ns-docker-wrapper 0.1.2

Run Nerfstudio inside docker as a python package

ns-docker-wrapper

ns-docker-wrapper is a Python library that simplifies using Nerfstudio through Docker. It provides a clean python API to run commands inside a Docker container, handling tasks like file copying, volume management, and command construction.

Requirements

• Python 3.10 or higher
• Docker

Installation

You can install the library using Pip:

pip install ns-docker-wrapper

Or if you want the latest development version directly from GitHub, you can use the following command:

pip install git+https://github.com/Jourdelune/ns_docker_wrapper.git

Usage Overview

The goal of ns-docker-wrapper is to streamline the process of running Nerfstudio commands in a Dockerized environment.

In this way, you can use Nerfstudio without compiling it locally, but you can still use it in a Pythonic way to integrate it into your Python projects as a regular library.

Documentation

A comprehensive documentation is available here.

Basic Example

Here’s a simple end-to-end example showing how to process a set of images and train a Nerfstudio model:

import os

import ns_docker_wrapper as nsdw
from ns_docker_wrapper.utils import select_largest_model

RAW_IMAGES_INPUT_PATH = "PATH_TO_YOUR_RAW_IMAGES"  # Replace this with your actual path
OUTPUT_BASE_PATH = "./nerfstudio_output"

nsdw.init(output_base_path=OUTPUT_BASE_PATH)


nsdw.process_data("images", nsdw.path(RAW_IMAGES_INPUT_PATH)).output_dir(
    "processed_data"
).run()

# fix colmap sparse issue from nerfstudio https://github.com/nerfstudio-project/nerfstudio/issues/3435

nsdw.process_data("images", nsdw.path(RAW_IMAGES_INPUT_PATH)).output_dir(
    "processed_data"
).skip_image_processing().skip_colmap().colmap_model_path(
    nsdw.path(str(select_largest_model()))
).run()


exit_code, output = (
    nsdw.train("splatfacto-big")
    .data(nsdw.path("./nerfstudio_output/processed_data"))
    .output_dir("trained_models")
    .pipeline.model.use_scale_regularization(True)
    .pipeline.model.use_bilateral_grid(True)
    .viewer.quit_on_train_completion(True)
    .pipeline.model.strategy("mcmc")
    .pipeline.model.rasterize_mode("antialiased")
    .pipeline.model.cull_alpha_thresh(0.005)
    .run()
)

# extract the config file path from the output
for line in output.splitlines():
    if "Config File" in line:
        path = line.split("│")[2].strip()
        config_file = os.path.join("/workspace", path)

        print(f"Config file generated: {config_file}")


nsdw.custom_command("ns-export").add_positional_arg("gaussian-splat").load_config(
    config_file  # type: ignore # nsdw.path(config_file, copy_depth=5) if you want to copy the file from the host to the container
).output_dir("export").run()

# now in ./nerfstudio_output/export you will find the exported model (splat.py)

Available Commands

The nsdw object (short for ns_docker_wrapper) provides convenient factory methods for commonly used Nerfstudio commands.

nsdw.train(method: str) -> Command

Creates a ns-train command for training with the specified method (e.g., nerfacto, splatfacto).

nsdw.train("nerfacto")

nsdw.process_data(processor: str, data_path: Union[str, PathArgument]) -> Command

Launches ns-process-data with the specified processor (e.g., "images", "video").

If you're using a local file path, make sure to wrap it with nsdw.path() so it can be copied into the container:

nsdw.process_data("images", nsdw.path("/path/to/raw_images"))

nsdw.process_images(input_image_path: Union[str, PathArgument], output_dir: str) -> Command

A shortcut for ns-process-data images. It simplifies the common use case of processing raw images:

nsdw.process_images(nsdw.path("/path/to/images"), "processed_data")

nsdw.custom_command(command_string: str) -> Command

Use this if you want to run a custom Nerfstudio command that's not yet covered by a built-in method:

nsdw.custom_command("ns-export").add_positional_arg("colmap").run()

nsdw.path(local_path: str) -> PathArgument

Wrap any local file or directory path with this function. It ensures that the resource is copied into the Docker container and mapped correctly.

nsdw.train("nerfacto").data(nsdw.path("/your/local/data"))

Adding Arguments to Commands

Once you create a command object, you can chain argument methods to it. The library handles kebab-case conversion and supports various argument types.

Simple Named Arguments

Use method calls that correspond to argument names. For example:

nsdw.train("nerfacto").viewer_port(7008).run()
# Translates to: ns-train nerfacto --viewer-port 7008

If you don’t pass a value, the argument will be included as a flag:

nsdw.train("nerfacto").no_debug().run()
# Translates to: ns-train nerfacto --no-debug

Nested Arguments (Dot-Notation)

Use chained methods to build nested CLI options:

nsdw.train("nerfacto").pipeline.model.field_of_view(60).run()
# Translates to: ns-train nerfacto --pipeline.model.field-of-view 60

Positional Arguments

For positional CLI arguments (those without --key=value), use .add_positional_arg():

nsdw.custom_command("ns-export").add_positional_arg("colmap").run()
# Translates to: ns-export colmap

Local Paths and Volumes

When providing a local path, always wrap it with nsdw.path(). This ensures the file or directory is made accessible inside the container:

nsdw.train("nerfacto").data(nsdw.path("./datasets/my_scene")).run()

License

This project is licensed under the MIT License. See the LICENSE file for details.

Contributing

We welcome contributions! If you have suggestions or improvements, please open an issue or submit a pull request.