generating clearml-reports about clearml-server HOT 13 OPEN

root@104ab3100c7d:~/yolov5# python3 train.py --img 320 --batch 2 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache --project container --name testing
train: weights=yolov5s.pt, cfg=, data=coco128.yaml, hyp=data/hyps/hyp.scratch-low.yaml, epochs=3, batch_size=2, imgsz=320, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=container, name=testing, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: ⚠️ YOLOv5 is out of date by 743 commits. Use `git pull` or `git clone https://github.com/ultralytics/yolov5` to update.
YOLOv5 🚀 v6.1-211-gcee5959c Python-3.8.10 torch-1.11.0+cu102 CPU

hyperparameters: lr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0
Weights & Biases: run 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)
TensorBoard: Start with 'tensorboard --logdir container', view at http://localhost:6006/

Dataset not found ⚠, missing paths ['/root/datasets/coco128/images/train2017']
Downloading https://ultralytics.com/assets/coco128.zip to coco128.zip...
100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████| 6.66M/6.66M [00:00<00:00, 7.08MB/s]
Dataset download success ✅ (3.7s), saved to /root/datasets

.........

Model summary: 270 layers, 7235389 parameters, 7235389 gradients, 16.6 GFLOPs

Transferred 349/349 items from yolov5s.pt
Scaled weight_decay = 0.0005
optimizer: SGD with parameter groups 57 weight (no decay), 60 weight, 60 bias
train: Scanning '/root/datasets/coco128/labels/train2017' images and labels...126 found, 2 missing, 0 empty, 0 corrupt: 100%|██████████| 128/128 [00:0
train: New cache created: /root/datasets/coco128/labels/train2017.cache
train: Caching images (0.0GB ram): 100%|██████████| 128/128 [00:00<00:00, 2060.30it/s]                                                                
val: Scanning '/root/datasets/coco128/labels/train2017.cache' images and labels... 126 found, 2 missing, 0 empty, 0 corrupt: 100%|██████████| 128/128 
val: Caching images (0.0GB ram): 100%|██████████| 128/128 [00:00<00:00, 1655.63it/s]                                                                  
Plotting labels to container/testing3/labels.jpg... 

AutoAnchor: 3.96 anchors/target, 0.957 Best Possible Recall (BPR). Anchors are a poor fit to dataset ⚠️, attempting to improve...
AutoAnchor: WARNING: Extremely small objects found: 35 of 929 labels are < 3 pixels in size
AutoAnchor: Running kmeans for 9 anchors on 927 points...
AutoAnchor: Evolving anchors with Genetic Algorithm: fitness = 0.6699: 100%|██████████| 1000/1000 [00:00<00:00, 4459.00it/s]                          
AutoAnchor: thr=0.25: 0.9935 best possible recall, 3.75 anchors past thr
AutoAnchor: n=9, img_size=320, metric_all=0.263/0.670-mean/best, past_thr=0.477-mean: 5,7, 12,11, 16,27, 43,36, 54,72, 62,145, 146,107, 166,191, 298,218
AutoAnchor: Done ✅ (optional: update model *.yaml to use these anchors in the future)
Image sizes 320 train, 320 val
Using 2 dataloader workers
Logging results to container/testing3
Starting training for 3 epochs...

..........

3 epochs completed in 0.013 hours.
Optimizer stripped from container/testing3/weights/last.pt, 14.7MB
Optimizer stripped from container/testing3/weights/best.pt, 14.7MB

Validating container/testing3/weights/best.pt...
Fusing layers... 
Model summary: 213 layers, 7225885 parameters, 0 gradients, 16.4 GFLOPs
               Class     Images     Labels          P          R     [email protected] [email protected]:.95:   0%|          | 0/32 [00:00<?, ?it/s]  

..........
Results saved to container/testing3

# ClearML SDK configuration file
api {
    # Notice: 'host' is the api server (default port 8008), not the web server.
    api_server: http://<public_ip>:8008
    web_server: http://<public_ip>:8082
    files_server: http://<public_ip>:8081
    # Credentials are generated using the webapp, http://<public_ip>:8082/settings
    # Override with os environment: CLEARML_API_ACCESS_KEY / CLEARML_API_SECRET_KEY
    credentials {"access_key": "OTVT8HO*********5RXU", "secret_key": "OZClLTtposYbKC5UVA*****************4LvM7yhiaFm06"}
}
sdk {
    # ClearML - default SDK configuration

    storage {
        cache {
            # Defaults to <system_temp_folder>/clearml_cache
            default_base_dir: "~/.clearml/cache"
            # default_cache_manager_size: 100
        }

        direct_access: [
            # Objects matching are considered to be available for direct access, i.e. they will not be downloaded
            # or cached, and any download request will return a direct reference.
            # Objects are specified in glob format, available for url and content_type.
            { url: "file://*" }  # file-urls are always directly referenced
        ]
    }