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GCP Quickstart
To get started using this repo quickly using a Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM) follow the instructions below. New GCP users are eligible for a $300 free credit offer. Other quickstart options for this repo include our Jupyter Notebook and our Docker image at https://hub.docker.com/r/ultralytics/yolov3
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Select a PyTorch >=1.7 image from the GCP marketplace, check 'install GPU driver automatically', and select a 300 GB SSD for sufficient I/O speed. All dependencies are typically included in the preinstalled Anaconda distribution.
![Screen Shot 2019-11-23 at 11 16 01 AM](https://user-images.githubusercontent.com/26833433/69485379-c198ad80-0de2-11ea-9a23-a581d4fa83a7.png)
Clone this repo and install requirements.txt dependencies, including Python>=3.8 and PyTorch>=1.7.
$ git clone https://github.com/ultralytics/yolov3 # clone repo
$ cd yolov3
$ pip install -r requirements.txt # install dependencies
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Train:
$ python train.py
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Test:
$ python test.py
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Detect:
$ python detect.py
![Screen Shot 2019-11-30 at 2 25 06 PM](https://user-images.githubusercontent.com/26833433/69906773-700d9700-137d-11ea-9a5f-b5c541b61930.png)
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
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