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Showing 184 open source projects for "data"
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Boltons
250+ constructs, recipes, and snippets which extend the Python library
Boltons is a set of pure-Python utilities in the same spirit as, and yet conspicuously missing from, the standard library. Due to the nature of utilities, application developers might want to consider other integration options. Boltons is tested against Python 2.6-2.7, 3.4-3.7, and PyPy. The majority of boltons strive to be “good enough” for a wide range of basic uses, leaving advanced use cases to Python’s myriad specialized 3rd-party libraries. In many cases the respective boltons module... -
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Google Toolbox for Mac
Google Toolbox for Mac
...It consolidates reusable code components drawn from various internal Google projects, offering developers a wide range of tools for building efficient, maintainable Apple platform software. The library includes modules for networking, logging, testing, data handling, and user interface extensions, helping developers avoid reinventing common functionality. Its modular design allows developers to integrate only the components they need, improving project flexibility and performance. With well-documented interfaces and consistent coding standards, Google Toolbox for Mac serves as a reliable foundation for both small and large-scale applications. ... -
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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... -
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DGL
Python package built to ease deep learning on graph
...We also want to make the combination of graph based modules and tensor based modules (PyTorch or MXNet) as smooth as possible. DGL provides a powerful graph object that can reside on either CPU or GPU. It bundles structural data as well as features for a better control. We provide a variety of functions for computing with graph objects including efficient and customizable message passing primitives for Graph Neural Networks. -
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FairChem
FAIR Chemistry's library of machine learning methods for chemistry
FAIRChem is a unified library for machine learning in chemistry and materials, consolidating data, pretrained models, demos, and application code into a single, versioned toolkit. Version 2 modernizes the stack with a cleaner core package and breaking changes relative to V1, focusing on simpler installs and a stable API surface for production and research. 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. ... -
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Otter-Grader
A Python and R autograding solution
Otter Grader is a light-weight, modular open-source autograder developed by the Data Science Education Program at UC Berkeley. It is designed to work with classes at any scale by abstracting away the autograding internals in a way that is compatible with any instructor's assignment distribution and collection pipeline. 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. ... -
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Python Outlier Detection
A Python toolbox for scalable outlier detection
PyOD is a comprehensive and scalable Python toolkit for detecting outlying objects in multivariate data. This exciting yet challenging field is commonly referred as outlier detection or anomaly 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]. -
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grafanalib
Python library for building Grafana dashboards
Grafanalib is a Python library for building Grafana dashboards programmatically, allowing users to automate dashboard creation and configuration. -
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Dominate
Dominate is a Python library for creating and manipulating HTML docs
Dominate is a Python library for creating and manipulating HTML documents using an elegant DOM API. It allows you to write HTML pages in pure Python very concisely, which eliminates the need to learn another template language, and lets you take advantage of the more powerful features of Python. Dominate can also use keyword arguments to append attributes onto your tags. Most of the attributes are a direct copy from the HTML spec with a few variations. Through the use of the += operator and... -
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Glumpy
Python+Numpy+OpenGL, scalable and beautiful scientific visualization
...It abstracts complex OpenGL tasks into Pythonic constructs, making it easier for scientists, artists, and developers to harness the power of the GPU for real-time rendering and data visualization. Glumpy is particularly well-suited for rapid prototyping of graphical applications, and its integration with NumPy and shader programming makes it a powerful tool for both research and creative exploration. -
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eCxx
A C++ library for AVR and NodeMCU
NOTE: This project is marked with 'Status: Abandoned' on SourceForge because not enough time can be dedicated to this project. However it may still get sporadic commits to the repository. eCxx is a library for AVR and NodeMCU tailored for micro LED displays and lighting effects. eCxx is utilizing Makefile build system. Java and Python based applications/tools are also included to ease the development and debugging process using the host PC. On one side, eCxx supports the original... -
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databooks
A CLI tool to reduce the friction between data scientists
databooks is a package to ease the collaboration between data scientists using Jupyter notebooks, by reducing the number of git conflicts between different notebooks and resolution of git conflicts when encountered. Simply specify the paths for notebook files to remove metadata. By doing so, we can already avoid many of the conflicts. Specify the paths for notebook files with conflicts to be fixed. -
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fastMRI
A large open dataset + tools to speed up MRI scans using ML
...By enabling reconstruction of high-fidelity MR images from significantly fewer measurements, fastMRI aims to make MRI scanning faster, cheaper, and more accessible in clinical settings. The repository provides an open-source PyTorch framework with data loaders, subsampling utilities, reconstruction models, and evaluation metrics, supporting both research reproducibility and practical experimentation. It includes reference implementations for key MRI reconstruction architectures such as U-Net and Variational Networks (VarNet), along with example scripts for model training and evaluation using the PyTorch Lightning framework. ... -
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learn2learn
A PyTorch Library for Meta-learning Research
...It provides reusable components and meta-learning algorithms, making it easier to build, train, and evaluate models that can quickly adapt to new tasks with minimal data. Learn2Learn is widely used in research for tasks such as few-shot classification, reinforcement learning, and optimization. -
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iJEPA
Official codebase for I-JEPA
...The design scales naturally with Vision Transformer backbones and flexible masking strategies, and it trains stably at large batch sizes. i-JEPA’s predictions are made in embedding space, which is computationally efficient and better aligned with downstream discrimination tasks. The repository provides training recipes, data pipelines, and evaluation code that clarify which masking patterns and architectural choices matter most. -
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Robin-Stocks API Library
This is a library to use with Robinhood Financial App
This is a library to use with Robinhood Financial App. It currently supports trading crypto-currencies, options, and stocks. In addition, it can be used to get real-time ticker information, assess the performance of your portfolio, and can also get tax documents, total dividends paid, and more. The code is simple to use, easy to understand, and easy to modify. With this library, you can view information on stocks, options, and cryptocurrencies in real-time, create your own robo-investor or... -
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CARTOframes
CARTO Python package for data scientists
A Python package for integrating CARTO maps, analysis, and data services into data science workflows. Python data analysis workflows often rely on the de facto standards pandas and Jupyter notebooks. Integrating CARTO into this workflow saves data scientists time and energy by not having to export datasets as files or retain multiple copies of the data. Instead, CARTOframes give the ability to communicate reproducible analysis while providing the ability to gain from CARTO's services like hosted, dynamic or static maps and Data Observatory augmentation. -
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DIG
A library for graph deep learning research
...If you are working or plan to work on research in graph deep learning, DIG enables you to develop your own methods within our extensible framework, and compare with current baseline methods using common datasets and evaluation metrics without extra efforts. It includes unified implementations of data interfaces, common algorithms, and evaluation metrics for several advanced tasks. Our goal is to enable researchers to easily implement and benchmark algorithms. -
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Alpa
Training and serving large-scale neural networks
Alpa is a system for training and serving large-scale neural networks. Scaling neural networks to hundreds of billions of parameters has enabled dramatic breakthroughs such as GPT-3, but training and serving these large-scale neural networks require complicated distributed system techniques. Alpa aims to automate large-scale distributed training and serving with just a few lines of code. -
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Neural Network Visualization
Project for processing neural networks and rendering to gain insights
nn_vis is a minimalist visualization tool for neural networks written in Python using OpenGL and Pygame. It provides an interactive, graphical representation of how data flows through neural network layers, offering a unique educational experience for those new to deep learning or looking to explain it visually. By animating input, weights, activations, and outputs, the tool demystifies neural network operations and helps users intuitively grasp complex concepts. Its lightweight codebase is great for customization and teaching purposes. -
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FairScale
PyTorch extensions for high performance and large scale training
FairScale is a collection of PyTorch performance and scaling primitives that pioneered many of the ideas now used for large-model training. It introduced Fully Sharded Data Parallel (FSDP) style techniques that shard model parameters, gradients, and optimizer states across ranks to fit bigger models into the same memory budget. The library also provides pipeline parallelism, activation checkpointing, mixed precision, optimizer state sharding (OSS), and auto-wrapping policies that reduce boilerplate in complex distributed setups. ... -
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quantitative
Quantized transactions python3
...The repo is evidently tied to a popular video series (on Bilibili) that reportedly drew substantial attention, suggesting the material is meant to be both educational and hands-on. The README and associated lessons walk the user through implementing algorithms, likely covering data handling, backtesting, and maybe simple trading logic. As an open-source educational resource, it’s designed for Python users interested in automatic trading, algorithmic strategies, and financial data analysis. -
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Jraph
A Graph Neural Network Library in Jax
Jraph (pronounced “giraffe”) is a lightweight JAX library developed by Google DeepMind for building and experimenting with graph neural networks (GNNs). It provides an efficient and flexible framework for representing, manipulating, and training models on graph-structured data. The core of Jraph is the GraphsTuple data structure, which enables users to define graphs with arbitrary node, edge, and global attributes, and to batch variable-sized graphs efficiently for JAX’s just-in-time compilation. The library includes a comprehensive set of utilities for batching, padding, masking, and partitioning graph data, making it ideal for distributed and large-scale GNN experiments. ... -
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Fairseq
Facebook AI Research Sequence-to-Sequence Toolkit written in Python
Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers. Recent work by Microsoft and Google has shown that data parallel training can be made significantly more efficient by sharding the model parameters and optimizer state across data parallel workers. These ideas are encapsulated in the new FullyShardedDataParallel (FSDP) wrapper provided by fairscale. Fairseq can be extended through user-supplied plug-ins. Models define the neural network architecture and encapsulate all of the learnable parameters. ... -
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Whisper Library
Whisper is a file-based time-series database format for Graphite
Whisper is one of three components within the Graphite project. Whisper is a fixed-size database, similar in design and purpose to RRD (round-robin-database). It provides fast, reliable storage of numeric data over time. Whisper allows for higher resolution (seconds per point) of recent data to degrade into lower resolutions for long-term retention of historical data. Copies data from src in dst, if missing. Unlike whisper-merge, don't overwrite data that's already present in the target file, but instead, only add the missing data (e.g. where the gaps in the target file are). ...
