Search Results for "python-ldap" - Page 6
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Showing 1565 open source projects for "python-ldap"
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Circuitscape.jl
Algorithms from circuit theory to predict connectivity
Circuitscape is an open-source program that uses circuit theory to model connectivity in heterogeneous landscapes. Its most common applications include modeling the movement and gene flow of plants and animals, as well as identifying areas important for connectivity conservation. The new Circuitscape is built entirely in the Julia language, a new programming language for technical computing. Julia is built from the ground up to be fast. As such, this offers a number of advantages over the... -
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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,... -
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AI Hedge Fund
An AI Hedge Fund Team
This repository demonstrates how to build a simplified, automated hedge fund strategy powered by AI/ML. It integrates financial data collection, preprocessing, feature engineering, and predictive modeling to simulate decision-making in trading. The code shows workflows for pulling stock or market data, applying machine learning algorithms to forecast trends, and generating buy/sell/hold signals based on the predictions. Its structure is educational: intended more as a proof-of-concept than a... -
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Kronos
A Foundation Model for the Language of Financial Markets
Kronos is a specialized open-source foundation model designed for analyzing and predicting financial market data using time-series representations of candlestick patterns. It is built as a decoder-only Transformer model trained specifically on K-line data, which captures open, high, low, close, and volume information across multiple global exchanges. The system introduces a novel tokenization approach that converts continuous financial data into discrete tokens, enabling the model to process... -
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Recap
Recap tracks and transform schemas across your whole application
Recap is a schema language and multi-language toolkit to track and transform schemas across your whole application. Your data passes through web services, databases, message brokers, and object stores. Recap describes these schemas in a single language, regardless of which system your data passes through. Recap schemas can be defined in YAML, TOML, JSON, XML, or any other compatible language. -
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electricityMap
A real-time visualisation of the CO2 emissions of electricity
Real-time visualization of the Greenhouse Gas (in terms of CO2 equivalent) footprint of electricity consumption built with d3.js and mapbox GL. Real-time data is defined as a data source with an hourly (or better) frequency, delayed by less than 2hrs. It should provide a breakdown by generation type. Often fossil fuel generation (coal/gas/oil) is combined under a single heading like 'thermal' or 'conventional', this is not a problem. Citizens should not be responsible for the emissions... -
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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... -
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tsfresh
Automatic extraction of relevant features from time series
tsfresh is a python package. It automatically calculates a large number of time series characteristics, the so called features. tsfresh is used to to extract characteristics from time series. Without tsfresh, you would have to calculate all characteristics by hand. With tsfresh this process is automated and all your features can be calculated automatically. -
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Neuroglancer
WebGL-based viewer for volumetric data
Neuroglancer is a WebGL-based visualization tool designed for exploring large-scale volumetric and neuroimaging datasets directly in the browser. It allows users to interactively view arbitrary 2D and 3D cross-sections of volumetric data alongside 3D meshes and skeleton models, enabling precise examination of neural structures and biological imaging results. Its multi-pane interface synchronizes multiple orthogonal views with a central 3D viewport, making it ideal for analyzing complex brain... -
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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,... -
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SageMaker Training Toolkit
Train machine learning models within Docker containers
Train machine learning models within a Docker container using Amazon SageMaker. Amazon SageMaker is a fully managed service for data science and machine learning (ML) workflows. You can use Amazon SageMaker to simplify the process of building, training, and deploying ML models. To train a model, you can include your training script and dependencies in a Docker container that runs your training code. A container provides an effectively isolated environment, ensuring a consistent runtime and... -
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loonflow
A workflow engine base on django python
a workflow engine base on django The django-based workflow engine system (called through the http interface, can be used as a unified workflow engine within the enterprise, providing all workflows such as permission application, resource application, release application, leave, reimbursement, it service, etc. Scenario services), if there is a certain development capability, it is recommended to use only the back-end engine function, and the front-end customized development according to the... -
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classic.tplx
A more accurate representation of jupyter notebooks
A more accurate representation of Jupyter notebooks when converting to pdfs. This template was designed to make converted Jupyter notebooks look (almost) identical to the actual notebook. If something doesn't exist in the original notebook then it doesn't belong in the conversion. As of nbconvert 5.5.0, the majority of these improvements have been merged into nbconvert's default template. Version 3.x of this package will continue to support nbconvert 5.5.0 and lower, whereas in the future... -
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Luigi
Python module that helps you build complex pipelines of batch jobs
Luigi is a Python (3.6, 3.7, 3.8, 3.9 tested) package that helps you build complex pipelines of batch jobs. It handles dependency resolution, workflow management, visualization, handling failures, command line integration, and much more. The purpose of Luigi is to address all the plumbing typically associated with long-running batch processes. You want to chain many tasks, automate them, and failures will happen. -
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Sundials.jl
Julia interface to Sundials, including a nonlinear solver
This is a suite for numerically solving differential equations written in Julia and available for use in Julia, Python, and R. The purpose of this package is to supply efficient Julia implementations of solvers for various differential equations. -
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OptScale
FinOps and MLOps platform to run ML/AI and regular cloud workloads
Run ML/AI or any type of workload with optimal performance and infrastructure cost. OptScale allows ML teams to multiply the number of ML/AI experiments running in parallel while efficiently managing and minimizing costs associated with cloud and infrastructure resources. OptScale MLOps capabilities include ML model leaderboards, performance bottleneck identification and optimization, bulk run of ML/AI experiments, experiment tracking, and more. The solution enables ML/AI engineers to run... -
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ValueCell
Community-driven, multi-agent platform for financial applications
ValueCell is a community-driven multi-agent AI platform focused on financial research, analysis, and decision-making that lets users leverage multiple specialized AI agents for tasks like data retrieval, investment research, strategy execution, and market tracking. The system brings together a suite of collaborative agents—such as research agents that gather and interpret fundamentals, strategy agents that implement trading logic, and news agents that deliver personalized updates—to help... -
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CUDOS Framework
Command Line Interface tool for Cloud Intelligence Dashboards
The AWS Cloud Intelligence Dashboards Framework is a set of open-source tools and templates designed to help organizations deploy and manage advanced data visualization dashboards that offer insights into cost, usage, governance, and operational health across AWS environments. It is part of the AWS Solutions Library and includes CloudFormation templates, CLI commands, and pre-built dashboards that collect, process, and visualize data from AWS billing, cost management, budgets, and usage... -
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StackStorm
Connect all your apps, services, and workflows
StackStorm is a platform for integration and automation for numerous services and tools. It takes actions in response to events, and is composed of a rules engine, workflow, and 160 integration packs with more than 6000 actions. This makes StackStorm very versatile, useful in a wide array of business applications. StackStorm can be used to automate and streamline nearly any part of a business. It can be used for automated remediation to immediately troubleshoot and fix problems when... -
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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... -
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UnROOT.jl
Native Julia I/O package to work with CERN ROOT files objects
UnROOT.jl is a reader for the CERN ROOT file format written entirely in Julia, without any dependence on ROOT or Python. -
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AI Data Science Team
An AI-powered data science team of agents
AI Data Science Team is a Python library and agent ecosystem designed to accelerate and automate common data science workflows by modeling them as specialized AI “agents” that can be orchestrated to perform tasks like data cleaning, transformation, analysis, visualization, and machine learning. It provides a modular agent framework where each agent focuses on a step in the typical data science pipeline — for example, loading data from CSV/Excel files, cleaning and wrangling messy datasets, engineering predictive features, building models with AutoML, connecting to SQL databases, and producing visual outputs — all driven by natural language or programmatic instructions. ... -
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ClearML
Streamline your ML workflow
...It is designed as an end-to-end MLOps suite allowing you to focus on developing your ML code & automation, while ClearML ensures your work is reproducible and scalable. The ClearML Python Package for integrating ClearML into your existing scripts by adding just two lines of code, and optionally extending your experiments and other workflows with ClearML powerful and versatile set of classes and methods. The ClearML Server storing experiment, model, and workflow data, and supports the Web UI experiment manager, and ML-Ops automation for reproducibility and tuning. ... -
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DearPyGui
Graphical User Interface Toolkit for Python with minimal dependencies
Dear PyGui is an easy-to-use, dynamic, GPU-Accelerated, cross-platform graphical user interface toolkit(GUI) for Python. It is “built with” Dear ImGui. Features include traditional GUI elements such as buttons, radio buttons, menus, and various methods to create a functional layout. Additionally, DPG has an incredible assortment of dynamic plots, tables, drawings, debuggers, and multiple resource viewers. DPG is well suited for creating simple user interfaces as well as developing complex and demanding graphical interfaces. ... -
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ArviZ.jl
Exploratory analysis of Bayesian models with Julia
ArviZ.jl (pronounced "AR-vees") is a Julia package for exploratory analysis of Bayesian models. It includes functions for posterior analysis, model checking, comparison and diagnostics.
