Update Dockerfile

Dockerfile

@@ -1,89 +1,89 @@
-# This is the same dockerfile from `Helper_Files/Dockerfiles/tldw-nvidia_amd64_Dockerfile`. c/p here so people see a 'Dockerfile' in the root directory and know what to do.
-# Usage
-# docker build -t tldw-nvidia_amd64 .
-# docker run --gpus=all -p 7860:7860 -v tldw_volume:/tldw tldw-nvidia_amd64
-#
-# If the above command doesn't work and it hangs on start, use the following command:
-#
-#   sudo docker run -it -p 7860:7860 -v tldw_volume:/tdlw tldw-nvidia_amd64 bash
-#
-# Once in the container, run the following command:
-#
-#   python summarize.py -gui
-#
-# And you should be good.
-
-# Use Nvidia image:
-FROM nvidia/cuda:12.6.1-cudnn-runtime-ubuntu24.04
-
-# Use an official Python runtime as a parent image
-#FROM python:3.10.15-slim-bookworm
-
-
-# Set build arguments
-ARG REPO_URL=https://github.com/rmusser01/tldw.git
-ARG BRANCH=main
-ARG GPU_SUPPORT=cpu
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y \
-    ffmpeg \
-    libsqlite3-dev \
-    build-essential \
-    git \
-    python3 \
-    python3-pyaudio \
-    portaudio19-dev \
-    python3-pip \
-    portaudio19-dev \
-    python3-venv \
-    && rm -rf /var/lib/apt/lists/*
-
-# Set the working directory in the container
-WORKDIR /tldw
-
-# Clone the repository
-RUN git clone -b ${BRANCH} ${REPO_URL} .
-
-# Create and activate virtual environment
-RUN python3 -m venv ./venv
-ENV PATH="/tldw/venv/bin:$PATH"
-
-# Upgrade pip and install wheel
-RUN pip install --upgrade pip wheel
-
-# Install CUDA
-RUN pip install nvidia-cublas-cu12 nvidia-cudnn-cu12
-
-# setup PATH
-RUN export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
-
-
-# Install PyTorch based on GPU support
-RUN if [ "$GPU_SUPPORT" = "cuda" ]; then \
-        pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu123; \
-    elif [ "$GPU_SUPPORT" = "amd" ]; then \
-        pip install torch-directml; \
-    else \
-        pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cpu; \
-    fi
-
-# Install other requirements
-RUN pip install -r requirements.txt
-
-# Update config.txt for CPU if needed
-RUN if [ "$GPU_SUPPORT" = "cpu" ]; then \
-        sed -i 's/cuda/cpu/' ./Config_Files/config.txt; \
-    fi
-
-# Create a volume for persistent storage
-VOLUME /tldw
-
-# Make port 7860 available to the world outside this container
-EXPOSE 7860
-
-# Set listening to all interfaces
-ENV GRADIO_SERVER_NAME="0.0.0.0"
-
-# Run the application
-CMD ["python", "summarize.py", "-gui"]
+# This is the same dockerfile from `Helper_Files/Dockerfiles/tldw-nvidia_amd64_Dockerfile`. c/p here so people see a 'Dockerfile' in the root directory and know what to do.
+# Usage
+# docker build -t tldw-nvidia_amd64 .
+# docker run --gpus=all -p 7860:7860 -v tldw_volume:/tldw tldw-nvidia_amd64
+#
+# If the above command doesn't work and it hangs on start, use the following command:
+#
+#   sudo docker run -it -p 7860:7860 -v tldw_volume:/tdlw tldw-nvidia_amd64 bash
+#
+# Once in the container, run the following command:
+#
+#   python summarize.py -gui
+#
+# And you should be good.
+
+# Use Nvidia image:
+FROM nvidia/cuda:12.6.1-cudnn-runtime-ubuntu24.04
+
+# Use an official Python runtime as a parent image
+#FROM python:3.10.15-slim-bookworm
+
+
+# Set build arguments
+ARG REPO_URL=https://github.com/rmusser01/tldw.git
+ARG BRANCH=main
+ARG GPU_SUPPORT=cpu
+
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    ffmpeg \
+    libsqlite3-dev \
+    build-essential \
+    git \
+    python3 \
+    python3-pyaudio \
+    portaudio19-dev \
+    python3-pip \
+    portaudio19-dev \
+    python3-venv \
+    && rm -rf /var/lib/apt/lists/*
+
+# Set the working directory in the container
+WORKDIR /tldw
+
+# Clone the repository
+RUN git clone -b ${BRANCH} ${REPO_URL} .
+
+# Create and activate virtual environment
+RUN python3 -m venv ./venv
+ENV PATH="/tldw/venv/bin:$PATH"
+
+# Upgrade pip and install wheel
+RUN pip install --upgrade pip wheel
+
+# Install CUDA
+RUN pip install nvidia-cublas-cu12 nvidia-cudnn-cu12
+
+# setup PATH
+RUN export LD_LIBRARY_PATH=`python3 -c 'import os; import nvidia.cublas.lib; import nvidia.cudnn.lib; print(os.path.dirname(nvidia.cublas.lib.__file__) + ":" + os.path.dirname(nvidia.cudnn.lib.__file__))'`
+
+
+# Install PyTorch based on GPU support
+RUN if [ "$GPU_SUPPORT" = "cuda" ]; then \
+        pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cu123; \
+    elif [ "$GPU_SUPPORT" = "amd" ]; then \
+        pip install torch-directml; \
+    else \
+        pip install torch==2.2.2 torchvision==0.17.2 torchaudio==2.2.2 --index-url https://download.pytorch.org/whl/cpu; \
+    fi
+
+# Install other requirements
+RUN pip install -r requirements.txt
+
+# Update config.txt for CPU if needed
+RUN if [ "$GPU_SUPPORT" = "cpu" ]; then \
+        sed -i 's/cuda/cpu/' ./Config_Files/config.txt; \
+    fi
+
+# Create a volume for persistent storage
+VOLUME /tldw
+
+# Make port 7860 available to the world outside this container
+EXPOSE 7860
+
+# Set listening to all interfaces
+ENV GRADIO_SERVER_NAME="0.0.0.0"
+
+# Run the application
+CMD ["python", "summarize.py", "-gui", "-log DEBUG"]