Error: fp16 mixed precision requires a GPU; exit status 1

Trying to train on an AMD RX6600XT on arch linux. Xformers is disabled, and in the startup script, when it prompts which GPU I want to use, I've tried entering "all", "[all]", "0", and "1", but none of them make the script recognize my GPU and I get `ValueError: fp16 mixed precision requires a GPU` every time. Any idea of what the issue could be?

The full output is as follows:

```accelerate launch main.py
2023-03-02 17:54:36.961454: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-03-02 17:54:37.454232: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2023-03-02 17:54:37.454271: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2023-03-02 17:54:37.454277: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
2023-03-02 17:54:39.110471: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2023-03-02 17:54:39.580158: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2023-03-02 17:54:39.580198: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2023-03-02 17:54:39.580204: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
WARNING[XFORMERS]: xFormers can't load C++/CUDA extensions. xFormers was built for:
PyTorch 1.13.1+cu117 with CUDA 1107 (you have 2.0.0.dev20230128+cu118)
Python 3.10.9 (you have 3.10.9)
Please reinstall xformers (see https://github.com/facebookresearch/xformers#installing-xformers)
Memory-efficient attention, SwiGLU, sparse and more won't be available.
Set XFORMERS_MORE_DETAILS=1 for more details
/home/steve/Desktop/stable-diffusion-webui/LoRA_Easy_Training_Scripts/training_image_folder/image_dir/
10_training
Created a txt file named last.txt in the output folder
prepare tokenizer
update token length: 150
Use DreamBooth method.
prepare train images.
found directory 10_training contains 25 image files
250 train images with repeating.
loading image sizes.
100%|█████████████████████████████| 25/25 [00:00<00:00, 7339.89it/s]
make buckets
number of images (including repeats) / 各bucketの画像枚数（繰り返し回数を含む）bucket 0: resolution (448, 576), count: 20
bucket 1: resolution (512, 512), count: 90
bucket 2: resolution (576, 448), count: 80
bucket 3: resolution (640, 384), count: 60
mean ar error (without repeats): 0.07514270473459406
prepare accelerator
╭─────────────── Traceback (most recent call last) ────────────────╮
│ /home/steve/Desktop/stable-diffusion-webui/LoRA_Easy_Training_Sc │
│ ripts/main.py:143 in │
│ │
│ 140 │
│ 141 │
│ 142 if __name__ == "__main__": │
│ ❱ 143 │ main() │
│ 144 │
│ │
│ /home/steve/Desktop/stable-diffusion-webui/LoRA_Easy_Training_Sc │
│ ripts/main.py:80 in main │
│ │
│ 77 │ │
│ 78 │ args = parser.create_args(ArgStore.change_dict_to_inte │
│ 79 │ # print(args) │
│ ❱ 80 │ train_network.train(args) │
│ 81 │
│ 82 │
│ 83 def ensure_file_paths(args: dict) -> None: │
│ │
│ /home/steve/Desktop/stable-diffusion-webui/LoRA_Easy_Training_Sc │
│ ripts/sd_scripts/train_network.py:90 in train │
│ │
│ 87 │
│ 88 # acceleratorを準備する │
│ 89 print("prepare accelerator") │
│ ❱ 90 accelerator, unwrap_model = train_util.prepare_accelerat │
│ 91 │
│ 92 # mixed precisionに対応した型を用意しておき適宜castする │
│ 93 weight_dtype, save_dtype = train_util.prepare_dtype(args │
│ │
│ /home/steve/.local/lib/python3.10/site-packages/library/train_ut │
│ il.py:1817 in prepare_accelerator │
│ │
│ 1814 │ log_prefix = "" if args.log_prefix is None else args. │
│ 1815 │ logging_dir = args.logging_dir + "/" + log_prefix + t │
│ 1816 │
│ ❱ 1817 accelerator = Accelerator(gradient_accumulation_steps=a │
│ 1818 │ │ │ │ │ │ │ log_with=log_with, logging_di │
│ 1819 │
│ 1820 # accelerateの互換性問題を解決する │
│ │
│ /home/steve/.local/lib/python3.10/site-packages/accelerate/accel │
│ erator.py:355 in __init__ │
│ │
│ 352 │ │ if self.state.mixed_precision == "fp16" and self. │
│ 353 │ │ │ self.native_amp = True │
│ 354 │ │ │ if not torch.cuda.is_available() and not pars │
│ ❱ 355 │ │ │ │ raise ValueError(err.format(mode="fp16", │
│ 356 │ │ │ kwargs = self.scaler_handler.to_kwargs() if s │
│ 357 │ │ │ if self.distributed_type == DistributedType.F │
│ 358 │ │ │ │ from torch.distributed.fsdp.sharded_grad_ │
╰──────────────────────────────────────────────────────────────────╯
ValueError: fp16 mixed precision requires a GPU
╭─────────────── Traceback (most recent call last) ────────────────╮
│ /home/steve/.local/bin/accelerate:8 in │
│ │
│ 5 from accelerate.commands.accelerate_cli import main │
│ 6 if __name__ == '__main__': │
│ 7 │ sys.argv[0] = re.sub(r'(-script\.pyw|\.exe)?$', '', sys. │
│ ❱ 8 │ sys.exit(main()) │
│ 9 │
│ │
│ /home/steve/.local/lib/python3.10/site-packages/accelerate/comma │
│ nds/accelerate_cli.py:45 in main │
│ │
│ 42 │ │ exit(1) │
│ 43 │ │
│ 44 │ # Run │
│ ❱ 45 │ args.func(args) │
│ 46 │
│ 47 │
│ 48 if __name__ == "__main__": │
│ │
│ /home/steve/.local/lib/python3.10/site-packages/accelerate/comma │
│ nds/launch.py:1104 in launch_command │
│ │
│ 1101 │ elif defaults is not None and defaults.compute_enviro │
│ 1102 │ │ sagemaker_launcher(defaults, args) │
│ 1103 │ else: │
│ ❱ 1104 │ │ simple_launcher(args) │
│ 1105 │
│ 1106 │
│ 1107 def main(): │
│ │
│ /home/steve/.local/lib/python3.10/site-packages/accelerate/comma │
│ nds/launch.py:567 in simple_launcher │
│ │
│ 564 │ process = subprocess.Popen(cmd, env=current_env) │
│ 565 │ process.wait() │
│ 566 │ if process.returncode != 0: │
│ ❱ 567 │ │ raise subprocess.CalledProcessError(returncode=pr │
│ 568 │
│ 569 │
│ 570 def multi_gpu_launcher(args): │
╰──────────────────────────────────────────────────────────────────╯
CalledProcessError: Command '['/usr/bin/python3', 'main.py']'
returned non-zero exit status 1.

Start with the traceback in main.py and sd_scripts/train_network.py, then inspect library/train_util.py around prepare_accelerator and the Accelerate initialization. Reproduce the failure with the reported AMD RX6600XT environment and check the GPU availability reported by the involved Python packages; done means identifying the cause and documenting or validating a supported resolution.

Written by the indexing model from the issue text.