How to Fix CUDA Error 209 in Kohya SS LoRA Training on Windows 11

Understanding CUDA Error 209: What It Means and Why It Happens

CUDA error 209 specifically means "no kernel image is available for execution on the device." In simpler terms, your GPU cannot find or run the necessary code (kernel) it needs to perform the requested operations. This error is particularly common when working with AI training frameworks like Kohya SS for LoRA training.

Common Causes of CUDA Error 209

Before diving into specific solutions, it's important to understand that this error often occurs because Kohya SS is trying to use CUDA functions that were compiled for a different GPU architecture than what you have installed. This mismatch is the root cause we need to address.

Prerequisites: What You'll Need to Fix CUDA Error 209

Supported NVIDIA GPUs for Kohya SS

Minimum Software Requirements

Solution 1: Verify Your GPU and CUDA Configuration

The first step in resolving CUDA error 209 is to verify your current GPU and CUDA configuration. This will help identify mismatches between your hardware, drivers, and software that might be causing the error.

Check Your GPU Model and Driver Version

Open Command Prompt or PowerShell and run the following command to check your GPU model and driver version:

This command will display your GPU model, driver version, and current CUDA version supported by the driver. Make note of these details as you'll need them for later steps.

Verify CUDA Toolkit Installation

Next, check which CUDA toolkit version is installed on your system by running:

If this command isn't recognized, it means the CUDA toolkit isn't properly installed or isn't in your system PATH. In that case, you'll need to install or reinstall the CUDA toolkit.

Check PyTorch CUDA Compatibility

To verify if PyTorch is correctly using CUDA, open a Python prompt and run:

The first line should return True if PyTorch can access your GPU. The second line shows which CUDA version PyTorch was built with, and the third line displays your GPU model name.

Solution 2: Update NVIDIA Drivers and CUDA Toolkit

Outdated or mismatched drivers and CUDA toolkit versions are the most common causes of CUDA error 209. In this section, we'll update both components to ensure compatibility.

Update NVIDIA GPU Drivers

Install the Correct CUDA Toolkit Version

For Kohya SS LoRA training on Windows 11, CUDA 11.8 is generally the most stable version, though newer versions like CUDA 12.1 may work with the latest PyTorch builds.

Verify PATH Environment Variables

After installation, verify that your system PATH includes the correct CUDA directories:

Solution 3: Install the Correct PyTorch Version

The PyTorch version you're using must be compatible with your CUDA toolkit. Mismatches between these versions are a common cause of CUDA error 209 in Kohya SS LoRA training.

Uninstall Existing PyTorch

First, remove any existing PyTorch installations to avoid conflicts:

Install PyTorch with Matching CUDA Version

Visit the PyTorch installation page and use the selector to generate the correct installation command for your CUDA version. For example, for PyTorch with CUDA 11.8 support:

For CUDA 12.1 support:

Verify PyTorch CUDA Compatibility

After installation, verify that PyTorch can access your GPU and is using the correct CUDA version:

This should return True followed by your CUDA version and GPU name. If it returns False, there's still an issue with your PyTorch CUDA configuration.

Solution 4: Modify Kohya SS Configuration

If you've updated your drivers, CUDA toolkit, and PyTorch but still encounter CUDA error 209, you may need to modify the Kohya SS configuration to match your GPU architecture.

Identify Your GPU's Compute Capability

Each NVIDIA GPU has a specific compute capability version. You can find yours in the NVIDIA CUDA GPUs list or by running:

Make note of your GPU's compute capability (e.g., 8.6 for RTX 3080, 8.9 for RTX 4090).

Modify the Makefile or Build Configuration

In Kohya SS, you may need to modify the CUDA architecture settings in the build configuration. Look for a file named Makefile or similar in the Kohya SS directory.

Or:

Rebuild or Reinstall Kohya SS

After modifying the configuration, you'll need to rebuild or reinstall Kohya SS:

Or if using a Python package:

Solution 5: Adjust Training Parameters

If you're still encountering CUDA error 209, the issue might be related to your training parameters. Adjusting batch size, resolution, and other settings can help avoid GPU memory issues that trigger this error.

Reduce Batch Size

A common cause of CUDA errors is setting a batch size that's too large for your GPU's VRAM. Try reducing your batch size in the training configuration:

Lower Training Resolution

Higher resolutions require more VRAM. Try reducing your training resolution:

Disable Gradient Checkpointing

While gradient checkpointing saves memory, it can sometimes cause CUDA errors. Try disabling it in your training configuration:

Adjust Optimizer Settings

Some optimizers require more GPU memory. Try switching to a memory-efficient optimizer:

Or:

Solution 6: Windows 11 Specific Optimizations

Windows 11 has some specific settings that can affect CUDA performance and stability. Adjusting these settings can help resolve CUDA error 209.

Modify TDR Delay Settings

Windows has a feature called Timeout Detection and Recovery (TDR) that automatically resets the GPU if it doesn't respond within a certain timeframe. This can interrupt long CUDA operations during training. To increase the timeout:

Disable Hardware-Accelerated GPU Scheduling

Windows 11's hardware-accelerated GPU scheduling can sometimes conflict with CUDA operations:

Set NVIDIA GPU to Maximum Performance

Ensure your NVIDIA GPU is set to maximum performance mode:

Disable Windows Security Real-time Protection Temporarily

Windows Security can sometimes interfere with CUDA operations. Temporarily disabling it during training can help:

Solution 7: Alternative Approaches and Community Solutions

If you've tried all the previous solutions and are still encountering CUDA error 209, here are some alternative approaches and community-sourced solutions that have worked for others.

Use a Different PyTorch Build

Some users have reported success with specific PyTorch builds. Try the following:

Or for newer GPUs:

Try a Docker Container

Using a Docker container with a pre-configured environment can bypass many compatibility issues:

Use WSL2 (Windows Subsystem for Linux)

Some users find better CUDA stability using WSL2 instead of native Windows:

Community Workarounds

Here are some additional workarounds reported by the community:

Preventing Future CUDA Error 209 Occurrences

Once you've resolved the current CUDA error 209, follow these best practices to prevent it from happening again in the future.

Create a Stable Environment

Update Components Carefully

Optimize Training Workflow

Regular Maintenance

Troubleshooting Flowchart: Quick Reference Guide

Use this flowchart as a quick reference guide when troubleshooting CUDA error 209 in Kohya SS LoRA training on Windows 11.

Quick Checklist

Conclusion: Mastering CUDA Error 209 in Kohya SS LoRA Training

CUDA error 209 ("no kernel image is available for execution on the device") can be frustrating when you're trying to train LoRA models with Kohya SS on Windows 11. However, as we've seen in this guide, the error is usually caused by compatibility issues between your GPU, drivers, CUDA toolkit, and PyTorch installation.

By systematically working through the solutions provided—from verifying your configuration and updating components to adjusting training parameters and optimizing Windows 11 settings—you should be able to resolve this error and get your LoRA training running smoothly.

Remember that AI model training is a complex process that pushes your hardware to its limits. Taking the time to set up a stable, compatible environment will save you countless hours of troubleshooting in the long run and allow you to focus on creating amazing AI models instead of fighting with technical errors.

With the knowledge and solutions provided in this guide, you're now equipped to tackle CUDA error 209 and other similar issues that might arise during your AI training journey. Happy training!