Update documents (#402)

* Update index.rst * Update docs * Fix dependencies
aistairc · Dec 9, 2024 · f5c2f88 · f5c2f88
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diff --git a/docs/source/api_reference/index.rst b/docs/source/api_reference/index.rst
@@ -3,15 +3,10 @@
 API Reference
 *************
 
-* :ref:`genindex`
-* :ref:`modindex`
-
-----
-
 
 .. toctree::
    :maxdepth: 2
 
    torch
    hpo
-   .. utils
+   utils
diff --git a/docs/source/api_reference/torch.rst b/docs/source/api_reference/torch.rst
@@ -1,5 +1,5 @@
-PyTorch Utilities
-=================
+PyTorch/Lightning Toolkit
+=========================
 
 
 Datasets
@@ -29,4 +29,4 @@ Lightning Utilities
 
    ABCIEnvironment
    OptimizerLightningModule
-   OptimizerConfig
+   OptimizerConfig
diff --git a/docs/source/api_reference/utils.rst b/docs/source/api_reference/utils.rst
@@ -0,0 +1,13 @@
+Miscellaneous Utilities
+=======================
+
+
+Config Utilities
+----------------
+
+.. currentmodule:: aiaccel.utils
+.. autosummary::
+   :toctree: generated/
+
+   print_config
+   pathlib2str_config
diff --git a/docs/source/conf.py b/docs/source/conf.py
@@ -43,6 +43,7 @@
     "myst_parser",
     "pydata_sphinx_theme",
     "sphinx.ext.doctest",
+    "sphinx_design",
 ]
 
 # Add any paths that contain templates here, relative to this directory.

diff --git a/docs/source/index.rst b/docs/source/index.rst
@@ -4,18 +4,34 @@
    contain the root `toctree` directive.
 
 
-aiaccel Documentation
-===================
+Aiaccel
+=======
+
+AIST toolkit for accelerating machine learning research.
+
+
+:octicon:`cpu;1em;sd-text-primary` High-Performance Computing (HPC)
+   Intended to use in the HPC clusters including AI Bridging Cloud Infrastructure (ABCI).
+
+:octicon:`server;1em;sd-text-primary` Highly Modular Design
+   Designed to let you pick up any part of aiaccel with minimal dependencies as you prefer.
+
+:octicon:`zap;1em;sd-text-primary` High Compatibility
+   Compatible with the modern standard frameworks such as PyTorch Lightning and Optuna.
 
-Overview
---------
 
-This software is a hyperparameter optimization library designed for the AI Bridging Cloud Infrastructure (ABCI).
-It addresses hyperparameter optimization challenges in AI technologies such as deep learning and multi-agent simulations.
-Currently, it supports five optimization algorithms: Random Search, Grid Search, Sobol Sequence, Nelder-Mead Method, and TPE.
+.. grid::
 
+   .. grid-item-card:: PyTorch/Lightning Toolkit
+      :link: api_reference/torch.html
+
+      Training toolkit for HPC clusters.
+
+   .. grid-item-card:: Hyperparameter Optimization (HPO)
+      :link: api_reference/hpo.html
+
+      Ready-to-use HPO algorithms/tools.
 
-.. _`AI Bridging Cloud Infrastructure (ABCI)`: https://abci.ai/
 
 
 Getting Started
@@ -49,20 +65,12 @@ API Reference
 Contribution Guide
 --------
 
-
 .. toctree::
    :maxdepth: 2
 
    contribution_guide.md
 
 
-Index and Search
---------
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
-
 Acknowledgments
 --------
 * Part of this work was developed under a commissioned project of the New Energy and Industrial Technology Development Organization (NEDO).
diff --git a/pyproject.toml b/pyproject.toml
@@ -49,6 +49,7 @@ dev = [
     "sphinx-fontawesome",
     "sphinx-rtd-theme",
     "pydata-sphinx-theme",
+    "sphinx_design",
     "types-colorama",
     "undecorated",
     "pandas",