Search Results for "conda"
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Showing 29 open source projects for "conda"
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SKUDONET ADC, operates at the application layer, efficiently distributing network load and application load across multiple servers. This not only enhances the performance of your application but also ensures that your web servers can handle more traffic seamlessly.
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Google Cloud’s AI-powered APIs help you translate documents, websites, apps, audio files, videos, and more at scale with best-in-class quality and enterprise-grade control and security.
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Conda
OS-agnostic, system-level binary package manager
Conda is an open-source package management system and environment management system that runs on Windows, macOS, and Linux. Conda quickly installs, runs, and updates packages and their dependencies. Conda easily creates, saves, loads, and switches between environments on your local computer. It was created for Python programs but it can package and distribute software for any language. Conda as a package manager helps you find and install packages. If you need a package that requires... -
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SHAP
A game theoretic approach to explain the output of ml models
SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions. While SHAP can explain the output of any machine learning model, we have developed a high-speed exact algorithm for tree ensemble methods. Fast C++ implementations are supported for XGBoost, LightGBM, CatBoost, scikit-learn and pyspark... -
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word_cloud
A little word cloud generator in Python
A little word cloud generator in Python. The code is tested against Python 2.7, 3.4, 3.5, 3.6 and 3.7. If you are using conda, you can install from the conda-forge channel. wordcloud depends on numpy and pillow. To save the wordcloud into a file, matplotlib can also be installed. If there are no wheels available for your version of python, installing the package requires having a C compiler set up. Before installing a compiler, report an issue describing the version of python and operating... -
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DeepChem
Democratizing Deep-Learning for Drug Discovery, Quantum Chemistry, etc
DeepChem aims to provide a high-quality open-source toolchain that democratizes the use of deep learning in drug discovery, materials science, quantum chemistry, and biology. DeepChem currently supports Python 3.7 through 3.9 and requires these packages on any condition. DeepChem has a number of "soft" requirements. If you face some errors like ImportError: This class requires XXXX, you may need to install some packages. Deepchem provides support for TensorFlow, PyTorch, JAX and each... -
This means every device on your network, and every interface on every device is automatically analyzed for performance, errors, QoS, and configuration.
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pypandoc
Thin wrapper for "pandoc" (MIT)
..., and if both are the same, the already installed version. Pypandoc is included in conda-forge. The conda packages will also install the pandoc package, so pandoc is available in the installation. -
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Diffusers
State-of-the-art diffusion models for image and audio generation
... lines of code. Interchangeable noise schedulers for different diffusion speeds and output quality. Pretrained models that can be used as building blocks, and combined with schedulers, for creating your own end-to-end diffusion systems. We recommend installing Diffusers in a virtual environment from PyPi or Conda. For more details about installing PyTorch and Flax, please refer to their official documentation. -
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torchvision
Datasets, transforms and models specific to Computer Vision
The torchvision package consists of popular datasets, model architectures, and common image transformations for computer vision. We recommend Anaconda as Python package management system. Torchvision currently supports Pillow (default), Pillow-SIMD, which is a much faster drop-in replacement for Pillow with SIMD, if installed will be used as the default. Also, accimage, if installed can be activated by calling torchvision.set_image_backend('accimage'), libpng, which can be installed via conda... -
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Gorilla CLI
LLMs for your CLI
Gorilla CLI powers your command-line interactions with a user-centric tool. Simply state your objective, and Gorilla CLI will generate potential commands for execution. Gorilla today supports ~1500 APIs, including Kubernetes, AWS, GCP, Azure, GitHub, Conda, Curl, Sed, and many more. No more recalling intricate CLI arguments. -
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OpenWPM
A web privacy measurement framework
... officially support other platforms, conda is a cross-platform utility and the install script can be expected to work on OSX and other Linux distributions. OpenWPM does not support windows. The main pre-requisite for OpenWPM is conda, a cross-platform package management tool. -
Save time, money and your sanity. Deluxe Payment Exchange+ (DPX+) is our integrated payments solution that streamlines and automates your accounts payable (AP) disbursements. DPX+ ensures secure payments and offers suppliers alternate ways to receive funds, including mailed checks, ACH, virtual credit cards, debit cards, or eCheck payments. By simply integrating with your existing accounting software like QuickBooks®, you’ll implement efficient payment solutions for AP with ease—without costly development fees or untimely delays.
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Pymunk
Pymunk is a easy-to-use pythonic 2d physics library
Pymunk is an easy-to-use Pythonic 2D physics library that can be used whenever you need 2D rigid body physics from Python. Perfect when you need 2D physics in your game, demo or simulation! It is built on top of the very capable 2D physics library Chipmunk2D. The first version was released in 2007 and Pymunk is still actively developed and maintained today, more than 15 years of active development. Pymunk has been used with success in many projects, big and small. For example: 3 Pyweek game... -
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ipychart
The power of Chart.js with Python
Create charts with Python in a very similar way to creating charts using Chart.js. The charts created are fully configurable, interactive, and modular and are displayed directly in the output of the cells of your jupyter notebook environment. Charts are fully interactive, you can hover it to display tooltips and select the information you want to see directly from the output cell of your notebook. All the types of charts present in Chart.js are exposed in ipychart. Even complex features such... -
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segment-geospatial
A Python package for segmenting geospatial data with the SAM
The segment-geospatial package draws its inspiration from segment-anything-eo repository authored by Aliaksandr Hancharenka. To facilitate the use of the Segment Anything Model (SAM) for geospatial data, I have developed the segment-anything-py and segment-geospatial Python packages, which are now available on PyPI and conda-forge. My primary objective is to simplify the process of leveraging SAM for geospatial data analysis by enabling users to achieve this with minimal coding effort. I have... -
13
SDGym
Benchmarking synthetic data generation methods
... the SDV project, or input your own data. Choose from any of the SDV synthesizers and baselines. Or write your own custom machine learning model. In addition to performance and memory usage, you can also measure synthetic data quality and privacy through a variety of metrics. Install SDGym using pip or conda. We recommend using a virtual environment to avoid conflicts with other software on your device. -
14
garak
Developers and anyone seeking an LLM solution to scan for vulnerabilit
.... garak has its own dependencies, you can to install garak in its own Conda environment. garak needs to know what model to scan, and by default, it'll try all the probes it knows on that model, using the vulnerability detectors recommended by each probe. For each probe loaded, garak will print a progress bar as it generates. Once the generation is complete, a row evaluating the probe's results on each detector is given. -
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PySR
High-Performance Symbolic Regression in Python and Julia
PySR is an open-source tool for Symbolic Regression: a machine learning task where the goal is to find an interpretable symbolic expression that optimizes some objective. Over a period of several years, PySR has been engineered from the ground up to be (1) as high-performance as possible, (2) as configurable as possible, and (3) easy to use. PySR is developed alongside the Julia library SymbolicRegression.jl, which forms the powerful search engine of PySR. The details of these algorithms are... -
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MLflow
Open source platform for the machine learning lifecycle
MLflow is a platform to streamline machine learning development, including tracking experiments, packaging code into reproducible runs, and sharing and deploying models. MLflow offers a set of lightweight APIs that can be used with any existing machine learning application or library (TensorFlow, PyTorch, XGBoost, etc), wherever you currently run ML code (e.g. in notebooks, standalone applications or the cloud). -
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PyScaffold
Python project template generator with batteries included
... yet, also have a look at these reasons to use PyScaffold. After cd-ing into your new project and creating (or activating) an isolated development environment (with virtualenv, conda or your preferred tool), you can do the usual editable install. All configuration can be done in setup.cfg like changing the description, URL, classifiers, installation requirements and so on as defined by setuptools. That means in most cases it is not necessary to tamper with setup.py. -
18
RecBole
A unified, comprehensive and efficient recommendation library
... 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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torchtext
Data loaders and abstractions for text and NLP
...++ compiler as the one used to build PyTorch. A simple way is to build PyTorch from source and use the same environment to build torchtext. If you are using the nightly build of PyTorch, check out the environment it was built with conda (here) and pip (here). Text classification: SST2, AG_NEWS, SogouNews, DBpedia, YelpReviewPolarity, YelpReviewFull, YahooAnswers, AmazonReviewPolarity, AmazonReviewFull, IMDB, etc. -
20
Darts
A python library for easy manipulation and forecasting of time series
... be trained on potentially large datasets containing multiple time series, and some of the models offer a rich support for probabilistic forecasting. We recommend to first setup a clean Python environment for your project with at least Python 3.7 using your favorite tool (conda, venv, virtualenv with or without virtualenvwrapper). -
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SageMaker Scikit-Learn Extension
A library of additional estimators and SageMaker tools based on scikit
... the mlio version 0.7 package via conda. The mlio package is only available through conda at the moment. You can also install from source by cloning this repository and running a pip install command in the root directory of the repository. For unit tests, tox will use pytest to run the unit tests in a Python 3.7 interpreter. tox will also run flake8 and pylint for style checks. -
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Spleeter
Deezer source separation library including pretrained models
... than real-time when run on a GPU. We designed Spleeter so you can use it straight from command line as well as directly in your own development pipeline as a Python library. It can be installed with Conda, with pip or be used with Docker. -
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arxiv-collector
Little Python script to collect LaTeX sources for upload to the arXiv
A little Python script to collect LaTeX sources for upload to the arXiv. Install with pip install arxiv-collector or conda install -c conda-forge arxiv-collector, or just download arxiv_collector.py, it's a stand-alone script with no dependencies. Works with any reasonable version of Python 3, or 2.7 if you really must. -
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Zipline
Zipline, a Pythonic algorithmic trading library
Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backtesting and live-trading engine powering Quantopian -- a free, community-centered, hosted platform for building and executing trading strategies. Quantopian also offers a fully managed service for professionals that includes Zipline, Alphalens, Pyfolio, FactSet data, and more. Installing Zipline is slightly more involved than the average Python... -
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Python4Proteomics Course
Python course for Proteomics analysis
Python course (in Spanish) for Proteomics analysis using basically Jupyter NoteBooks. For more information, you can have a look at the readme.md file in the source code tree: https://sourceforge.net/p/lp-csic-uab/p4p/code/ci/default/tree/readme.md