Search Results for "linux windows like" - Page 3
Sort By:
Showing 414 open source projects for "linux windows like"
View related business solutions-
MongoDB Atlas runs apps anywhereMongoDB Atlas gives you the freedom to build and run modern applications anywhere—across AWS, Azure, and Google Cloud. With global availability in over 115 regions, Atlas lets you deploy close to your users, meet compliance needs, and scale with confidence across any geography.
-
Gemini 3 and 200+ AI Models on One PlatformBuild generative AI apps with Vertex AI. Switch between models without switching platforms.
-
1
miepython
Mie scattering of light by perfect spheres
miepython is a pure Python module to calculate light scattering for non-absorbing, partially-absorbing, or perfectly-conducting spheres. Mie theory is used, following the procedure described by Wiscombe. This code has been validated against his results. This code provides functions for calculating the extinction efficiency, scattering efficiency, backscattering, and scattering asymmetry. Moreover, a set of angles can be given to calculate the scattering for a sphere at each of those angles. -
2
CellTypist
A tool for semi-automatic cell type classification, harmonization
CellTypist is an automated tool for cell type classification, harmonization, and integration. Classification, transfer cell type labels from the reference to query dataset. Harmonization, match and harmonize cell types defined by independent datasets. integration, integrate cell and cell types with supervision from harmonization. CellTypist recapitulates cell type structure and biology of independent datasets. Regularised linear models with Stochastic Gradient Descent provide a fast and... -
3
DataChain
AI-data warehouse to enrich, transform and analyze unstructured data
Datachain enables multimodal API calls and local AI inferences to run in parallel over many samples as chained operations. The resulting datasets can be saved, versioned, and sent directly to PyTorch and TensorFlow for training. Datachain can persist features of Python objects returned by AI models, and enables vectorized analytical operations over them. The typical use cases are data curation, LLM analytics and validation, image segmentation, pose detection, and GenAI alignment. Datachain... -
4
Fondant
Production-ready data processing made easy and shareable
Fondant is a modular, pipeline-based framework designed to simplify the preparation of large-scale datasets for training machine learning models, especially foundation models. It offers an end-to-end system for ingesting raw data, applying transformations, filtering, and formatting outputs—all while remaining scalable and traceable. Fondant is designed with reproducibility in mind and supports containerized steps using Docker, making it easy to share and reuse data processing components.... -
Full-stack observability with actually useful AI | Grafana CloudBuilt on open standards like Prometheus and OpenTelemetry, Grafana Cloud includes Kubernetes Monitoring, Application Observability, Incident Response, plus the AI-powered Grafana Assistant. Get started with our generous free tier today.
-
5
atpbar
Progress bars for threading and multiprocessing tasks on terminal
Progress bars for threading and multiprocessing tasks on the terminal and Jupyter Notebook. atpbar can display multiple progress bars simultaneously growing to show the progresses of iterations of loops in threading or multiprocessing tasks. atpbar can display progress bars on the terminal and Jupyter Notebook. atpbar can be used with Mantichora. atpbar started its development in 2015 as part of Alphatwirl. atpbar prevented physicists from terminating their running analysis codes, which... -
6
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... -
7
Encord Active
The toolkit to test, validate, and evaluate your models and surface
Encord Active is an open-source toolkit to test, validate, and evaluate your models and surface, curate, and prioritize the most valuable data for labeling to supercharge model performance. Encord Active has been designed as a all-in-one open source toolkit for improving your data quality and model performance. Use the intuitive UI to explore your data or access all the functionalities programmatically. Discover errors, outliers, and edge-cases within your data - all in one open source... -
8
Bytewax
Python Stream Processing
Bytewax is a Python framework that simplifies event and stream processing. Because Bytewax couples the stream and event processing capabilities of Flink, Spark, and Kafka Streams with the friendly and familiar interface of Python, you can re-use the Python libraries you already know and love. Connect data sources, run stateful transformations, and write to various downstream systems with built-in connectors or existing Python libraries. Bytewax is a Python framework and Rust distributed... -
9
GemGIS
Spatial data processing for geomodeling
GemGIS is a Python-based, open-source geographic information processing library. It is capable of preprocessing spatial data such as vector data (shape files, geojson files, geopackages,…), raster data (tif, png,…), data obtained from online services (WCS, WMS, WFS) or XML/KML files (soon). Preprocessed data can be stored in a dedicated Data Class to be passed to the geomodeling package GemPy in order to accelerate the model-building process. Postprocessing of model results will allow export... -
Custom VMs From 1 to 96 vCPUs With 99.95% UptimeLive migration and automatic failover keep workloads online through maintenance. One free e2-micro VM every month.
-
10
Population Shift Monitoring
Monitor the stability of a Pandas or Spark dataframe
popmon is a package that allows one to check the stability of a dataset. popmon works with both pandas and spark datasets. popmon creates histograms of features binned in time-slices, and compares the stability of the profiles and distributions of those histograms using statistical tests, both over time and with respect to a reference. It works with numerical, ordinal, categorical features, and the histograms can be higher-dimensional, e.g. it can also track correlations between any two... -
11
whylogs
The open standard for data logging
whylogs is an open-source library for logging any kind of data. With whylogs, users are able to generate summaries of their datasets (called whylogs profiles) which they can use to track changes in their dataset Create data constraints to know whether their data looks the way it should. Quickly visualize key summary statistics about their datasets. whylogs profiles are the core of the whylogs library. They capture key statistical properties of data, such as the distribution (far beyond... -
12
BertViz
BertViz: Visualize Attention in NLP Models (BERT, GPT2, BART, etc.)
BertViz is an interactive tool for visualizing attention in Transformer language models such as BERT, GPT2, or T5. It can be run inside a Jupyter or Colab notebook through a simple Python API that supports most Huggingface models. BertViz extends the Tensor2Tensor visualization tool by Llion Jones, providing multiple views that each offer a unique lens into the attention mechanism. The head view visualizes attention for one or more attention heads in the same layer. It is based on the... -
13
HyperTools
A Python toolbox for gaining geometric insights
HyperTools is a library for visualizing and manipulating high-dimensional data in Python. It is built on top of matplotlib (for plotting), seaborn (for plot styling), and scikit-learn (for data manipulation). Functions for plotting high-dimensional datasets in 2/3D. Static and animated plots. Simple API for customizing plot styles. Set of powerful data manipulation tools including hyperalignment, k-means clustering, normalizing and more. Support for lists of Numpy arrays, Pandas dataframes,... -
14
Sweetviz
Visualize and compare datasets, target values and associations
Sweetviz is an open-source Python library that generates beautiful, high-density visualizations to kickstart EDA (Exploratory Data Analysis) with just two lines of code. Output is a fully self-contained HTML application. The system is built around quickly visualizing target values and comparing datasets. Its goal is to help quick analysis of target characteristics, training vs testing data, and other such data characterization tasks. Shows how a target value (e.g. "Survived" in the Titanic... -
15
nb-clean
Clean Jupyter notebooks of outputs, metadata, and empty cells
nb-clean cleans Jupyter notebooks of cell execution counts, metadata, outputs, and (optionally) empty cells, preparing them for committing to version control. It provides both a Git filter and pre-commit hook to automatically clean notebooks before they're staged, and can also be used with other version control systems, as a command line tool, and as a Python library. It can determine if a notebook is clean or not, which can be used as a check in your continuous integration pipelines.... -
16
gusty
Making DAG construction easier
gusty allows you to control your Airflow DAGs, Task Groups, and Tasks with greater ease. gusty manages collections of tasks, represented as any number of YAML, Python, SQL, Jupyter Notebook, or R Markdown files. A directory of task files is instantly rendered into a DAG by passing a file path to gusty's create_dag function. gusty also manages dependencies (within one DAG) and external dependencies (dependencies on tasks in other DAGs) for each task file you define. All you have to do is... -
17
Union Pandera
Light-weight, flexible, expressive statistical data testing library
The open-source framework for precision data testing for data scientists and ML engineers. Pandera provides a simple, flexible, and extensible data-testing framework for validating not only your data but also the functions that produce them. A simple, zero-configuration data testing framework for data scientists and ML engineers seeking correctness. Access a comprehensive suite of built-in tests, or easily create your own validation rules for your specific use cases. Validate the functions... -
18
Gretel Synthetics
Synthetic data generators for structured and unstructured text
Unlock unlimited possibilities with synthetic data. Share, create, and augment data with cutting-edge generative AI. Generate unlimited data in minutes with synthetic data delivered as-a-service. Synthesize data that are as good or better than your original dataset, and maintain relationships and statistical insights. Customize privacy settings so that data is always safe while remaining useful for downstream workflows. Ensure data accuracy and privacy confidently with expert-grade reports.... -
19
Great Expectations
Always know what to expect from your data
Great Expectations helps data teams eliminate pipeline debt, through data testing, documentation, and profiling. Software developers have long known that testing and documentation are essential for managing complex codebases. Great Expectations brings the same confidence, integrity, and acceleration to data science and data engineering teams. Expectations are assertions for data. They are the workhorse abstraction in Great Expectations, covering all kinds of common data issues. Expectations... -
20
Cookiecutter Data Science
Project structure for doing and sharing data science work
A logical, reasonably standardized, but flexible project structure for doing and sharing data science work. When we think about data analysis, we often think just about the resulting reports, insights, or visualizations. While these end products are generally the main event, it's easy to focus on making the products look nice and ignore the quality of the code that generates them. Because these end products are created programmatically, code quality is still important! And we're not talking... -
21
Positron
Positron, a next-generation data science IDE
Positron is a next-generation integrated development environment (IDE) created by Posit PBC (formerly RStudio Inc) specifically tailored for data science workflows in Python, R, and multi-language ecosystems. It aims to unify exploratory data analysis, production code, and data-app authoring in a single environment so that data scientists move from “question → insight → application” without switching tools. Built on the open-source Code-OSS foundation, Positron provides a familiar coding... -
22
airda
airda(Air Data Agent
airda(Air Data Agent) is a multi-smart body for data analysis, capable of understanding data development and data analysis needs, understanding data, generating data-oriented queries, data visualization, machine learning and other tasks of SQL and Python codes. -
23
Cleanlab
The standard data-centric AI package for data quality and ML
cleanlab helps you clean data and labels by automatically detecting issues in a ML dataset. To facilitate machine learning with messy, real-world data, this data-centric AI package uses your existing models to estimate dataset problems that can be fixed to train even better models. cleanlab cleans your data's labels via state-of-the-art confident learning algorithms, published in this paper and blog. See some of the datasets cleaned with cleanlab at labelerrors.com. This package helps you... -
24
NannyML
Detecting silent model failure. NannyML estimates performance
NannyML is an open-source python library that allows you to estimate post-deployment model performance (without access to targets), detect data drift, and intelligently link data drift alerts back to changes in model performance. Built for data scientists, NannyML has an easy-to-use interface, and interactive visualizations, is completely model-agnostic, and currently supports all tabular classification use cases. NannyML closes the loop with performance monitoring and post deployment data... -
25
AutoGluon
AutoGluon: AutoML for Image, Text, and Tabular Data
AutoGluon enables easy-to-use and easy-to-extend AutoML with a focus on automated stack ensembling, deep learning, and real-world applications spanning image, text, and tabular data. Intended for both ML beginners and experts, AutoGluon enables you to quickly prototype deep learning and classical ML solutions for your raw data with a few lines of code. Automatically utilize state-of-the-art techniques (where appropriate) without expert knowledge. Leverage automatic hyperparameter tuning,...
