When I run the same notebook with the same jupyter kernel (custom conda env) in vscode and in jupyterlab, it only creates a wandb run in vscode, but not in jupyterlab.
Output in jupyterlab (no output from wandb):
Training with yolov5: yolov5_model_size: yolov5s batch size: 8 for 180 epochs
train: weights=yolov5s.pt, cfg=, data=dataset/yolov5_config.yml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=180, batch_size=8, imgsz=1280, rect=True, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v7.0-71-gc442a2e Python-3.10.9 torch-1.13.1+cu117 CUDA:0 (NVIDIA GeForce GTX 1070 with Max-Q Design, 8120MiB)
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
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
Overriding model.yaml nc=80 with nc=1
from n params module arguments
Output in vscode jupyter notebook (shows output from wandb with link to run):
Output exceeds the size limit. Open the full output data in a text editor
Training with yolov5: yolov5_model_size: yolov5s batch size: 8 for 180 epochs
wandb: Currently logged in as: tleyden (eyepi). Use `wandb login --relogin` to force relogin
train: weights=yolov5s.pt, cfg=, data=dataset/yolov5_config.yml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=180, batch_size=8, imgsz=1280, rect=True, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest
github: up to date with https://github.com/ultralytics/yolov5 ✅
YOLOv5 🚀 v7.0-71-gc442a2e Python-3.10.9 torch-1.13.1+cu117 CUDA:0 (NVIDIA GeForce GTX 1070 with Max-Q Design, 8120MiB)
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
ClearML: run 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML
Comet: run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet
TensorBoard: Start with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/
wandb: Tracking run with wandb version 0.13.9
wandb: Run data is saved locally in /home/tleyden/Development/seal-face-detection/wandb/run-20230118_182447-02ee6gxj
wandb: Run `wandb offline` to turn off syncing.
wandb: Syncing run kind-meadow-202
wandb: ⭐️ View project at https://wandb.ai/<projectname>/train
wandb: 🚀 View run at https://wandb.ai/<projectname>/train/runs/02ee6gxj
Overriding model.yaml nc=80 with nc=1
from n params module arguments
Is there anything I can try in jupyterlab to get it to work? If needed, I don’t mind running it only in jupyterlab if it’s not possible to switch back and forth between the two editing environments.