Merge pull request #10 from ins-amu/develop

Develop

Dockerfile

@@ -1,28 +1,42 @@
-# Use the smallest official Python image
-FROM python:3.9-slim
+FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu20.04
 
-# Set environment variables to avoid interactive prompts
+# Set environment to avoid interactive prompts
 ENV DEBIAN_FRONTEND=noninteractive
 
-# Set the working directory inside the container
-WORKDIR /app
-
-RUN apt-get update && apt-get install -y --no-install-recommends \
-    swig \
+# Install system dependencies
+RUN apt-get update && apt-get install -y \
+    python3.10 \
+    python3-pip \
     build-essential \
-    git \
+    gcc \
+    g++ \
+    libatlas-base-dev \
+    libopenblas-dev \
+    libhdf5-dev \
+    swig \
+    tzdata \
+    && ln -s /usr/bin/python3 /usr/bin/python \
     && rm -rf /var/lib/apt/lists/*
 
+# Set timezone (e.g., UTC) to avoid configuration prompts
+RUN echo "Etc/UTC" > /etc/timezone && \
+    ln -fs /usr/share/zoneinfo/Etc/UTC /etc/localtime && \
+    dpkg-reconfigure -f noninteractive tzdata
 
-# Copy only the package files (without installing dependencies)
-COPY . .
+WORKDIR /app
+
+RUN pip install --upgrade pip
 
-# Install Python dependencies
-RUN pip install --no-cache-dir hatchling setuptools wheel && \
-    pip install .
+RUN pip install --no-cache-dir \
+    hatchling \
+    setuptools>=45 \
+    wheel \
+    swig>=4.0
+
+COPY . .
 
-# Optional: Support GPU version (comment out if not needed)
-ARG GPU_SUPPORT=false
-RUN if [ "$GPU_SUPPORT" = "true" ]; then pip install cupy; fi
+RUN pip install . --no-cache-dir
+RUN pip install cupy-cuda11x
 
+# Set the default command (modify as needed)
 CMD ["python", "-c", "from vbi.utils import test_imports; test_imports()"]

README.md

@@ -27,7 +27,33 @@
 To use the Docker image, you can pull it from the GitHub Container Registry and run it as follows:
 
 ```bash
-    docker run --rm -it ghcr.io/ins-amu/vbi:main python3 -c 'from vbi.utils import test_imports; test_imports()'
+    docker build -t vbi-project .         # build
+    docker run --gpus all -it vbi-project # use with gpu
+
+    # Output something like this:
+
+                Dependency Check              
+
+    Package      Version       Status        
+  ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 
+    vbi          v0.1.3        ✅ Available  
+    numpy        1.24.4        ✅ Available  
+    scipy        1.10.1        ✅ Available  
+    matplotlib   3.7.5         ✅ Available  
+    sbi          0.22.0        ✅ Available  
+    torch        2.4.1+cu121   ✅ Available  
+    cupy         12.3.0        ✅ Available  
+
+    Torch GPU available: True
+    Torch device count: 1
+    Torch CUDA version: 12.1
+    CuPy GPU available: True
+    CuPy device count: 1
+    CUDA Version: 11.8
+    Device Name: NVIDIA RTX A5000
+    Total Memory: 23.68 GB
+    Compute Capability: 8.6
+
 ```
 
 

docs/index.rst

@@ -12,6 +12,10 @@ Virtual Brain Inference (VBI)
    :width: 200px
    :align: center
 
+
+The **Virtual Brain Inference (VBI)** toolkit is an open-source, flexible solution tailored for probabilistic inference on virtual brain models. It integrates computational models with personalized anatomical data to deepen the understanding of brain dynamics and neurological processes. VBI supports **fast simulations**, comprehensive **feature extraction**, and employs **deep neural density estimators** to handle various neuroimaging data types. Its goal is to bridge the gap in solving the inverse problem of identifying control parameters that best explain observed data, thereby making these models applicable for clinical settings. VBI leverages high-performance computing through GPU acceleration and C++ code to ensure efficiency in processing.
+
+
 Workflow
 ========