, govern, and optimize agents and models. Choose from Google's most advanced models and third-party models like Anthropic's Claude Model Family.
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ClassyVision
An end-to-end PyTorch framework for image and video classification
Classy Vision is a PyTorch-based framework designed for large-scale training and deployment of state-of-the-art image and video classification models. Developed by Facebook Research, it serves as an end-to-end system that simplifies the process of training at scale, reducing redundancy and friction in moving from research to production. Unlike traditional computer vision libraries that focus solely on modular components, Classy Vision provides a complete and unified framework, featuring... -
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FastAPI CRUD Router
A dynamic FastAPI router that automatically creates CRUD routes
...As an extension to the APIRouter included with FastAPI, the FastAPI CRUDRouter will automatically generate and document your CRUD routes for you, all you have to do is pass your model and maybe your database connection. fastapi-crudrouter provides a number of features that allow you to get the most out of your automatically generated CRUD routes. The CRUDRouter is able to dynamically generate detailed documentation based on the models given to it. By default, all routes generated by the CRUDRouter will be documented according to OpenAPI spec. ... -
7
Wooey
A Django app that creates automatic web UIs for Python scripts
...Enable the easy wrapping of any program in simple python instead of having to use language specific to existing tools such as Galaxy. Enable fellow lab members with no command line experience to utilize python scripts. Autodocument workflows for data analysis (simple model saving). -
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Blend_My_NFTs
Easily generate thousands of 3D models, images, and animation NFTs
...Any rendering engine works; Cycles, Eevee, and Octane have all been used by the community. This add-on only works in Blender, a Cinima 4D port is being investigated. Every object, model, texture, camera, light etc. must be in the same .blend file! If you have mulitiple .blend files, Blend_My_NFTs is unable to open and process them. It's recommended to keep your file's total size 5gb. -
9
Blankly
Easily build, backtest and deploy your algo in just a few lines
...Blankly is the first framework to enable developers to backtest, paper trade, and go live across exchanges without modifying a single line of trading logic on stocks, crypto, and forex. Every model needs to figure out how to buy and sell. We make it super easy for you so you can focus on building better trading algos. Your models can run on any platform, and on any supported exchange. We make that as easy as just changing one line of code. -
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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Model Search
Framework that implements AutoML algorithms
...The framework logs trials, metrics, and artifacts so you can analyze what the search learned and why certain designs dominate. It’s intended as a platform for method development as much as for model discovery. -
11
django-summernote
Simply integrate Summernote editor with Django project
Summernote is a simple WYSIWYG editor. Django-summernote allows you to embed Summernote into Django very handy. Support admin mixins and widgets. Although Post model has several TextField, only the content field will have SummernoteWidget. Apply summernote to all TextField in the model. Please mind, that the widget does not provide any escaping. If you expose the widget to external users without taking care of this, it could potentially lead to an injection vulnerability. Therefore you can use the SummernoteTextFormField or SummernoteTextField, which escape all harmful tags through mozilla's package bleach. django-summernote is served with Bootstrap3 by default, but you can choose other options. -
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TorchGAN
Research Framework for easy and efficient training of GANs
...This package provides an easy-to-use API which can be used to train popular GANs as well as develop newer variants. The core idea behind this project is to facilitate easy and rapid generative adversarial model research. TorchGAN is a Pytorch-based framework for designing and developing Generative Adversarial Networks. This framework has been designed to provide building blocks for popular GANs and also to allow customization for cutting-edge research. Using TorchGAN's modular structure allows. -
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Pytorch Points 3D
Pytorch framework for doing deep learning on point clouds
...We aim to build a tool that can be used for benchmarking SOTA models, while also allowing practitioners to efficiently pursue research into point cloud analysis, with the end goal of building models which can be applied to real-life applications. Task driven implementation with dynamic model and dataset resolution from arguments. Core implementation of common components for point cloud deep learning - greatly simplifying the creation of new models. 4 Base Convolution base classes to simplify the implementation of new convolutions. Each base class supports a different data format. -
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Gluon CV Toolkit
Gluon CV Toolkit
...It features training scripts that reproduce SOTA results reported in latest papers, a large set of pre-trained models, carefully designed APIs and easy-to-understand implementations and community support. From fundamental image classification, object detection, semantic segmentation and pose estimation, to instance segmentation and video action recognition. The model zoo is the one-stop shopping center for many models you are expecting. GluonCV embraces a flexible development pattern while is super easy to optimize and deploy without retaining a heavyweight deep learning framework. -
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Flask-MongoEngine
MongoEngine flask extension with WTF model forms support
Flask-MongoEngine is a Flask extension that provides integration with MongoEngine, WtfForms and FlaskDebugToolbar. By default, Flask-MongoEngine will install integration only between Flask and MongoEngine. Integration with WTFForms and FlaskDebugToolbar are optional and should be selected as extra option, if required. This is done by users request, to limit amount of external dependencies in different production setup environments. All methods end extras described below are compatible... -
16
MMdnn
Tools to help users inter-operate among deep learning frameworks
...During the model conversion, we generate some code snippets to simplify later retraining or inference. We provide a model collection to help you find some popular models. We provide a model visualizer to display the network architecture more intuitively. We provide some guidelines to help you deploy DL models to another hardware platform. -
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HyperGAN
Composable GAN framework with api and user interface
A composable GAN built for developers, researchers, and artists. HyperGAN builds generative adversarial networks in PyTorch and makes them easy to train and share. HyperGAN is currently in pre-release and open beta. Everyone will have different goals when using hypergan. HyperGAN is currently beta. We are still searching for a default cross-data-set configuration. Each of the examples supports search. Automated search can help find good configurations. If you are unsure, you can start with... -
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PyText
A natural language modeling framework based on PyTorch
...PyText addresses the often-conflicting requirements of enabling rapid experimentation and of serving models at scale. It achieves this by providing simple and extensible interfaces and abstractions for model components, and by using PyTorch’s capabilities of exporting models for inference via the optimized Caffe2 execution engine. We use PyText at Facebook to iterate quickly on new modeling ideas and then seamlessly ship them at scale. Distributed-training support built on the new C10d backend in PyTorch 1.0. Mixed precision training support through APEX (trains faster with less GPU memory on NVIDIA Tensor Cores). ... -
19
CrypTen
A framework for Privacy Preserving Machine Learning
...Designed to make secure computation accessible to machine learning practitioners, CrypTen introduces a CrypTensor object that behaves like a regular PyTorch tensor, allowing users to seamlessly apply automatic differentiation and neural network operations. Its design mirrors PyTorch’s modular and library-based structure, enabling flexible experimentation, debugging, and model development. The framework supports both encryption and decryption of tensors and operations such as addition and multiplication over encrypted values. Although not yet production-ready, CrypTen focuses on advancing real-world secure ML applications, such as training and inference over private datasets, without exposing sensitive data. -
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abu
Abu quantitative trading system (stocks, options, futures, bitcoin)
Abu Quantitative Integrated AI Big Data System, K-Line Pattern System, Classic Indicator System, Trend Analysis System, Time Series Dimension System, Statistical Probability System, and Traditional Moving Average System conduct in-depth quantitative analysis of investment varieties, completely crossing the user's complex code quantification stage, more suitable for ordinary people to use, towards the era of vectorization 2.0. The above system combines hundreds of seed quantitative models, such as financial time series loss model, deep pattern quality assessment model, long and short pattern combination evaluation model, long pattern stop-loss strategy model, short pattern covering strategy model, big data K-line pattern Historical portfolio fitting model, trading position mentality model, dopamine quantification model, inertial residual resistance support model, long-short swap revenge probability model, strong and weak confrontation model, trend angle change rate model, etc. -
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MMF
A modular framework for vision & language multimodal research
...MMF contains reference implementations of state-of-the-art vision and language models and has powered multiple research projects at Facebook AI Research. MMF is designed from ground up to let you focus on what matters, your model, by providing boilerplate code for distributed training, common datasets and state-of-the-art pre-trained baselines out-of-the-box. MMF is built on top of PyTorch that brings all of its power in your hands. MMF is not strongly opinionated. So you can use all of your PyTorch knowledge here. MMF is created to be easily extensible and composable. ... -
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django-dynamic-scraper
Creating Scrapy scrapers via the Django admin interface
...Since it simplifies things DDS is not usable for all kinds of scrapers, but it is well suited for the relatively common case of regularly scraping a website with a list of updated items (e.g. news, events, etc.) and then dig into the detail page to scrape some more infos for each item. Django Dynamic Scraper tries to keep its data structure in the database as separated as possible from the models in your app, so it comes with its own Django model classes for defining scrapers, runtime information related to your scraper runs and classes. -
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Gin Config
Gin provides a lightweight configuration framework for Python
Gin Config is a lightweight and flexible configuration framework for Python built around dependency injection. It enables developers to manage complex parameter hierarchies—particularly common in machine learning experiments—without relying on boilerplate configuration classes or protos. By decorating functions and classes with @gin.configurable, Gin allows their parameters to be overridden using simple configuration files (.gin) or command-line bindings. Users can define default parameter... -
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Intel neon
Intel® Nervana™ reference deep learning framework
neon is Intel's reference deep learning framework committed to best performance on all hardware. Designed for ease of use and extensibility. See the new features in our latest release. We want to highlight that neon v2.0.0+ has been optimized for much better performance on CPUs by enabling Intel Math Kernel Library (MKL). The DNN (Deep Neural Networks) component of MKL that is used by neon is provided free of charge and downloaded automatically as part of the neon installation. The gpu... -
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Rekall
Rekall Memory Forensic Framework
...Extensibility is a core theme, with a plugin API and notebook-friendly workflows for custom hunts and triage. Used well, it compresses what would be hours of manual sleuthing into scripted passes over a consistent object model.
