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Driver >=560.35.05

PyTorch 2.9.1 + CUDA 12.6

Generate a production-ready Dockerfile with verified compatibility

Configuration Summary

Framework
PyTorch 2.9.1
CUDA Version
12.6
Python Support
3.10, 3.11, 3.12
Min Driver
>=560.35.05

Note: 稳定生产环境，适合Hopper/Ampere架构

What's in PyTorch 2.9.1

  • Official CUDA 12.8 (cu128) wheels with Blackwell (10.0) native support
  • Python 3.10-3.12 support (3.9 deprecated)
  • Enhanced Hopper (H100/H200) and Blackwell (B200/GB200) architecture optimizations
  • cuDNN 9.x performance improvements with Ubuntu 24.04
  • Advanced torch.compile() with improved inductor optimizations
  • Enhanced FlexAttention API for efficient custom attention patterns

Performance: Up to 3x faster on Blackwell GPUs compared to PyTorch 2.4

Best For

This Version

  • • Blackwell B200/GB200 GPU deployments (2025 latest hardware)
  • • Hopper H100/H200 production inference and training
  • • Modern Python 3.11/3.12 environments with Ubuntu 24.04
  • • LLM inference requiring maximum CUDA 12.8 performance

CUDA 12.6

  • • Stable production environments for Hopper/Ampere
  • • Ubuntu 22.04 deployments with proven stability
  • • Alternative when 12.8 driver requirements not met
Note: No Blackwell native support, consider 12.8 for B200/GB200

Generate Dockerfile

Configuration

Local GPU or CPU environment

稳定生产环境，适合Hopper/Ampere架构

Requires NVIDIA Driver >=560.35.05
Dockerfile
1# syntax=docker/dockerfile:1
2# ^ Required for BuildKit cache mounts and advanced features
3 
4# Generated by DockerFit (https://tools.eastondev.com/docker)
5# PYTORCH 2.9.1 + CUDA 12.6 | Python 3.11
6# Multi-stage build for optimized image size
7 
8# ==============================================================================
9# Stage 1: Builder - Install dependencies and compile
10# ==============================================================================
11FROM nvidia/cuda:12.6.3-cudnn-devel-ubuntu22.04 AS builder
12 
13# Build arguments
14ARG DEBIAN_FRONTEND=noninteractive
15 
16# Environment variables
17ENV PYTHONUNBUFFERED=1
18ENV PYTHONDONTWRITEBYTECODE=1
19ENV TORCH_CUDA_ARCH_LIST="8.0;8.6;8.9;9.0"
20 
21# 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    git
31    && rm -rf /var/lib/apt/lists/*
32 
33# Create virtual environment
34ENV VIRTUAL_ENV=/opt/venv
35RUN python3.11 -m venv $VIRTUAL_ENV
36ENV PATH="$VIRTUAL_ENV/bin:$PATH"
37 
38# Upgrade pip
39RUN pip install --no-cache-dir --upgrade pip setuptools wheel
40 
41# Install PyTorch with BuildKit cache
42RUN --mount=type=cache,target=/root/.cache/pip \
43    pip install torch torchvision torchaudio \
44    --index-url https://download.pytorch.org/whl/cu126
45 
46# Install project dependencies
47COPY requirements.txt .
48RUN --mount=type=cache,target=/root/.cache/pip \
49    pip install -r requirements.txt
50 
51# ==============================================================================
52# Stage 2: Runtime - Minimal production image
53# ==============================================================================
54FROM nvidia/cuda:12.6.3-cudnn-runtime-ubuntu22.04 AS runtime
55 
56# Labels
57LABEL maintainer="Generated by DockerFit"
58LABEL version="2.9.1"
59LABEL description="PYTORCH 2.9.1 + CUDA 12.6"
60 
61# Environment variables
62ENV PYTHONUNBUFFERED=1
63ENV PYTHONDONTWRITEBYTECODE=1
64ENV NVIDIA_VISIBLE_DEVICES=all
65ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
66 
67# Install Python 3.11 runtime from deadsnakes PPA (Ubuntu 22.04)
68RUN apt-get update && apt-get install -y --no-install-recommends \
69    software-properties-common \
70    && add-apt-repository -y ppa:deadsnakes/ppa \
71    && apt-get update && apt-get install -y --no-install-recommends \
72    python3.11 \
73    libgomp1
74    && apt-get remove -y software-properties-common \
75    && apt-get autoremove -y \
76    && rm -rf /var/lib/apt/lists/*
77 
78# Create non-root user for security
79ARG USERNAME=appuser
80ARG USER_UID=1000
81ARG USER_GID=$USER_UID
82RUN groupadd --gid $USER_GID $USERNAME \
83    && useradd --uid $USER_UID --gid $USER_GID -m $USERNAME
84 
85# Copy virtual environment from builder
86COPY --from=builder --chown=$USERNAME:$USERNAME /opt/venv /opt/venv
87ENV VIRTUAL_ENV=/opt/venv
88ENV PATH="$VIRTUAL_ENV/bin:$PATH"
89 
90# Set working directory
91WORKDIR /app
92 
93# Copy application code
94COPY --chown=$USERNAME:$USERNAME . .
95 
96# Switch to non-root user
97USER $USERNAME
98 
99# Expose port
100EXPOSE 8000
101 
102# Default command
103CMD ["python", "main.py"]
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Frequently Asked Questions

What NVIDIA driver version do I need?

For PyTorch 2.9.1 with CUDA 12.6, you need NVIDIA driver version >=560.35.05 or higher.

Run nvidia-smi to check your current driver version.

Which Python version should I use?

PyTorch 2.9.1 supports Python versions: 3.10, 3.11, 3.12.

We recommend using Python 3.11 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.