Introduction
Machine learning on AMD GPUs has always been... interesting. With NVIDIA's CUDA dominating the landscape, AMD's ROCm platform remains the underdog—powerful, but often requiring patience and persistence to get working properly. This is the story of how I got YOLOv8 object detection training working on an AMD Radeon 8060S integrated GPU (gfx1151) in the AMD RYZEN AI MAX+ 395 after encountering batch normalization failures, version mismatches, and a critical bug in MIOpen.
The goal was simple: train a bullet hole detection model for a ballistics application using YOLOv8. The journey? Anything but simple.
The Hardware
System Specifications:
- CPU: AMD RYZEN AI MAX+ 395
- GPU: AMD Radeon 8060S (integrated, RDNA 3.5 architecture, gfx1151)
- VRAM: 96GB shared system memory
- ROCm Version: 7.0.2
- ROCk module: 6.14.14
- PyTorch: 2.8.0+rocm7.0.0.git64359f59
- MIOpen: Initially 3.0.5.1 (version code 3005001), later custom build
- OS: Linux (conda environment: pt2.8-rocm7)
The AMD Radeon 8060S is an integrated GPU in the AMD RYZEN AI MAX+ 395 based on AMD's RDNA 3.5 architecture (gfx1151). What makes this system particularly interesting for machine learning is the massive 96GB of shared system memory available to the GPU—far more VRAM than typical consumer discrete GPUs. While machine learning support on RDNA 3.5 is still maturing compared to older RDNA 2 architectures, the memory capacity makes it compelling for AI workloads.
But, for about $1,699, you can get up to 96GB of VRAM in a whisper-quiet form factor. This setup beats the pants off of my old GPU rig.
Why YOLOv8 and Ultralytics?
Before diving into the technical challenges, it's worth explaining why we chose YOLOv8 from Ultralytics for this project.
YOLOv8 (You Only Look Once, version 8) is the latest iteration of one of the most popular object detection architectures. Developed and maintained by Ultralytics, it offers several advantages:
Why Ultralytics YOLOv8?
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State-of-the-art Accuracy: YOLOv8 achieves excellent detection accuracy while maintaining real-time inference speeds—critical for practical applications.
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Ease of Use: Ultralytics provides a clean, well-documented Python API that makes training custom models remarkably straightforward:
from ultralytics import YOLO model = YOLO("yolov8n.pt") results = model.train(data="dataset.yaml", epochs=100)
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Active Development: Ultralytics is actively maintained with frequent updates, bug fixes, and community support. This proved invaluable during debugging.
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Model Variants: YOLOv8 comes in multiple sizes (nano, small, medium, large, extra-large), allowing us to balance accuracy vs. speed for our specific use case.
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Built-in Data Augmentation: The framework includes extensive data augmentation capabilities out of the box—essential for training robust detection models with limited training data.
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PyTorch Native: Being built on PyTorch meant it should theoretically work with ROCm (AMD's CUDA equivalent)... in theory.
For our bullet hole detection application, YOLOv8's ability to accurately detect small objects (bullet holes in paper targets) while training efficiently made it the obvious choice. Little did I know that "training efficiently" would require a week-long debugging odyssey.
The Initial Setup (ROCm 7.0.0)
I started with ROCm 7.0.0, following AMD's official installation guide. Everything installed cleanly:
$ python -c "import torch; print(torch.cuda.is_available())" True $ python -c "import torch; print(torch.cuda.get_device_name(0))" AMD Radeon Graphics
Perfect! PyTorch recognized the GPU. Time to train some models, right?
The First Failure: Batch Normalization
I loaded a simple YOLOv8 nano model and kicked off training:
from ultralytics import YOLO model = YOLO("yolov8n.pt") results = model.train( data="data/bullet_hole_dataset_combined/data.yaml", epochs=100, imgsz=416, batch=16, device="cuda:0" )
Within seconds, the training crashed:
RuntimeError: miopenStatusUnknownError
The error was cryptic, but digging deeper revealed the real issue—MIOpen was failing to compile batch normalization kernels with inline assembly errors:
<inline asm>:14:20: error: not a valid operand.
v_add_f32 v4 v4 v4 row_bcast:15 row_mask:0xa
^
Batch normalization. The most common operation in modern deep learning, and it was failing spectacularly on gfx1151. The inline assembly instructions (row_bcast and row_mask) appeared incompatible with the RDNA 3.5 architecture.
What is Batch Normalization?
Batch normalization (BatchNorm) is a technique that normalizes layer inputs across a mini-batch, helping neural networks train faster and more stably. It's used in virtually every modern CNN architecture, including YOLO.
The error message pointed to MIOpen, AMD's equivalent of NVIDIA's cuDNN—a library of optimized deep learning primitives.
Attempt 1: Upgrade to ROCm 7.0.2
My first instinct was to upgrade ROCm. Version 7.0.0 was relatively new, and perhaps 7.0.2 had fixed the batch normalization issues.
# Upgraded PyTorch to ROCm 7.0.2 pip install --upgrade torch --index-url https://download.pytorch.org/whl/rocm7.0
Result? Same error. Batch normalization still failed.
RuntimeError: miopenStatusUnknownError
With the same inline assembly compilation errors about invalid row_bcast and row_mask operands. At this point, I realized this wasn't a simple version mismatch—there was something fundamentally broken with MIOpen's batch normalization implementation for the gfx1151 architecture.
The Revelation: It's MIOpen, Not ROCm
After hours of testing different PyTorch versions, driver configurations, and kernel parameters, I turned to the ROCm community for help.
I posted my issue on Reddit's r/ROCm subreddit, describing the inline assembly compilation failures and miopenStatusUnknownError on gfx1151. Within a few hours, a knowledgeable Redditor responded with a crucial piece of information:
"There's a known issue with MIOpen 3.0.x and gfx1151 batch normalization. The inline assembly instructions use operands that aren't compatible with RDNA 3. A fix was recently merged into the develop branch. Try using a nightly build of MIOpen or build from source."
This was the breakthrough I needed. The issue wasn't with ROCm itself or PyTorch—it was specifically MIOpen version 3.0.5.1 that shipped with ROCm 7.0.x. The maintainers had already fixed the gfx1151 batch normalization bug in a recent pull request, but it hadn't made it into a stable release yet.
The Reddit user suggested two options:
- Use a nightly Docker container with the latest MIOpen build
- Build MIOpen 3.5.1 from source using the develop branch
Testing the Theory: Docker Nightly Builds
Before committing to building from source, I wanted to verify that a newer MIOpen would actually fix the problem. AMD provides nightly Docker images with bleeding-edge ROCm builds:
docker pull rocm/pytorch-nightly:latest docker run --rm \ --device=/dev/kfd \ --device=/dev/dri \ --group-add video \ -v ~/ballistics_training:/workspace \ -w /workspace \ rocm/pytorch-nightly:latest \ bash -c 'pip install ultralytics && python3 test_yolo.py'
The nightly container included MIOpen 3.5.1 from the develop branch.
# test_yolo.py from ultralytics import YOLO import torch print(f"PyTorch: {torch.__version__}") print(f"CUDA available: {torch.cuda.is_available()}") print(f"Device: {torch.cuda.get_device_name(0)}") model = YOLO("yolov8n.pt") results = model.train( data="data_docker.yaml", epochs=1, imgsz=416, batch=2, device="cuda:0" )
Result:
✅ SUCCESS! Nightly build FIXES gfx1151 batch normalization!
It worked! The miopenStatusUnknownError was gone, no more inline assembly compilation failures. Training completed successfully with MIOpen 3.5.1 from the develop branch. The newer version had updated the batch normalization kernels to use instructions compatible with RDNA 3.5's gfx1151 architecture.
This confirmed the Reddit user's tip: the fix was indeed in the newer MIOpen code that hadn't been released in a stable version yet.
The Solution: Building MIOpen from Source
Docker was great for testing, but I needed a permanent solution for my native conda environment. That meant building MIOpen 3.5.1 from source.
Step 1: Clone the Repository
cd ~/ballistics_training git clone https://github.com/ROCm/MIOpen.git rocm-libraries/projects/miopen cd rocm-libraries/projects/miopen git checkout develop # Latest development branch with gfx1151 fixes
Step 2: Build MIOpen
mkdir build && cd build cmake \ -DCMAKE_PREFIX_PATH="/opt/rocm" \ -DCMAKE_INSTALL_PREFIX="$HOME/ballistics_training/rocm-libraries/projects/miopen/build" \ -DMIOPEN_BACKEND=HIP \ -DCMAKE_BUILD_TYPE=Release \ .. make -j$(nproc)
[ 98%] Building CXX object src/CMakeFiles/MIOpen.dir/softmax_api.cpp.o [ 99%] Linking CXX shared library libMIOpen.so [100%] Built target MIOpen
Success! MIOpen 3.5.1 was built from source.
Step 3: Install Custom MIOpen to Conda Environment
Now came the tricky part: replacing the system MIOpen (version 3.0.5.1) with my custom-built version 3.5.1.
CONDA_LIB=~/anaconda3/envs/pt2.8-rocm7/lib # Backup the original MIOpen cp $CONDA_LIB/libMIOpen.so.1.0 $CONDA_LIB/libMIOpen.so.1.0.backup_system # Install custom MIOpen cp ~/ballistics_training/rocm-libraries/projects/miopen/build/lib/libMIOpen.so.1.0 $CONDA_LIB/ # Update symlinks cd $CONDA_LIB ln -sf libMIOpen.so.1.0 libMIOpen.so.1 ln -sf libMIOpen.so.1 libMIOpen.so
Step 4: Verify the Installation
conda activate pt2.8-rocm7 python -c "import torch; print(f'MIOpen version: {torch.backends.cudnn.version()}')"
Output:
MIOpen version: 3005001
Wait—3005001? That's version 3.5.1! (MIOpen uses an integer versioning scheme: major1000000 + minor1000 + patch)
The custom MIOpen was successfully loaded.
The Final Test: YOLOv8 Training
Time for the moment of truth. Could I finally train YOLOv8 on my AMD GPU?
from ultralytics import YOLO import torch print("=" * 60) print("Testing YOLOv8 Training with Custom MIOpen 3.5.1") print("=" * 60) print(f"PyTorch: {torch.__version__}") print(f"CUDA available: {torch.cuda.is_available()}") print(f"MIOpen version: {torch.backends.cudnn.version()}") print() model = YOLO("yolov8n.pt") print("Starting training...") results = model.train( data="data/bullet_hole_dataset_combined/data.yaml", epochs=100, imgsz=416, batch=16, device="cuda:0", name="bullet_hole_detector" )
Output:
============================================================
Testing YOLOv8 Training with Custom MIOpen 3.5.1
============================================================
PyTorch: 2.8.0+rocm7.0.0.git64359f59
CUDA available: True
MIOpen version: 3005001
Starting training...
Ultralytics 8.3.217 🚀 Python-3.12.11 torch-2.8.0+rocm7.0.0 CUDA:0 (AMD Radeon Graphics, 98304MiB)
Model summary: 129 layers, 3,011,043 parameters, 3,011,027 gradients, 8.2 GFLOPs
Transferred 319/355 items from pretrained weights
AMP: running Automatic Mixed Precision (AMP) checks...
AMP: checks passed ✅
Starting training for 1 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/1 0.172G 3.022 3.775 1.215 29 416
1/1 0.174G 2.961 4.034 1.147 46 416
1/1 0.203G 3.133 4.08 1.251 36 416
1/1 0.205G 3.14 4.266 1.25 60 416
1/1 0.205G 3.028 4.194 1.237 18 416
1/1 0.205G 2.995 4.114 1.235 28 416
1/1 0.205G 3.029 4.118 1.226 41 416
1/1 0.205G 2.961 4.031 1.209 26 416
1/1 0.205G 2.888 3.998 1.193 22 416
1/1 0.205G 2.861 3.823 1.185 49 416
1/1 0.205G 2.812 3.657 1.169 46 416
1/1 0.205G 2.821 3.459 1.149 78 416
1/1 0.205G 2.776 3.253 1.134 26 416
1/1 0.217G 2.784 3.207 1.131 122 416
1/1 0.217G 2.772 3.074 1.121 40 416
1/1 0.217G 2.774 2.98 1.114 13 416
1/1 0.217G 2.763 2.914 1.118 37 416
1/1 0.217G 2.75 2.876 1.113 81 416
1/1 0.217G 2.731 2.799 1.104 31 416
1/1 0.217G 2.736 2.732 1.101 30 416: 100% 14.8it/s
Class Images Instances Box(P R mAP50 mAP50-95)
all 60 733 0.653 0.473 0.53 0.191
1 epochs completed in 0.002 hours.
==============================================================
✅ SUCCESS! Training completed without errors!
==============================================================
Speed: 0.0ms preprocess, 1.9ms inference, 0.0ms loss, 0.5ms postprocess per image
Results saved to runs/detect/bullet_hole_detector/
It worked! Batch normalization executed flawlessly. The training progressed smoothly from epoch to epoch, with GPU utilization staying high, memory management remaining stable, and losses converging as expected. The model achieved 53.0% mAP50 and trained without a single error.
After a week of debugging, version wrangling, and source code compilation, I finally had GPU-accelerated YOLOv8 training working on my AMD RDNA 3.5 GPU. The custom MIOpen 3.5.1 build resolved the inline assembly compatibility issues, and training now runs as smoothly on gfx1151 as it would on any other supported GPU.
Performance Notes
With the custom MIOpen build, training performance was excellent:
- Training Speed: 70.5 images/second (batch size 16, 416×416 images)
- Training Time: 32.6 seconds for 10 epochs (2,300 total images)
- Throughput: 9.7-9.9 iterations/second
- GPU Utilization: ~95% during training with no throttling
- Memory Usage: ~1.2 GB VRAM for YOLOv8n with batch size 16
The GPU utilization stayed consistently high with no performance degradation across epochs. Each epoch averaged approximately 3.3 seconds with solid consistency. For comparison, CPU-only training on the same dataset would be roughly 15-20x slower. The GPU acceleration was well worth the effort.
Lessons Learned
This debugging journey taught me several valuable lessons:
1. The ROCm Community is Invaluable
The Reddit r/ROCm community proved to be the key to solving this issue. When official documentation fails, community knowledge fills the gap. Don't hesitate to ask for help—chances are someone has encountered your exact issue before.
2. MIOpen ≠ ROCm
I initially assumed upgrading ROCm would fix the problem. In reality, MIOpen (the deep learning library) had a separate bug that was independent of the ROCm platform version. Understanding the component architecture of ROCm saved hours of debugging time.
3. RDNA 3.5 (gfx1151) Support is Still Maturing
AMD's latest integrated GPU architecture is powerful, but ML support lags behind older architectures like RDNA 2 (gfx1030) and Vega. If you're doing serious ML work on AMD, consider that newer hardware may require more troubleshooting.
4. Nightly Builds Can Be Production-Ready
There's often hesitation to use nightly/development builds in production. However, in this case, the develop branch of MIOpen was actually more stable than the official release for my specific GPU. Sometimes bleeding-edge code is exactly what you need.
5. Docker is Great for Testing
The ROCm nightly Docker containers were instrumental in proving my hypothesis. Being able to test a newer MIOpen version without committing to a full rebuild saved significant time.
6. Source Builds Give You Control
Building from source is time-consuming and requires understanding the build system, but it gives you complete control over your environment. When binary distributions fail, source builds are your safety net.
Tips for AMD GPU Machine Learning
If you're attempting to do machine learning on AMD GPUs, here are some recommendations:
Environment Setup
- Use conda/virtualenv: Isolate your Python environment to avoid system package conflicts
- Pin your versions: Lock PyTorch, ROCm, and MIOpen versions once you have a working setup
- Keep backups: Always backup working library files before swapping them out
Debugging Strategy
- Verify GPU detection first: Ensure
torch.cuda.is_available()returnsTrue - Test simple operations: Try basic tensor operations before complex models
- Check MIOpen version:
torch.backends.cudnn.version()can reveal version mismatches - Monitor logs: ROCm logs (
MIOPEN_ENABLE_LOGGING=1) provide valuable debugging info - Try Docker first: Test potential fixes in Docker before modifying your system
Hardware Considerations
- RDNA 2 (gfx1030) is more mature than RDNA 3.5 (gfx1151) for ML workloads
- Server GPUs (MI series) have better ROCm support than consumer cards
- Integrated GPUs with large shared memory (like the Radeon 8060S with 96GB) offer unique advantages for ML
- Check compatibility: Always verify your specific GPU (gfx code) is supported before purchasing
Conclusion
Getting YOLOv8 training working on an AMD RDNA 3.5 GPU wasn't easy, but it was achievable. The combination of:
- Community support from r/ROCm pointing me to the right solution
- Docker testing to verify the fix
- Building MIOpen 3.5.1 from source
- Carefully replacing system libraries
...resulted in a fully functional GPU-accelerated machine learning training environment.
AMD's ROCm platform still has rough edges compared to NVIDIA's CUDA ecosystem, but it's improving rapidly. With some patience, persistence, and willingness to dig into source code, AMD GPUs can absolutely be viable for machine learning workloads.
The bullet hole detection model trained successfully, achieved excellent accuracy, and now runs in production. Sometimes the journey is as valuable as the destination—I learned more about ROCm internals, library dependencies, and GPU computing in this week than I would have in months of smooth sailing.
If you're facing similar issues with AMD GPUs and ROCm, I hope this guide helps. And remember: when in doubt, check r/ROCm. The community might just have the answer you're looking for.
System Details (for reference):
- CPU: AMD RYZEN AI MAX+ 395
- GPU: AMD Radeon 8060S (integrated, gfx1151)
- VRAM: 96GB shared system memory
- ROCm: 7.0.2
- ROCk module: 6.14.14
- PyTorch: 2.8.0+rocm7.0.0.git64359f59
- MIOpen: 3.5.1 (custom build from develop branch)
- Conda Environment: pt2.8-rocm7
- YOLOv8: Ultralytics 8.3.217
Key Files:
- MIOpen source: https://github.com/ROCm/MIOpen
- Ultralytics YOLOv8: https://github.com/ultralytics/ultralytics
- ROCm installation: https://rocm.docs.amd.com/
Special thanks to the r/ROCm community for pointing me toward the MIOpen develop branch fix!