Restructure Docs

docs/tutorials/quickstart.md

@@ -1,10 +1,118 @@
-# Quickstart
+# Installation
 
-```{include} ../../README.md
-:relative-docs: docs/
-:relative-images:
-:start-after: quick_tour.html).
-:end-before: 6. In-depth
+This repository is tested with CUDA 11.3. Make sure to install [Conda](https://docs.conda.io/en/latest/miniconda.html#linux-installers) before preceding.
+
+<details>
+<summary>Installing Conda</summary>
+
+    This step is fairly self-explanatory, but here are the basic steps. You can also find countless tutorials online.
+
+    ```bash
+    cd /path/to/install/miniconda
+
+    mkdir -p miniconda3
+    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O miniconda3/miniconda.sh
+    bash miniconda3/miniconda.sh -b -u -p miniconda3
+    rm -rf miniconda/miniconda.sh
+    ```
+
+</details>
+
+Create the python environment
+
+```bash
+conda create --name pyrad python=3.8.13
+conda activate pyrad
 ```
 
-For more complex changes (e.g. running with your own data/ setting up a new NeRF graph, please continue to the next page.
+Clone the repo
+
+```bash
+git clone git@github.com:plenoptix/pyrad.git
+```
+
+Install dependencies
+
+```bash
+cd pyrad
+pip install -r environment/requirements.txt
+```
+
+Install pyrad as a library
+
+```bash
+pip install -e .
+```
+
+Install library with CUDA development support. Change setup.py to `USE_CUDA = True` and then
+
+```bash
+python setup.py develop
+```
+
+Install tiny-cuda-nn (tcnn) to use with the graph_instant_ngp.yaml config
+
+```bash
+pip install git+https://github.com/NVlabs/tiny-cuda-nn/#subdirectory=bindings/torch
+```
+
+# Downloading data
+
+Download the original [NeRF dataset](https://drive.google.com/drive/folders/128yBriW1IG_3NJ5Rp7APSTZsJqdJdfc1) and unfold it in the following format. This is for the blender dataset type. We support the major datasets and allow users to create their own dataset, described in detail [here](data/creating_dataset.md).
+
+```
+|─ pyrad/
+   ├─ data/
+   |  ├─ blender/
+   |     ├─ fern/
+   |     ├─ lego/
+         ...
+      |- <dataset_format>/
+         |- <scene>
+         ...
+```
+
+# Training a model
+
+To run with all the defaults, e.g. vanilla nerf method with the blender lego image
+
+Run a vanilla nerf model.
+
+```bash
+python scripts/run_train.py
+```
+
+Run a faster version with instant ngp using tcnn (without the viewer).
+
+```bash
+python scripts/run_train.py --config-name=graph_instant_ngp.yaml
+```
+
+Run with the viewer. However, you'll have to start the viewer server first. (See the docs.)
+
+```bash
+python scripts/run_train.py --config-name=graph_instant_ngp.yaml viewer.enable=true
+```
+
+With support for [Hydra](https://hydra.cc/), you can run with other configurations by changing appropriate configs defined in `configs/` or by setting flags via command-line arguments.
+
+# Visualizing training runs
+
+If you run everything with the default configuration, by default, we use [TensorBoard](https://www.tensorflow.org/tensorboard) to log all training curves, test images, and other stats. Once the job is launched, you will be able to track training by launching the tensorboard in `outputs/blender_lego/vanilla_nerf/<timestamp>/<events.tfevents>`.
+
+```bash
+tensorboard --logdir outputs
+```
+
+Or the following:
+
+```bash
+export TENSORBOARD_PORT=<port>
+bash environment/run_tensorboard.sh
+```
+
+# Rendering a Trajectory
+
+```bash
+python scripts/run_eval.py --method=traj --traj=spiral --output-filename=output.mp4 --config-name=graph_instant_ngp.yaml trainer.resume_train.load_dir=outputs/blender_lego/instant_ngp/2022-07-07_230905/checkpoints
+```