Merged remote changes and resolved conflicts

Dockerfile

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+# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+# SPDX-License-Identifier: Apache-2.0
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+# Use NVIDIA PyTorch container as base image
+FROM nvcr.io/nvidia/pytorch:24.10-py3
+
+# Install basic tools and clean apt cache
+RUN apt-get update && apt-get install -y git tree ffmpeg wget \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# Create symlinks for bash and libcuda
+RUN rm /bin/sh && ln -s /bin/bash /bin/sh && ln -s /lib64/libcuda.so.1 /lib64/libcuda.so
+
+# Set the working directory inside the container
+WORKDIR /app
+
+# Copy the cosmos-predict1.yaml and requirements.txt files to the container root
+COPY ./cosmos-predict1.yaml /cosmos-predict1.yaml
+COPY ./requirements.txt /requirements.txt
+
+# Copy all your project files (including gui/, cosmos_predict1/, etc.) into the /app directory.
+COPY . /app
+
+# Step 1: Install Miniconda
+# Install Miniconda to /opt/miniconda3 for consistency
+RUN echo "Installing Miniconda..." && \
+    mkdir -p /opt/miniconda3 && \
+    wget https://repo.anaconda.com/miniconda/Miniconda3-latest-Linux-x86_64.sh -O /opt/miniconda3/miniconda.sh && \
+    bash /opt/miniconda3/miniconda.sh -b -u -p /opt/miniconda3 && \
+    rm /opt/miniconda3/miniconda.sh
+
+# Step 2: Configure Conda and set PATH
+# Add Miniconda to PATH for current and subsequent RUN commands
+ENV PATH="/opt/miniconda3/bin:$PATH"
+RUN echo "Configuring Conda..." && \
+    conda config --set auto_activate_base false && \
+    conda config --set auto_update_conda false && \
+    conda config --set show_channel_urls true && \
+    conda config --set channel_priority strict && \
+    conda config --set safety_checks disabled && \
+    conda config --add channels conda-forge && \
+    conda config --set always_yes true && \
+    conda config --set restore_free_channel true && \
+    conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/main && \
+    conda tos accept --override-channels --channel https://repo.anaconda.com/pkgs/r
+
+# Step 3: Create the conda environment from the YAML file
+RUN echo "Creating conda environment from YAML..." && \
+    conda env create --file /cosmos-predict1.yaml
+
+# Step 4: Activate environment and install core pip requirements
+# Add the new environment's bin directory to PATH for subsequent `pip` calls
+ENV PATH="/opt/miniconda3/envs/cosmos-predict1/bin:$PATH"
+RUN echo "Installing core pip requirements from requirements.txt..." && \
+    pip install --no-cache-dir -r /requirements.txt
+
+# Step 5: Create necessary symlinks
+# Use CONDA_PREFIX if set, otherwise fallback to expected path
+RUN echo "Creating symlinks..." && \
+    ln -sf ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/lib/python3.10/site-packages/nvidia/*/include/* ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/include/ && \
+    ln -sf ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/lib/python3.10/site-packages/nvidia/*/include/* ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/include/python3.10 && \
+    ln -sf ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/lib/python3.10/site-packages/triton/backends/nvidia/include/* ${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1}/include/
+
+# Step 6: Install specific pip packages
+RUN echo "Installing specific pip packages..." && \
+    pip install "fastapi[standard]" && \
+    pip install pyexr && \
+    pip install transformer-engine[pytorch]==1.12.0
+
+# Step 7: Clone and install Apex
+RUN echo "Cloning and installing Apex..." && \
+    git clone https://github.com/NVIDIA/apex /app/apex_temp && \
+    cd /app/apex_temp && \
+    CUDA_HOME=${CONDA_PREFIX:-/opt/miniconda3/envs/cosmos-predict1} pip install -v --disable-pip-version-check --no-cache-dir --no-build-isolation --config-settings "--build-option=--cpp_ext" --config-settings "--build-option=--cuda_ext" . && \
+    rm -rf /app/apex_temp # Clean up cloned repo after install
+
+# Step 8: Install MoGe
+RUN echo "Installing MoGe..." && \
+    pip install git+https://github.com/microsoft/MoGe.git
+
+# Set environment variables for your application at runtime
+# Assumes your checkpoints are in /app/checkpoints after COPY . /app
+ENV GEN3C_CKPT_PATH="/app/checkpoints"
+# Specify GPU count if your app needs it (e.g., "ALL" for all available)
+ENV GEN3C_GPU_COUNT="ALL"
+# Standard path for CUDA_HOME in NVIDIA PyTorch Docker images
+ENV CUDA_HOME="/usr/local/cuda"
+
+# Expose the port your FastAPI server will listen on
+EXPOSE 7860
+
+# Default command: Activate the conda environment and then run the FastAPI server.
+# This ensures all your dependencies are available and the server starts correctly.
+# 'gui.api.server:app' is the correct path for your FastAPI application.
+CMD ["bash", "-c", "source /opt/miniconda3/bin/activate cosmos-predict1 && uvicorn gui.api.server:app --host 0.0.0.0 --port 7860"]