Introduction

Running TensorFlow in Docker on Windows fails most commonly due to WSL 2 backend issues, GPU driver mismatches, or Docker configuration problems. Docker Desktop for Windows uses WSL 2 to run Linux containers, and TensorFlow's GPU support requires the NVIDIA Container Toolkit with specific driver versions. This guide covers the most frequent errors and their fixes.

Prerequisites Checklist

Before troubleshooting, verify these requirements:

1. Docker Desktop installed with WSL 2 backend enabled
2. WSL 2 installed (not WSL 1) — check with wsl --list --verbose
3. NVIDIA GPU drivers updated (for GPU containers)
4. NVIDIA Container Toolkit installed inside WSL 2

1# Check WSL version
2wsl --list --verbose
3
4# Expected output shows VERSION 2
5#   NAME      STATE    VERSION
6#   Ubuntu    Running  2

Fix 1: Ensure WSL 2 Backend Is Active

Docker Desktop requires WSL 2. If you are running WSL 1, TensorFlow containers fail to start.

1# Upgrade a distribution from WSL 1 to WSL 2
2wsl --set-version Ubuntu 2
3
4# Set WSL 2 as default for new distributions
5wsl --set-default-version 2

Then restart Docker Desktop and verify it shows "WSL 2 based engine" in Settings > General.

Fix 2: Run the Correct TensorFlow Image

TensorFlow provides separate images for CPU and GPU.

1# CPU-only (works without NVIDIA drivers)
2docker run -it tensorflow/tensorflow:latest-jupyter
3
4# GPU (requires NVIDIA Container Toolkit)
5docker run --gpus all -it tensorflow/tensorflow:latest-gpu bash

If you do not have an NVIDIA GPU, use the CPU image. Attempting to run the GPU image without proper drivers produces errors like Failed to initialize NVML or no CUDA-capable device is detected.

Fix 3: Install NVIDIA Container Toolkit in WSL 2

For GPU support, install the NVIDIA Container Toolkit inside your WSL 2 distribution (not in Windows).

1# Inside WSL 2 (Ubuntu)
2distribution=$(. /etc/os-release; echo $ID$VERSION_ID)
3curl -fsSL https://nvidia.github.io/libnvidia-container/gpgkey | sudo gpg --dearmor -o /usr/share/keyrings/nvidia-container-toolkit-keyring.gpg
4curl -s -L https://nvidia.github.io/libnvidia-container/$distribution/libnvidia-container.list | \
5    sed 's#deb https://#deb [signed-by=/usr/share/keyrings/nvidia-container-toolkit-keyring.gpg] https://#g' | \
6    sudo tee /etc/apt/sources.list.d/nvidia-container-toolkit.list
7
8sudo apt-get update
9sudo apt-get install -y nvidia-container-toolkit
10sudo nvidia-ctk runtime configure --runtime=docker

Restart Docker Desktop after installation.

Fix 4: Verify GPU Access Inside Container

Test that the container can see the GPU before running TensorFlow.

docker run --gpus all nvidia/cuda:12.2.0-base-ubuntu22.04 nvidia-smi

If nvidia-smi works, the GPU passthrough is functioning. Then test TensorFlow:

1docker run --gpus all -it tensorflow/tensorflow:latest-gpu python3 -c "
2import tensorflow as tf
3print('TensorFlow version:', tf.__version__)
4print('GPU available:', tf.config.list_physical_devices('GPU'))
5"

Fix 5: Memory and Resource Limits

TensorFlow containers need sufficient memory. Docker Desktop defaults to 2 GB RAM, which may be too low.

In Docker Desktop: Settings > Resources > WSL Integration, increase memory to at least 4 GB (8 GB recommended for training).

Alternatively, create or edit ~/.wslconfig in Windows:

[wsl2]
memory=8GB
processors=4

Then restart WSL with wsl --shutdown and reopen Docker Desktop.

Fix 6: Resolve Port and Volume Mounting Issues

Common Docker run options for TensorFlow with Jupyter:

docker run -it -p 8888:8888 \
    -v /mnt/c/Users/myuser/projects:/tf/notebooks \
    tensorflow/tensorflow:latest-jupyter

On Windows, mount paths use /mnt/c/ format inside WSL 2. If volume mounting fails, enable the WSL 2 file sharing integration in Docker Desktop settings.

Common Pitfalls

• Running Docker with WSL 1 backend instead of WSL 2 — Linux containers fail or run with limited functionality on WSL 1.
• Using the GPU TensorFlow image without NVIDIA drivers or the Container Toolkit — the container starts but TensorFlow cannot detect any GPU.
• Not restarting Docker Desktop after installing the NVIDIA Container Toolkit — Docker must reload its runtime configuration.
• Allocating too little RAM to the WSL 2 VM — TensorFlow's memory allocation fails silently or throws cryptic errors.
• Forgetting the --gpus all flag in docker run — without it, the GPU is not passed through to the container even if drivers are correctly installed.

Summary

• Use WSL 2 backend for Docker Desktop on Windows — verify with wsl --list --verbose.
• Choose the correct TensorFlow Docker image (CPU vs GPU) based on your hardware.
• Install the NVIDIA Container Toolkit inside WSL 2 for GPU support, and always use --gpus all.
• Increase Docker Desktop memory allocation to at least 4-8 GB for TensorFlow workloads.
• Test GPU access with nvidia-smi inside a container before running TensorFlow.