命令根据fastai GCP setup, 并针对我的任务进行了修改。
Google Cloud’s command line interface (CLI)安装配置部分见fastai GCP setup
另外因为火星网络环境不够稳定, 我在wsl中配置了proxychains4, 连到Windows主机的sock5代理.
创建抢占式实例
export IMAGE_FAMILY="pytorch-latest-gpu" # or "pytorch-latest-cpu" for non-GPU instances
export ZONE="asia-southeast1-b" # budget: "us-west1-b"
export INSTANCE_NAME="kaggle-rsna"
export INSTANCE_TYPE="n1-highmem-8" # budget: "n1-highmem-4"
# budget: 'type=nvidia-tesla-k80,count=1'
proxychains4 gcloud compute instances create $INSTANCE_NAME --zone=$ZONE --image-family=$IMAGE_FAMILY --image-project=deeplearning-platform-release --maintenance-policy=TERMINATE --accelerator="type=nvidia-tesla-p4,count=1" --machine-type=$INSTANCE_TYPE --boot-disk-size=800GB --metadata="install-nvidia-driver=True" --preemptible
创建完成之后, ssh连接上去并开启端口转发, 创建的主机默认有jupyter用户, 并且在8080端口开了jupyter lab. 这里就用jupyter用户连上去, 并开启本地端口转发.
proxychains4 gcloud beta compute --project "limongty" ssh --zone "asia-southeast1-b" jupyter@"kaggle-rsna" -- -L 8080:localhost:8080
连接成功后本地浏览器打开localhost:8080可以使用远程环境.
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