remove dockerfile, revert to reqs.txt

Dockerfile

@@ -1,98 +0,0 @@
-# Use a CUDA base image without preinstalled cuDNN to avoid conflicts
-FROM nvidia/cuda:12.3.2-devel-ubuntu22.04
-
-# Set environment variables
-ENV DEBIAN_FRONTEND=noninteractive
-ENV PYTHONUNBUFFERED=1
-ENV PYTHONDONTWRITEBYTECODE=1
-ENV TF_FORCE_GPU_ALLOW_GROWTH=true
-
-# Install system dependencies
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    git wget curl ca-certificates \
-    python3 python3-pip python3-dev \
-    ffmpeg libsm6 libxext6 libgl1-mesa-glx && \
-    apt-get clean && rm -rf /var/lib/apt/lists/*
-
-# --- Download and install cuDNN 9.3.0 ---
-# Download the archive directly from NVIDIA
-RUN wget -O /tmp/cudnn-linux-x86_64-9.3.0.75_cuda12-archive.tar.xz https://developer.download.nvidia.com/compute/cudnn/redist/cudnn/linux-x86_64/cudnn-linux-x86_64-9.3.0.75_cuda12-archive.tar.xz && \
-    tar -xJvf /tmp/cudnn-linux-x86_64-9.3.0.75_cuda12-archive.tar.xz -C /tmp && \
-    cp -P /tmp/cudnn-linux-x86_64-9.3.0.75_cuda12-archive/cuda/include/cudnn*.h /usr/local/cuda/include && \
-    cp -P /tmp/cudnn-linux-x86_64-9.3.0.75_cuda12-archive/cuda/lib64/libcudnn* /usr/local/cuda/lib64 && \
-    chmod a+r /usr/local/cuda/include/cudnn*.h /usr/local/cuda/lib64/libcudnn* && \
-    rm -rf /tmp/cudnn-linux-x86_64-9.3.0.75_cuda12-archive.tar.xz /tmp/cudnn-linux-x86_64-9.3.0.75_cuda12-archive
-
-# Set environment variables for CUDA/cuDNN libraries
-ENV LD_LIBRARY_PATH=/usr/local/cuda/lib64:/usr/lib/x86_64-linux-gnu:${LD_LIBRARY_PATH}
-ENV PATH=/usr/local/cuda/bin:${PATH}
-ENV CUDA_VISIBLE_DEVICES=0
-
-# Set working directory
-WORKDIR /app
-
-# Copy requirements and install Python dependencies
-COPY requirements.txt /app/
-RUN pip3 install --no-cache-dir --upgrade pip setuptools wheel
-# Install TensorFlow GPU support (using version 2.15.0 here for compatibility)
-RUN pip3 install --no-cache-dir tensorflow==2.15.0
-# Install the remaining packages from requirements.txt (skip dependency resolution)
-RUN pip3 install --no-cache-dir --no-deps -r requirements.txt
-RUN pip3 install --no-cache-dir tensorflow-hub==0.14.0
-RUN pip3 install --no-cache-dir opencv-python-headless opencv-contrib-python-headless
-
-# Copy the application code
-COPY . /app/
-
-# Create a CPU fallback setup for TensorFlow
-RUN echo 'import tensorflow as tf\n\
-import os\n\
-os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2"\n\
-\ndef setup_tensorflow():\n\
-    try:\n\
-        physical_devices = tf.config.list_physical_devices("GPU")\n\
-        if physical_devices:\n\
-            print(f"Found {len(physical_devices)} GPU(s)")\n\
-            for device in physical_devices:\n\
-                tf.config.experimental.set_memory_growth(device, True)\n\
-                print(f"Enabled memory growth for {device}")\n\
-        else:\n\
-            print("No GPU found. Running on CPU.")\n\
-    except Exception as e:\n\
-        print(f"Error setting up TensorFlow: {e}")\n\
-        print("Disabling GPU and falling back to CPU")\n\
-        os.environ["CUDA_VISIBLE_DEVICES"] = "-1"\n\
-\n\
-setup_tensorflow()\n' > /app/tf_setup.py
-
-# Patch FILM.py to ensure proper GPU/CPU fallback, if the file exists
-RUN if [ -f "/app/FILM.py" ]; then \
-    sed -i '1s/^/import tensorflow as tf\nfrom tf_setup import setup_tensorflow\n/' /app/FILM.py && \
-    sed -i '/def __init__/a\        # Check if GPU is disabled and use CPU if needed\n        if "CUDA_VISIBLE_DEVICES" in os.environ and os.environ["CUDA_VISIBLE_DEVICES"] == "-1":\n            print("GPU is disabled, using CPU for FILM")\n            self._device = "/cpu:0"\n        else:\n            self._device = "/gpu:0"\n        print(f"FILM will use device: {self._device}")' /app/FILM.py && \
-    sed -i '/def __call__/a\        with tf.device(self._device):' /app/FILM.py && \
-    sed -i 's/        result = self._model/            try:\n                result = self._model/g' /app/FILM.py && \
-    sed -i '/result = self._model/a\            except Exception as e:\n                print(f"Error during model inference: {e}, trying CPU fallback")\n                with tf.device("/cpu:0"):\n                    result = self._model(inputs, training=False)' /app/FILM.py && \
-    sed -i '1s/^/import os\n/' /app/FILM.py; \
-fi
-
-# Create a startup script that checks CUDA/cuDNN status and launches Streamlit
-RUN echo '#!/bin/bash\n\
-set -e\n\
-\n\
-echo "CUDA libraries:"\n\
-ldconfig -p | grep cuda\n\
-echo "cuDNN libraries:"\n\
-ldconfig -p | grep cudnn\n\
-\n\
-python3 -c "import tensorflow as tf; print(\\"Num GPUs Available: \\", len(tf.config.list_physical_devices(\\"GPU\\")))" || {\n\
-  echo "TensorFlow GPU test failed, falling back to CPU"\n\
-  export CUDA_VISIBLE_DEVICES=-1\n\
-}\n\
-\n\
-exec streamlit run app.py --server.port=8501 --server.address=0.0.0.0\n' > /app/start.sh && chmod +x /app/start.sh
-
-# Expose the port for Streamlit
-EXPOSE 8501
-
-# Use the startup script as the container's entrypoint
-CMD ["/app/start.sh"]

requirements.txt

@@ -14,6 +14,6 @@ torch
 torchvision
 lpips
 # peft
-# tensorflow==2.18.0
-# tensorflow_hub==0.16.1
+tensorflow==2.18.0
+tensorflow_hub==0.16.1
 opencv_python