Tancik/update docs (#12)

Update docs structure.

docs/quickstart/getting_around.rst

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+.. _getting_around:
+
+Getting around the codebase
+=====================================
+
+The entry point for training starts at ``scripts/run_train.py``, which spawns instances of our ``Trainer()`` class (in ``nerf/trainer.py``). The ``Trainer()`` is responsible for setting up the datasets and NeRF graph depending on the config specified. If you are planning on just using our codebase to build a new NeRF method or use an existing implementation, we've abstracted away the training routine in these two files and chances are you will not need to touch them.
+
+The NeRF graph definitions can be found in ``nerf/graph/``. Each implementation of NeRF is definined in its own file. For instance, ``nerf/graph/instant_ngp.py`` contains populates the ``NGPGraph()`` class with all of the appropriate fields, colliders, and misc. modules.
+
+* Fields (``nerf/fields/``): composed of field modules (``nerf/field_modules/``) and represents the radiance field of the NeRF.
+* Misc. Modules (``nerf/misc_modules``- TODO(maybe move to misc_modules? better organization)): any remaining module in the NeRF (e.g. renderers, samplers, losses, and metrics).
+
+To implement any pre-existing NeRF that we have not yet implemented under `nerf/graph/`, create a new graph structure by using provided modules or any new module you define. Then create an associated config making sure ``__target__`` points to your NeRF class (see [here](./configs/README.md) for more info on how to create the config). Then run training as described above.

docs/quickstart/installation.rst

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+.. _installation:
+
+Installation
+=================
+
+Clone the repo::
+
+   git clone --recurse-submodules git@github.com:ethanweber/pyrad.git
+
+Create the python environment::
+
+   conda create --name pyrad python=3.8.13
+   conda activate pyrad
+   pip install -r environment/requirements.txt
+
+Install pyrad as a library::
+
+   pip install -e .
+
+Install library with CUDA support. Change setup.py to ``USE_CUDA = True`` and then::
+
+   python setup.py develop
+
+Running the test cases::
+
+   pytest tests
+   Field Modules
+
+Getting the data
+------------------
+
+Download the original NeRF dataset and put it in the following format.::
+
+
+   - data/
+      - blender/
+         - fern/
+         - lego/
+
+Training a model
+------------------
+
+Run with default config::
+
+   python scripts/run_train.py
+
+Run with config changes::
+
+   python scripts/run_train.py machine_config.num_gpus=1
+   python scripts/run_train.py data.dataset.downscale_factor=1
+
+Run with different datasets::
+
+   python scripts/run_train.py data/dataset=blender_lego
+   python scripts/run_train.py data/dataset=friends_TBBT-big_living_room
+
+Run with different datasets and config changes::
+
+   python scripts/run_train.py data/dataset=friends_TBBT-big_living_room graph.network.far_plane=14