我必须部署一个堆栈，我们将其称为一个区域中的父堆栈。需要在另一个区域中部署第二个堆栈（子堆栈）。第二个堆栈（子堆栈）的区域不能包含部署父堆栈的区域。第二个堆栈可以部署在多个区域。

然而，第二个堆栈需要来自第一个堆栈的道具。具体来说，它需要一个 ARN 值。默认区域是us-east-1. 这就是部署父堆栈的地方。

为了解决这个问题我尝试了以下方法

1-第一次尝试：使用 cfnOutput

• 在父级和子级中创建了一个cfnOutput 我捕获的值cdk.Fn.ImportValue()
• 结果：出现错误，因为 cfnOutput 无法在不同区域的堆栈之间使用，如CloudFormation 用户指南中所述

2-第二次尝试：使用 StackProps

• 在父堆栈中创建一个继承自 StackProps 的接口，设置公共属性并将 ARN 值放在那里

来自 lib/mystack 文件

export interface myStackProps extends cdk.StackProps {
 principalKeyArn: string
}

• 将值作为 props 以及包含区域的 env 键传递给第二个堆栈，如下所示：

来自 bin/myapp 文件

const app = new cdk.App();
const regions = ["us-east-2"]

const primaryMRKey = new KmsMultiregionPrincipalKey(app, 'KmsMultiregionKeyStack')

for (let region of regions){
 const envToDeploy = {region: region, account: "123456789123"}
 new  KmsReplicaKey(app, …

在我正在学习的 ML 课程中，我有 100 个数据条目，并且我在感知器算法中使用它。\n我想要的是展示这样的情节。

正如您在上面看到的，我们有由红色和蓝色点表示的数据以及最小化误差的不同计算线。这是我想要的输出。这是我的数据和代码。

数据.csv

0.78051,-0.063669,1\n0.28774,0.29139,1\n0.40714,0.17878,1\n0.2923,0.4217,1\n0.50922,0.35256,1\n0.27785,0.10802,1\n0.27527,0.33223,1\n0.43999,0.31245,1\n0.33557,0.42984,1\n0.23448,0.24986,1\n0.0084492,0.13658,1\n0.12419,0.33595,1\n0.25644,0.42624,1\n0.4591,0.40426,1\n0.44547,0.45117,1\n0.42218,0.20118,1\n0.49563,0.21445,1\n0.30848,0.24306,1\n0.39707,0.44438,1\n0.32945,0.39217,1\n0.40739,0.40271,1\n0.3106,0.50702,1\n0.49638,0.45384,1\n0.10073,0.32053,1\n0.69907,0.37307,1\n0.29767,0.69648,1\n0.15099,0.57341,1\n0.16427,0.27759,1\n0.33259,0.055964,1\n0.53741,0.28637,1\n0.19503,0.36879,1\n0.40278,0.035148,1\n0.21296,0.55169,1\n0.48447,0.56991,1\n0.25476,0.34596,1\n0.21726,0.28641,1\n0.67078,0.46538,1\n0.3815,0.4622,1\n0.53838,0.32774,1\n0.4849,0.26071,1\n0.37095,0.38809,1\n0.54527,0.63911,1\n0.32149,0.12007,1\n0.42216,0.61666,1\n0.10194,0.060408,1\n0.15254,0.2168,1\n0.45558,0.43769,1\n0.28488,0.52142,1\n0.27633,0.21264,1\n0.39748,0.31902,1\n0.5533,1,0\n0.44274,0.59205,0\n0.85176,0.6612,0\n0.60436,0.86605,0\n0.68243,0.48301,0\n1,0.76815,0\n0.72989,0.8107,0\n0.67377,0.77975,0\n0.78761,0.58177,0\n0.71442,0.7668,0\n0.49379,0.54226,0\n0.78974,0.74233,0\n0.67905,0.60921,0\n0.6642,0.72519,0\n0.79396,0.56789,0\n0.70758,0.76022,0\n0.59421,0.61857,0\n0.49364,0.56224,0\n0.77707,0.35025,0\n0.79785,0.76921,0\n0.70876,0.96764,0\n0.69176,0.60865,0\n0.66408,0.92075,0\n0.65973,0.66666,0\n0.64574,0.56845,0\n0.89639,0.7085,0\n0.85476,0.63167,0\n0.62091,0.80424,0\n0.79057,0.56108,0\n0.58935,0.71582,0\n0.56846,0.7406,0\n0.65912,0.71548,0\n0.70938,0.74041,0\n0.59154,0.62927,0\n0.45829,0.4641,0\n0.79982,0.74847,0\n0.60974,0.54757,0\n0.68127,0.86985,0\n0.76694,0.64736,0\n0.69048,0.83058,0\n0.68122,0.96541,0\n0.73229,0.64245,0\n0.76145,0.60138,0\n0.58985,0.86955,0\n0.73145,0.74516,0\n0.77029,0.7014,0\n0.73156,0.71782,0\n0.44556,0.57991,0\n0.85275,0.85987,0\n0.51912,0.62359,0\n

现在这是我的代码。第一部分

0.78051,-0.063669,1\n0.28774,0.29139,1\n0.40714,0.17878,1\n0.2923,0.4217,1\n0.50922,0.35256,1\n0.27785,0.10802,1\n0.27527,0.33223,1\n0.43999,0.31245,1\n0.33557,0.42984,1\n0.23448,0.24986,1\n0.0084492,0.13658,1\n0.12419,0.33595,1\n0.25644,0.42624,1\n0.4591,0.40426,1\n0.44547,0.45117,1\n0.42218,0.20118,1\n0.49563,0.21445,1\n0.30848,0.24306,1\n0.39707,0.44438,1\n0.32945,0.39217,1\n0.40739,0.40271,1\n0.3106,0.50702,1\n0.49638,0.45384,1\n0.10073,0.32053,1\n0.69907,0.37307,1\n0.29767,0.69648,1\n0.15099,0.57341,1\n0.16427,0.27759,1\n0.33259,0.055964,1\n0.53741,0.28637,1\n0.19503,0.36879,1\n0.40278,0.035148,1\n0.21296,0.55169,1\n0.48447,0.56991,1\n0.25476,0.34596,1\n0.21726,0.28641,1\n0.67078,0.46538,1\n0.3815,0.4622,1\n0.53838,0.32774,1\n0.4849,0.26071,1\n0.37095,0.38809,1\n0.54527,0.63911,1\n0.32149,0.12007,1\n0.42216,0.61666,1\n0.10194,0.060408,1\n0.15254,0.2168,1\n0.45558,0.43769,1\n0.28488,0.52142,1\n0.27633,0.21264,1\n0.39748,0.31902,1\n0.5533,1,0\n0.44274,0.59205,0\n0.85176,0.6612,0\n0.60436,0.86605,0\n0.68243,0.48301,0\n1,0.76815,0\n0.72989,0.8107,0\n0.67377,0.77975,0\n0.78761,0.58177,0\n0.71442,0.7668,0\n0.49379,0.54226,0\n0.78974,0.74233,0\n0.67905,0.60921,0\n0.6642,0.72519,0\n0.79396,0.56789,0\n0.70758,0.76022,0\n0.59421,0.61857,0\n0.49364,0.56224,0\n0.77707,0.35025,0\n0.79785,0.76921,0\n0.70876,0.96764,0\n0.69176,0.60865,0\n0.66408,0.92075,0\n0.65973,0.66666,0\n0.64574,0.56845,0\n0.89639,0.7085,0\n0.85476,0.63167,0\n0.62091,0.80424,0\n0.79057,0.56108,0\n0.58935,0.71582,0\n0.56846,0.7406,0\n0.65912,0.71548,0\n0.70938,0.74041,0\n0.59154,0.62927,0\n0.45829,0.4641,0\n0.79982,0.74847,0\n0.60974,0.54757,0\n0.68127,0.86985,0\n0.76694,0.64736,0\n0.69048,0.83058,0\n0.68122,0.96541,0\n0.73229,0.64245,0\n0.76145,0.60138,0\n0.58985,0.86955,0\n0.73145,0.74516,0\n0.77029,0.7014,0\n0.73156,0.71782,0\n0.44556,0.57991,0\n0.85275,0.85987,0\n0.51912,0.62359,0\n

当您运行此代码时，您会正确得到

所以现在我想在同一个图中绘制代表每次迭代最佳函数的线。为此，我评论了 plt.show() 上面的最后一行并做了

import numpy as np\nimport pandas as pd\n# Setting the random seed, feel free to change it and see different solutions.\nnp.random.seed(42)\nimport matplotlib.pyplot as plt\n\n\ndef stepFunction(t):\n    return 1 if t >= 0 else 0\n\n\ndef prediction(X, W, b):\n    return stepFunction((np.matmul(X, W) + b)[0])\n\n# TODO: Fill in the code below to implement the perceptron trick.\n# INPUTS\n# data X, the labels …

使用flask_marshmallow进行输入验证，并使用scheme.load()，我无法捕获模型中@validates装饰器生成的错误

我捕获了资源中的结果和错误，但错误会直接发送给用户

==========模型.py==========

```python

from sqlalchemy.orm import validates

from sqlalchemy import Column, ForeignKey, Integer, String, DateTime
from sqlalchemy.orm import relationship, backref
from sqlalchemy import create_engine
from sqlalchemy.sql import func

from flask_marshmallow import Marshmallow
from flask_sqlalchemy import SQLAlchemy
from datetime import datetime
from sqlalchemy.orm import joinedload


db = SQLAlchemy()
ma = Marshmallow()

class Company(db.Model):

    __tablename__ = "company"

    id = db.Column(db.Integer, primary_key=True)
    name = db.Column(db.String(250), nullable=False)
    addressLine1 = db.Column(db.String(250), nullable=False)
    addressLine2 = db.Column(db.String(250), nullable=True)
    city = db.Column(db.String(250), nullable=False)
    state = db.Column(db.String(250), nullable=False) …

我在 AWS EKS 中创建了一个集群和节点。我将部署应用到该集群，如下所示

kubectl apply -f deployment.yaml

其中 deployment.yaml 包含容器的规范以及 DockerHub 存储库和图像

但是，我在deployment.yaml中犯了一个错误，需要重新应用到配置中

我的问题是：

1 - 如何使用 kubectl 将 deployment.yaml 重新应用到 AWS EKS 集群？只是运行上面的命令是行不通的 ( kubectl apply -f deployment.yaml)

2- 在我重新应用 deployment.yaml 之后，节点会去获取 DockerHub 镜像还是我还需要做其他事情（假设所有其他细节都可以）

下面的一些输出：

>> kubectl get pods

my-app-786dc95d8f-b6w4h   0/1     ImagePullBackOff   0          9h
my-app-786dc95d8f-w8hkg   0/1     ImagePullBackOff   0          9h

kubectl describe pod my-app-786dc95d8f-b6w4h                                                                            
Name:         my-app-786dc95d8f-b6w4h
Namespace:    default
Priority:     0
Node:         ip-192-168-24-13.ec2.internal/192.168.24.13
Start Time:   Fri, 10 Jul 2020 12:54:38 -0400
Labels:       app=my-app
              pod-template-hash=786dc95d8f
Annotations:  kubernetes.io/psp: eks.privileged
Status:       Pending …