title: YoloV5 killed at first epoch
body: <p>I'm using a virtual machine on Windows 10 with this config:</p>
  <pre><code>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
  </code></pre>
  <p>I installed docker for Ubuntu as described in <a href="https://docs.docker.com/engine/install/ubuntu/" rel="nofollow noreferrer">the official documentation</a>.<br>
  I pulled the docker image as described on the <a href="https://github.com/ultralytics/yolov5/wiki/Docker-Quickstart" rel="nofollow noreferrer">yolo github section for docker</a>.<br>
  Since I have no NVIDIA GPU I could not install a driver or CUDA.
  I pulled the aquarium from <a href="https://public.roboflow.com/object-detection/aquarium" rel="nofollow noreferrer">roboflow</a> and installed it on a folde aquarium.
  I ran this command to start the image and have my aquarium folder mounted</p>
  <pre><code>sudo docker run --ipc=host -it -v &quot;$(pwd)&quot;/Desktop/yolo/aquarium:/usr/src/app/aquarium ultralytics/yolov5:latest
  </code></pre>
  <p>And was greeted with this banner</p>
  <blockquote>
  <h1>=============
  == PyTorch ==</h1>
  <p>NVIDIA Release 21.03 (build 21060478) PyTorch Version 1.9.0a0+df837d0</p>
  <p>Container image Copyright (c) 2021, NVIDIA CORPORATION.  All rights
  reserved.</p>
  <p>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<br />
  (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.</p>
  <p>NVIDIA Deep Learning Profiler (dlprof) Copyright (c) 2021, NVIDIA
  CORPORATION.  All rights reserved.</p>
  <p>Various files include modifications (c) NVIDIA CORPORATION.  All
  rights reserved.</p>
  <p>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:
  <a href="https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license" rel="nofollow noreferrer">https://developer.nvidia.com/ngc/nvidia-deep-learning-container-license</a></p>
  <p>WARNING: The NVIDIA Driver was not detected.  GPU functionality will
  not be available.    Use 'nvidia-docker run' to start this container;
  see    <a href="https://github.com/NVIDIA/nvidia-docker/wiki/nvidia-docker" rel="nofollow noreferrer">https://github.com/NVIDIA/nvidia-docker/wiki/nvidia-docker</a> .</p>
  <p>NOTE: MOFED driver for multi-node communication was not detected.
  Multi-node communication performance may be reduced.</p>
  </blockquote>
  <p>So no error there.<br>
  I installed pip and with pip wandb I added wandb. I used <code>wandb login</code> and set my API key.<br><br>
  I ran following command:</p>
  <pre><code># python train.py --img 640 --batch 16 --epochs 10 --data ./aquarium/data.yaml --weights yolov5s.pt --project ip5 --name aquarium5 --nosave --cache
  </code></pre>
  <p>And received this output:</p>
  <pre><code>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 '
  </code></pre>
  <p>From this output I would think that there were 0 epochs completed.<br>
  My data.yaml contains this code:</p>
  <pre><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']
  </code></pre>
  <p><a href="https://wandb.ai/" rel="nofollow noreferrer">wandb.ai</a> does not display any metrics, but I have the files config.yaml, requirements.txt, wandb-metadata.json and wandb-summary.json.</p>
  <p>Why am I not getting any output?<br>
  Has there in fact be no training at all?<br>
  If there was a training, how can I use my model?</p>