Tutorial docs (#217)

docs/requirements.txt

@@ -1,3 +1,6 @@
 sphinx-autodoc-typehints
 sphinx-material
-autoapi
+sphinx-autoapi
+sphinx-click
+sphinx-rtd-theme
+myst-parser

docs/source/index.rst

@@ -7,6 +7,9 @@
    :hidden:
    :caption: DaCapo API:
 
+   overview
+   install
+   tutorial
    autoapi/index
    cli
 

docs/source/install.rst

@@ -0,0 +1,21 @@
+.. _sec_install:
+
+Installation
+============
+
+.. automodule:: gunpowder
+   :noindex:
+
+For the latest version:
+
+.. code-block:: bash
+
+   pip install git+https://github.com/janelia-cellmap/dacapo
+
+Optional(Web GUI):
+
+For the latest version:
+
+.. code-block:: bash
+
+   pip install git+https://github.com/funkelab/dacapo-dashboard

docs/source/overview.rst

@@ -0,0 +1,58 @@
+.. _sec_overview:
+
+Overview
+========
+
+What is DaCapo?
+^^^^^^^^^^^^^^^
+
+DaCapo is a framework that allows for easy configuration and
+execution of established machine learning techniques on
+arbitrarily large volumes of multi-dimensional images.
+
+DaCapo has 4 major configurable components:
+
+1. :class:`dacapo.datasplits.DataSplit`
+
+2. :class:`dacapo.architectures.Architecture`
+
+3. :class:`dacapo.tasks.Task`
+
+4. :class:`dacapo.trainers.Trainer`
+
+These are then combined in a single :class:`dacapo.experiments.Run` that
+includes your starting point (whether you want to start training from
+scratch or continue off of a previously trained model) and stopping
+criterion (the number of iterations you want to train).
+
+How does DaCapo work?
+^^^^^^^^^^^^^^^^^^^^^
+
+Each of the major components can be configured separately allowing you to define
+your job in a nicely structured format. Here we define what each component is
+responsible for:
+
+1. | **DataSplit**: Where can you find your data? What format is it in? Does it need
+   | to be normalized? What data do you want to use for validation?
+
+2. | **Architecture**: Biomedical image to image translation often utilizes a UNet,
+   | but even after choosing a UNet you still need to provide some additional parameters.
+   | How much do you want to downsample? How many convolutional layers do you want?
+
+3. | **Task**: What do you want to learn? An instance segmentation? If so how? Affinities,
+   | Distance Transform, Foreground/Background, etc. Each of these tasks are commonly learned
+   | and evaluated with specific loss functions and evaluation metrics. Some tasks may
+   | also require specific non-linearities or output formats from your model.
+
+4. | **Trainer**: How do you want to train? This config defines the training loop
+   | and how the other three components work together. What sort of augmentations
+   | to apply during training, what learning rate and optimizer to use, what batch size
+   | to train with.
+
+DaCapo allows you to define each of these configurations separately, and give them
+unique names. These configurations are then stored in a mongodb or on your filesystem,
+allowing you to retrieve configs by name and easily start multitudes of jobs as
+combinations of `Datasplits`, `Architectures`, `Tasks`, and `Trainers`.
+
+The :ref:`Simple Experiment using Python<sec_tutorial_simple_experiment_python>` demonstrates how such
+an experiment is assembled in ``dacapo``