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Road map (#330)
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Adds a roadmap to the documentation
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mzouink authored Nov 12, 2024
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3 changes: 2 additions & 1 deletion docs/source/index.rst
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docker
aws
cosem_starter
roadmap
autoapi/index
cli

.. include:: ../../README.md
:parser: myst_parser.sphinx_
:parser: myst_parser.sphinx_
77 changes: 77 additions & 0 deletions docs/source/roadmap.rst
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.. _sec_roadmap:

Road Map
========

Overview
--------

+-----------------------------------+------------------+-------------------------------+
| Task | Priority | Current State |
+===================================+==================+===============================+
| Write Documentation | High | Started with a long way to go |
+-----------------------------------+------------------+-------------------------------+
| Simplify configurations | High | First draft complete |
+-----------------------------------+------------------+-------------------------------+
| Develop Data Conventions | High | First draft complete |
+-----------------------------------+------------------+-------------------------------+
| Improve Blockwise Post-Processing | Low | Not Started |
+-----------------------------------+------------------+-------------------------------+
| Simplify Array handling | High | Almost done (Up/Down sampling)|
+-----------------------------------+------------------+-------------------------------+

Detailed Road Map
-----------------

- [ ] Write Documentation
- [ ] tutorials: not more than three, simple and continuously tested (with Github actions, small U-Net on CPU could work)
- [x] Basic tutorial: train a U-Net on a toy dataset
- [ ] Parametrize the basic tutorial across tasks (instance/semantic segmentation).
- [ ] Improve visualizations. Move some simple plotting functions to DaCapo.
- [ ] Add a pure pytorch implementation to show benefits side-by-side
- [ ] Track performance metrics (e.g., loss, accuracy, etc.) so we can make sure we aren't regressing
- [ ] semantic segmentation (LM and EM)
- [ ] instance segmentation (LM or EM, can be simulated)
- [ ] general documentation of CLI, also API for developers (curate docstrings)
- [x] Simplify configurations
- [x] Depricate old configs
- [x] Add simplified config for simple cases
- [x] can still get rid of `*Config` classes
- [x] Develop Data Conventions
- [x] document conventions
- [ ] convenience scripts to convert dataset into our convention (even starting from directories of PNG files)
- [ ] Improve Blockwise Post-Processing
- [ ] De-duplicate code between “in-memory” and “block-wise” processing
- [ ] have only block-wise algorithms, use those also for “in-memory”
- [ ] no more “in-memory”, this is just a run with a different Compute Context
- [ ] Incorporate `volara` into DaCapo (embargo until January)
- [ ] Improve debugging support (logging of chain of commands for reproducible runs)
- [ ] Split long post-processing steps into several smaller ones for composability (e.g., support running each step independently if we want to support choosing between `waterz` and `mutex_watershed` for fragment generation or agglomeration)
- [x] Incorporate `funlib.persistence` adaptors.
- [x] all of those can be adapters:
- [x] Binarize Labels into Mask
- [x] Scale/Shift intensities
- [ ] Up/Down sample (if easily possible)
- [ ] DVID source
- [x] Datatype conversions
- [x] everything else
- [x] simplify array configs accordingly

Can Have
--------

- [ ] Support other stats stores. Too much time, effort and code was put into the stats and didn’t provide a very nice interface:
- [ ] defining variables to store
- [ ] efficiently batch writing, storing and reading stats to both files and mongodb
- [ ] visualizing stats.
- [ ] Jeff and Marwan suggest MLFlow instead of WandB
- [ ] Support for slurm clusters
- [ ] Support for cloud computing (AWS)
- [ ] Lazy loading of dependencies (import takes too long)
- [ ] Support bioimage model spec for model dissemination

Non-Goals (for v1.0)
--------------------

- custom dash board
- GUI to run experiments

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