Google Cloud Platform
内容
Google Cloud Platform¶
This quickstart guide helps new users run YOLOv5 🚀 on a Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM) ⭐. New GCP users are eligible for a $300 free credit offer. Other quickstart options for YOLOv5 include our Colab Notebook 
and our Docker image at https://hub.docker.com/r/ultralytics/yolov5 
1. Create VM¶
Select a Deep Learning VM from the GCP marketplace, select an n1-standard-8 instance (with 8 vCPUs and 30 GB memory), add a GPU of your choice, check ‘Install NVIDIA GPU driver automatically on first startup?’, and select a 300 GB SSD Persistent Disk for sufficient I/O speed, then click ‘Deploy’. All dependencies are included in the preinstalled Anaconda Python environment.
2. Setup VM¶
Clone this repo and install requirements.txt dependencies, including Python>=3.8 and PyTorch>=1.7.
$ git clone https://github.com/ultralytics/yolov5 # clone repo
$ cd yolov5
$ pip install -r requirements.txt # install dependencies
3. Run Commands¶
$ python train.py # train a model
$ python test.py --weights yolov5s.pt # test a model for Precision, Recall and mAP
$ python detect.py --weights yolov5s.pt --source path/to/images # run inference on images and videos
Optional Extras¶
Add 64GB of swap memory (to --cache large datasets).
sudo fallocate -l 64G /swapfile
sudo chmod 600 /swapfile
sudo mkswap /swapfile
sudo swapon /swapfile
free -h # check memory
Mount local SSD
lsblk
sudo mkfs.ext4 -F /dev/nvme0n1
sudo mkdir -p /mnt/disks/nvme0n1
sudo mount /dev/nvme0n1 /mnt/disks/nvme0n1
sudo chmod a+w /mnt/disks/nvme0n1
cp -r coco /mnt/disks/nvme0n1