Troubleshooting AudioCraft: Reinstallation Using Docker

The other day, AudioCraft (MusicGen), which I had been able to use until now, stopped running with the following error.

Preparing metadata (setup.py) … error
error: subprocess-exited-with-error

× python setup.py egg_info did not run successfully.
│ exit code: 1
╰─> [9 lines of output]
No CUDA runtime is found, using CUDA_HOME=’C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.8′
Traceback (most recent call last):
File “”, line 2, in
File “”, line 34, in
File “C:\Users\minok\AppData\Local\Temp\pip-install-jutczj_m\xformers_7556bee2c41042458481e5ebb4b54959\setup.py”, line 239, in
ext_modules=get_extensions(),
File “C:\Users\minok\AppData\Local\Temp\pip-install-jutczj_m\xformers_7556bee2c41042458481e5ebb4b54959\setup.py”, line 157, in get_extensions
raise RuntimeError(
RuntimeError: CUTLASS submodule not found. Did you forget to run git submodule update –init –recursive ?
[end of output]

note: This error originates from a subprocess, and is likely not a problem with pip.
error: metadata-generation-failed

× Encountered error while generating package metadata.
╰─> See above for output.

note: This is an issue with the package mentioned above, not pip.
hint: See above for details.

This environment was created in a Python virtual environment.

Checking the official site, it appears that the page has changed and the recommended python version is 3.9 and not the previous 3.10.

GitHub - facebookresearch/audiocraft: Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec audio compressor / tokenizer, along with MusicGen, a simple and controllable music generation LM with textual and melodic conditioning.

Audiocraft is a library for audio processing and generation with deep learning. It features the state-of-the-art EnCodec...

The solution to this is to install the recommended version, but I was having trouble because some programs will not work with this version. There is a way to install multiple python versions and switch between them, but I will use Docker to achieve this. Using the recommended version of the image solves this problem and works even if the version of CUDA is different from the host.

Environment reproducibility: Docker containers allow dependencies and application environments to be defined in code, making environments more reproducible. This makes it easy to share environments between different machines or team members and run code under the same conditions.
Environment isolation: Because Docker containers are isolated from the host system, it is easier to maintain different environments for different projects or tasks. This avoids dependency conflicts and versioning issues.
Portability: Docker containers can be easily moved between different operating systems and cloud platforms. This simplifies application deployment and scaling.
Efficiency and Speed: Docker containers are lightweight and fast to launch. This increases the efficiency of deployment, testing, and continuous integration/continuous delivery (CI/CD) pipelines.
Version Control: Docker images are version controlled, making it easy to track, roll back, and manage different versions of applications and environments.
Efficient use of resources: Docker allows efficient sharing and utilization of resources, making more efficient use of resources on the host machine.
Simplified development and deployment: Docker makes it easy to move applications between development, test, and production environments. This simplifies the deployment process and reduces errors.
Community and Ecosystem: Docker has an active community and extensive ecosystem with many ready-made Docker images, Docker Compose templates, and other resources available.

So, start a Windows WSL and install Docker.

Create an appropriate directory for easier management.

mkdir audio

Go to the empty directory you created and create a Dockerfile.

cd audio
sudo vi Dockerfile

The contents of the Dockerfile are as follows. I encountered a lot of troubles, but it worked with these contents.

# Specify the base image
FROM python:3.9-slim-buster

# Install necessary packages
RUN apt-get update && \
    apt-get install -y git ffmpeg && \
    pip install torch==2.0.0+cu118 torchvision==0.15.1+cu118 torchaudio==2.0.1 --index-url https://download.pytorch.org/whl/cu118

# Clone the AudioCraft repository
RUN git clone https://github.com/facebookresearch/audiocraft /usr/src/app

# Set the working directory
WORKDIR /usr/src/app

# Install AudioCraft
RUN pip install -U .

# Start the server
CMD ["python", "./demos/musicgen_app.py", "--listen", "0.0.0.0", "--server_port", "7860"]

Build a Docker image based on the created Dockerfile.

docker build -t audiocraft .

Next, run the container.

docker run -d --name audio -p 7860:7860 audiocraft

Check the WSL IP address.

ip a

After confirming the IP address, access it with a browser.

http://IPアドレス:7860

To use a GPU with Docker, install the NVIDIA Container Toolkit.

If you have installed it correctly, the following command will show you the information.

nvidia-container-cli info

mamushi@n4060:~/audio$ nvidia-container-cli info
NVRM version: 537.42
CUDA version: 12.2

Device Index: 0
Device Minor: 0
Model: NVIDIA GeForce RTX 4060 Laptop GPU
Brand: GeForce
GPU UUID: GPU-73f6ad9a-b926-49a8-5fbb-ce9cb487757b
Bus Location: 00000000:01:00.0
Architecture: 8.9

The previous docker run command uses CPU, so stop the container and remove it.

docker stop audio
docker rm audio

Start the container with the following command

docker run --name audio --gpus all -p 7860:7860 -d audiocraft

Check the GPU usage while creating a song in the browser’s web UI.

nvidia-smi

The nvidia-smi command allows you to check GPU status and usage directly from within WSL. This will give you detailed information such as

Basic GPU information: View basic information about the GPU, such as name, ID, and driver version.
GPU utilization: Check GPU utilization, memory usage, and other resource usage.
Running Processes: See which processes are running on the GPU and how much GPU resources they are consuming.
Temperature and Power: Check GPU temperature and power consumption.
Errors and Warnings: Check for GPU-related errors and warnings.

This information is very useful for monitoring and troubleshooting GPU performance and health. In addition, nvidia-smi commands are updated in real time, providing real-time feedback and a more accurate picture of GPU behavior.

Windows Task Manager, on the other hand, only provides basic GPU utilization and memory usage, and does not provide as much detail as nvidia-smi. Therefore, if you need more detailed GPU status and usage information, it is recommended that you use the nvidia-smi command.