YoloV5 killed at first epoch
I'm using a virtual machine on Windows 10 with this config:
Memory 7.8 GiB
Processor Intel® Core™ i5-6600K CPU @ 3.50GHz × 3
Graphics llvmpipe (LLVM 11.0.0, 256 bits)
Disk Capcity 80.5 GB
OS Ubuntu 20.10 64 Bit
Virtualization Oracle

I installed docker for Ubuntu as described in the official documentation.
I pulled the docker image as described on the yolo github section for docker.
Since I have no NVIDIA GPU I could not install a driver or CUDA.
I pulled the aquarium from roboflow and installed it on a folde aquarium.
I ran this command to start the image and have my aquarium folder mounted
sudo docker run --ipc=host -it -v &quot;$(pwd)&quot;/Desktop/yolo/aquarium:/usr/src/app/aquarium ultralytics/yolov5:latest

And was greeted with this banner

=============
== PyTorch ==
NVIDIA Release 21.03 (build 21060478) PyTorch Version 1.9.0a0+df837d0
Container image Copyright (c) 2021, NVIDIA CORPORATION.  All rights
reserved.
Copyright (c) 2014-2021 Facebook Inc. Copyright (c) 2011-2014 Idiap
Research Institute (Ronan Collobert) Copyright (c) 2012-2014 Deepmind
Technologies    (Koray Kavukcuoglu) Copyright (c) 2011-2012 NEC
Laboratories America (Koray Kavukcuoglu) Copyright (c) 2011-2013 NYU
(Clement Farabet) Copyright (c) 2006-2010 NEC Laboratories America
(Ronan Collobert, Leon Bottou, Iain Melvin, Jason Weston) Copyright
(c) 2006      Idiap Research Institute (Samy Bengio) Copyright (c)
2001-2004 Idiap Research Institute (Ronan Collobert, Samy Bengio,
Johnny Mariethoz) Copyright (c) 2015      Google Inc. Copyright (c)
2015      Yangqing Jia Copyright (c) 2013-2016 The Caffe contributors
All rights reserved.
NVIDIA Deep Learning Profiler (dlprof) Copyright (c) 2021, NVIDIA
CORPORATION.  All rights reserved.
Various files include modifications (c) NVIDIA CORPORATION.  All
rights reserved.
This container image and its contents are governed by the NVIDIA Deep
Learning Container License. By pulling and using the container, you
accept the terms and conditions of this license:
https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license
WARNING: The NVIDIA Driver was not detected.  GPU functionality will
not be available.    Use 'nvidia-docker run' to start this container;
see    https://github.com/NVIDIA/nvidia-docker/wiki/nvidia-docker .
NOTE: MOFED driver for multi-node communication was not detected.
Multi-node communication performance may be reduced.

So no error there.
I installed pip and with pip wandb I added wandb. I used wandb login and set my API key.
I ran following command:
# python train.py --img 640 --batch 16 --epochs 10 --data ./aquarium/data.yaml --weights yolov5s.pt --project ip5 --name aquarium5 --nosave --cache

And received this output:
github: skipping check (Docker image)
YOLOv5  v5.0-14-g238583b torch 1.9.0a0+df837d0 CPU

Namespace(adam=False, artifact_alias='latest', batch_size=16, bbox_interval=-1, bucket='', cache_images=True, cfg='', data='./aquarium/data.yaml', device='', entity=None, epochs=10, evolve=False, exist_ok=False, global_rank=-1, hyp='data/hyp.scratch.yaml', image_weights=False, img_size=[640, 640], label_smoothing=0.0, linear_lr=False, local_rank=-1, multi_scale=False, name='aquarium5', noautoanchor=False, nosave=True, notest=False, project='ip5', quad=False, rect=False, resume=False, save_dir='ip5/aquarium5', save_period=-1, single_cls=False, sync_bn=False, total_batch_size=16, upload_dataset=False, weights='yolov5s.pt', workers=8, world_size=1)
tensorboard: Start with 'tensorboard --logdir ip5', view at http://localhost:6006/
hyperparameters: lr0=0.01, lrf=0.2, 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
wandb: Currently logged in as: pebs (use `wandb login --relogin` to force relogin)
wandb: Tracking run with wandb version 0.10.26
wandb: Syncing run aquarium5
wandb: ⭐️ View project at https://wandb.ai/pebs/ip5
wandb:  View run at https://wandb.ai/pebs/ip5/runs/1c2j80ii
wandb: Run data is saved locally in /usr/src/app/wandb/run-20210419_102642-1c2j80ii
wandb: Run `wandb offline` to turn off syncing.

Overriding model.yaml nc=80 with nc=7

                 from  n    params  module                                  arguments                     
  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    
  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                
  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   
  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               
  4                -1  1    156928  models.common.C3                        [128, 128, 3]                 
  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              
  6                -1  1    625152  models.common.C3                        [256, 256, 3]                 
  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              
  8                -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        
  9                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              
 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 12           [-1, 6]  1         0  models.common.Concat                    [1]                           
 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          
 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              
 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          
 16           [-1, 4]  1         0  models.common.Concat                    [1]                           
 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          
 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              
 19          [-1, 14]  1         0  models.common.Concat                    [1]                           
 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          
 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              
 22          [-1, 10]  1         0  models.common.Concat                    [1]                           
 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          
 24      [17, 20, 23]  1     32364  models.yolo.Detect                      [7, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]
[W NNPACK.cpp:80] Could not initialize NNPACK! Reason: Unsupported hardware.
Model Summary: 283 layers, 7079724 parameters, 7079724 gradients, 16.4 GFLOPS

Transferred 356/362 items from yolov5s.pt
Scaled weight_decay = 0.0005
Optimizer groups: 62 .bias, 62 conv.weight, 59 other
train: Scanning '/usr/src/app/aquarium/train/labels.cache' images and labels... 448 found, 0 missing, 1 empty, 0 corrupted: 100%|█| 448/448 [00:00&lt;?, ?
train: Caching images (0.4GB): 100%|████████████████████████████████████████████████████████████████████████████████| 448/448 [00:01&lt;00:00, 313.77it/s]
val: Scanning '/usr/src/app/aquarium/valid/labels.cache' images and labels... 127 found, 0 missing, 0 empty, 0 corrupted: 100%|█| 127/127 [00:00&lt;?, ?it
val: Caching images (0.1GB): 100%|██████████████████████████████████████████████████████████████████████████████████| 127/127 [00:00&lt;00:00, 141.31it/s]
Plotting labels... 

autoanchor: Analyzing anchors... anchors/target = 5.17, Best Possible Recall (BPR) = 0.9997
Image sizes 640 train, 640 test
Using 3 dataloader workers
Logging results to ip5/aquarium5
Starting training for 10 epochs...

     Epoch   gpu_mem       box       obj       cls     total    labels  img_size
  0%|                                                                                                                           | 0/28 [00:00&lt;?, ?it/s]Killed
root@cf40a6498016:~# /opt/conda/lib/python3.8/multiprocessing/resource_tracker.py:216: UserWarning: resource_tracker: There appear to be 6 leaked semaphore objects to clean up at shutdown
  warnings.warn('resource_tracker: There appear to be %d '

From this output I would think that there were 0 epochs completed.
My data.yaml contains this code:
train: /usr/src/app/aquarium/train/images
val: /usr/src/app/aquarium/valid/images

nc: 7
names: ['fish', 'jellyfish', 'penguin', 'puffin', 'shark', 'starfish', 'stingray']

wandb.ai does not display any metrics, but I have the files config.yaml, requirements.txt, wandb-metadata.json and wandb-summary.json.
Why am I not getting any output?
Has there in fact be no training at all?
If there was a training, how can I use my model?

The problem was, that the VM ran out of RAM. The solution was to create 16 GB of swap memory, so the machine can use the virtual harddrive as RAM.
