Search Results for "file python"
Sort By:
Showing 46 open source projects for "file python"
View related business solutions-
BrandMail Email Signatures for OutlookBrandMail®, developed by BrandQuantum, is a software solution that seamlessly integrates with Microsoft Outlook to empower every employee in the organisation to automatically create consistently branded emails via a single toolbar that provides access to brand standards and the latest pre-approved content.
-
Stigg | SaaS Monetization and Entitlements APIA monetization platform is a standalone middleware that sits between your application and your business applications, as part of the modern enterprise billing stack. Stigg unifies all the APIs and abstractions billing and platform engineers had to build and maintain in-house otherwise. Acting as your centralized source of truth, with a highly scalable and flexible entitlements management, rolling out any pricing and packaging change is now a self-service, risk-free, exercise.
-
1
tika-python
Python binding to the Apache Tika™ REST services
A Python port of the Apache Tika library that makes Tika available using the Tika REST Server. This makes Apache Tika available as a Python library, installable via Setuptools, Pip and easy to install. To use this library, you need to have Java 7+ installed on your system as tika-python starts up the Tika REST server in the background. To get this working in a disconnected environment, download a tika server file (both tika-server.jar and tika-server.jar.md5, which can be found here) and set the TIKA_SERVER_JAR environment variable to TIKA_SERVER_JAR="file:////tika-server.jar" which successfully tells python-tika to "download" this file and move it to /tmp/tika-server.jar and run as a background process. ... -
2
python-small-examples
Focus on creating classic Python small examples and cases
...Each example highlights a specific function or programming pattern so that learners can quickly understand how to apply Python features in real-world scenarios. The repository includes examples covering topics such as file processing, JSON manipulation, data visualization, and library usage. The examples are intentionally short and easy to read, making them useful for beginners who want to understand Python syntax and programming logic step by step. The repository is organized as a large collection of small scripts and notes that can be browsed individually without needing to study a full project. -
3
Basic Pitch
A lightweight audio-to-MIDI converter with pitch bend detection
...Provide a compatible audio file and a basic-pitch will generate a MIDI file, complete with pitch bends. The basic pitch is instrument-agnostic and supports polyphonic instruments, so you can freely enjoy transcription of all your favorite music, no matter what instrument is used. Basic pitch works best on one instrument at a time. -
4
supabase-py
Python Client for Supabase. Query Postgres from Flask, Django
Python Client for Supabase. Query Postgres from Flask, Django, FastAPI. Python user authentication, security policies, edge functions, file storage, and realtime data streaming. Good first issue. -
Houzz Pro is the #1 business management software for home construction and design professionals.Get an all-in-one solution that spans the full customer lifecycle, including marketing, CRM, estimation & proposal building, project management, a 3D Floor Plan builder, an online invoicing and payment portal, as well as a client portal and collaboration tools. Start a free trial today to see why thousands of Pros run their business on Houzz Pro. Plans available for all business sizes.
-
5
pycm
Multi-class confusion matrix library in Python
PyCM is a multi-class confusion matrix library written in Python that supports both input data vectors and direct matrix, and a proper tool for post-classification model evaluation that supports most classes and overall statistics parameters. PyCM is the swiss-army knife of confusion matrices, targeted mainly at data scientists that need a broad array of metrics for predictive models and an accurate evaluation of large variety of classifiers. -
6
autoresearch
AI agents autonomously run and improve ML experiments overnight
autoresearch is an experimental framework that enables AI agents to autonomously conduct machine learning research by iteratively modifying and training models. Created by Andrej Karpathy, the project allows an agent to edit the model training code, run short experiments, evaluate results, and repeat the process without human intervention. Each experiment runs for a fixed five-minute training window, enabling rapid iteration and consistent comparison across architectural or hyperparameter... -
7
Pedalboard
A Python library for audio
pedalboard is a Python library for working with audio: reading, writing, rendering, adding effects, and more. It supports the most popular audio file formats and a number of common audio effects out of the box and also allows the use of VST3® and Audio Unit formats for loading third-party software instruments and effects. pedalboard was built by Spotify’s Audio Intelligence Lab to enable using studio-quality audio effects from within Python and TensorFlow. -
8
OpenBB
Investment Research for Everyone, Everywhere
Customize and speed up your analysis, bring your own data, and create instant reports to gain a competitive edge. Whether it’s a CSV file, a private endpoint, an RSS feed, or even embed an SEC filing directly. Chat with financial data using large language models. Don’t waste time reading, create summaries in seconds and ask how that impacts investments. Create your dashboard with your favorite widgets. Create charts directly from raw data in seconds. Create charts directly from raw data in... -
9
OpenCLIP
An open source implementation of CLIP
The goal of this repository is to enable training models with contrastive image-text supervision and to investigate their properties such as robustness to distribution shift. Our starting point is an implementation of CLIP that matches the accuracy of the original CLIP models when trained on the same dataset. Specifically, a ResNet-50 model trained with our codebase on OpenAI's 15 million image subset of YFCC achieves 32.7% top-1 accuracy on ImageNet. OpenAI's CLIP model reaches 31.3% when... -
Information Security Made Simple and Affordable | CarbideGet expert guidance and smart tools to launch or level up your security and compliance efforts without the complexity.
-
10
Pandas Profiling
Create HTML profiling reports from pandas DataFrame objects
pandas-profiling generates profile reports from a pandas DataFrame. The pandas df.describe() function is handy yet a little basic for exploratory data analysis. pandas-profiling extends pandas DataFrame with df.profile_report(), which automatically generates a standardized univariate and multivariate report for data understanding. High correlation warnings, based on different correlation metrics (Spearman, Pearson, Kendall, Cramér’s V, Phik). Most common categories (uppercase, lowercase,... -
11
The SpeechBrain Toolkit
A PyTorch-based Speech Toolkit
SpeechBrain is an open-source and all-in-one conversational AI toolkit. It is designed to be simple, extremely flexible, and user-friendly. Competitive or state-of-the-art performance is obtained in various domains. SpeechBrain supports state-of-the-art methods for end-to-end speech recognition, including models based on CTC, CTC+attention, transducers, transformers, and neural language models relying on recurrent neural networks and transformers. Speaker recognition is already deployed in a... -
12
deepjazz
Deep learning driven jazz generation using Keras & Theano
deepjazz is a deep learning project that generates jazz music using recurrent neural networks trained on MIDI files. The repository demonstrates how machine learning can learn musical structure and produce original compositions. It uses the Keras and Theano libraries to build a two-layer Long Short-Term Memory network capable of learning temporal patterns in music. The system analyzes musical sequences from an input MIDI file and then generates new musical notes that follow similar stylistic... -
13
seq2seq-couplet
Play couplet with seq2seq model
seq2seq-couplet is a deep learning application that generates Chinese couplet responses using a sequence-to-sequence model built with TensorFlow. Its purpose is not general machine translation, but a specialized text generation task in which the model produces a matching second line for a given first line in the style of traditional couplets. The repository includes the code needed to train the model, configure file paths and hyperparameters, and evaluate progress through loss and BLEU score... -
14
Colossal-AI
Making large AI models cheaper, faster and more accessible
The Transformer architecture has improved the performance of deep learning models in domains such as Computer Vision and Natural Language Processing. Together with better performance come larger model sizes. This imposes challenges to the memory wall of the current accelerator hardware such as GPU. It is never ideal to train large models such as Vision Transformer, BERT, and GPT on a single GPU or a single machine. There is an urgent demand to train models in a distributed environment.... -
15
Zylthra
Zylthra: A PyQt6 app to generate synthetic datasets with DataLLM.
Welcome to Zylthra, a powerful Python-based desktop application built with PyQt6, designed to generate synthetic datasets using the DataLLM API from data.mostly.ai. This tool allows users to create custom datasets by defining columns, configuring generation parameters, and saving setups for reuse, all within a sleek, dark-themed interface. -
16
Uranie
Uranie is CEA's uncertainty analysis platform, based on ROOT
...It provides various tools for: - data analysis - sampling - statistical modeling - optimisation - sensitivity analysis - uncertainty analysis - running code on high performance computers - etc. Thanks to ROOT, it is easily scriptable in CINT (c++ like syntax) and Python. Is is available both for Unix and Windows platforms (a dedicated platform archive is available on request). Note : if you have downloaded version 3.12 before the 8th of february, a patch exists for a minor bug on TOutputFileKey file, don't hesitate to ask us. -
17
GNNPCSAFT
Smart Thermodynamic Modeling with Graph Neural Networks
The GNNPCSAFT app is an implementation of our project that focuses on using Graph Neural Networks (GNN) to estimate the pure-component parameters of the Equation of State PC-SAFT. We developed this app so the scientific community can access the model's results easily. In this app, the estimated pure-component parameters can be used to calculate thermodynamic properties and compare them with experimental data from the ThermoML Archive. To install the GNNPCSAFT app, download the... -
18
SSD in PyTorch 1.0
High quality, fast, modular reference implementation of SSD in PyTorch
This repository implements SSD (Single Shot MultiBox Detector). The implementation is heavily influenced by the projects ssd.pytorch, pytorch-ssd and maskrcnn-benchmark. This repository aims to be the code base for research based on SSD. Multi-GPU training and inference: We use DistributedDataParallel, you can train or test with arbitrary GPU(s), the training schema will change accordingly. Add your own modules without pain. We abstract backbone, Detector, BoxHead, BoxPredictor, etc. You can... -
19
TensorFlow Addons
Useful extra functionality for TensorFlow 2.x maintained by SIG-addons
TensorFlow Addons is a repository of contributions that conform to well-established API patterns but implement new functionality not available in core TensorFlow. TensorFlow natively supports a large number of operators, layers, metrics, losses, and optimizers. However, in a fast-moving field like ML, there are many interesting new developments that cannot be integrated into core TensorFlow (because their broad applicability is not yet clear, or it is mostly used by a smaller subset of the... -
20
MMClassification
OpenMMLab Image Classification Toolbox and Benchmark
...We wish that the toolbox and benchmark could serve the growing research community by providing a flexible toolkit to re-implement existing methods and develop their own new classifiers. MMClassification mainly uses python files as configs. The design of our configuration file system integrates modularity and inheritance, facilitating users to conduct various experiments. -
21
minimalRL-pytorch
Implementations of basic RL algorithms with minimal lines of codes
minimalRL is a lightweight reinforcement learning repository that implements several classic algorithms using minimal PyTorch code. The project is designed primarily as an educational resource that demonstrates how reinforcement learning algorithms work internally without the complexity of large frameworks. Each algorithm implementation is contained within a single file and typically ranges from about 100 to 150 lines of code, making it easy for learners to inspect the entire implementation... -
22
CleanRL
High-quality single file implementation of Deep Reinforcement Learning
CleanRL is a Deep Reinforcement Learning library that provides high-quality single-file implementation with research-friendly features. The implementation is clean and simple, yet we can scale it to run thousands of experiments using AWS Batch. CleanRL is not a modular library and therefore it is not meant to be imported. At the cost of duplicate code, we make all implementation details of a DRL algorithm variant easy to understand, so CleanRL comes with its own pros and cons. You should... -
23
StudioGAN
StudioGAN is a Pytorch library providing implementations of networks
StudioGAN is a Pytorch library providing implementations of representative Generative Adversarial Networks (GANs) for conditional/unconditional image generation. StudioGAN aims to offer an identical playground for modern GANs so that machine learning researchers can readily compare and analyze a new idea. Moreover, StudioGAN provides an unprecedented-scale benchmark for generative models. The benchmark includes results from GANs (BigGAN-Deep, StyleGAN-XL), auto-regressive models (MaskGIT,... -
24
ModelFox
ModelFox makes it easy to train, deploy, and monitor ML models
ModelFox makes it easy to train, deploy, and monitor machine learning models. Train a model from a CSV file on the command line. Make predictions from Elixir, Go, JavaScript, PHP, Python, Ruby, or Rust. Learn about your models and monitor them in production from your browser. ModelFox makes it easy to train, deploy, and monitor machine learning models. You can install the modelfox CLI by either downloading the binary from the latest GitHub release or by building from source. ... -
25
The fastai book
The fastai book, published as Jupyter Notebooks
...These notebooks are used for a MOOC and form the basis of this book, which is currently available for purchase. It does not have the same GPL restrictions that are on this repository. The code in the notebooks and python .py files is covered by the GPL v3 license; see the LICENSE file for details. The remainder (including all markdown cells in the notebooks and other prose) is not licensed for any redistribution or change of format or medium, other than making copies of the notebooks or forking this repo for your own private use. No commercial or broadcast use is allowed. ...
