Driver >=520.61.05
PyTorch 2.1.2 + CUDA 11.8
Generate a production-ready Dockerfile with verified compatibility
Configuration Summary
Framework
PyTorch 2.1.2
CUDA Version
11.8
Python Support
3.8, 3.9, 3.10, 3.11
Min Driver
>=520.61.05
What's in PyTorch 2.1.2
- Mature torch.compile() support
- Wide ecosystem compatibility
- Proven production stability
Best For
This Version
- • Legacy systems migration
- • Maximum compatibility with scientific packages
- • Environments with strict dependency constraints
CUDA 11.8
- • T4, V100, and older GPUs
- • RTX 20xx/30xx consumer GPUs
- • Cost-effective inference deployments
Note: Missing newer CUDA features like FP8 support
Generate Dockerfile
Configuration
Local GPU or CPU environment
Requires NVIDIA Driver >=520.61.05
Dockerfile
1# syntax=docker/dockerfile:12# ^ Required for BuildKit cache mounts and advanced features34# Generated by DockerFit (https://tools.eastondev.com/docker)5# PYTORCH 2.1.2 + CUDA 11.8 | Python 3.116# Multi-stage build for optimized image size78# ==============================================================================9# Stage 1: Builder - Install dependencies and compile10# ==============================================================================11FROM nvidia/cuda:11.8.0-cudnn8-devel-ubuntu22.04 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 from deadsnakes PPA (Ubuntu 22.04)22RUN apt-get update && apt-get install -y --no-install-recommends \23 software-properties-common \24 && add-apt-repository -y ppa:deadsnakes/ppa \25 && apt-get update && apt-get install -y --no-install-recommends \26 python3.11 \27 python3.11-venv \28 python3.11-dev \29 build-essential \30 git31 && rm -rf /var/lib/apt/lists/*3233# Create virtual environment34ENV VIRTUAL_ENV=/opt/venv35RUN python3.11 -m venv $VIRTUAL_ENV36ENV PATH="$VIRTUAL_ENV/bin:$PATH"3738# Upgrade pip39RUN pip install --no-cache-dir --upgrade pip setuptools wheel4041# Install PyTorch with BuildKit cache42RUN --mount=type=cache,target=/root/.cache/pip \43 pip install torch torchvision torchaudio \44 --index-url https://download.pytorch.org/whl/cu1184546# Install project dependencies47COPY requirements.txt .48RUN --mount=type=cache,target=/root/.cache/pip \49 pip install -r requirements.txt5051# ==============================================================================52# Stage 2: Runtime - Minimal production image53# ==============================================================================54FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04 AS runtime5556# Labels57LABEL maintainer="Generated by DockerFit"58LABEL version="2.1.2"59LABEL description="PYTORCH 2.1.2 + CUDA 11.8"6061# Environment variables62ENV PYTHONUNBUFFERED=163ENV PYTHONDONTWRITEBYTECODE=164ENV NVIDIA_VISIBLE_DEVICES=all65ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility6667# Install Python 3.11 runtime from deadsnakes PPA (Ubuntu 22.04)68RUN apt-get update && apt-get install -y --no-install-recommends \69 software-properties-common \70 && add-apt-repository -y ppa:deadsnakes/ppa \71 && apt-get update && apt-get install -y --no-install-recommends \72 python3.11 \73 libgomp174 && apt-get remove -y software-properties-common \75 && apt-get autoremove -y \76 && rm -rf /var/lib/apt/lists/*7778# Create non-root user for security79ARG USERNAME=appuser80ARG USER_UID=100081ARG USER_GID=$USER_UID82RUN groupadd --gid $USER_GID $USERNAME \83 && useradd --uid $USER_UID --gid $USER_GID -m $USERNAME8485# Copy virtual environment from builder86COPY --from=builder --chown=$USERNAME:$USERNAME /opt/venv /opt/venv87ENV VIRTUAL_ENV=/opt/venv88ENV PATH="$VIRTUAL_ENV/bin:$PATH"8990# Set working directory91WORKDIR /app9293# Copy application code94COPY --chown=$USERNAME:$USERNAME . .9596# Switch to non-root user97USER $USERNAME9899# Expose port100EXPOSE 8000101102# Default command103CMD ["python", "main.py"]
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Frequently Asked Questions
What NVIDIA driver version do I need?
For PyTorch 2.1.2 with CUDA 11.8, you need NVIDIA driver version >=520.61.05 or higher.
Run nvidia-smi to check your current driver version.
Which Python version should I use?
PyTorch 2.1.2 supports Python versions: 3.8, 3.9, 3.10, 3.11.
We recommend using Python 3.10 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.