Driver >=510.47.03
PyTorch 1.13.1 + CUDA 11.6
Generate a production-ready Dockerfile with verified compatibility
Configuration Summary
Framework
PyTorch 1.13.1
CUDA Version
11.6
Python Support
3.7, 3.8, 3.9, 3.10
Min Driver
>=510.47.03
What's in PyTorch 1.13.1
- Last major PyTorch 1.x version
- Complete CUDA 11.x support
- Mature production ecosystem
Best For
This Version
- • Maintaining 1.x systems
- • Maximum compatibility with legacy code
- • Projects unable to migrate to 2.x
CUDA 11.6
- • PyTorch 1.13.x deployments
- • Maximum backward compatibility
- • Older Ubuntu 20.04 systems
Note: Deprecated CUDA version, use 11.7+ when possible
Generate Dockerfile
Configuration
Local GPU or CPU environment
Requires NVIDIA Driver >=510.47.03
Dockerfile
1# syntax=docker/dockerfile:12# ^ Required for BuildKit cache mounts and advanced features34# Generated by DockerFit (https://tools.eastondev.com/docker)5# PYTORCH 1.13.1 + CUDA 11.6 | Python 3.116# Multi-stage build for optimized image size78# ==============================================================================9# Stage 1: Builder - Install dependencies and compile10# ==============================================================================11FROM nvidia/cuda:11.6.2-cudnn8-devel-ubuntu20.04 AS builder1213# Build arguments14ARG DEBIAN_FRONTEND=noninteractive1516# Environment variables17ENV PYTHONUNBUFFERED=118ENV PYTHONDONTWRITEBYTECODE=119ENV TORCH_CUDA_ARCH_LIST="7.5;8.0;8.6"2021# Install Python 3.11 and build tools (native)22RUN apt-get update && apt-get install -y --no-install-recommends \23 python3.11 \24 python3.11-venv \25 python3.11-dev \26 build-essential \27 git28 && rm -rf /var/lib/apt/lists/*2930# Create virtual environment31ENV VIRTUAL_ENV=/opt/venv32RUN python3.11 -m venv $VIRTUAL_ENV33ENV PATH="$VIRTUAL_ENV/bin:$PATH"3435# Upgrade pip36RUN pip install --no-cache-dir --upgrade pip setuptools wheel3738# Install PyTorch with BuildKit cache39RUN --mount=type=cache,target=/root/.cache/pip \40 pip install torch torchvision torchaudio \41 --index-url https://download.pytorch.org/whl/cu1164243# Install project dependencies44COPY requirements.txt .45RUN --mount=type=cache,target=/root/.cache/pip \46 pip install -r requirements.txt4748# ==============================================================================49# Stage 2: Runtime - Minimal production image50# ==============================================================================51FROM nvidia/cuda:11.6.2-cudnn8-runtime-ubuntu20.04 AS runtime5253# Labels54LABEL maintainer="Generated by DockerFit"55LABEL version="1.13.1"56LABEL description="PYTORCH 1.13.1 + CUDA 11.6"5758# Environment variables59ENV PYTHONUNBUFFERED=160ENV PYTHONDONTWRITEBYTECODE=161ENV NVIDIA_VISIBLE_DEVICES=all62ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility6364# Install runtime dependencies (native Python 3.11)65RUN apt-get update && apt-get install -y --no-install-recommends \66 python3.11 \67 libgomp168 && rm -rf /var/lib/apt/lists/*6970# Create non-root user for security71ARG USERNAME=appuser72ARG USER_UID=100073ARG USER_GID=$USER_UID74RUN groupadd --gid $USER_GID $USERNAME \75 && useradd --uid $USER_UID --gid $USER_GID -m $USERNAME7677# Copy virtual environment from builder78COPY --from=builder --chown=$USERNAME:$USERNAME /opt/venv /opt/venv79ENV VIRTUAL_ENV=/opt/venv80ENV PATH="$VIRTUAL_ENV/bin:$PATH"8182# Set working directory83WORKDIR /app8485# Copy application code86COPY --chown=$USERNAME:$USERNAME . .8788# Switch to non-root user89USER $USERNAME9091# Expose port92EXPOSE 80009394# Default command95CMD ["python", "main.py"]
🚀 Recommended
High-Performance GPU Cloud
Deploy your Docker containers with powerful NVIDIA GPUs. A100/H100 available, 32+ global locations.
- NVIDIA A100/H100 GPU instances
- Hourly billing, starting at $0.004/h
- 32+ global data centers
- One-click container & bare metal deployment
Frequently Asked Questions
What NVIDIA driver version do I need?
For PyTorch 1.13.1 with CUDA 11.6, you need NVIDIA driver version >=510.47.03 or higher.
Run nvidia-smi to check your current driver version.
Which Python version should I use?
PyTorch 1.13.1 supports Python versions: 3.7, 3.8, 3.9, 3.10.
We recommend using Python 3.9 for the best balance of compatibility and features.
How do I verify GPU access in the container?
After building your image, run:
docker run --gpus all your-image python -c "import torch; print(torch.cuda.is_available())"
This should print True if GPU is accessible.