Driver >=520.61.05

PyTorch 2.4.1 + CUDA 11.8

Q: Which Python versions are supported by PyTorch 2.4.1?

PyTorch 2.4.1 supports Python versions: 3.8, 3.10, 3.11, 3.12. We recommend using Python 3.11 for the best balance of compatibility and features.

Q: How do I verify GPU access in the Docker 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.

Generate a production-ready Dockerfile with verified compatibility

Configuration Summary

Framework

PyTorch 2.4.1

CUDA Version

11.8

Python Support

3.8, 3.10, 3.11, 3.12

Min Driver

>=520.61.05

Note: 传统硬件支持

What's in PyTorch 2.4.1

torch.compile() with improved performance and broader model support
FlexAttention API for custom attention patterns
Enhanced CUDA 12.4 support with improved memory efficiency
Better integration with NVIDIA Hopper architecture (H100)

Performance: Up to 2x faster inference compared to PyTorch 2.0 with torch.compile

Best For

This Version

• Production LLM inference with torch.compile optimization
• Custom transformer architectures with FlexAttention
• H100/A100 GPU deployments requiring maximum performance

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

Deployment Target

Local GPU or CPU environment

Framework

Version

CUDA Version

传统硬件支持

Python Version

Requires NVIDIA Driver >=520.61.05

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 2.4.1 + CUDA 11.8 | 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:11.8.0-cudnn8-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="7.5;8.0;8.6"
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:$PATH"
37 
38# Upgrade pip
39RUN pip install --no-cache-dir --upgrade pip setuptools wheel
40 
41# Install PyTorch with BuildKit cache
42RUN --mount=type=cache,target=/root/.cache/pip \
43    pip install torch torchvision torchaudio \
44    --index-url https://download.pytorch.org/whl/cu118
45 
46# Install project dependencies
47COPY requirements.txt .
48RUN --mount=type=cache,target=/root/.cache/pip \
49    pip install -r requirements.txt
50 
51# ==============================================================================
52# Stage 2: Runtime - Minimal production image
53# ==============================================================================
54FROM nvidia/cuda:11.8.0-cudnn8-runtime-ubuntu22.04 AS runtime
55 
56# Labels
57LABEL maintainer="Generated by DockerFit"
58LABEL version="2.4.1"
59LABEL description="PYTORCH 2.4.1 + CUDA 11.8"
60 
61# Environment variables
62ENV PYTHONUNBUFFERED=1
63ENV PYTHONDONTWRITEBYTECODE=1
64ENV NVIDIA_VISIBLE_DEVICES=all
65ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
66 
67# Install Python 3.11 runtime from deadsnakes PPA (Ubuntu 22.04)
68RUN apt-get update && apt-get install -y --no-install-recommends \
69    software-properties-common \
70    && add-apt-repository -y ppa:deadsnakes/ppa \
71    && apt-get update && apt-get install -y --no-install-recommends \
72    python3.11 \
73    libgomp1
74    && apt-get remove -y software-properties-common \
75    && apt-get autoremove -y \
76    && rm -rf /var/lib/apt/lists/*
77 
78# Create non-root user for security
79ARG USERNAME=appuser
80ARG USER_UID=1000
81ARG USER_GID=$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 2.4.1 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 2.4.1 supports Python versions: 3.8, 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.