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Common Error

Fix: Docker GPU Not Found

Resolve the "could not select device driver" or "nvidia-container-cli" error when running Docker with GPU

Error Message

docker: Error response from daemon: could not select device driver "" with capabilities: [[gpu]]

or

nvidia-container-cli: initialization error

or

torch.cuda.is_available() returns False

Root Cause

This error occurs when Docker cannot access the host GPU. Common causes:

  • NVIDIA Container Toolkit not installed - Required for GPU passthrough
  • Docker daemon not configured - Missing nvidia runtime configuration
  • Missing --gpus flag - Container launched without GPU access
  • Driver not loaded - NVIDIA kernel module not active

Solution

Step 1: Verify NVIDIA Driver

nvidia-smi

Should show your GPU and driver version. If not, install NVIDIA drivers first.

Step 2: Install NVIDIA Container Toolkit

# Add repository

curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg

curl -s -L https://nvidia.github.io/libnvidia-container/stable/deb/nvidia-container-toolkit.list | \

sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \

sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list

# Install toolkit

sudo apt-get update

sudo apt-get install -y nvidia-container-toolkit

Step 3: Configure Docker

sudo nvidia-ctk runtime configure --runtime=docker

sudo systemctl restart docker

Step 4: Test GPU Access

docker run --rm --gpus all nvidia/cuda:12.1.1-base-ubuntu22.04 nvidia-smi

Step 5: Run Your Container

Always use the --gpus all flag:

docker run --gpus all your-image python -c "import torch; print(torch.cuda.is_available())"

Quick Checklist

  • nvidia-smi works on host
  • nvidia-container-toolkit installed
  • Docker daemon restarted after toolkit install
  • Using --gpus all or --gpus "device=0"

Generate GPU-Ready Dockerfile

Configuration

Local GPU or CPU environment

2025推荐，Blackwell(10.0)原生支持，官方cu128编译包

Requires NVIDIA Driver >=570.26.00
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 12.8 | 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:12.8.0-cudnn-devel-ubuntu24.04 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;10.0"
20 
21# Install Python 3.11 from deadsnakes PPA (Ubuntu 24.04)
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:$PATH"
37 
38# Upgrade pip
39RUN pip install --no-cache-dir --upgrade pip setuptools wheel
40 
41# Install PyTorch with BuildKit cache
42RUN --mount=type=cache,target=/root/.cache/pip \
43    pip install torch torchvision torchaudio \
44    --index-url https://download.pytorch.org/whl/cu128
45 
46# Install project dependencies
47COPY requirements.txt .
48RUN --mount=type=cache,target=/root/.cache/pip \
49    pip install -r requirements.txt
50 
51# ==============================================================================
52# Stage 2: Runtime - Minimal production image
53# ==============================================================================
54FROM nvidia/cuda:12.8.0-cudnn-runtime-ubuntu24.04 AS runtime
55 
56# Labels
57LABEL maintainer="Generated by DockerFit"
58LABEL version="2.9.1"
59LABEL description="PYTORCH 2.9.1 + CUDA 12.8"
60 
61# Environment variables
62ENV PYTHONUNBUFFERED=1
63ENV PYTHONDONTWRITEBYTECODE=1
64ENV NVIDIA_VISIBLE_DEVICES=all
65ENV NVIDIA_DRIVER_CAPABILITIES=compute,utility
66 
67# Install Python 3.11 runtime from deadsnakes PPA (Ubuntu 24.04)
68RUN apt-get update && apt-get install -y --no-install-recommends \
69    software-properties-common \
70    && add-apt-repository -y ppa:deadsnakes/ppa \
71    && apt-get update && apt-get install -y --no-install-recommends \
72    python3.11 \
73    libgomp1
74    && apt-get remove -y software-properties-common \
75    && apt-get autoremove -y \
76    && rm -rf /var/lib/apt/lists/*
77 
78# Create non-root user for security
79ARG USERNAME=appuser
80ARG USER_UID=1000
81ARG USER_GID=$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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Related Issues

PyTorch CUDA Mismatch

PyTorch and CUDA version incompatibility

CUDA Out of Memory

Model or batch size too large for available VRAM