Showing 746 open source projects for "python-snap7"

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  • 1
    Elyra

    Elyra

    Elyra extends JupyterLab with an AI centric approach

    Elyra is a set of AI-centric extensions to JupyterLab Notebooks. The Elyra Getting Started Guide includes more details on these features. A version-specific summary of new features is located on the releases page.
    Downloads: 0 This Week
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  • 2
    APKiD

    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.
    Downloads: 4 This Week
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  • 3
    Hamilton DAGWorks

    Hamilton DAGWorks

    Helps scientists define testable, modular, self-documenting dataflow

    Hamilton is a lightweight Python library for directed acyclic graphs (DAGs) of data transformations. Your DAG is portable; it runs anywhere Python runs, whether it's a script, notebook, Airflow pipeline, FastAPI server, etc. Your DAG is expressive; Hamilton has extensive features to define and modify the execution of a DAG (e.g., data validation, experiment tracking, remote execution).
    Downloads: 0 This Week
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  • 4
    TextAttack

    TextAttack

    Python framework for adversarial attacks, and data augmentation

    Generating adversarial examples for NLP models. TextAttack is a Python framework for adversarial attacks, data augmentation, and model training in NLP.
    Downloads: 0 This Week
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  • 5
    stable-diffusion-videos

    stable-diffusion-videos

    Create videos with Stable Diffusion

    Create videos with Stable Diffusion by exploring the latent space and morphing between text prompts. Try it yourself in Colab.
    Downloads: 2 This Week
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  • 6
    SKORCH

    SKORCH

    A scikit-learn compatible neural network library that wraps PyTorch

    A scikit-learn compatible neural network library that wraps PyTorch.
    Downloads: 0 This Week
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  • 7
    Apache Hamilton

    Apache Hamilton

    Helps data scientists define testable self-documenting dataflows

    Apache Hamilton is an open-source Python framework designed to simplify the creation and management of dataflows used in analytics, machine learning pipelines, and data engineering workflows. The framework enables developers to define data transformations as simple Python functions, where each function represents a node in a dataflow graph and its parameters define dependencies on other nodes.
    Downloads: 0 This Week
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  • 8
    TorchRL

    TorchRL

    A modular, primitive-first, python-first PyTorch library

    TorchRL is an open-source Reinforcement Learning (RL) library for PyTorch. TorchRL provides PyTorch and python-first, low and high-level abstractions for RL that are intended to be efficient, modular, documented, and properly tested. The code is aimed at supporting research in RL. Most of it is written in Python in a highly modular way, such that researchers can easily swap components, transform them, or write new ones with little effort.
    Downloads: 0 This Week
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  • 9
    TPOT

    TPOT

    A Python Automated Machine Learning tool that optimizes ML

    Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming. TPOT stands for Tree-based Pipeline Optimization Tool. Consider TPOT your Data Science Assistant. TPOT is a Python Automated Machine Learning tool that optimizes machine learning pipelines using genetic programming.
    Downloads: 0 This Week
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    Build Securely on AWS with Proven Frameworks

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  • 10
    GPflow

    GPflow

    Gaussian processes in TensorFlow

    GPflow is a package for building Gaussian process models in Python. It implements modern Gaussian process inference for composable kernels and likelihoods. GPflow builds on TensorFlow 2.4+ and TensorFlow Probability for running computations, which allows fast execution on GPUs.
    Downloads: 0 This Week
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  • 11
    Pedalboard

    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.
    Downloads: 1 This Week
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  • 12
    RAGFlow

    RAGFlow

    RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine

    RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine based on deep document understanding. It offers a streamlined RAG workflow for businesses of any scale, combining LLM (Large Language Models) to provide truthful question-answering capabilities, backed by well-founded citations from various complex formatted data.
    Downloads: 6 This Week
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  • 13
    Label Studio

    Label Studio

    Label Studio is a multi-type data labeling and annotation tool

    The most flexible data annotation tool. Quickly installable. Build custom UIs or use pre-built labeling templates. Detect objects on image, bboxes, polygons, circular, and keypoints supported. Partition image into multiple segments. Use ML models to pre-label and optimize the process. Label Studio is an open-source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can...
    Downloads: 21 This Week
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  • 14
    TabPFN

    TabPFN

    Foundation Model for Tabular Data

    TabPFN is an open-source machine learning system that introduces a foundation model designed specifically for tabular data analysis. The model is based on transformer architectures and implements a prior-data fitted network that can perform supervised learning tasks such as classification and regression with minimal configuration. Unlike many traditional machine learning workflows that require extensive hyperparameter tuning and training cycles, TabPFN is pre-trained to perform inference...
    Downloads: 2 This Week
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  • 15
    Ray

    Ray

    A unified framework for scalable computing

    ...Accelerate your hyperparameter search workloads with Ray Tune. Find the best model and reduce training costs by using the latest optimization algorithms. Deploy your machine learning models at scale with Ray Serve, a Python-first and framework agnostic model serving framework. Scale reinforcement learning (RL) with RLlib, a framework-agnostic RL library that ships with 30+ cutting-edge RL algorithms including A3C, DQN, and PPO. Easily build out scalable, distributed systems in Python with simple and composable primitives in Ray Core.
    Downloads: 1 This Week
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  • 16
    TensorRT Backend For ONNX

    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. ...
    Downloads: 3 This Week
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  • 17
    AutoKeras

    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. AutoKeras would search for the best detailed configuration for you. ...
    Downloads: 0 This Week
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  • 18
    Mlxtend

    Mlxtend

    A library of extension and helper modules for Python's data analysis

    Mlxtend (machine learning extensions) is a Python library of useful tools for day-to-day data science tasks.
    Downloads: 0 This Week
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  • 19
    ChatterBot

    ChatterBot

    Machine learning, conversational dialog engine for creating chat bots

    ChatterBot is a Python library that makes it easy to generate automated responses to a user’s input. ChatterBot uses a selection of machine learning algorithms to produce different types of responses. This makes it easy for developers to create chat bots and automate conversations with users. For more details about the ideas and concepts behind ChatterBot see the process flow diagram.
    Downloads: 2 This Week
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  • 20
    Homemade Machine Learning

    Homemade Machine Learning

    Python examples of popular machine learning algorithms

    ...The purpose is pedagogical: you’ll see linear regression, logistic regression, k-means clustering, neural nets, decision trees, etc., built in Python using fundamentals like NumPy and Matplotlib, not hidden behind API calls. It is well suited for learners who want to move beyond library usage to understand how algorithms operate internally—how cost functions, gradients, updates and predictions work.
    Downloads: 0 This Week
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  • 21
    Petastorm

    Petastorm

    Petastorm library enables single machine or distributed training

    Petastorm library enables single machine or distributed training and evaluation of deep learning models from datasets in Apache Parquet format. It supports ML frameworks such as Tensorflow, Pytorch, and PySpark and can be used from pure Python code. Petastorm is an open-source data access library developed at Uber ATG. This library enables single machine or distributed training and evaluation of deep learning models directly from datasets in Apache Parquet format. Petastorm supports popular Python-based machine learning (ML) frameworks such as Tensorflow, PyTorch, and PySpark. ...
    Downloads: 0 This Week
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  • 22
    GluonTS

    GluonTS

    Probabilistic time series modeling in Python

    GluonTS is a Python package for probabilistic time series modeling, focusing on deep learning based models. GluonTS requires Python 3.6 or newer, and the easiest way to install it is via pip. We train a DeepAR-model and make predictions using the simple "airpassengers" dataset. The dataset consists of a single time-series, containing monthly international passengers between the years 1949 and 1960, a total of 144 values (12 years * 12 months).
    Downloads: 0 This Week
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  • 23
    Feature-engine

    Feature-engine

    Feature engineering package with sklearn like functionality

    Feature-engine is a Python library with multiple transformers to engineer and select features for use in machine learning models. Feature-engine's transformers follow Scikit-learn's functionality with fit() and transform() methods to learn the transforming parameters from the data and then transform it.
    Downloads: 0 This Week
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  • 24
    PySINDy

    PySINDy

    A package for the sparse identification of nonlinear dynamical systems

    ...The library provides tools for constructing libraries of candidate functions, performing sparse regression, and validating discovered models against observed data. It integrates with standard Python scientific computing libraries, making it easy to apply to experimental datasets or simulated systems.
    Downloads: 0 This Week
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  • 25
    GPU Puzzles

    GPU Puzzles

    Solve puzzles. Learn CUDA

    ...Instead of presenting traditional lecture-style explanations, the project immerses learners directly in hands-on programming tasks that demonstrate how GPU computation works. The exercises are implemented using Python with the Numba CUDA interface, which allows Python code to compile into GPU kernels that run on CUDA-enabled hardware. By solving progressively more complex puzzles, learners gain a practical understanding of how parallel algorithms operate on graphics processing units. The project emphasizes experimentation and problem solving, encouraging learners to discover GPU programming techniques through trial and exploration. ...
    Downloads: 0 This Week
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