Dcokerfile created

Dockerfile

@@ -0,0 +1,52 @@
+ARG ROS_DISTRO=humble
+FROM ros:${ROS_DISTRO} AS deps
+
+# Create ros2_ws and copy files
+WORKDIR /root/ros2_ws
+SHELL ["/bin/bash", "-c"]
+COPY . /root/ros2_ws/src
+
+# Install dependencies
+RUN apt-get update \
+    && apt-get -y --quiet --no-install-recommends install \
+    gcc \
+    git \
+    wget \
+    python3 \
+    python3-pip
+RUN pip3 install -r src/requirements.txt
+RUN rosdep install --from-paths src --ignore-src -r -y
+
+# Install CUDA nvcc
+ARG USE_CUDA
+ARG CUDA_VERSION=12-6
+
+RUN if [ "$USE_CUDA" = "1" ]; then \
+    wget https://developer.download.nvidia.com/compute/cuda/repos/ubuntu2004/x86_64/cuda-keyring_1.1-1_all.deb && \
+    dpkg -i cuda-keyring_1.1-1_all.deb && \
+    rm cuda-keyring_1.1-1_all.deb; \
+    apt-get update && apt-get install -y cuda-toolkit-$CUDA_VERSION; \
+    echo "export PATH=/usr/local/cuda/bin${PATH:+:${PATH}}" >> ~/.bashrc; \
+    echo "export LD_LIBRARY_PATH=/usr/local/cuda/lib64${LD_LIBRARY_PATH:+:${LD_LIBRARY_PATH}}" >> ~/.bashrc; \
+    fi
+
+# Colcon the ws
+FROM deps AS builder
+ARG CMAKE_BUILD_TYPE=Release
+
+ENV PATH=/usr/local/cuda/bin${PATH:+:${PATH}}
+ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64
+
+RUN source /opt/ros/${ROS_DISTRO}/setup.bash && \
+    if [ "$USE_CUDA" = "1" ]; then \
+    source ~/.bashrc && \
+    colcon build --cmake-args -DGGML_CUDA=ON; \
+    else \
+    colcon build; \
+    fi
+
+# Source the ROS 2 setup file
+RUN echo "source /root/ros2_ws/install/setup.bash" >> ~/.bashrc
+
+# Run a default command, e.g., starting a bash shell
+CMD ["bash"]

README.md

@@ -6,16 +6,17 @@ This repository provides a set of ROS 2 packages to integrate [whisper.cpp](http
 
 1. [Related Projects](#related-projects)
 2. [Installation](#installation)
-3. [Usage](#usage)
-4. [Demos](#demos)
+3. [Docker](#docker)
+4. [Usage](#usage)
+5. [Demos](#demos)
 
 ## Related Projects
 
 - [chatbot_ros](https://github.com/mgonzs13/chatbot_ros) → This chatbot, integrated into ROS 2, uses whisper_ros, to listen to people speech; and [llama_ros](https://github.com/mgonzs13/llama_ros/tree/main), to generate responses. The chatbot is controlled by a state machine created with [YASMIN](https://github.com/uleroboticsgroup/yasmin).
 
 ## Installation
 
-To run llama_ros with CUDA, first, you must install the [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit).
+To run whisper_ros with CUDA, first, you must install the [CUDA Toolkit](https://developer.nvidia.com/cuda-toolkit).
 
 ```shell
 $ cd ~/ros2_ws/src
@@ -28,6 +29,20 @@ $ cd ~/ros2_ws
 $ colcon build --cmake-args -DGGML_CUDA=ON # add this for CUDA
 ```
 
+## Docker
+
+Build the whisper_ros docker. Additionally, you can choose to build whisper_ros with CUDA (`USE_CUDA`) and choose the CUDA version (`CUDA_VERSION`). Remember that you have to use `DOCKER_BUILDKIT=0` to compile whisper_ros with CUDA when building the image.
+
+```shell
+$ DOCKER_BUILDKIT=0 docker build -t whisper_ros --build-arg USE_CUDA=1 --build-arg CUDA_VERSION=12-6 .
+```
+
+Run the docker container. If you want to use CUDA, you have to install the [NVIDIA Container Tollkit](https://docs.nvidia.com/datacenter/cloud-native/container-toolkit/latest/install-guide.html) and add `--gpus all`.
+
+```shell
+$ docker run -it --rm --gpus all whisper_ros
+```
+
 ## Usage
 
 Run Silero for VAD and Whisper for STT: