Search Results for "define" - Page 3
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Showing 232 open source projects for "define"
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fugue
A unified interface for distributed computing
Fugue is a unified interface for distributed computing that lets users execute Python, Pandas, and SQL code on Spark, Dask, and Ray with minimal rewrites. -
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KerasTuner
A Hyperparameter Tuning Library for Keras
KerasTuner is an easy-to-use, scalable hyperparameter optimization framework that solves the pain points of hyperparameter search. Easily configure your search space with a define-by-run syntax, then leverage one of the available search algorithms to find the best hyperparameter values for your models. KerasTuner comes with Bayesian Optimization, Hyperband, and Random Search algorithms built-in, and is also designed to be easy for researchers to extend in order to experiment with new search algorithms. -
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GenericAgent
Self-evolving autonomous agent framework
The GenericAgent project is a flexible framework for building autonomous AI agents that can operate across diverse tasks and environments. It is designed around modularity, allowing developers to define agents with interchangeable components such as tools, memory systems, and reasoning strategies. The architecture emphasizes generality, enabling the same agent framework to be adapted for different domains including coding, research, and task automation. It integrates with modern language models to provide planning, execution, and iterative reasoning capabilities, making it suitable for complex workflows. ... -
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Kornia
Open Source Differentiable Computer Vision Library
...It consists of a set of routines and differentiable modules to solve generic computer vision problems. At its core, the package uses PyTorch as its main backend both for efficiency and to take advantage of the reverse-mode auto-differentiation to define and compute the gradient of complex functions. Inspired by existing packages, this library is composed by a subset of packages containing operators that can be inserted within neural networks to train models to perform image transformations, epipolar geometry, depth estimation, and low-level image processing such as filtering and edge detection that operate directly on tensors. ... -
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LangGraph
Build resilient language agents as graphs
LangGraph is a library for building stateful, multi-actor applications with LLMs, used to create agent and multi-agent workflows. Compared to other LLM frameworks, it offers these core benefits: cycles, controllability, and persistence. LangGraph allows you to define flows that involve cycles, essential for most agentic architectures, differentiating it from DAG-based solutions. As a very low-level framework, it provides fine-grained control over both the flow and state of your application, crucial for creating reliable agents. Additionally, LangGraph includes built-in persistence, enabling advanced human-in-the-loop and memory features. -
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cmake-conan
CMake wrapper for conan C and C++ package manager
cmake-conan is a CMake wrapper for the Conan C and C++ package manager, designed to streamline the integration of Conan into CMake-based projects. It automates the process of installing and managing dependencies, allowing developers to define their requirements in Conan and have them seamlessly integrated into their CMake build process. This tool simplifies dependency management and enhances reproducibility in C++ projects. -
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attrs
Python Classes Without Boilerplate
attrs is a Python package that lets you write classes without all the usual drudgery. Its ultimate goal is to help you write concise and correct software without slowing down your code. attrs provides a class decorator and a means to declaratively define the attributes on that class. This results in a concise and explicit overview of the class's attributes, a human-readable __repr__, a complete set of comparison methods and more, all without having to repetitively write dull boilerplate code and without negatively affecting your runtime performance. With attrs you can write correct and self-documenting code, and you'll find joy in writing classes again! -
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Harvey LAB
A benchmark built to evaluate and improve agent capabilities
...It combines a dataset of legal assignments with an execution harness that runs agents against those tasks. Each benchmark task includes instructions, working documents, and rubrics that define successful completion. The environment is designed to test practical legal workflows rather than isolated question-answering ability. Evaluation tools score agent outputs and support reports and comparative experiment runs. Documentation includes an end-to-end M&A data-room example covering setup, execution, scoring, and result analysis. ... -
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requests-cache
Persistent HTTP cache for python requests
...Works with several storage backends including SQLite, Redis, MongoDB, and DynamoDB; or save responses as plain JSON files, YAML, and more. Use Cache-Control and other standard HTTP headers, define your own expiration schedule, and keep your cache clutter-free with backends that natively support TTL or any combination of strategies. Works out of the box with zero config, but with a robust set of features for configuring and extending the library to suit your needs. -
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SLM Lab
Modular Deep Reinforcement Learning framework in PyTorch
...It provides implementations of various state-of-the-art RL algorithms and emphasizes reproducibility, scalability, and detailed experiment tracking. SLM Lab is structured around a flexible experiment management system, allowing users to define, run, and analyze RL experiments efficiently. -
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ffmpy
Pythonic interface for FFmpeg/FFprobe command line
ffmpy is a Python wrapper that provides a simple and Pythonic interface for constructing and executing FFmpeg and FFprobe command-line operations. It abstracts command generation into structured Python objects, making it easier to define inputs, outputs, and parameters programmatically. The library uses Python’s subprocess module to run compiled commands, ensuring compatibility with standard FFmpeg installations. It is designed for developers who want to automate media processing tasks without manually writing complex command strings. ffmpy supports flexible argument configuration, enabling a wide range of transcoding and analysis workflows. ... -
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TreeQuest
A Tree Search Library with Flexible API for LLM Inference-Time Scaling
TreeQuest, developed by SakanaAI, is a versatile Python library implementing adaptive tree search algorithms—such as AB‑MCTS—for enhancing inference-time performance of large language models (LLMs). It allows developers to define custom state-generation and scoring functions (e.g., via LLMs), and then efficiently explores possible answer trees during runtime. With support for multi-LLM collaboration, checkpointing, and mixed policies, TreeQuest enables smarter, trial‑and‑error question answering by leveraging both breadth (multiple attempts) and depth (iterative refinement) strategies to find better outputs dynamically -
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arxiv_latex_cleaner
arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper
This tool allows you to easily clean the LaTeX code of your paper to submit to arXiv. From a folder containing all your code, e.g. /path/to/latex/, it creates a new folder /path/to/latex_arXiv/, that is ready to ZIP and upload to arXiv. -
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Robusta
Kubernetes observability and automation
...By listening to all the events in your cluster, Robusta can tell you why alerts fired, what happened at the same time, and what you can do about it. Robusta can either improve your existing alerts or be used to define new alerts triggered by APIServer changes. -
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Habitat-Lab
A modular high-level library to train embodied AI agents
...Allowing users to train agents in a wide variety of single and multi-agent tasks (e.g. navigation, rearrangement, instruction following, question answering, human following), as well as define novel tasks. Configuring and instantiating a diverse set of embodied agents, including commercial robots and humanoids, specifying their sensors and capabilities. Providing algorithms for single and multi-agent training (via imitation or reinforcement learning, or no learning at all as in SensePlanAct pipelines), as well as tools to benchmark their performance on the defined tasks using standard metrics. -
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Flask-SocketIO
Socket.IO integration for Flask applications
Flask-SocketIO is an extension for the Flask web framework that enables real-time bi-directional communication between clients and servers using WebSockets or long-polling fallbacks, making it possible to build interactive applications like chat systems, live dashboards, and collaborative tools. It abstracts the complexities of asynchronous sockets by providing a familiar Flask-style API where developers can define event handlers that trigger on client messages, broadcast to connected users, and manage namespaces and rooms. The extension supports multiple asynchronous workers through integrations with popular async servers like eventlet or gevent, allowing scalable handling of concurrent connections. It also includes features such as session and user tracking across socket connections, JSON message support, and simple decorators to bind events to handler functions. -
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cloud-morph
Decentralize, Self-host Cloud Gaming/Application
...It leverages containerization and distributed architecture to handle workloads efficiently across multiple nodes. cloud-morph focuses on flexibility, enabling users to define custom processing pipelines for different media scenarios. It can be used for applications such as streaming, video processing, and content delivery optimization. Overall, it provides a scalable solution for handling multimedia transformations in cloud environments. -
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spacy-llm
Integrating LLMs into structured NLP pipelines
Large Language Models (LLMs) feature powerful natural language understanding capabilities. With only a few (and sometimes no) examples, an LLM can be prompted to perform custom NLP tasks such as text categorization, named entity recognition, coreference resolution, information extraction and more. This package integrates Large Language Models (LLMs) into spaCy, featuring a modular system for fast prototyping and prompting, and turning unstructured responses into robust outputs for various... -
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django-prometheus
Export Django monitoring metrics for Prometheus.io
Export Django monitoring metrics for Prometheus.io. This library provides Prometheus metrics for Django-related operations. Prometheus uses Histogram based grouping for monitoring latencies. You can define custom buckets for latency, adding more buckets decreases performance but increases accuracy. SQLite, MySQL, and PostgreSQL databases can be monitored. Just replace the ENGINE property of your database, replacing django.db.backends with django_prometheus.db.backends. You may want to monitor the creation/deletion/update rate for your model. ... -
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Agent Control
Centralized agent control plane for governing runtime agent behavior
Agent Control is a centralized control plane for governing AI agent behavior at runtime across different frameworks and deployment environments. It lets teams define controls once and apply them consistently to agents without rewriting the agent’s core code. The platform evaluates agent inputs and outputs against configurable policies to reduce risks such as prompt injection, unsafe responses, sensitive data exposure, and policy drift. It is designed for production environments where organizations need observability, enforcement, and governance around autonomous or semi-autonomous AI systems. ... -
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gain
Asyncio-based Python framework for building fast web crawling spiders
...It is built on top of asynchronous technologies such as asyncio, aiohttp, and uvloop to support high-performance crawling with concurrent network requests. It provides a structured framework for creating spiders that can navigate websites, extract structured data, and process the collected results. Developers define crawlers using components such as spiders, parsers, and items, allowing them to organize crawling logic and data extraction rules clearly. Gain supports CSS selectors and XPath expressions for parsing page content and extracting specific elements. Gain also allows developers to configure headers, concurrency levels, and proxy settings to control how crawlers interact with target websites. ... -
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Flax
Flax is a neural network library for JAX
...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. Flax emphasizes composability: optimizers, training loops, and checkpointing are provided as examples or utilities rather than monolithic frameworks, encouraging research-friendly customization. ... -
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Fairlearn
A Python package to assess and improve fairness of ML models
...Besides the source code, this repository also contains Jupyter notebooks with examples of Fairlearn usage. An AI system can behave unfairly for a variety of reasons. In Fairlearn, we define whether an AI system is behaving unfairly in terms of its impact on people – i.e., in terms of harm. Fairness of AI systems is about more than simply running lines of code. In each use case, both societal and technical aspects shape who might be harmed by AI systems and how. There are many complex sources of unfairness and a variety of societal and technical processes for mitigation, not just the mitigation algorithms in our library. -
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Sacred
Sacred is a tool to help you configure, andorganize IDSIA experiments
...It is designed to do all the tedious overhead work that you need to do around your actual experiment. A very convenient way of the local variables in a function to define the parameters your experiment uses. You can access all parameters of your configuration from every function. They are automatically injected by name. You get a powerful command-line interface for each experiment that you can use to change parameters and run different variants. Observers that log all kinds of information about your experiment, its dependencies, the configuration you used, the machine it is run on, and of course the result. ... -
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Database-backed Periodic Tasks
Celery Periodic Tasks backed by the Django ORM
This extension enables you to store the periodic task schedule in the database. The periodic tasks can be managed from the Django Admin interface, where you can create, edit and delete periodic tasks and how often they should run. Usage and installation instructions for this extension are available from the Celery documentation. If you change the Django TIME_ZONE setting your periodic task schedule will still be based on the old timezone. To create a periodic task executing at an interval...
