Search Results for "python-kinterbasdb" - Page 64
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Showing 19108 open source projects for "python-kinterbasdb"
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1
AutoCrop-Vertical
Smart video converter using YOLOv8 and FFmpeg
AutoCrop-Vertical is a Python-based video processing tool that automatically converts horizontal videos into vertical formats optimized for social media platforms. It uses computer vision techniques and AI models such as YOLOv8 to analyze each frame, detect subjects, and dynamically adjust cropping decisions. Instead of applying a static center crop, the system intelligently tracks people or key objects to preserve visual focus and composition. -
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MedgeClaw
Open-source AI research assistant for biomedicine
...The system connects conversational interfaces with computational environments, allowing users to initiate research tasks through messaging platforms while the backend executes analyses using tools like R and Python. It includes a real-time dashboard that displays progress, generated code, and outputs, providing transparency throughout the research process. MedgeClaw also supports reproducibility by generating structured reports and maintaining consistent environments through containerization. Its architecture combines conversational AI, automated pipelines, and scientific tooling into a unified workflow. -
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MCPM.sh
CLI MCP package manager & registry for all platforms and all clients
...With its advanced router and profile features, mcpm.sh simplifies the management of complex MCP environments, supporting clients like Claude Desktop, Cursor, and Windsurf. The tool is built with Python and leverages the Click framework for its CLI, ensuring a robust and user-friendly experience. -
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AndroidEnv
RL research on Android devices
android_env is a reinforcement learning (RL) environment developed by Google DeepMind that enables agents to interact with Android applications directly as a learning environment. It provides a standardized API for training agents to perform tasks on Android apps, supporting tasks ranging from games to productivity apps, making it suitable for research in real-world RL settings. -
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TorchDistill
A coding-free framework built on PyTorch
torchdistill (formerly kdkit) offers various state-of-the-art knowledge distillation methods and enables you to design (new) experiments simply by editing a declarative yaml config file instead of Python code. Even when you need to extract intermediate representations in teacher/student models, you will NOT need to reimplement the models, which often change the interface of the forward, but instead specify the module path(s) in the yaml file. In addition to knowledge distillation, this framework helps you design and perform general deep learning experiments (WITHOUT coding) for reproducible deep learning studies. i.e., it enables you to train models without teachers simply by excluding teacher entries from a declarative yaml config file. -
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mypy-baseline
Integrate mypy in seconds with existing codebase
A CLI tool for painless integration of mypy with an existing Python project. When you run it for the first time, it will remember all types of errors that you already have in the project (generate “baseline”). All consecutive runs will ignore these errors and report only the ones that you introduced after that. -
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uAgents
A fast and lightweight framework for creating decentralized agents
uAgents is a library developed by Fetch.ai that allows for creating autonomous AI agents in Python. With simple and expressive decorators, you can have an agent that performs various tasks on a schedule or takes action on various events. -
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sparkmagic
Jupyter magics and kernels for working with remote Spark clusters
...Automatic visualization of SQL queries in the PySpark, Spark and SparkR kernels; use an easy visual interface to interactively construct visualizations, no code required. Ability to capture the output of SQL queries as Pandas dataframes to interact with other Python libraries (e.g. matplotlib). Send local files or dataframes to a remote cluster (e.g. sending pretrained local ML model straight to the Spark cluster) Authenticate to Livy via Basic Access authentication or via Kerberos. -
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Perceval
An open source framework for programming photonic quantum computers
An open-source framework for programming photonic quantum computers. Through a simple object-oriented Python API, Perceval provides tools for composing circuits from linear optical components, defining single-photon sources, manipulating Fock states, running simulations, reproducing published experimental papers and experimenting with a new generation of quantum algorithms. It aims to be a companion tool for developing photonic circuits – for simulating and optimizing their design, modeling both the ideal and realistic behaviors, and proposing a normalized interface to control them through the concept of backends. -
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Apache Airflow Provider
Great Expectations Airflow operator
Due to apply_default decorator removal, this version of the provider requires Airflow 2.1.0+. If your Airflow version is 2.1.0, and you want to install this provider version, first upgrade Airflow to at least version 2.1.0. Otherwise, your Airflow package version will be upgraded automatically, and you will have to manually run airflow upgrade db to complete the migration. This operator currently works with the Great Expectations V3 Batch Request API only. If you would like to use the... -
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QtAwesome
Iconic fonts in PyQt and PySide applications
QtAwesome enables iconic fonts such as Font Awesome and Elusive Icons in PyQt and PySide applications. It started as a Python port of the QtAwesome C++ library by Rick Blommers. QtAwesome identifies icons by their prefix and their icon name, separated by a period (.) character. Use Font Awesome, Elusive Icons, Material Design Icons, Phosphor, Remix Icon or Microsoft's Codicons. QtAwesome comes bundled with Font Awesome, Elusive Icons, Material Design Icons, Phosphor, Remix Icon and Microsoft's Codicons but it can also be used with other iconic fonts. ... -
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Flask RESTX
Fully featured framework for fast, easy and documented API development
Fork of Flask-RESTPlus fully featured framework for fast, easy and documented API development with Flask. Flask-RESTX is an extension for Flask that adds support for quickly building REST APIs. Flask-RESTX encourages best practices with minimal setup. If you are familiar with Flask, Flask-RESTX should be easy to pick up. It provides a coherent collection of decorators and tools to describe your API and expose its documentation properly using Swagger. With Flask-RESTX, you only import the api... -
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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... -
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django-money
Money fields for Django forms and models
A little Django app that uses py-moneyed to add support for Money fields in your models and forms. The default currency code length is 3 but you can change it with the CURRENCY_CODE_MAX_LENGTH setting. Currencies are listed on moneyed, and these modules use this to provide a choice list on the admin, also for validation. Django-money leaves you to use any custom model managers you like for your models, but it needs to wrap some of the methods to allow searching for models with money values.... -
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Wemake Django Template
Bleeding edge django template focused on code quality and security
What this project is all about? The main idea of this project is to provide a fully configured template for django projects, where code quality, testing, documentation, security, and scalability are number one priorities. This template is a result of implementing our processes, it should not be considered as an independent part. When developing this template we had several goals in mind. Development environment should be bootstrapped easily, so we use docker-compose for that. Development... -
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Ajenti 2
Ajenti Core and stock plugins
...Does not overwrite your config files, options and comments. All changes are non-destructive. Includes lots of plugins for system and software configuration, monitoring and management. Ajenti 2 is easily extensible using Python. Plugin development is quick and pleasant with Ajenti APIs. Write your first plugin. Pleasant to look at, satisfying to click and accessible anywhere from tablets and mobile. Small memory footprint and CPU usage. Runs on low-end machines, wall plugs, routers and so on. -
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Devon
Open source AI pair programmer for coding, debugging, automation
...It operates as an agent-based system that can explore codebases, edit files, and execute development workflows with minimal manual intervention. Devon uses a client-server architecture with a Python backend and multiple user interfaces, including a terminal interface and an Electron-based desktop application. Devon integrates with multiple large language models, allowing users to choose between different providers for performance, cost, and latency considerations. It is capable of performing tasks such as debugging, writing tests, analyzing code structure, and navigating complex repositories. ... -
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OpenAI-Compatible Edge-TTS API
Free, high-quality text-to-speech API endpoint to replace OpenAI
OpenAI-Compatible Edge-TTS API is a local, OpenAI-compatible text-to-speech API that uses edge-tts—Microsoft Edge’s online TTS service—as the backend. The project emulates the /v1/audio/speech endpoint used by OpenAI, so any client that can talk to the OpenAI TTS API can be redirected to this service with minimal changes. It exposes parameters for input text, voice selection, audio format, and playback speed, mirroring the OpenAI interface while mapping popular OpenAI voice names to... -
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kubectl-aliases
Programmatically generated handy kubectl aliases
...This dramatically speeds up interactive Kubernetes workflows, especially when you spend a lot of time inspecting pods, services, deployments, and logs. The repository also includes a Python script used to generate the aliases, which advanced users can tweak if they want a custom alias set. It is distributed under the Apache-2.0 license and is widely used by Kubernetes practitioners. -
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The Grand Complete Data Science Guide
Data Science Guide With Videos And Materials
The Grand Complete Data Science Materials is a repository curated by a data-science educator that aggregates a wide range of learning resources — from basic programming and math foundation to advanced topics in machine learning, deep learning, natural language processing, computer vision, and deployment practices — into a structured, centralized collection aimed at learners seeking a comprehensive path to data science mastery. The repository bundles tutorials, lecture notes, project outlines, course materials, and references across topics like Python, statistics, ML algorithms, deep learning, NLP, data preprocessing, model evaluation, and real-world problem solving. Its broad scope makes it particularly suitable for beginners or self-taught programmers who want an end-to-end learning track — from fundamentals all the way to building and deploying ML or AI systems. -
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NVIDIA NeMo Framework
Scalable generative AI framework built for researchers and developers
...It provides collections of domain-specific modules and reference implementations that make it easier to pre-train, fine-tune, and deploy very large models on multi-GPU and multi-node infrastructure. NeMo 2.0 introduces a Python-based configuration system, replacing YAML with more flexible, programmable configs that can be versioned and composed for different experiments. The framework builds on PyTorch Lightning–style modular abstractions, so training scripts are composed from reusable components for data loading, models, optimizers, and schedulers, which simplifies experimentation and adaptation. ... -
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Optopsy
A nimble options backtesting library for Python
Optopsy is a Python-based, nimble backtesting and statistics library focused on evaluating options trading strategies like calls, puts, straddles, spreads, and more, using pandas-driven analysis. The csv_data() function is a convenience function. Under the hood it uses Panda's read_csv() function to do the import. There are other parameters that can help with loading the csv data, consult the code/future documentation to see how to use them. -
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Norfair
Lightweight Python library for adding real-time multi-object tracking
Norfair is a customizable lightweight Python library for real-time multi-object tracking. Using Norfair, you can add tracking capabilities to any detector with just a few lines of code. Any detector expressing its detections as a series of (x, y) coordinates can be used with Norfair. This includes detectors performing tasks such as object or keypoint detection. It can easily be inserted into complex video processing pipelines to add tracking to existing projects. -
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FLAML
A fast library for AutoML and tuning
FLAML is a lightweight Python library that finds accurate machine learning models automatically, efficiently and economically. It frees users from selecting learners and hyperparameters for each learner. For common machine learning tasks like classification and regression, it quickly finds quality models for user-provided data with low computational resources. -
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Jittor
Jittor is a high-performance deep learning framework
...Jittor also contains a wealth of high-performance model libraries, including image recognition, detection, segmentation, generation, differentiable rendering, geometric learning, reinforcement learning, etc. The front-end language is Python. Module Design and Dynamic Graph Execution is used in the front-end, which is the most popular design for deep learning framework interface. The back-end is implemented by high-performance languages, such as CUDA, C++. Jittor'op is similar to NumPy. Let's try some operations. We create Var a and b via operation jt.float32, and add them. ...
