ctr image import stmp.tar 可以加上-n k8s.io这个namespace ctr -n k8s.io image import stmp.tar #ctr工具导出镜像命令 ctr images export stmp.tar # 拉取镜像 ctr -n k8s.io image pull docker.io/library/nginx:alpine # 删除镜像 ctr -n k8s.io images rm k8s.gcr.io/pause:3.2例子 ctr i pu...
镜像导入/导出 ctr images import/exporter docker load/save 镜像拉取/推送 ctr images pull/push docker pull/push 镜像tag ctr images tag docker tag 这里需注意的是,由于Containerd也有namespaces的概念,对于上层编排系统的支持,主要区分了3个命名空间分别是k8s.io、moby和 default,以上我们用crictl操作的均在k8s...
alpine ctr images push harbor.junengcloud.com/tmp/redis:alpine # 离线导⼊ docker 镜像,在其他 docker 上导出, containerd 镜像导⼊ docker save -o rabbitmq_3.7.8.tar harbor.junengcloud.com/rabbitmq/rabbitmq:3.7.8 ctr images import rabbitmq_3.7.8.tar ctr images ls -q ...
# 导入 ctr -n k8s.ioimage import xxx.tar # 查看镜像列表 ctr -n k8s.ioimage ls # 拉取(需要注意的是镜像地址需要加上地址,如:DockerHub 的要加 docker.io) ctr -n k8s.ioimages pull docker.io/kubebiz/pause:3.2
ctr images push harbor.junengcloud.com/tmp/redis:alpine# 离线导入 docker 镜像,在其他 docker 上导出, containerd 镜像导入docker save -o rabbitmq_3.7.8.tar harbor.junengcloud.com/rabbitmq/rabbitmq:3.7.8 ctr images import rabbitmq_3.7.8.tar ...
常用指令的简单用法,如想详细了解某条指令可以单独查阅。 方法dockercrictlctr查看镜像列表docker imagescrictl imagesctr -n http://k8s.io i ls查看镜像详情docker inspect IMAGE IDdocker inspect IMAGE ID拉…
1. 导入k8s.io镜像 [root@k8smaster1 ~]# ctr -n=k8s.io images import calico.tar.gz 1. 查看镜像 [root@k8smaster1 ~]# ctr -n k8s.io images list 1. 删除镜像 [root@k8smaster1 ~]# ctr images rm registry.cn-hangzhou.aliyuncs.com/google_containers/nginx-ingress-controller:v1.1.0 ...
import numpy as np from dataprocess import DataLoad # 自定义的npy数据读取类 class CtrDataset(Dataset): """ Custom dataset class for dataset in order to use efficient dataloader tool provided by PyTorch. """ def __init__(self, train=True,split_=0.8): ...
import paddle import numpy as np import paddle.nn.functional as F from visualdl import LogWriter from tqdm import tqdm log_writer = LogWriter("./log/gnet") class Dataset(paddle.io.Dataset): def __init__(self,is_train = True): data = np.load("/home/aistudio/data/data104925/train_dat...
from DataParse import DataParse from tqdm import tqdm np.random.seed(2018)class PNN(BaseEstimator, TransformerMixin): def __init__(self, feature_size, field_size, embedding_size=8, deep_init_size=50, deep_layers=[32, 32], dropout_deep=[1.0, 1.0, 1.0], ...