GCP Quickstart

This quickstart guide 📚 helps new users run YOLOv3 🚀 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 YOLOv3 include our Colab Notebook and our Docker image at https://hub.docker.com/r/ultralytics/yolov3 .

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

Install YOLOv3: Python>=3.6.0 with all requirements.txt installed including PyTorch>=1.7. To get started:

$ git clone https://github.com/ultralytics/yolov3
$ cd yolov3
$ pip install -r requirements.txt

3. Run YOLOv3 🚀

Start training, testing, detecting and exporting YOLOv3 models on your VM!

$ python train.py  # train a model
$ python val.py --weights yolov3.pt  # validate a model for Precision, Recall and mAP
$ python detect.py --weights yolov3.pt --source path/to/images  # run inference on images and videos
$ python export.py --weights yolov3.pt --include onnx coreml tflite  # export models to other formats

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