0.太长不看版
radeon云打开ultralytics镜像，并执行yolo train model=yolov8n.pt data=coco.yaml epochs=100 imgsz=640
1.摘要
本文介绍使用radeon云线程的镜像惊醒yolo系列模型的训练测试等任务。
2.创建和打开环境
- 创建环境
- 进入radeon云
- 点击右上角自己的 头像和名字
- 点击
Profile - 在
My Templates选项卡中点击Add Templates - 在弹出小窗中
Container Image中选择ultralytics(ultralytics_yolo26:latest) - 此时在
tile中取一个名字，如ultralytics - 点击
Add Templates以新建模板
- 查看环境
- 此时在
My Templates会出现刚刚新建的环境
- 此时在
- 进入环境
- 点击刚刚新建环境的右侧的蓝色的
launch按钮，启动环境 - 稍等片刻此页面右上角
Active Instance中会出现对应此环境而启动的实例 - 点击蓝色的
Open Notebook在新的页面打开jupyter环境
- 点击刚刚新建环境的右侧的蓝色的
3. 确定依赖和数据集
- 确定依赖
pip list- 注意torch等三者要是rocm版本的：
torch 2.10.0+rocm7.2.4.lw.git3d3aa833torchaudio 2.10.0+rocm7.2.4.git5047768ftorchvision 0.25.0+rocm7.2.4.git82df5f59
- 注意
ultralytics已经安装ultralytics 8.4.60
root@u-1254-152e0e51:/workspace# pip list Package Version ------------------------- ------------------------------- anyio 4.13.0 apex 1.10.0+rocm7.2.4.git751f5dd5 argon2-cffi 25.1.0 argon2-cffi-bindings 25.1.0 arrow 1.4.0 asttokens 3.0.1 async-lru 2.3.0 attrs 26.1.0 babel 2.18.0 beautifulsoup4 4.15.0 bleach 6.4.0 build 1.5.0 certifi 2026.5.20 cffi 2.0.0 charset-normalizer 3.4.7 comm 0.2.3 contourpy 1.3.3 cxxfilt 0.3.0 cycler 0.12.1 debugpy 1.8.21 decorator 5.3.1 defusedxml 0.7.1 executing 2.2.1 fastjsonschema 2.21.2 filelock 3.29.0 flatbuffers 25.12.19 fonttools 4.63.0 fqdn 1.5.1 fsspec 2026.4.0 h11 0.16.0 httpcore 1.0.9 httpx 0.28.1 hypothesis 6.155.0 idna 3.18 iniconfig 2.3.0 ipykernel 7.2.0 ipython 9.14.1 ipython_pygments_lexers 1.1.1 ipywidgets 8.1.8 isoduration 20.11.0 jedi 0.20.0 Jinja2 3.1.6 json5 0.14.0 jsonpointer 3.1.1 jsonschema 4.26.0 jsonschema-specifications 2025.9.1 jupyter 1.1.1 jupyter_client 8.9.1 jupyter-console 6.6.3 jupyter_core 5.9.1 jupyter-events 0.12.1 jupyter-lsp 2.3.1 jupyter_server 2.19.0 jupyter_server_terminals 0.5.4 jupyterlab 4.5.8 jupyterlab_pygments 0.3.0 jupyterlab_server 2.28.0 jupyterlab_widgets 3.0.16 kiwisolver 1.5.0 lark 1.3.1 MarkupSafe 3.0.3 matplotlib 3.10.9 matplotlib-inline 0.2.2 mistune 3.2.1 ml_dtypes 0.5.4 mpmath 1.3.0 nbclient 0.11.0 nbconvert 7.17.1 nbformat 5.10.4 nest-asyncio 1.6.0 networkx 3.6.1 ninja 1.13.0 notebook 7.5.7 notebook_shim 0.2.4 numpy 2.4.6 onnx 1.21.0 onnxruntime-migraphx 1.25.0 opencv-python 4.13.0.92 packaging 26.2 pandas 3.0.3 pandocfilters 1.5.1 parso 0.8.7 pexpect 4.9.0 pillow 12.2.0 pip 26.1.2 platformdirs 4.10.0 pluggy 1.6.0 polars 1.41.2 polars-runtime-32 1.41.2 prometheus_client 0.25.0 prompt_toolkit 3.0.52 protobuf 7.35.0 psutil 7.2.2 ptyprocess 0.7.0 pure_eval 0.2.3 py-cpuinfo 9.0.0 pycparser 3.0 Pygments 2.20.0 pyparsing 3.3.2 pyproject_hooks 1.2.0 pytest 9.0.3 python-dateutil 2.9.0.post0 python-json-logger 4.1.0 PyYAML 6.0.3 pyzmq 27.1.0 referencing 0.37.0 requests 2.34.2 rfc3339-validator 0.1.4 rfc3986-validator 0.1.1 rfc3987-syntax 1.1.0 rpds-py 2026.5.1 scipy 1.17.1 Send2Trash 2.1.0 setuptools 82.0.1 six 1.17.0 sortedcontainers 2.4.0 soupsieve 2.8.4 stack-data 0.6.3 sympy 1.14.0 terminado 0.18.1 tinycss2 1.5.1 torch 2.10.0+rocm7.2.4.lw.git3d3aa833 torchaudio 2.10.0+rocm7.2.4.git5047768f torchvision 0.25.0+rocm7.2.4.git82df5f59 tornado 6.5.7 tqdm 4.67.3 traitlets 5.15.1 triton 3.6.0+rocm7.2.4.git4ed88892 typing_extensions 4.15.0 tzdata 2026.2 ultralytics 8.4.60 ultralytics-thop 2.0.20 uri-template 1.3.0 urllib3 2.7.0 wcwidth 0.8.1 webcolors 25.10.0 webencodings 0.5.1 websocket-client 1.9.0 wheel 0.47.0 widgetsnbextension 4.0.15 root@u-1254-152e0e51:/workspace# - 注意torch等三者要是rocm版本的：
- 转到coco数据集（已经默认下好了）：
cd /datasets/coco/ - 复制
ultra了lytics需要的数据及yml文件:cp /opt/venv/lib/python3.12/site-packages/ultralytics/cfg/datasets/coco.yaml . - 查看数据集：ls /datasets/coco/
root@u-1254-152e0e51:/datasets/coco# ls LICENSE README.txt annotations coco.yaml images labels runs test-dev2017.txt train2017.txt val2017.txt yolov8n.pt
4. 执行训练
cd /datasets/coco/
cp /opt/venv/lib/python3.12/site-packages/ultralytics/cfg/datasets/coco.yaml .
yolo train model=yolov8n.pt data=/datasets/coco epochs=100 imgsz=640
- 训练日志如下
- 此时已经训练到
2/100 epoch
- 此时已经训练到
root@u-1254-152e0e51:/datasets/coco# yolo train model=yolov8n.pt data=/datasets/coco epochs=100 imgsz=640
New https://pypi.org/project/ultralytics/8.4.76 available 😃 Update with 'pip install -U ultralytics'
Ultralytics 8.4.60 🚀 Python-3.12.3 torch-2.10.0+rocm7.2.4.git3d3aa833 CUDA:0 (AMD Radeon Graphics, 49136MiB)
engine/trainer: agnostic_nms=False, amp=True, angle=1.0, augment=False, auto_augment=randaugment, batch=16, bgr=0.0, box=7.5, cache=False, cfg=None, classes=None, close_mosaic=10, cls=0.5, cls_pw=0.0, compile=False, conf=None, copy_paste=0.0, copy_paste_mode=flip, cos_lr=False, cutmix=0.0, data=/datasets/coco, degrees=0.0, deterministic=True, device=None, dfl=1.5, dnn=False, dropout=0.0, dynamic=False, embed=None, end2end=None, epochs=100, erasing=0.4, exist_ok=False, fliplr=0.5, flipud=0.0, format=torchscript, fraction=1.0, freeze=None, half=False, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, imgsz=640, int8=False, iou=0.7, keras=False, kobj=1.0, line_width=None, lr0=0.01, lrf=0.01, mask_ratio=4, max_det=300, mixup=0.0, mode=train, model=yolov8n.pt, momentum=0.937, mosaic=1.0, multi_scale=0.0, name=train, nbs=64, nms=False, opset=None, optimize=False, optimizer=auto, overlap_mask=True, patience=100, perspective=0.0, plots=True, pose=12.0, pretrained=True, profile=False, project=None, rect=False, resume=False, retina_masks=False, rle=1.0, save=True, save_conf=False, save_crop=False, save_dir=/datasets/coco/runs/detect/train, save_frames=False, save_json=False, save_period=-1, save_txt=False, scale=0.5, seed=0, shear=0.0, show=False, show_boxes=True, show_conf=True, show_labels=True, simplify=True, single_cls=False, source=None, split=val, stream_buffer=False, task=detect, time=None, tracker=botsort.yaml, translate=0.1, val=True, verbose=True, vid_stride=1, visualize=False, warmup_bias_lr=0.1, warmup_epochs=3.0, warmup_momentum=0.8, weight_decay=0.0005, workers=8, workspace=None
from n params module arguments
0 -1 1 464 ultralytics.nn.modules.conv.Conv [3, 16, 3, 2]
1 -1 1 4672 ultralytics.nn.modules.conv.Conv [16, 32, 3, 2]
2 -1 1 7360 ultralytics.nn.modules.block.C2f [32, 32, 1, True]
3 -1 1 18560 ultralytics.nn.modules.conv.Conv [32, 64, 3, 2]
4 -1 2 49664 ultralytics.nn.modules.block.C2f [64, 64, 2, True]
5 -1 1 73984 ultralytics.nn.modules.conv.Conv [64, 128, 3, 2]
6 -1 2 197632 ultralytics.nn.modules.block.C2f [128, 128, 2, True]
7 -1 1 295424 ultralytics.nn.modules.conv.Conv [128, 256, 3, 2]
8 -1 1 460288 ultralytics.nn.modules.block.C2f [256, 256, 1, True]
9 -1 1 164608 ultralytics.nn.modules.block.SPPF [256, 256, 5]
10 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
11 [-1, 6] 1 0 ultralytics.nn.modules.conv.Concat [1]
12 -1 1 148224 ultralytics.nn.modules.block.C2f [384, 128, 1]
13 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest']
14 [-1, 4] 1 0 ultralytics.nn.modules.conv.Concat [1]
15 -1 1 37248 ultralytics.nn.modules.block.C2f [192, 64, 1]
16 -1 1 36992 ultralytics.nn.modules.conv.Conv [64, 64, 3, 2]
17 [-1, 12] 1 0 ultralytics.nn.modules.conv.Concat [1]
18 -1 1 123648 ultralytics.nn.modules.block.C2f [192, 128, 1]
19 -1 1 147712 ultralytics.nn.modules.conv.Conv [128, 128, 3, 2]
20 [-1, 9] 1 0 ultralytics.nn.modules.conv.Concat [1]
21 -1 1 493056 ultralytics.nn.modules.block.C2f [384, 256, 1]
22 [15, 18, 21] 1 897664 ultralytics.nn.modules.head.Detect [80, 16, None, [64, 128, 256]]
Model summary: 130 layers, 3,157,200 parameters, 3,157,184 gradients, 8.9 GFLOPs
Transferred 355/355 items from pretrained weights
Freezing layer 'model.22.dfl.conv.weight'
AMP: running Automatic Mixed Precision (AMP) checks...
WARNING ⚠️ Download failure, retrying 1/3 https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt... <urlopen error [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate (_ssl.c:1000)>
curl: (60) SSL certificate problem: self-signed certificate
More details here: https://curl.se/docs/sslcerts.html
curl failed to verify the legitimacy of the server and therefore could not
establish a secure connection to it. To learn more about this situation and
how to fix it, please visit the web page mentioned above.
WARNING ⚠️ Download failure, retrying 2/3 https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt... Curl return value 60
curl: (60) SSL certificate problem: self-signed certificate
More details here: https://curl.se/docs/sslcerts.html
curl failed to verify the legitimacy of the server and therefore could not
establish a secure connection to it. To learn more about this situation and
how to fix it, please visit the web page mentioned above.
WARNING ⚠️ Download failure, retrying 3/3 https://github.com/ultralytics/assets/releases/download/v8.4.0/yolo26n.pt... Curl return value 60
curl: (60) SSL certificate problem: self-signed certificate
More details here: https://curl.se/docs/sslcerts.html
curl failed to verify the legitimacy of the server and therefore could not
establish a secure connection to it. To learn more about this situation and
how to fix it, please visit the web page mentioned above.
WARNING ⚠️ AMP: checks skipped. Offline and unable to download YOLO26n for AMP checks. Setting 'amp=True'. If you experience zero-mAP or NaN losses you can disable AMP with amp=False.
train: Fast image access ✅ (ping: 0.0±0.0 ms, read: 2471.2±682.0 MB/s, size: 195.4 KB)
train: Scanning /datasets/coco/labels/train2017... 117266 images, 1021 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 118287/118287 2.5Kit/s 47.2s
train: New cache created: /datasets/coco/labels/train2017.cache
val: Fast image access ✅ (ping: 0.0±0.0 ms, read: 853.8±40.9 MB/s, size: 147.9 KB)
val: Scanning /datasets/coco/labels/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% ━━━━━━━━━━━━ 5000/5000 2.0Kit/s 2.5s
val: New cache created: /datasets/coco/labels/val2017.cache
optimizer: 'optimizer=auto' found, ignoring 'lr0=0.01' and 'momentum=0.937' and determining best 'optimizer', 'lr0' and 'momentum' automatically...
optimizer: MuSGD(lr=0.01, momentum=0.9) with parameter groups 57 weight(decay=0.0), 64 weight(decay=0.0005), 63 bias(decay=0.0)
Plotting labels to /datasets/coco/runs/detect/train/labels.jpg...
Image sizes 640 train, 640 val
Using 8 dataloader workers
Logging results to /datasets/coco/runs/detect/train
Starting training for 100 epochs...
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
1/100 0.344G 1.164 1.466 1.195 15 640: 100% ━━━━━━━━━━━━ 7393/7393 7.0it/s 17:40
Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 157/157 1.1it/s 2:19
all 5000 36335 0.551 0.387 0.411 0.276
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
2/100 2.93G 1.254 1.671 1.255 16 640: 16% ━╸────────── 1153/7393 8.1it/s 2:23<12:48
- 最终结果
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
99/100 4.36G 1.111 1.206 1.168 15 640: 100% ━━━━━━━━━━━━ 7393/7393 9.0it/s 13:41
Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 157/157 12.1it/s 13.0s
all 5000 36335 0.627 0.469 0.512 0.362
Epoch GPU_mem box_loss cls_loss dfl_loss Instances Size
100/100 4.36G 1.107 1.195 1.163 15 640: 100% ━━━━━━━━━━━━ 7393/7393 9.0it/s 13:42
Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 157/157 12.1it/s 13.0s
all 5000 36335 0.627 0.469 0.513 0.362
100 epochs completed in 23.581 hours.
Optimizer stripped from /datasets/coco/runs/detect/train/weights/last.pt, 6.5MB
Optimizer stripped from /datasets/coco/runs/detect/train/weights/best.pt, 6.5MB
Validating /datasets/coco/runs/detect/train/weights/best.pt...
Ultralytics 8.4.60 🚀 Python-3.12.3 torch-2.10.0+rocm7.2.4.git3d3aa833 CUDA:0 (AMD Radeon Graphics, 49136MiB)
Model summary (fused): 73 layers, 3,151,904 parameters, 0 gradients, 8.7 GFLOPs
Class Images Instances Box(P R mAP50 mAP50-95): 100% ━━━━━━━━━━━━ 157/157 9.0it/s 17.4s
all 5000 36335 0.629 0.468 0.512 0.362
person 2693 10777 0.753 0.67 0.739 0.504
bicycle 149 314 0.672 0.36 0.438 0.26
car 535 1918 0.655 0.527 0.564 0.357
motorcycle 159 367 0.691 0.573 0.655 0.415
airplane 97 143 0.814 0.748 0.816 0.622
bus 189 283 0.757 0.675 0.744 0.621
train 157 190 0.789 0.763 0.836 0.649
truck 250 414 0.517 0.37 0.429 0.285
boat 121 424 0.566 0.333 0.385 0.211
traffic light 191 634 0.628 0.336 0.393 0.202
fire hydrant 86 101 0.849 0.683 0.783 0.631
stop sign 69 75 0.683 0.631 0.68 0.612
parking meter 37 60 0.688 0.483 0.548 0.432
bench 235 411 0.561 0.246 0.289 0.193
bird 125 427 0.635 0.347 0.416 0.271
cat 184 202 0.788 0.817 0.85 0.642
dog 177 218 0.664 0.707 0.721 0.572
horse 128 272 0.716 0.64 0.721 0.543
sheep 65 354 0.609 0.692 0.68 0.468
cow 87 372 0.63 0.568 0.647 0.468
elephant 89 252 0.689 0.833 0.806 0.601
bear 49 71 0.851 0.732 0.834 0.676
zebra 85 266 0.824 0.82 0.883 0.656
giraffe 101 232 0.833 0.806 0.863 0.667
backpack 228 371 0.495 0.167 0.196 0.103
umbrella 174 407 0.632 0.514 0.529 0.344
handbag 292 540 0.481 0.12 0.159 0.082
tie 145 252 0.659 0.345 0.399 0.249
suitcase 105 299 0.56 0.401 0.463 0.314
frisbee 84 115 0.751 0.704 0.76 0.587
skis 120 241 0.606 0.307 0.352 0.183
snowboard 49 69 0.483 0.348 0.39 0.279
sports ball 169 260 0.691 0.438 0.469 0.326
kite 91 327 0.563 0.535 0.561 0.368
baseball bat 97 145 0.559 0.407 0.391 0.228
baseball glove 100 148 0.692 0.446 0.489 0.28
skateboard 127 179 0.726 0.623 0.669 0.463
surfboard 149 267 0.61 0.446 0.486 0.312
tennis racket 167 225 0.698 0.591 0.641 0.389
bottle 379 1013 0.576 0.404 0.447 0.295
wine glass 110 341 0.661 0.372 0.415 0.269
cup 390 895 0.574 0.433 0.482 0.34
fork 155 215 0.526 0.284 0.344 0.239
knife 181 325 0.505 0.157 0.189 0.118
spoon 153 253 0.376 0.107 0.15 0.0877
bowl 314 623 0.598 0.485 0.522 0.387
banana 103 370 0.524 0.295 0.348 0.216
apple 76 236 0.437 0.216 0.233 0.154
sandwich 98 177 0.575 0.435 0.45 0.327
orange 85 285 0.471 0.4 0.376 0.291
broccoli 71 312 0.475 0.331 0.368 0.208
carrot 81 365 0.449 0.261 0.287 0.176
hot dog 51 125 0.6 0.376 0.436 0.315
pizza 153 284 0.663 0.62 0.654 0.486
donut 62 328 0.52 0.479 0.502 0.394
cake 124 310 0.574 0.397 0.435 0.299
chair 580 1771 0.58 0.333 0.384 0.241
couch 195 261 0.591 0.567 0.57 0.404
potted plant 172 342 0.535 0.365 0.37 0.212
bed 149 163 0.582 0.547 0.595 0.421
dining table 501 695 0.514 0.432 0.421 0.282
toilet 149 179 0.751 0.743 0.761 0.615
tv 207 288 0.725 0.646 0.709 0.547
laptop 183 231 0.686 0.632 0.684 0.554
mouse 88 106 0.682 0.689 0.704 0.542
remote 145 283 0.492 0.244 0.298 0.174
keyboard 106 153 0.604 0.575 0.642 0.463
cell phone 214 262 0.522 0.34 0.385 0.27
microwave 54 55 0.635 0.564 0.629 0.497
oven 115 143 0.574 0.441 0.494 0.325
toaster 8 9 0.766 0.222 0.448 0.287
sink 187 225 0.611 0.471 0.516 0.335
refrigerator 101 126 0.79 0.597 0.654 0.496
book 230 1129 0.449 0.104 0.18 0.0842
clock 204 267 0.719 0.613 0.663 0.458
vase 137 274 0.561 0.409 0.433 0.3
scissors 28 36 0.739 0.333 0.363 0.297
teddy bear 94 190 0.633 0.542 0.591 0.401
hair drier 9 11 1 0 0.00419 0.00225
toothbrush 34 57 0.378 0.193 0.189 0.117
Speed: 0.1ms preprocess, 0.4ms inference, 0.0ms loss, 0.6ms postprocess per image
Saving /datasets/coco/runs/detect/train/predictions.json...
Evaluating faster-coco-eval mAP using /datasets/coco/runs/detect/train/predictions.json and /datasets/coco/annotations/instances_val2017.json...
requirements: Ultralytics requirement ['faster-coco-eval>=1.6.7'] not found, attempting AutoUpdate...
Looking in indexes: https://pypi.tuna.tsinghua.edu.cn/simple
Collecting faster-coco-eval>=1.6.7
Downloading faster_coco_eval-1.7.2-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (588 kB)
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 588.1/588.1 kB 11.6 MB/s 0:00:00
Requirement already satisfied: numpy in /opt/venv/lib/python3.12/site-packages (from faster-coco-eval>=1.6.7) (2.4.6)
Installing collected packages: faster-coco-eval
Successfully installed faster-coco-eval-1.7.2
requirements: AutoUpdate success ✅ 6.7s
WARNING ⚠️ requirements: Restart runtime or rerun command for updates to take effect
Evaluate annotation type *bbox*
COCOeval_opt.evaluate() finished...
DONE (t=7.77s).
Accumulating evaluation results...
COCOeval_opt.accumulate() finished...
DONE (t=0.00s).
Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.368
Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.520
Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.401
Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.185
Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.404
Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.525
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.316
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.531
Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.586
Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.358
Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.651
Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.772
Average Recall (AR) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.811
Average Recall (AR) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.639
Results saved to /datasets/coco/runs/detect/train
💡 Learn more at https://docs.ultralytics.com/modes/train
root@u-1254-152e0e51:/datasets/coco#
amd-smi结果-
Every 0.1s: amd-smi u-1254-152e0e51: Wed Jun 24 16:12:58 2026 +------------------------------------------------------------------------------+ | AMD-SMI 26.2.2+97f5574fe2 amdgpu version: 6.14.14 ROCm version: 7.2.4 | | VBIOS version: 00162356 | | Platform: Linux Baremetal | |-------------------------------------+----------------------------------------| | BDF GPU-Name | Mem-Uti Temp UEC Power-Usage | | GPU HIP-ID OAM-ID Partition-Mode | GFX-Uti Fan Mem-Usage | |=====================================+========================================| | 0000:03:00.0 AMD Radeon Graphics | 11 % 55 °C 0 138/241 W | | 0 0 N/A N/A | 91 % 20.0 % 4153/49136 MB | +-------------------------------------+----------------------------------------+ +------------------------------------------------------------------------------+ | Processes: | | GPU PID Process Name GTT_MEM VRAM_MEM MEM_USAGE CU % | |==============================================================================| | 0 307548 N/A 0.0 B 0.0 B 0.0 B N/A | | 0 1270381 N/A 0.0 B 4.0 GB 4.0 GB N/A | | 0 1667982 N/A 0.0 B 0.0 B 0.0 B N/A | +------------------------------------------------------------------------------+ Process Name may require elevated permissions.
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