Reverse Dependencies of nbconvert

The following projects have a declared dependency on nbconvert:

• abqpy — Type hints for Abaqus/Python scripting
• abqpy2016 — Type hints for Abaqus/Python scripting
• abqpy2017 — Type hints for Abaqus/Python scripting
• abqpy2018 — Type hints for Abaqus/Python scripting
• abqpy2019 — Type hints for Abaqus/Python scripting
• abqpy2020 — Type hints for Abaqus/Python scripting
• abqpy2021 — Type hints for Abaqus/Python scripting
• abqpy2022 — Type hints for Abaqus/Python scripting
• abqpy2023 — Type hints for Abaqus/Python scripting
• abqpy2024 — Type hints for Abaqus/Python scripting
• abqpy2025 — Type hints for Abaqus/Python scripting
• abracadabra — Making hypothesis and AB testing magically simple!
• academic — Import Bibtex publications and Jupyter Notebook blog posts into your Markdown-formatted website or book.
• agent-cloud — no summary
• agent-cloud-os — no summary
• agent-context — no summary
• agent-management-system — no summary
• agent.ngo — no summary
• agent-system — no summary
• agent0 — Agent interface for on-chain protocols.
• agentbox — no summary
• agentDB — no summary
• agentvm — no summary
• ai-economist — Foundation: An Economics Simulation Framework
• ai-gradio — A Python package for creating Gradio applications with AI models
• ai-python — Microsoft AI Python Package
• ai4scr-athena — ATHENA package provides methods to analyse spatial heterogeneity in spatial omics data
• aiutil — A utils Python package for data scientists.
• aleph-alpha-client — python client to interact with Aleph Alpha api endpoints
• alphainspect — factor performance visualization
• ams-core — no summary
• ams-python — no summary
• aneris-iamc — Harmonize Integrated Assessment Model Emissions Trajectories
• animal-classification — classification of animals using machine learning models
• ansys-geometry-core — A python wrapper for Ansys Geometry service
• apache-beam — Apache Beam SDK for Python
• apache-beam-ai2 — A FORK! for testing with different dill version
• appnext — no summary
• appyter — no summary
• arb-tehran-finance — Statistics and Analysis Module for Financial Markets and Economy in Iran
• argiope — A framework for simpler finite element processing
• art-training — ART - Actually Robust Training framework - a framework that teaches good practices when training deep neural networks.
• asekuro — CLI util to deal with Jupyter Notebooks
• asf-search — Python wrapper for ASF's SearchAPI
• asmc-data-module — Something
• astir — no summary
• astro-science-ppt — no summary
• astrograppa — no summary
• astroraytrace — Ray tracing for astronomical optics and instruments
• asynciojobs — A simplistic orchestration engine for asyncio-based jobs
• auroris — Data Curation in Polaris
• auto-ams — no summary
• autogen-agentchat-um — Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
• autollm — Ship RAG based LLM Web API's, in seconds.
• autopc — An image recognition framework running on a computer
• autopilotml — A package for automating machine learning tasks
• autoprognosis — A system for automating the design of predictive modeling pipelines tailored for clinical prognosis.
• autora-doc — Automatic documentation generator from AutoRA code
• ax-platform — Adaptive Experimentation
• axiomic — Primitives for Genreative AI.
• aztools — no summary
• azure-arm-nb-extensions — Jupyter notebook extensions for azure arm.
• azure-ml-component — Azure Machine Learning Component SDK
• azureml-contrib-notebook — no summary
• azureml-pipeline-wrapper — This package have been deprecated. Please use azure-ml-component instead.
• backtesting — Backtest trading strategies in Python
• balance — balance is a Python package offering a simple workflow and methods for dealing with biased data samples when looking to infer from them to some target population of interest.
• bark — Bark text to audio model
• batchflow — ML pipelines, model configuration and batch management
• batsim-py — Batsim-py allows using Batsim from Python 3.
• bayesnf — Scalable spatiotemporal prediction with Bayesian neural fields
• big-graph-dataset — A collection of graph datasets in torch_geometric format.
• BioCantor — Flexible feature arithmetic, seamlessly integrated with nested coordinate systems.
• biocrnpyler — A chemical reaction network compiler for generating large biological circuit models
• bioinf-common — Aggregation of functionalities needed in multiple projects
• birdhouse-birdy — Birdy provides a command-line tool to work with Web Processing Services.
• birdhouse-finch — A Web Processing Service for Climate Indicators.
• bisheng-pyautogen — Enabling Next-Gen LLM Applications via Multi-Agent Conversation Framework
• bitfount — Machine Learning and Federated Learning Library.
• blab — Various Jupyter Tools
• black-nbconvert — Apply black to ipynb files
• blogger-cli — Blogger cli is a CLI tool to convert ipynb, md, html file to responsive html files.
• blue-prints — Blueprints: Python AEC library.
• boilercore — Common functionality of boiler repositories
• boinor — Utilities and Python wrappers for Orbital Mechanics.
• bookbook — Tools to use a collection of notebooks as 'chapters'
• bpnet — BPNet: toolkit to learn motif synthax from high-resolution functional genomics data using convolutional neural networks
• bunkatopics — Bunkatopics is a Topic Modeling package and Exploration Module
• c4v-py — Code for Venezuela python library.
• CADET-RDM — A Python toolbox for research data management.
• calkit-python — Reproducibility simplified.
• callisto-nbviewer — Jupyter notebook viewer
• cap-fairing — CAP Fairing Python SDK.
• carpo — Run, profile, and save Jupyter notebooks from the command line
• catalystcoop.cheshire — Replace this text with a one line description of the package.
• catalystcoop.pudl — An open data processing pipeline for US energy data
• cebra — Consistent Embeddings of high-dimensional Recordings using Auxiliary variables
• cegalprizm-investigator — Cegal Prizm Investigator
• cellbender — A software package for eliminating technical artifacts from high-throughput single-cell RNA sequencing (scRNA-seq) data
• celldisect — Cell DISentangled Experts for Covariate counTerfactuals (CellDISECT). Causal generative model designed to disentangle known covariate variations from unknown ones at test time while simultaneously learning to make counterfactual predictions.