added config docs (#644)

* added config docs

* fixed first_nerf

docs/developer_guides/config.md

@@ -1 +1,105 @@
-# Customizable Configs
+# Customizable configs
+
+Our python dataclass configs allow you to easily plug in different permutations of models, dataloaders, modules, etc.
+and modify all parameters from a typed CLI supported by [dcargs](https://pypi.org/project/dcargs/).
+
+### Base components
+
+All basic, reusable config components can be found in `nerfstudio/configs/base_config.py`. The `Config` class at the bottom of the file is the upper-most config level and stores all of the sub-configs needed to get started with training. 
+
+You can browse this file and read the attribute annotations to see what configs are available and what each specifies.
+
+### Creating new configs
+
+If you are interested in creating a brand new model or data format, you will need to create a corresponding config with associated parameters you want to expose as configurable.
+
+Let's say you want to create a new model called Nerfacto. You can create a new `Model` class that extends the base class as described [here](pipelines/models.ipynb). Before the model definition, you define the actual `NerfactoModelConfig` which points to the `NerfactoModel` class (make sure to wrap the `_target` classes in a `field` as shown below).
+
+:::{admonition} Tip
+:class: info
+
+You can then enable type/auto complete on the config passed into the `NerfactoModel` by specifying the config type below the class definition.
+  :::
+
+```python
+"""nerfstudio/models/nerfacto.py"""
+
+@dataclass
+class NerfactoModelConfig(ModelConfig):
+    """Nerfacto Model Config"""
+
+    _target: Type = field(default_factory=lambda: NerfactoModel)
+    ...
+
+class NerfactoModel(Model):
+     """Nerfacto model
+
+    Args:
+        config: Nerfacto configuration to instantiate model
+    """
+
+    config: NerfactoModelConfig
+    ...
+```
+
+The same logic applies to all other custom configs you want to create. For more examples, you can see `nerfstudio/data/dataparsers/nerfstudio_dataparsers.py`, `nerfstudio/data/datamanagers.py`.
+
+:::{admonition} See Also
+:class: seealso
+
+For how to create the actual data and model classes that follow the configs, please refer to [pipeline overview](pipelines/index.rst).
+  :::
+
+### Updating model configs
+
+If you are interested in creating a new model config, you will have to modify the `nerfstudio/configs/model_configs.py` file. This is where all of the configs for implemented models are housed. You can browse this file to see how we construct various existing models by modifying the `Config` class and specifying new or modified default components. 
+
+For instance, say we created a brand new model called Nerfacto that has an associated `NerfactoModelConfig`, we can specify the following new Config by overriding the pipeline and optimizers attributes appropriately.
+
+```python
+"""nerfstudio/configs/model_configs.py"""
+
+model_configs["nerfacto"] = Config(
+    method_name="nerfacto",
+    pipeline=VanillaPipelineConfig(
+        model=NerfactoModelConfig(eval_num_rays_per_chunk=1 << 14),
+    ),
+    optimizers={
+        "proposal_networks": {
+            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
+            "scheduler": None,
+        },
+        "fields": {
+            "optimizer": AdamOptimizerConfig(lr=1e-2, eps=1e-15),
+            "scheduler": None,
+        },
+    },
+)
+```
+
+After placing your new `Config` class into the `model_configs` dictionary, you can provide a description for the model by updating the `descriptions` dictionary at the top of the file.
+
+
+### Modifying from CLI
+Often times, you just want to play with the parameters of an existing model without having to specify a new one. You can easily do so via CLI. Below, we showcase some useful CLI commands.
+
+```bash
+# list out all existing models
+ns-train --help
+
+# list out all exist configurable parameters for {MODEL_NAME}
+ns-train {MODEL_NAME} --help
+
+# change the train/eval dataset
+ns-train {MODEL_NAME} --data DATA_PATH
+
+# enable the viewer
+ns-train {MODEL_NAME} --vis viewer
+
+# see what options are available for the specified dataparser (e.g. blender-data)
+ns-train {MODEL_NAME} {DATA_PARSER} --help
+
+# run with changed dataparser attributes and viewer on
+# NOTE: the dataparser and associated configurations go at the end of the command
+ns-train {MODEL_NAME} --vis viewer {DATA_PARSER} --scale-factor 0.5
+```