# Installation

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 nerfactory python=3.8.13
conda activate nerfactory
```

Clone the repo

```bash
git clone git@github.com:plenoptix/nerfactory.git
```

Install dependencies and nerfactory 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).

```
|─ nerfactory/
   ├─ 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 [viewer docs](../tutorials/viewer/viewer_quickstart.md))

```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 using a fast NeRF variant (ie. Instant-NGP), we reccomend using our viewer. See our [viewer docs](../tutorials/viewer/viewer_quickstart.md) for more details. The viewer will allow interactive visualization of training in realtime.

Additionally, 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
```

# Rendering a Trajectory

To evaluate the trained NeRF, we provide an evaluation script that allows you to do benchmarking (see our [benchmarking workflow](../tooling/benchmarking.md)) or to render out the scene with a custom trajectory and save the output to a video.

```bash
python scripts/run_eval.py render-trajectory --load-config=outputs/blender_lego/instant_ngp/2022-07-07_230905/config.yml --traj=spiral --output-path=output.mp4
```

Please note, this quickstart allows you to preform everything in a headless manner. We also provide a web-based visualizer that allows you to easily monitor training or render out trajectories. See our [viewer docs](../tutorials/viewer/viewer_quickstart.md) for more.

# FAQ

* [TinyCUDA installation errors out with cuda mismatch](tiny-cuda-error)

(tiny-cuda-error)=
#### TinyCUDA installation errors out with cuda mismatch

While installing tiny-cuda, you run into: `The detected CUDA version mismatches the version that was used to compile PyTorch (10.2). Please make sure to use the same CUDA versions.`

**Solution**:
```
pip install torch==1.11.0+cu113 torchvision==0.12.0+cu113 -f https://download.pytorch.org/whl/torch_stable.html
```