Search Results for "python::module" - Page 6
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Showing 529 open source projects for "python::module"
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Audiomentations
A Python library for audio data augmentation
A Python library for audio data augmentation. Inspired by albumentations. Useful for deep learning. Runs on CPU. Supports mono audio and multichannel audio. Can be integrated in training pipelines in e.g. Tensorflow/Keras or Pytorch. Has helped people get world-class results in Kaggle competitions. Is used by companies making next-generation audio products. -
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TorchRec
Pytorch domain library for recommendation systems
TorchRec is a PyTorch domain library built to provide common sparsity & parallelism primitives needed for large-scale recommender systems (RecSys). It allows authors to train models with large embedding tables sharded across many GPUs. Parallelism primitives that enable easy authoring of large, performant multi-device/multi-node models using hybrid data-parallelism/model-parallelism. The TorchRec sharder can shard embedding tables with different sharding strategies including data-parallel,... -
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PennyLane
A cross-platform Python library for differentiable programming
A cross-platform Python library for differentiable programming of quantum computers. Train a quantum computer the same way as a neural network. Built-in automatic differentiation of quantum circuits, using the near-term quantum devices directly. You can combine multiple quantum devices with classical processing arbitrarily! Support for hybrid quantum and classical models, and compatible with existing machine learning libraries. -
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PromptTools
Open-source tools for prompt testing and experimentation
Welcome to prompttools created by Hegel AI! This repo offers a set of open-source, self-hostable tools for experimenting with, testing, and evaluating LLMs, vector databases, and prompts. The core idea is to enable developers to evaluate using familiar interfaces like code, notebooks, and a local playground. -
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Lazy Predict
Lazy Predict help build a lot of basic models without much code
Lazy Predict helps build a lot of basic models without much code and helps understand which models work better without any parameter tuning. -
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MLPerf
Reference implementations of MLPerf™ training benchmarks
This is a repository of reference implementations for the MLPerf training benchmarks. These implementations are valid as starting points for benchmark implementations but are not fully optimized and are not intended to be used for "real" performance measurements of software frameworks or hardware. Benchmarking the performance of training ML models on a wide variety of use cases, software, and hardware drives AI performance across the tech industry. The MLPerf Training working group draws on... -
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Nixtla ML
TimeGPT-1: production ready pre-trained Time Series Foundation Model
TimeGPT is a production ready, generative pretrained transformer for time series. It's capable of accurately predicting various domains such as retail, electricity, finance, and IoT with just a few lines of code. Whether you're a bank forecasting market trends or a startup predicting product demand, TimeGPT democratizes access to cutting-edge predictive insights, eliminating the need for a dedicated team of machine learning engineers. A generative model for time series. TimeGPT is capable of... -
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Chronos Forecasting
Pretrained (Language) Models for Probabilistic Time Series Forecasting
Chronos is a family of pretrained time series forecasting models based on language model architectures. A time series is transformed into a sequence of tokens via scaling and quantization, and a language model is trained on these tokens using the cross-entropy loss. Once trained, probabilistic forecasts are obtained by sampling multiple future trajectories given the historical context. Chronos models have been trained on a large corpus of publicly available time series data, as well as... -
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HDBSCAN
A high performance implementation of HDBSCAN clustering
HDBSCAN - Hierarchical Density-Based Spatial Clustering of Applications with Noise. Performs DBSCAN over varying epsilon values and integrates the result to find a clustering that gives the best stability over epsilon. This allows HDBSCAN to find clusters of varying densities (unlike DBSCAN), and be more robust to parameter selection. In practice this means that HDBSCAN returns a good clustering straight away with little or no parameter tuning -- and the primary parameter, minimum cluster... -
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pomegranate
Fast, flexible and easy to use probabilistic modelling in Python
pomegranate is a library for probabilistic modeling defined by its modular implementation and treatment of all models as the probability distributions they are. The modular implementation allows one to easily drop normal distributions into a mixture model to create a Gaussian mixture model just as easily as dropping a gamma and a Poisson distribution into a mixture model to create a heterogeneous mixture. But that's not all! Because each model is treated as a probability distribution,... -
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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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dm_control
DeepMind's software stack for physics-based simulation
DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo. DeepMind's software stack for physics-based simulation and Reinforcement Learning environments, using MuJoCo physics. The MuJoCo Python bindings support three different OpenGL rendering backends: EGL (headless, hardware-accelerated), GLFW (windowed, hardware-accelerated), and OSMesa (purely software-based). At least one of these three backends must be available in order render through dm_control. Hardware rendering with a windowing system is supported via GLFW and GLEW. ... -
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APKiD
Android Application Identifier for Packers, Protectors and Obfuscators
APKiD gives you information about how an APK was made. It identifies many compilers, packers, obfuscators, and other weird stuff. It's PEiD for Android. -
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Materials Discovery: GNoME
AI discovers 520000 stable inorganic crystal structures for research
Materials Discovery (GNoME) is a large-scale research initiative by Google DeepMind focused on applying graph neural networks to accelerate the discovery of stable inorganic crystal materials. The project centers on Graph Networks for Materials Exploration (GNoME), a message-passing neural network architecture trained on density functional theory (DFT) data to predict material stability and energy formation. Using GNoME, DeepMind identified 381,000 new stable materials, later expanding the... -
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Diffgram
Training data (data labeling, annotation, workflow) for all data types
From ingesting data to exploring it, annotating it, and managing workflows. Diffgram is a single application that will improve your data labeling and bring all aspects of training data under a single roof. Diffgram is world’s first truly open source training data platform that focuses on giving its users an unlimited experience. This is aimed to reduce your data labeling bills and increase your Training Data Quality. Training Data is the art of supervising machines through data. This... -
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Weights and Biases
Tool for visualizing and tracking your machine learning experiments
Use W&B to build better models faster. Track and visualize all the pieces of your machine learning pipeline, from datasets to production models. Quickly identify model regressions. Use W&B to visualize results in real time, all in a central dashboard. Focus on the interesting ML. Spend less time manually tracking results in spreadsheets and text files. Capture dataset versions with W&B Artifacts to identify how changing data affects your resulting models. Reproduce any model, with saved... -
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Raster Vision
Open source framework for deep learning satellite and aerial imagery
Raster Vision is an open source framework for Python developers building computer vision models on satellite, aerial, and other large imagery sets (including oblique drone imagery). There is built-in support for chip classification, object detection, and semantic segmentation using PyTorch. Raster Vision allows engineers to quickly and repeatably configure pipelines that go through core components of a machine learning workflow: analyzing training data, creating training chips, training models, creating predictions, evaluating models, and bundling the model files and configuration for easy deployment. ... -
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Albumentations
Fast image augmentation library and an easy-to-use wrapper
Albumentations is a computer vision tool that boosts the performance of deep convolutional neural networks. Albumentations is a Python library for fast and flexible image augmentations. Albumentations efficiently implements a rich variety of image transform operations that are optimized for performance, and does so while providing a concise, yet powerful image augmentation interface for different computer vision tasks, including object classification, segmentation, and detection. ... -
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higgsfield
Fault-tolerant, highly scalable GPU orchestration
Higgsfield is an open-source, fault-tolerant, highly scalable GPU orchestration, and a machine learning framework designed for training models with billions to trillions of parameters, such as Large Language Models (LLMs). -
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ZenML
Build portable, production-ready MLOps pipelines
A simple yet powerful open-source framework that scales your MLOps stack with your needs. Set up ZenML in a matter of minutes, and start with all the tools you already use. Gradually scale up your MLOps stack by switching out components whenever your training or deployment requirements change. Keep up with the latest changes in the MLOps world and easily integrate any new developments. Define simple and clear ML workflows without wasting time on boilerplate tooling or infrastructure code.... -
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AutoMLOps
Build MLOps Pipelines in Minutes
AutoMLOps is a service that generates, provisions, and deploys CI/CD integrated MLOps pipelines, bridging the gap between Data Science and DevOps. AutoMLOps provides a repeatable process that dramatically reduces the time required to build MLOps pipelines. The service generates a containerized MLOps codebase, provides infrastructure-as-code to provision and maintain the underlying MLOps infra, and provides deployment functionalities to trigger and run MLOps pipelines. AutoMLOps gives... -
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AtomAI
Deep and Machine Learning for Microscopy
AtomAI is a Pytorch-based package for deep and machine-learning analysis of microscopy data that doesn't require any advanced knowledge of Python or machine learning. The intended audience is domain scientists with a basic understanding of how to use NumPy and Matplotlib. It was developed by Maxim Ziatdinov at Oak Ridge National Lab. The purpose of the AtomAI is to provide an environment that bridges the instrument-specific libraries and general physical analysis by enabling the seamless deployment of machine learning algorithms including deep convolutional neural networks, invariant variational autoencoders, and decomposition/unmixing techniques for image and hyperspectral data analysis. ... -
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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... -
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RecBole
A unified, comprehensive and efficient recommendation library
...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. RecBole is developed based on Python and PyTorch for reproducing and developing recommendation algorithms in a unified, comprehensive and efficient framework for research purpose. It can be installed from pip, conda and source, and is easy to use. We have implemented more than 100 recommender system models, covering four common recommender system categories in RecBole and eight toolkits of RecBole2.0, including General Recommendation, Sequential Recommendation, Context-aware Recommendation, and Knowledge-based Recommendation and sub-packages. -
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TensorRT Backend For ONNX
ONNX-TensorRT: TensorRT backend for ONNX
...For previous versions of TensorRT, refer to their respective branches. Building INetwork objects in full dimensions mode with dynamic shape support requires calling the C++ and Python API. Current supported ONNX operators are found in the operator support matrix. For building within docker, we recommend using and setting up the docker containers as instructed in the main (TensorRT repository). Note that this project has a dependency on CUDA. By default the build will look in /usr/local/cuda for the CUDA toolkit installation. ...
