Reverse Dependencies of kaleido
The following projects have a declared dependency on kaleido:
- fmbench — Benchmark performance of **any model** deployed on **Amazon SageMaker** or available on **Amazon Bedrock** or deployed by you on an AWS service of choice (such as Amazon EKS or Amazon EC2) a.k.a **Bring your own endpoint**.
- folioflex — A collection of portfolio tracking capabilities
- forexflaggr — A minimal package to pull and analyse financial (exchange rate) data.
- freyja-plot — Plotting lineage abundance for individual samples from aggregated freyja demix results
- fsqc — Quality control scripts for FastSurfer and FreeSurfer structural MRI data
- fudstop — no summary
- fugw — A collection of gpu-compatible solvers for fused unbalanced gromov-wasserstein optimization problems
- functime — Time-series machine learning at scale.
- gam — Global Explanations for Deep Neural Networks
- gator-eda — Hierarchical job execution and logging
- gennet-forked — Framework for Interpretable Neural Networks
- geohexviz — A library for the visualization of hexagonally binned data sets.
- gimodules — Python package to deliver a Gantner cloud interface
- giotto-deep — Toolbox for Deep Learning and Topological Data Analysis.
- gitly — This is a lib to help you plot your fency graphs from plotly in github while using Google Colab notebook.
- gnss-lib-py — Modular Python tool for parsing, analyzing, and visualizing Global Navigation Satellite Systems (GNSS) data and state estimates
- gofigr — GoFigr client library
- google-calendar-analytics — A Python library for analyzing Google Calendar data.
- govdata — no summary
- GPS-mapping — Genetics-informed pathogenic spatial mapping
- gradientcobra — Python implementation for Gradient COBRA by S. Has (2023) with other aggregation and kernel methods.
- graph-express — Python package for the analysis and visualization of network graphs.
- graph-jsp-env — A flexible enviorment for job shop scheduling using the disjunctive graph apporach.
- graphxl — Workbench for Deep Learning on network datasets
- grouped-query-attention-pytorch — grouped-query-attention-pytorch
- GSG — no summary
- gsMap — Genetics-informed pathogenic spatial mapping
- gtbook — Frank Dellaerts book support lib, made with nbdev
- gym-PBN — A Gymnasium environment modelling Probabilistic Boolean Networks and Probabilistic Boolean Control Networks.
- halib — Small library for common tasks
- hipsta — A python package for hippocampal shape and thickness analysis
- Hive-ML — Python package to run Machine Learning Experiments, within the Hive Framework.
- hotaru — High performance Optimizer to extract spike Timing And cell location from calcium imaging data via lineaR impUlse
- huawei-fusionsolar — API to the Huawei Fusion Solar web interface.
- huble — no summary
- ia-sdk — SDK for Intelligent Artifact's GAIuS agents.
- ie-package — Insight Extractor Package
- ifermi — Fermi surface plotting tool from DFT output
- imsy-htc — Framework for automatic classification and segmentation of hyperspectral images.
- insight-extractor-packaage — Insight Extractor Package
- insight-extractor-package — Insight Extractor Package
- inspirems — Helping to integrate Spectral Predictors and Rescoring.
- instacrawl — A simple CLI Instagram crawler with a focus on algorithm analytics.
- interplot — Create matplotlib and plotly charts with the same few lines of code.
- iprPy — Interatomic Potential Repository Python Property Calculations and Tools
- ire.py — Export tables and plots from Jupyter notebooks, along with metadata for embedding interactive tables in downstream apps.
- itba-cde-tpf-python-applications-fvidal90 — TP Vidal
- ITR-examples — Example code and user interface for the ITR project.
- jeddinformatics — Convert bioinformatics data to plots
- jetto-mobo — no summary
- jijbench — Experiment management and benchmark tools for mathematical optimization
- jrpyvisualisation — Jumping Rivers: Introduction to Visualisation in Python
- jsp-vis — A visualization tool for job shop scheduling problems.
- JSSEnv — An optimized OpenAi gym's environment to simulate the Job-Shop Scheduling problem.
- jupyter-docx-bundler — Jupyter bundler extension to export notebook as a docx file
- jupyter-Pandas-GUI — Pandas expression composers using Jupyter widgets.
- kaen — kaen is a friendly open source toolkit to help you train and deploy PyTorch deep learning models in public clouds
- kailo-beewell-dashboard — Tools to support creation of #BeeWell survey dashboards for Kailo
- kipet — An all-in-one tool for fitting kinetic models using spectral and other state data
- krisi — Testing and Reporting framework for Time Series Analysis
- laai — no summary
- ladybug-charts — Ladybug extension to generate 2D charts.
- lds-python — Linear Dynamical Systems
- leximpact-prepare-data — Prepare data for LexImpact
- lightning-pose — Semi-supervised pose estimation using pytorch lightning
- liveisstracker — A CLI to get the current position, speed, passing country and image of the location of International Space Station on a map [#liveisstracker]. Source: LiveIssTracker Project from https://gitlab.com/manojm18/liveisstracker.
- llama-index-packs-vanna — llama-index packs vanna integration
- lussac — Python package for automatic post-processing and merging of multiple spike-sorting analyses.
- lysis-curve — Lysis curve package
- machnamh — An ipywidgets based package for detecting bias in ML data and Models
- machnamh-unmakingyou — An ipywidgets based package for detecting bias in ML data and Models
- mafese — MAFESE: Metaheuristic Algorithm for Feature Selection - An Open Source Python Library
- mass-composition — For managing multi-dimensional mass-composition datasets, supporting weighted mathematical operations and visualisation.
- massql — Mass spectrometry query language python implementation
- mat_discover — Data-driven materials discovery based on composition or structure.
- matsci-opt-benchmarks — A collection of benchmarking problems and datasets for testing the performance of advanced optimization algorithms in the field of materials science and chemistry.
- maui-software — Eco-acoustics data visualization and analysis
- mba — Market Basket Analysis.
- mdml — Application of Deep learning on molecular dymanamics trajectories
- mechviz — MechViz -- Python-based toolkit for the analysis and visualization of mechanical properties of materials
- mellow-strategy-sdk — Framework for creating new Uniswap V3 strategies
- merph — Bayesian methods for inferring mass eruption rate for column height (or vice versa) for volcanic eruptions
- MetaCerberus — Versatile Functional Ontology Assignments for Metagenomes via Hidden Markov Model (HMM) searching with environmental focus of shotgun meta'omics data
- metacluster — MetaCluster: An Open-Source Python Library for Metaheuristic-based Clustering Problems
- metaflow-helper — Convenience utilities for common machine learning tasks on Metaflow
- methrandir — Python utility for understanding whole genome bisulfite data and viewing it as a whole
- metrics-render — no summary
- mg-pso-gui — GUI for MG-PSO
- MhcVizPipe — A reporting pipeline for visualization of immunopeptidomics MS data.
- mitzu — Product analytics over your data warehouse
- mkt-retv — market research TV
- mloptimizer — mloptimizer is a Python library for optimizing hyperparameters of machine learning algorithms using genetic algorithms.
- mlx.treemap — Sphinx extension to generate a treemap of Cobertura data
- modbamtools — A set of tools to manipulate and visualize data from base modification bam files
- MolParse — Package for parsing, writing, and modifying molecular structure files
- multi-vector-simulator — Multi-vector Simulation Tool assessing and optimizing Local Energy Systems (LES) for the E-LAND project
- multimedeval — A Python tool to evaluate the performance of VLM on the medical domain.
- multiqc — Create aggregate bioinformatics analysis reports across many samples and tools
- multiqc-sgr — Create aggregate bioinformatics analysis reports across many samples and tools
- MultiTrain — MultiTrain allows you to train multiple machine learning algorithms on a dataset all at once to determine the best for that particular use case