Search Results for "data" - Page 5
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Showing 629 open source projects for "data"
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Awesome Free ChatGPT
List of free ChatGPT mirror sites, continuously updated
This is a curated directory of freely accessible ChatGPT-style services and mirror sites that offer AI chatbot interfaces without login or payment requirements. Resources often support multiple models like GPT-4, Claude, Gemini, and more. Data collected from multiple independent sites with descriptions and tags. Includes services with image upload and drawing capabilities. Aggregates free, no-login-required ChatGPT-like web services. Continually updated mirror list to maintain availability. -
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BlenderProc
Blender pipeline for photorealistic training image generation
A procedural Blender pipeline for photorealistic training image generation. BlenderProc has to be run inside the blender python environment, as only there we can access the blender API. Therefore, instead of running your script with the usual python interpreter, the command line interface of BlenderProc has to be used. In general, one run of your script first loads or constructs a 3D scene, then sets some camera poses inside this scene and renders different types of images (RGB, distance,... -
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Mobly
E2E test framework for tests with complex environment requirements
Mobly is a Python-based test framework that specializes in supporting test cases that require multiple devices, complex environments, or custom hardware setups. P2P data transfer between two devices. Conference calls across three phones. Wearable device interacting with a phone. Internet-Of-Things devices interacting with each other. Testing RF characteristics of devices with special equipment. Testing LTE network by controlling phones, base stations, and eNBs. Mobly can support many different types of devices and equipment, and it's easy to plug your own device or custom equipment/service into Mobly. ... -
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AutoKeras
AutoML library for deep learning
AutoKeras: An AutoML system based on Keras. It is developed by DATA Lab at Texas A&M University. The goal of AutoKeras is to make machine learning accessible to everyone. AutoKeras only support Python 3. If you followed previous steps to use virtualenv to install tensorflow, you can just activate the virtualenv. Currently, AutoKeras is only compatible with Python >= 3.7 and TensorFlow >= 2.8.0. AutoKeras supports several tasks with extremely simple interface. -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
...Albumentations supports different computer vision tasks such as classification, semantic segmentation, instance segmentation, object detection, and pose estimation. Albumentations works well with data from different domains: photos, medical images, satellite imagery, manufacturing and industrial applications, Generative Adversarial Networks. Albumentations can work with various deep learning frameworks such as PyTorch and Keras. -
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SageMaker TensorFlow Training Toolkit
Toolkit for running TensorFlow training scripts on SageMaker
...After creating a SageMaker Model, you can use it to create SageMaker Batch Transform Jobs for offline inference, or create SageMaker Endpoints for real-time inference. A SageMaker Model contains references to a model.tar.gz file in S3 containing serialized model data, and a Docker image used to serve predictions with that model. A Batch Transform job runs an offline-inference job using your TensorFlow Serving model. Input data in S3 is converted to HTTP requests, and responses are saved to an output bucket in S3. -
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Pacu
The AWS exploitation framework, designed for testing security
...What used to take days to manually enumerate can be now be achieved in minutes. There are currently over 35 modules that range from reconnaissance, persistence, privilege escalation, enumeration, data exfiltration, log manipulation, and miscellaneous general exploitation. -
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cheat.sh
The only cheat sheet you need
...The repository contains the server and client code, instructions to run a local standalone instance (including Python virtualenv setup), and tooling to fetch or maintain the upstream cheat-sheet data; installation documentation explains disk-space needs and dependency setup for offline use. Cheat.sh is intentionally minimal and scriptable, so it fits naturally into shells, CI scripts, editors, and quick lookups without leaving the terminal, while also offering ways to extend or host personal cheat sheets. -
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Barfi
A Python visual Flow Based Programming library
...Firstly, each Block has Input and Output interfaces that link to other Blocks. Each Block can carry an executable function, that is specified by the user. This function can access/get data from the Input interface, perform computations or calculations, and set the Output interface. In general, Barfi is an abstraction of Graphical Programming, Flow-Based Programming, or Node programming. Where the Block is synonymous to a Node, and a Link (connection) is synonymous with an Edge. There are many ways to call this, each serving a specific need or a philosophy. -
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DeepSeed
Deep learning optimization library making distributed training easy
DeepSpeed is a deep learning optimization library that makes distributed training easy, efficient, and effective. DeepSpeed delivers extreme-scale model training for everyone, from data scientists training on massive supercomputers to those training on low-end clusters or even on a single GPU. Using current generation of GPU clusters with hundreds of devices, 3D parallelism of DeepSpeed can efficiently train deep learning models with trillions of parameters. With just a single GPU, ZeRO-Offload of DeepSpeed can train models with over 10B parameters, 10x bigger than the state of arts, democratizing multi-billion-parameter model training such that many deep learning scientists can explore bigger and better models. ... -
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PRML
PRML algorithms implemented in Python
...Rather than just summarizing concepts, the repository includes working code that demonstrates linear regression and classification, kernel methods, neural networks, graphical models, mixture models with EM algorithms, approximate inference, and sequential data methods — all following the book’s structure and notation. Many of these algorithms are paired with Jupyter notebooks that let users interact with the code, visualize results, and experiment with parameters in a way that deeply strengthens theoretical understanding. -
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rich
Rich is a Python library for rich text and beautiful formatting
...Effortlessly add rich output to your application, you can import the rich print method, which has the same signature as the builtin Python function. Rich can be installed in the Python REPL, so that any data structures will be pretty printed and highlighted. As you might expect, this will print "Hello World!" to the terminal. Note that unlike the builtin print function, Rich will word-wrap your text to fit within the terminal width. -
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segyio
Fast Python library for SEGY files
Segyio is a small LGPL-licensed C library for easy interaction with SEG-Y and Seismic Unix formatted seismic data, with language bindings for Python and Matlab. Segyio is an attempt to create an easy-to-use, embeddable, community-oriented library for seismic applications. Features are added as they are needed; suggestions and contributions of all kinds are very welcome. -
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Trame
Weave various components and technologies into a Web App
...It enables the integration of various components and technologies, such as VTK and ParaView, into web applications written entirely in Python. With best-in-class platforms at its core, trame provides complete control of 3D visualizations and data processing. Developers benefit from a write-once environment from trame. trame is an open source project licensed under Apache License Version 2.0 which allows users to create open source or commercial applications without any licensing worries. By relying simply on Python and HTML, trame focuses on one's data and associated analysis and visualizations while hiding the complications of web development. -
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Papis
Powerful and highly extensible command-line based document
...With Papis, you can search your library for books and papers, add documents and notes, import and export to and from other formats, and much much more. Papis uses a human-readable and easily hackable .yaml file to store each entry's bibliographical data. It strives to be easy to use while providing a wide range of features. And for those who still want more, Papis makes it easy to write scripts that extend its features even further. -
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Django Rules
Awesome Django authorization, without the database
...Dive in the code. You'll need 10 minutes to figure out how it works. Powerful. rules comes complete with advanced features, such as invocation context and storage for arbitrary data, skipping evaluation of predicates. -
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RecBole
A unified, comprehensive and efficient recommendation library
A unified, comprehensive and efficient recommendation library. We design general and extensible data structures to unify the formatting and usage of various recommendation datasets. We implement more than 100 commonly used recommendation algorithms and provide formatted copies of 28 recommendation datasets. We support a series of widely adopted evaluation protocols or settings for testing and comparing recommendation algorithms. -
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peewee
A small, expressive orm, which supports postgresql, mysql and sqlite
...You can override the default name by specifying a table_name attribute in the inner “Meta” class (alongside the database attribute). To learn more about how Peewee generates table names, refer to the Table Names section. There are lots of field types suitable for storing various types of data. Peewee handles converting between pythonic values and those used by the database, so you can use Python types in your code without having to worry. The real strength of our database is in how it allows us to retrieve data through queries. Relational databases are excellent for making ad-hoc queries. Peewee provides a magical helper fn(), which can be used to call any SQL function. -
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aws-cli
Universal Command Line Interface for Amazon Web Services
The AWS CLI is the universal command-line interface for managing AWS services, automating tasks, and scripting cloud workflows. It exposes nearly every public API from EC2 and S3 to IAM, Lambda, and beyond, providing parity with the service SDKs in a tool you can run anywhere. Profiles, regions, single-sign-on, and credential helpers make it straightforward to switch contexts securely across accounts and environments. Its output controls and JMESPath querying let you slice, filter, and... -
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Hypothesis
The property-based testing library for Python
Hypothesis is a powerful library for property-based testing in Python. Instead of writing specific test cases, users define properties and Hypothesis generates random inputs to uncover edge cases and bugs. It integrates with unittest and pytest, shrinking failing examples to minimal reproducible cases. Widely adopted in production systems, Hypothesis boosts code reliability by exploring input spaces far beyond manually crafted tests. -
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DBOS Transact PY
Lightweight Durable Python Workflows
...It simplifies building resilient and idempotent applications by enabling Python functions to retain their state, restart after failure, and guarantee consistency. It's designed for data-heavy and fault-intolerant use cases. -
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AutoPkg
Automating packaging and software distribution on macOS
AutoPkg is a system that automatically prepares software for distribution to managed clients. Recipes allow you to specify a series of simple actions which combined together can perform complex tasks, similar to Automator workflows or Unix pipes. -
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Numba
NumPy aware dynamic Python compiler using LLVM
...Just apply one of the Numba decorators to your Python function, and Numba does the rest. Numba is designed to be used with NumPy arrays and functions. Numba generates specialized code for different array data types and layouts to optimize performance. Special decorators can create universal functions that broadcast over NumPy arrays just like NumPy functions do. Numba also works great with Jupyter notebooks for interactive computing, and with distributed execution frameworks, like Dask and Spark. -
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Flama
Fire up your models with the flame
Flama is a python library which establishes a standard framework for development and deployment of APIs with special focus on machine learning (ML). The main aim of the framework is to make ridiculously simple the deployment of ML APIs, simplifying (when possible) the entire process to a single line of code. The library builds on Starlette, and provides an easy-to-learn philosophy to speed up the building of highly performant GraphQL, REST and ML APIs. Besides, it comprises an ideal solution... -
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Tunix
A JAX-native LLM Post-Training Library
...It embraces JAX’s strengths—functional programming, jit compilation, and effortless multi-device execution—so experiments scale from a single GPU to pods of TPUs with minimal code changes. The library is organized around modular pipelines for data loading, rollout, optimization, and evaluation, letting practitioners swap components without rewriting the whole stack. Examples and reference configs demonstrate end-to-end runs for common model families, helping teams reproduce baselines before customizing. Tunix also leans into research ergonomics: logging, checkpointing, and metrics are built in, and the code is written to be hackable rather than monolithic. ...
