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metrics | Capturing JVM and applicationlevel metrics | Analytics library

 by   dropwizard Java Version: v4.1.31 License: Apache-2.0

 by   dropwizard Java Version: v4.1.31 License: Apache-2.0

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kandi X-RAY | metrics Summary

metrics is a Java library typically used in Analytics, Prometheus applications. metrics has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has high support. You can download it from GitHub, GitLab.
For more information, please see [the documentation](https://metrics.dropwizard.io/).
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Quality
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kandi-support Support

  • metrics has a highly active ecosystem.
  • It has 7520 star(s) with 1793 fork(s). There are 384 watchers for this library.
  • There were 2 major release(s) in the last 6 months.
  • There are 9 open issues and 623 have been closed. On average issues are closed in 245 days. There are 17 open pull requests and 0 closed requests.
  • It has a positive sentiment in the developer community.
  • The latest version of metrics is v4.1.31
metrics Support
Best in #Analytics
Average in #Analytics
metrics Support
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Average in #Analytics

quality kandi Quality

  • metrics has 0 bugs and 0 code smells.
metrics Quality
Best in #Analytics
Average in #Analytics
metrics Quality
Best in #Analytics
Average in #Analytics

securitySecurity

  • metrics has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
  • metrics code analysis shows 0 unresolved vulnerabilities.
  • There are 0 security hotspots that need review.
metrics Security
Best in #Analytics
Average in #Analytics
metrics Security
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Average in #Analytics

license License

  • metrics is licensed under the Apache-2.0 License. This license is Permissive.
  • Permissive licenses have the least restrictions, and you can use them in most projects.
metrics License
Best in #Analytics
Average in #Analytics
metrics License
Best in #Analytics
Average in #Analytics

buildReuse

  • metrics releases are available to install and integrate.
  • Build file is available. You can build the component from source.
  • It has 41521 lines of code, 3414 functions and 513 files.
  • It has medium code complexity. Code complexity directly impacts maintainability of the code.
metrics Reuse
Best in #Analytics
Average in #Analytics
metrics Reuse
Best in #Analytics
Average in #Analytics
Top functions reviewed by kandi - BETA

kandi has reviewed metrics and discovered the below as its top functions. This is intended to give you an instant insight into metrics implemented functionality, and help decide if they suit your requirements.

  • Initialize the metrics .
  • Dump all threads information .
  • Adds metrics to the registry .
  • Converts a list of MetricTuple into a byte array .
  • Start the downloader .
  • Handle the request .
  • Removes all chunks from the beginning and endKey .
  • Creates an object name .
  • Builds a CloseableHttpClient that will be executed on the client .
  • Stops the executor .

metrics Key Features

:chart_with_upwards_trend: Capturing JVM- and application-level metrics. So you know what's going on.

Microk8s dashboard using nginx-ingress via http not working (Error: `no matches for kind "Ingress" in version "extensions/v1beta1"`)

copy iconCopydownload iconDownload
error: unable to recognize "ingress.yaml": no matches for kind "Ingress" in version "extensions/v1beta1"
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: minimal-ingress
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /
spec:
  rules:
  - http:
      paths:
      - path: /testpath
        pathType: Prefix
        backend:
          service:
            name: test
            port:
              number: 80
-----------------------
error: unable to recognize "ingress.yaml": no matches for kind "Ingress" in version "extensions/v1beta1"
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: minimal-ingress
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /
spec:
  rules:
  - http:
      paths:
      - path: /testpath
        pathType: Prefix
        backend:
          service:
            name: test
            port:
              number: 80
-----------------------
apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /$2
    nginx.ingress.kubernetes.io/configuration-snippet: |
      rewrite ^(/dashboard)$ $1/ redirect;
    nginx.ingress.kubernetes.io/backend-protocol: "HTTPS"
    kubernetes.io/ingress.class: public
  name: dashboard
  namespace: kube-system
spec:
  rules:
  - http:
      paths:
      - path: /dashboard(/|$)(.*)
        pathType: Prefix
        backend:
          service:
            name: kubernetes-dashboard
            port:
              number: 443

How to prevent Keras from computing metrics during training

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import tensorflow as tf
tf.compat.v1.disable_eager_execution()
def metric_graph(in1, in2, out):
    actual_metric = out * (in1 + in2)
    return K.switch(K.learning_phase(), tf.zeros((1,)), actual_metric) 
x1 = numpy.ones((5,3))
x2 = numpy.ones((5,3))
y = 3*numpy.ones((5,1))

vx1 = numpy.ones((5,3))
vx2 = numpy.ones((5,3))
vy = 3*numpy.ones((5,1))

def metric_eager(in1, in2, out):
    if (K.learning_phase()):
        return 0
    else:
        return out * (in1 + in2)

def metric_graph(in1, in2, out):
    actual_metric = out * (in1 + in2)
    return K.switch(K.learning_phase(), tf.zeros((1,)), actual_metric) 

ins1 = Input((3,))
ins2 = Input((3,))
outs = Concatenate()([ins1, ins2])
outs = Dense(1)(outs)
model = Model([ins1, ins2],outs)
model.add_metric(metric_graph(ins1, ins2, outs), name='my_metric', aggregation='mean')
model.compile(loss='mse', optimizer='adam')

model.fit([x1, x2],y, validation_data=([vx1, vx2], vy), epochs=3)
-----------------------
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
def metric_graph(in1, in2, out):
    actual_metric = out * (in1 + in2)
    return K.switch(K.learning_phase(), tf.zeros((1,)), actual_metric) 
x1 = numpy.ones((5,3))
x2 = numpy.ones((5,3))
y = 3*numpy.ones((5,1))

vx1 = numpy.ones((5,3))
vx2 = numpy.ones((5,3))
vy = 3*numpy.ones((5,1))

def metric_eager(in1, in2, out):
    if (K.learning_phase()):
        return 0
    else:
        return out * (in1 + in2)

def metric_graph(in1, in2, out):
    actual_metric = out * (in1 + in2)
    return K.switch(K.learning_phase(), tf.zeros((1,)), actual_metric) 

ins1 = Input((3,))
ins2 = Input((3,))
outs = Concatenate()([ins1, ins2])
outs = Dense(1)(outs)
model = Model([ins1, ins2],outs)
model.add_metric(metric_graph(ins1, ins2, outs), name='my_metric', aggregation='mean')
model.compile(loss='mse', optimizer='adam')

model.fit([x1, x2],y, validation_data=([vx1, vx2], vy), epochs=3)
-----------------------
import tensorflow as tf
tf.compat.v1.disable_eager_execution()
def metric_graph(in1, in2, out):
    actual_metric = out * (in1 + in2)
    return K.switch(K.learning_phase(), tf.zeros((1,)), actual_metric) 
x1 = numpy.ones((5,3))
x2 = numpy.ones((5,3))
y = 3*numpy.ones((5,1))

vx1 = numpy.ones((5,3))
vx2 = numpy.ones((5,3))
vy = 3*numpy.ones((5,1))

def metric_eager(in1, in2, out):
    if (K.learning_phase()):
        return 0
    else:
        return out * (in1 + in2)

def metric_graph(in1, in2, out):
    actual_metric = out * (in1 + in2)
    return K.switch(K.learning_phase(), tf.zeros((1,)), actual_metric) 

ins1 = Input((3,))
ins2 = Input((3,))
outs = Concatenate()([ins1, ins2])
outs = Dense(1)(outs)
model = Model([ins1, ins2],outs)
model.add_metric(metric_graph(ins1, ins2, outs), name='my_metric', aggregation='mean')
model.compile(loss='mse', optimizer='adam')

model.fit([x1, x2],y, validation_data=([vx1, vx2], vy), epochs=3)
-----------------------
class MyCustomMetricCallback(tf.keras.callbacks.Callback):

    def __init__(self, train=None, validation=None):
        super(MyCustomMetricCallback, self).__init__()
        self.train = train
        self.validation = validation

    def on_epoch_end(self, epoch, logs={}):

        mse = tf.keras.losses.mean_squared_error

        if self.train:
            logs['my_metric_train'] = float('inf')
            X_train, y_train = self.train[0], self.train[1]
            y_pred = self.model.predict(X_train)
            score = mse(y_train, y_pred)
            logs['my_metric_train'] = np.round(score, 5)

        if self.validation:
            logs['my_metric_val'] = float('inf')
            X_valid, y_valid = self.validation[0], self.validation[1]
            y_pred = self.model.predict(X_valid)
            val_score = mse(y_pred, y_valid)
            logs['my_metric_val'] = np.round(val_score, 5)
def build_model():

  inp1 = Input((5,))
  inp2 = Input((5,))
  out = Concatenate()([inp1, inp2])
  out = Dense(1)(out)

  model = Model([inp1, inp2], out)
  model.compile(loss='mse', optimizer='adam')

  return model
X_train1 = np.random.uniform(0,1, (100,5))
X_train2 = np.random.uniform(0,1, (100,5))
y_train = np.random.uniform(0,1, (100,1))

X_val1 = np.random.uniform(0,1, (100,5))
X_val2 = np.random.uniform(0,1, (100,5))
y_val = np.random.uniform(0,1, (100,1))
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train), validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train))])
-----------------------
class MyCustomMetricCallback(tf.keras.callbacks.Callback):

    def __init__(self, train=None, validation=None):
        super(MyCustomMetricCallback, self).__init__()
        self.train = train
        self.validation = validation

    def on_epoch_end(self, epoch, logs={}):

        mse = tf.keras.losses.mean_squared_error

        if self.train:
            logs['my_metric_train'] = float('inf')
            X_train, y_train = self.train[0], self.train[1]
            y_pred = self.model.predict(X_train)
            score = mse(y_train, y_pred)
            logs['my_metric_train'] = np.round(score, 5)

        if self.validation:
            logs['my_metric_val'] = float('inf')
            X_valid, y_valid = self.validation[0], self.validation[1]
            y_pred = self.model.predict(X_valid)
            val_score = mse(y_pred, y_valid)
            logs['my_metric_val'] = np.round(val_score, 5)
def build_model():

  inp1 = Input((5,))
  inp2 = Input((5,))
  out = Concatenate()([inp1, inp2])
  out = Dense(1)(out)

  model = Model([inp1, inp2], out)
  model.compile(loss='mse', optimizer='adam')

  return model
X_train1 = np.random.uniform(0,1, (100,5))
X_train2 = np.random.uniform(0,1, (100,5))
y_train = np.random.uniform(0,1, (100,1))

X_val1 = np.random.uniform(0,1, (100,5))
X_val2 = np.random.uniform(0,1, (100,5))
y_val = np.random.uniform(0,1, (100,1))
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train), validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train))])
-----------------------
class MyCustomMetricCallback(tf.keras.callbacks.Callback):

    def __init__(self, train=None, validation=None):
        super(MyCustomMetricCallback, self).__init__()
        self.train = train
        self.validation = validation

    def on_epoch_end(self, epoch, logs={}):

        mse = tf.keras.losses.mean_squared_error

        if self.train:
            logs['my_metric_train'] = float('inf')
            X_train, y_train = self.train[0], self.train[1]
            y_pred = self.model.predict(X_train)
            score = mse(y_train, y_pred)
            logs['my_metric_train'] = np.round(score, 5)

        if self.validation:
            logs['my_metric_val'] = float('inf')
            X_valid, y_valid = self.validation[0], self.validation[1]
            y_pred = self.model.predict(X_valid)
            val_score = mse(y_pred, y_valid)
            logs['my_metric_val'] = np.round(val_score, 5)
def build_model():

  inp1 = Input((5,))
  inp2 = Input((5,))
  out = Concatenate()([inp1, inp2])
  out = Dense(1)(out)

  model = Model([inp1, inp2], out)
  model.compile(loss='mse', optimizer='adam')

  return model
X_train1 = np.random.uniform(0,1, (100,5))
X_train2 = np.random.uniform(0,1, (100,5))
y_train = np.random.uniform(0,1, (100,1))

X_val1 = np.random.uniform(0,1, (100,5))
X_val2 = np.random.uniform(0,1, (100,5))
y_val = np.random.uniform(0,1, (100,1))
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train), validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train))])
-----------------------
class MyCustomMetricCallback(tf.keras.callbacks.Callback):

    def __init__(self, train=None, validation=None):
        super(MyCustomMetricCallback, self).__init__()
        self.train = train
        self.validation = validation

    def on_epoch_end(self, epoch, logs={}):

        mse = tf.keras.losses.mean_squared_error

        if self.train:
            logs['my_metric_train'] = float('inf')
            X_train, y_train = self.train[0], self.train[1]
            y_pred = self.model.predict(X_train)
            score = mse(y_train, y_pred)
            logs['my_metric_train'] = np.round(score, 5)

        if self.validation:
            logs['my_metric_val'] = float('inf')
            X_valid, y_valid = self.validation[0], self.validation[1]
            y_pred = self.model.predict(X_valid)
            val_score = mse(y_pred, y_valid)
            logs['my_metric_val'] = np.round(val_score, 5)
def build_model():

  inp1 = Input((5,))
  inp2 = Input((5,))
  out = Concatenate()([inp1, inp2])
  out = Dense(1)(out)

  model = Model([inp1, inp2], out)
  model.compile(loss='mse', optimizer='adam')

  return model
X_train1 = np.random.uniform(0,1, (100,5))
X_train2 = np.random.uniform(0,1, (100,5))
y_train = np.random.uniform(0,1, (100,1))

X_val1 = np.random.uniform(0,1, (100,5))
X_val2 = np.random.uniform(0,1, (100,5))
y_val = np.random.uniform(0,1, (100,1))
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train), validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train))])
-----------------------
class MyCustomMetricCallback(tf.keras.callbacks.Callback):

    def __init__(self, train=None, validation=None):
        super(MyCustomMetricCallback, self).__init__()
        self.train = train
        self.validation = validation

    def on_epoch_end(self, epoch, logs={}):

        mse = tf.keras.losses.mean_squared_error

        if self.train:
            logs['my_metric_train'] = float('inf')
            X_train, y_train = self.train[0], self.train[1]
            y_pred = self.model.predict(X_train)
            score = mse(y_train, y_pred)
            logs['my_metric_train'] = np.round(score, 5)

        if self.validation:
            logs['my_metric_val'] = float('inf')
            X_valid, y_valid = self.validation[0], self.validation[1]
            y_pred = self.model.predict(X_valid)
            val_score = mse(y_pred, y_valid)
            logs['my_metric_val'] = np.round(val_score, 5)
def build_model():

  inp1 = Input((5,))
  inp2 = Input((5,))
  out = Concatenate()([inp1, inp2])
  out = Dense(1)(out)

  model = Model([inp1, inp2], out)
  model.compile(loss='mse', optimizer='adam')

  return model
X_train1 = np.random.uniform(0,1, (100,5))
X_train2 = np.random.uniform(0,1, (100,5))
y_train = np.random.uniform(0,1, (100,1))

X_val1 = np.random.uniform(0,1, (100,5))
X_val2 = np.random.uniform(0,1, (100,5))
y_val = np.random.uniform(0,1, (100,1))
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train), validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train))])
-----------------------
class MyCustomMetricCallback(tf.keras.callbacks.Callback):

    def __init__(self, train=None, validation=None):
        super(MyCustomMetricCallback, self).__init__()
        self.train = train
        self.validation = validation

    def on_epoch_end(self, epoch, logs={}):

        mse = tf.keras.losses.mean_squared_error

        if self.train:
            logs['my_metric_train'] = float('inf')
            X_train, y_train = self.train[0], self.train[1]
            y_pred = self.model.predict(X_train)
            score = mse(y_train, y_pred)
            logs['my_metric_train'] = np.round(score, 5)

        if self.validation:
            logs['my_metric_val'] = float('inf')
            X_valid, y_valid = self.validation[0], self.validation[1]
            y_pred = self.model.predict(X_valid)
            val_score = mse(y_pred, y_valid)
            logs['my_metric_val'] = np.round(val_score, 5)
def build_model():

  inp1 = Input((5,))
  inp2 = Input((5,))
  out = Concatenate()([inp1, inp2])
  out = Dense(1)(out)

  model = Model([inp1, inp2], out)
  model.compile(loss='mse', optimizer='adam')

  return model
X_train1 = np.random.uniform(0,1, (100,5))
X_train2 = np.random.uniform(0,1, (100,5))
y_train = np.random.uniform(0,1, (100,1))

X_val1 = np.random.uniform(0,1, (100,5))
X_val2 = np.random.uniform(0,1, (100,5))
y_val = np.random.uniform(0,1, (100,1))
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train), validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(validation=([X_val1, X_val2],y_val))])
model = build_model()

model.fit([X_train1, X_train2], y_train, epochs=10, 
          callbacks=[MyCustomMetricCallback(train=([X_train1, X_train2],y_train))])

Keras AttributeError: 'Sequential' object has no attribute 'predict_classes'

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predict_x=model.predict(X_test) 
classes_x=np.argmax(predict_x,axis=1)
-----------------------
y_pred = model.predict(X_test)
y_pred = np.round(y_pred).astype(int)
-----------------------
predictions = model.predict_classess(x_test)
predictions = (model.predict(x_test) > 0.5).astype("int32")
-----------------------
predictions = model.predict_classess(x_test)
predictions = (model.predict(x_test) > 0.5).astype("int32")
-----------------------
y_predict = np.argmax(model.predict(x_test), axis=-1)
-----------------------
    predicted = np.argmax(model.predict(token_list),axis=1)
-----------------------
predictions = (model.predict(X_test) > 0.5)*1 

cannot import name '_registerMatType' from 'cv2.cv2'

copy iconCopydownload iconDownload
C:\Windows\system32>pip list |findstr opencv
opencv-python                 4.5.2.52
opencv-python-headless        4.5.5.62
pip uninstall opencv-python-headless==4.5.5.62
pip install opencv-python-headless==4.5.2.52
-----------------------
C:\Windows\system32>pip list |findstr opencv
opencv-python                 4.5.2.52
opencv-python-headless        4.5.5.62
pip uninstall opencv-python-headless==4.5.5.62
pip install opencv-python-headless==4.5.2.52
-----------------------
C:\Windows\system32>pip list |findstr opencv
opencv-python                 4.5.2.52
opencv-python-headless        4.5.5.62
pip uninstall opencv-python-headless==4.5.5.62
pip install opencv-python-headless==4.5.2.52
-----------------------
C:\Windows\system32>pip list |findstr opencv
opencv-python                 4.5.2.52
opencv-python-headless        4.5.5.62
-----------------------
pip uninstall opencv-python
pip install opencv-python
-----------------------
pip list | grep opencv 
opencv-contrib-python    4.5.3.56
opencv-python            4.5.5.62
python -m pip install --upgrade opencv-contrib-python
-----------------------
pip list | grep opencv 
opencv-contrib-python    4.5.3.56
opencv-python            4.5.5.62
python -m pip install --upgrade opencv-contrib-python
-----------------------
pip list | grep opencv 
opencv-contrib-python    4.5.3.56
opencv-python            4.5.5.62
python -m pip install --upgrade opencv-contrib-python

How to measure energy usage in Xcode 13 / iOS15?

copy iconCopydownload iconDownload
import MetricKit

...

// Somewhere in your application startup sequence:
MXMetricManager.shared.add(someObjectYouWantToHaveThisResponsibility)

...

extension SomeObjectYouWantToHaveThisResponsibility: MXMetricManagerSubscriber {
   func didReceive(_ payloads: [MXMetricPayload]) {
       for payload in payloads {
           // Parse the payload here
       }
   }
}

When recognizing hand gesture classes, I always get the same class in Keras

copy iconCopydownload iconDownload
feature1, feature2, feature3,y
aaa,bbb,3.0,2.0
bbb, ,4.1, 3.1
-----------------------
import tensorflow as tf
import pandas as pd

df_train = pd.read_csv('/content/training_set.csv', skiprows=1, index_col=0)
df_train = df_train.fillna(0)
x_train = df_train.drop(['138.1', '0.1'], axis=1)
y_train = df_train['138.1']
x_train = x_train / 310
y_train_cat = tf.keras.utils.to_categorical(y_train, 6)
model = tf.keras.Sequential([tf.keras.layers.Dense(64, input_shape=(41,), activation='relu'),
                                tf.keras.layers.Dense(32, activation='relu'),
                                tf.keras.layers.Dense(6, activation='softmax')])

model.compile(optimizer='adam',
             loss='categorical_crossentropy',
             metrics=['accuracy'])

model.fit(x_train, y_train_cat, batch_size=16, epochs=9, validation_split=0.2)
model.save("gestures_model.h5")

REV_CLASS_MAP = {
    0: "up",
    1: "down",
    2: "right",
    3: "left",
    4: "forward",
    5: "back"
}

def mapper(val):
    return REV_CLASS_MAP[val]

df_test = pd.read_csv('/content/testing_set.csv', skiprows=1, index_col=0)
df_test = df_test.fillna(0)
x_test = df_test.drop(['140', '0.1'], axis=1)
y_test = df_test['140']

model = tf.keras.models.load_model("/content/gestures_model.h5")

predicted_list = model.predict(x_test)

print(tf.argmax(predicted_list, axis=-1))
tf.Tensor(
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4
 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
 3 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 3 3 3 3 3 3 3
 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0
 0 0 0 1 1 0 0 4 4 4 0 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 1 1 1 3 4 4 4 4 1
 1 1 4 1 1 1 4], shape=(599,), dtype=int64)
-----------------------
import tensorflow as tf
import pandas as pd

df_train = pd.read_csv('/content/training_set.csv', skiprows=1, index_col=0)
df_train = df_train.fillna(0)
x_train = df_train.drop(['138.1', '0.1'], axis=1)
y_train = df_train['138.1']
x_train = x_train / 310
y_train_cat = tf.keras.utils.to_categorical(y_train, 6)
model = tf.keras.Sequential([tf.keras.layers.Dense(64, input_shape=(41,), activation='relu'),
                                tf.keras.layers.Dense(32, activation='relu'),
                                tf.keras.layers.Dense(6, activation='softmax')])

model.compile(optimizer='adam',
             loss='categorical_crossentropy',
             metrics=['accuracy'])

model.fit(x_train, y_train_cat, batch_size=16, epochs=9, validation_split=0.2)
model.save("gestures_model.h5")

REV_CLASS_MAP = {
    0: "up",
    1: "down",
    2: "right",
    3: "left",
    4: "forward",
    5: "back"
}

def mapper(val):
    return REV_CLASS_MAP[val]

df_test = pd.read_csv('/content/testing_set.csv', skiprows=1, index_col=0)
df_test = df_test.fillna(0)
x_test = df_test.drop(['140', '0.1'], axis=1)
y_test = df_test['140']

model = tf.keras.models.load_model("/content/gestures_model.h5")

predicted_list = model.predict(x_test)

print(tf.argmax(predicted_list, axis=-1))
tf.Tensor(
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4
 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
 3 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 3 3 3 3 3 3 3
 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0
 0 0 0 1 1 0 0 4 4 4 0 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 1 1 1 3 4 4 4 4 1
 1 1 4 1 1 1 4], shape=(599,), dtype=int64)
-----------------------
import tensorflow as tf
import pandas as pd

df_train = pd.read_csv('/content/training_set.csv', skiprows=1, index_col=0)
df_train = df_train.fillna(0)
x_train = df_train.drop(['138.1', '0.1'], axis=1)
y_train = df_train['138.1']
x_train = x_train / 310
y_train_cat = tf.keras.utils.to_categorical(y_train, 6)
model = tf.keras.Sequential([tf.keras.layers.Dense(64, input_shape=(41,), activation='relu'),
                                tf.keras.layers.Dense(32, activation='relu'),
                                tf.keras.layers.Dense(6, activation='softmax')])

model.compile(optimizer='adam',
             loss='categorical_crossentropy',
             metrics=['accuracy'])

model.fit(x_train, y_train_cat, batch_size=16, epochs=9, validation_split=0.2)
model.save("gestures_model.h5")

REV_CLASS_MAP = {
    0: "up",
    1: "down",
    2: "right",
    3: "left",
    4: "forward",
    5: "back"
}

def mapper(val):
    return REV_CLASS_MAP[val]

df_test = pd.read_csv('/content/testing_set.csv', skiprows=1, index_col=0)
df_test = df_test.fillna(0)
x_test = df_test.drop(['140', '0.1'], axis=1)
y_test = df_test['140']

model = tf.keras.models.load_model("/content/gestures_model.h5")

predicted_list = model.predict(x_test)

print(tf.argmax(predicted_list, axis=-1))
tf.Tensor(
[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 3 4 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0
 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 4 4 4 4 4 4 4 4 1 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 1 1 1 1 1 1 1 1 1 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1
 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4
 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 2 2 2 2 2 2 2 2 2 2 4 4 4 4 4 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2
 2 2 2 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3
 3 4 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 3 3 3 3 3 3 3
 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4
 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 0
 0 0 0 1 1 0 0 4 4 4 0 4 4 4 4 4 4 4 3 4 4 4 4 4 4 4 4 4 1 1 1 3 4 4 4 4 1
 1 1 4 1 1 1 4], shape=(599,), dtype=int64)

Spring Boot WebClient stops sending requests

copy iconCopydownload iconDownload
jstack <java pid> > ThredDump.txt
-----------------------
    "reactor-http-epoll-6@15467" daemon prio=5 tid=0xbe nid=NA waiting
  java.lang.Thread.State: WAITING
      at jdk.internal.misc.Unsafe.park(Unsafe.java:-1)
      at java.util.concurrent.locks.LockSupport.park(LockSupport.java:194)
      at java.util.concurrent.CompletableFuture$Signaller.block(CompletableFuture.java:1796)

Add Kubernetes scrape target to Prometheus instance that is NOT in Kubernetes

copy iconCopydownload iconDownload
- job_name: kubernetes
  kubernetes_sd_configs:
  - role: node
    api_server: https://kubernetes-cluster-api.com
    tls_config:
      insecure_skip_verify: true
      bearer_token: "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
  bearer_token: "xxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxxx"
  scheme: https
  tls_config:
    insecure_skip_verify: true
  relabel_configs:
  - separator: ;
    regex: __meta_kubernetes_node_label_(.+)
    replacement: $1
    action: labelmap
-----------------------
- job_name: 'kubelet-cadvisor'
  scheme: https

  kubernetes_sd_configs:
  - role: node
    api_server: https://api-server.example.com

    # TLS and auth settings to perform service discovery
    authorization:
      credentials_file: /kube/token  # the file with your service account token
    tls_config:
      ca_file: /kube/CA.crt  # the file with the CA certificate

  # The same as above but for actual scrape request.
  # We're going to send scrape requests back to the API-server
  # so the credentials are the same.
  bearer_token_file: /kube/token
  tls_config:
    ca_file: /kube/CA.crt

  relabel_configs:
  # This is just to drop this long __meta_kubernetes_node_label_ prefix
  - action: labelmap
    regex: __meta_kubernetes_node_label_(.+)

  # By default Prometheus goes to /metrics endpoint.
  # This relabeling changes it to /api/v1/nodes/[kubernetes_io_hostname]/proxy/metrics/cadvisor
  - source_labels: [kubernetes_io_hostname]
    replacement: /api/v1/nodes/$1/proxy/metrics/cadvisor
    target_label: __metrics_path__

  # This relabeling defines that Prometheus should connect to the
  # API-server instead of the actual instance. Together with the relabeling
  # from above this will make the scrape request proxied to the node kubelet.
  - replacement: api-server.example.com
    target_label: __address__
❯ kubectl config view --raw
apiVersion: v1
clusters:
- cluster:                      # you need this ⤋ long value 
    certificate-authority-data: LS0tLS1CRUdJTiBDRVJUSUZJ...
    server: https://api-server.example.com
  name: default
...
echo LS0tLS1CRUdJTiBDRVJUSUZJ... | base64 -d > CA.crt
-----------------------
- job_name: 'kubelet-cadvisor'
  scheme: https

  kubernetes_sd_configs:
  - role: node
    api_server: https://api-server.example.com

    # TLS and auth settings to perform service discovery
    authorization:
      credentials_file: /kube/token  # the file with your service account token
    tls_config:
      ca_file: /kube/CA.crt  # the file with the CA certificate

  # The same as above but for actual scrape request.
  # We're going to send scrape requests back to the API-server
  # so the credentials are the same.
  bearer_token_file: /kube/token
  tls_config:
    ca_file: /kube/CA.crt

  relabel_configs:
  # This is just to drop this long __meta_kubernetes_node_label_ prefix
  - action: labelmap
    regex: __meta_kubernetes_node_label_(.+)

  # By default Prometheus goes to /metrics endpoint.
  # This relabeling changes it to /api/v1/nodes/[kubernetes_io_hostname]/proxy/metrics/cadvisor
  - source_labels: [kubernetes_io_hostname]
    replacement: /api/v1/nodes/$1/proxy/metrics/cadvisor
    target_label: __metrics_path__

  # This relabeling defines that Prometheus should connect to the
  # API-server instead of the actual instance. Together with the relabeling
  # from above this will make the scrape request proxied to the node kubelet.
  - replacement: api-server.example.com
    target_label: __address__
❯ kubectl config view --raw
apiVersion: v1
clusters:
- cluster:                      # you need this ⤋ long value 
    certificate-authority-data: LS0tLS1CRUdJTiBDRVJUSUZJ...
    server: https://api-server.example.com
  name: default
...
echo LS0tLS1CRUdJTiBDRVJUSUZJ... | base64 -d > CA.crt
-----------------------
- job_name: 'kubelet-cadvisor'
  scheme: https

  kubernetes_sd_configs:
  - role: node
    api_server: https://api-server.example.com

    # TLS and auth settings to perform service discovery
    authorization:
      credentials_file: /kube/token  # the file with your service account token
    tls_config:
      ca_file: /kube/CA.crt  # the file with the CA certificate

  # The same as above but for actual scrape request.
  # We're going to send scrape requests back to the API-server
  # so the credentials are the same.
  bearer_token_file: /kube/token
  tls_config:
    ca_file: /kube/CA.crt

  relabel_configs:
  # This is just to drop this long __meta_kubernetes_node_label_ prefix
  - action: labelmap
    regex: __meta_kubernetes_node_label_(.+)

  # By default Prometheus goes to /metrics endpoint.
  # This relabeling changes it to /api/v1/nodes/[kubernetes_io_hostname]/proxy/metrics/cadvisor
  - source_labels: [kubernetes_io_hostname]
    replacement: /api/v1/nodes/$1/proxy/metrics/cadvisor
    target_label: __metrics_path__

  # This relabeling defines that Prometheus should connect to the
  # API-server instead of the actual instance. Together with the relabeling
  # from above this will make the scrape request proxied to the node kubelet.
  - replacement: api-server.example.com
    target_label: __address__
❯ kubectl config view --raw
apiVersion: v1
clusters:
- cluster:                      # you need this ⤋ long value 
    certificate-authority-data: LS0tLS1CRUdJTiBDRVJUSUZJ...
    server: https://api-server.example.com
  name: default
...
echo LS0tLS1CRUdJTiBDRVJUSUZJ... | base64 -d > CA.crt

Saving model on Tensorflow 2.7.0 with data augmentation layer

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import tensorflow as tf
import numpy as np

class RandomColorDistortion(tf.keras.layers.Layer):
    def __init__(self, contrast_range=[0.5, 1.5], 
                 brightness_delta=[-0.2, 0.2], **kwargs):
        super(RandomColorDistortion, self).__init__(**kwargs)
        self.contrast_range = contrast_range
        self.brightness_delta = brightness_delta
    
    def call(self, images, training=None):
        if not training:
            return images
        contrast = np.random.uniform(
            self.contrast_range[0], self.contrast_range[1])
        brightness = np.random.uniform(
            self.brightness_delta[0], self.brightness_delta[1])
        
        images = tf.image.adjust_contrast(images, contrast)
        images = tf.image.adjust_brightness(images, brightness)
        images = tf.clip_by_value(images, 0, 1)
        return images
    
    def get_config(self):
        config = super(RandomColorDistortion, self).get_config()
        config.update({"contrast_range": self.contrast_range, "brightness_delta": self.brightness_delta})
        return config
        
input_shape_rgb = (256, 256, 3)
data_augmentation_rgb = tf.keras.Sequential(
  [ 
    tf.keras.layers.RandomFlip("horizontal"),
    tf.keras.layers.RandomFlip("vertical"),
    tf.keras.layers.RandomRotation(0.5),
    tf.keras.layers.RandomZoom(0.5),
    tf.keras.layers.RandomContrast(0.5),
    RandomColorDistortion(name='random_contrast_brightness/none'),
  ]
)
input_shape = (256, 256, 3)
padding = 'same'
kernel_size = 3
model = tf.keras.Sequential([
  tf.keras.layers.Input(input_shape),
  data_augmentation_rgb,
  tf.keras.layers.Rescaling((1./255)),
  tf.keras.layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1, 
     data_format='channels_last'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(32, kernel_size, padding=padding, activation='relu'), # best 4
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(64, kernel_size, padding=padding, activation='relu'), # best 3
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(128, kernel_size, padding=padding, activation='relu'), # best 3
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(128, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(64, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(5, activation = 'softmax')
 ])

model.compile(loss='categorical_crossentropy', optimizer='adam')
model.summary()
model.save("test", save_format='h5')
model = tf.keras.models.load_model('test.h5', custom_objects={'RandomColorDistortion': RandomColorDistortion})
-----------------------
import tensorflow as tf
import numpy as np

class RandomColorDistortion(tf.keras.layers.Layer):
    def __init__(self, contrast_range=[0.5, 1.5], 
                 brightness_delta=[-0.2, 0.2], **kwargs):
        super(RandomColorDistortion, self).__init__(**kwargs)
        self.contrast_range = contrast_range
        self.brightness_delta = brightness_delta
    
    def call(self, images, training=None):
        if not training:
            return images
        contrast = np.random.uniform(
            self.contrast_range[0], self.contrast_range[1])
        brightness = np.random.uniform(
            self.brightness_delta[0], self.brightness_delta[1])
        
        images = tf.image.adjust_contrast(images, contrast)
        images = tf.image.adjust_brightness(images, brightness)
        images = tf.clip_by_value(images, 0, 1)
        return images
    
    def get_config(self):
        config = super(RandomColorDistortion, self).get_config()
        config.update({"contrast_range": self.contrast_range, "brightness_delta": self.brightness_delta})
        return config
        
input_shape_rgb = (256, 256, 3)
data_augmentation_rgb = tf.keras.Sequential(
  [ 
    tf.keras.layers.RandomFlip("horizontal"),
    tf.keras.layers.RandomFlip("vertical"),
    tf.keras.layers.RandomRotation(0.5),
    tf.keras.layers.RandomZoom(0.5),
    tf.keras.layers.RandomContrast(0.5),
    RandomColorDistortion(name='random_contrast_brightness/none'),
  ]
)
input_shape = (256, 256, 3)
padding = 'same'
kernel_size = 3
model = tf.keras.Sequential([
  tf.keras.layers.Input(input_shape),
  data_augmentation_rgb,
  tf.keras.layers.Rescaling((1./255)),
  tf.keras.layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1, 
     data_format='channels_last'),
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(32, kernel_size, padding=padding, activation='relu'), # best 4
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(64, kernel_size, padding=padding, activation='relu'), # best 3
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Conv2D(128, kernel_size, padding=padding, activation='relu'), # best 3
  tf.keras.layers.MaxPooling2D(),
  tf.keras.layers.BatchNormalization(),

  tf.keras.layers.Flatten(),
  tf.keras.layers.Dense(128, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(128, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(64, activation='relu'), # best 1
  tf.keras.layers.Dropout(0.1),
  tf.keras.layers.Dense(5, activation = 'softmax')
 ])

model.compile(loss='categorical_crossentropy', optimizer='adam')
model.summary()
model.save("test", save_format='h5')
model = tf.keras.models.load_model('test.h5', custom_objects={'RandomColorDistortion': RandomColorDistortion})

get cloudfront usage report via aws cli

copy iconCopydownload iconDownload
aws ce get-cost-and-usage \                                                                                                        
--time-period Start=2022-01-01,End=2022-01-03 \
--granularity MONTHLY \
--metrics "BlendedCost" "UnblendedCost" "UsageQuantity" \
--group-by Type=DIMENSION,Key=SERVICE Type=TAG,Key=Environment
-----------------------
curl 'https://console.aws.amazon.com/cloudfront/v3/api/cloudfrontreporting' \
  -H 'authority: console.aws.amazon.com' \
  -H 'sec-ch-ua: " Not;A Brand";v="99", "Google Chrome";v="97", "Chromium";v="97"' \
  -H 'content-type: application/json' \
  -H 'x-csrf-token: ${CSRF_TOKEN}' \
  -H 'accept: */*' \
  -H 'origin: https://console.aws.amazon.com' \
  -H 'sec-fetch-site: same-origin' \
  -H 'sec-fetch-mode: cors' \
  -H 'sec-fetch-dest: empty' \
  -H 'referer: https://console.aws.amazon.com/cloudfront/v3/home?region=eu-central-1' \
  -H 'accept-language: en-US,en;q=0.9' \
  -H 'cookie: ${COOKIE}' \
  --data-raw '{"headers":{"X-Amz-User-Agent":"aws-sdk-js/2.849.0 promise"},"path":"/2014-01-01/reports/series","method":"POST","region":"us-east-1","params":{},"contentString":"<DataPointSeriesRequestFilters xmlns=\"http://cloudfront.amazonaws.com/doc/2014-01-01/\"><Report>Usage</Report><StartTime>2022-01-28T11:23:35Z</StartTime><EndTime>2022-02-04T11:23:35Z</EndTime><TimeBucketSizeMinutes>ONE_DAY</TimeBucketSizeMinutes><ResourceId>All Web Distributions (excludes deleted)</ResourceId><Region>ALL</Region><Series><DataKey><Name>HTTP</Name><Description></Description></DataKey><DataKey><Name>HTTPS</Name><Description></Description></DataKey><DataKey><Name>HTTP-BYTES</Name><Description></Description></DataKey><DataKey><Name>HTTPS-BYTES</Name><Description></Description></DataKey><DataKey><Name>BYTES-OUT</Name><Description></Description></DataKey><DataKey><Name>BYTES-IN</Name><Description></Description></DataKey><DataKey><Name>FLE</Name><Description></Description></DataKey></Series></DataPointSeriesRequestFilters>","operation":"listDataPointSeries"}' \
  --compressed > report.xml

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QUESTION

Microk8s dashboard using nginx-ingress via http not working (Error: `no matches for kind &quot;Ingress&quot; in version &quot;extensions/v1beta1&quot;`)

Asked 2022-Apr-01 at 07:26

I have microk8s v1.22.2 running on Ubuntu 20.04.3 LTS.

Output from /etc/hosts:

127.0.0.1 localhost
127.0.1.1 main

Excerpt from microk8s status:

addons:
  enabled:
    dashboard            # The Kubernetes dashboard
    ha-cluster           # Configure high availability on the current node
    ingress              # Ingress controller for external access
    metrics-server       # K8s Metrics Server for API access to service metrics

I checked for the running dashboard (kubectl get all --all-namespaces):

NAMESPACE     NAME                                             READY   STATUS    RESTARTS   AGE
kube-system   pod/calico-node-2jltr                            1/1     Running   0          23m
kube-system   pod/calico-kube-controllers-f744bf684-d77hv      1/1     Running   0          23m
kube-system   pod/metrics-server-85df567dd8-jd6gj              1/1     Running   0          22m
kube-system   pod/kubernetes-dashboard-59699458b-pb5jb         1/1     Running   0          21m
kube-system   pod/dashboard-metrics-scraper-58d4977855-94nsp   1/1     Running   0          21m
ingress       pod/nginx-ingress-microk8s-controller-qf5pm      1/1     Running   0          21m

NAMESPACE     NAME                                TYPE        CLUSTER-IP       EXTERNAL-IP   PORT(S)    AGE
default       service/kubernetes                  ClusterIP   10.152.183.1     <none>        443/TCP    23m
kube-system   service/metrics-server              ClusterIP   10.152.183.81    <none>        443/TCP    22m
kube-system   service/kubernetes-dashboard        ClusterIP   10.152.183.103   <none>        443/TCP    22m
kube-system   service/dashboard-metrics-scraper   ClusterIP   10.152.183.197   <none>        8000/TCP   22m

NAMESPACE     NAME                                               DESIRED   CURRENT   READY   UP-TO-DATE   AVAILABLE   NODE SELECTOR            AGE
kube-system   daemonset.apps/calico-node                         1         1         1       1            1           kubernetes.io/os=linux   23m
ingress       daemonset.apps/nginx-ingress-microk8s-controller   1         1         1       1            1           <none>                   22m

NAMESPACE     NAME                                        READY   UP-TO-DATE   AVAILABLE   AGE
kube-system   deployment.apps/calico-kube-controllers     1/1     1            1           23m
kube-system   deployment.apps/metrics-server              1/1     1            1           22m
kube-system   deployment.apps/kubernetes-dashboard        1/1     1            1           22m
kube-system   deployment.apps/dashboard-metrics-scraper   1/1     1            1           22m

NAMESPACE     NAME                                                   DESIRED   CURRENT   READY   AGE
kube-system   replicaset.apps/calico-kube-controllers-69d7f794d9     0         0         0       23m
kube-system   replicaset.apps/calico-kube-controllers-f744bf684      1         1         1       23m
kube-system   replicaset.apps/metrics-server-85df567dd8              1         1         1       22m
kube-system   replicaset.apps/kubernetes-dashboard-59699458b         1         1         1       21m
kube-system   replicaset.apps/dashboard-metrics-scraper-58d4977855   1         1         1       21m

I want to expose the microk8s dashboard within my local network to access it through http://main/dashboard/

To do so, I did the following nano ingress.yaml:

apiVersion: extensions/v1beta1
kind: Ingress
metadata:
  annotations:
    kubernetes.io/ingress.class: public
    nginx.ingress.kubernetes.io/backend-protocol: "HTTPS"
  name: dashboard
  namespace: kube-system
spec:
  rules:
  - host: main
    http:
      paths:
      - backend:
          serviceName: kubernetes-dashboard
          servicePort: 443
        path: /

Enabling the ingress-config through kubectl apply -f ingress.yaml gave the following error:

error: unable to recognize "ingress.yaml": no matches for kind "Ingress" in version "extensions/v1beta1"

Help would be much appreciated, thanks!

Update: @harsh-manvar pointed out a mismatch in the config version. I have rewritten ingress.yaml to a very stripped down version:

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: dashboard
  namespace: kube-system
spec:
  rules:
  - http:
      paths:
      - path: /dashboard
        pathType: Prefix
        backend:
          service:
            name: kubernetes-dashboard
            port:
              number: 443

Applying this works. Also, the ingress rule gets created.

NAMESPACE     NAME        CLASS    HOSTS   ADDRESS     PORTS   AGE
kube-system   dashboard   public   *       127.0.0.1   80      11m

However, when I access the dashboard through http://<ip-of-kubernetes-master>/dashboard, I get a 400 error.

Log from the ingress controller:

192.168.0.123 - - [10/Oct/2021:21:38:47 +0000] "GET /dashboard HTTP/1.1" 400 54 "-" "Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/94.0.4606.71 Safari/537.36" 466 0.002 [kube-system-kubernetes-dashboard-443] [] 10.1.76.3:8443 48 0.000 400 ca0946230759edfbaaf9d94f3d5c959a

Does the dashboard also need to be exposed using the microk8s proxy? I thought the ingress controller would take care of this, or did I misunderstand this?

ANSWER

Answered 2021-Oct-10 at 18:29
error: unable to recognize "ingress.yaml": no matches for kind "Ingress" in version "extensions/v1beta1"

it' due to the mismatch in the ingress API version.

You are running the v1.22.2 while API version in YAML is old.

Good example : https://kubernetes.io/docs/tasks/access-application-cluster/ingress-minikube/

you are using the older ingress API version in your YAML which is extensions/v1beta1.

You need to change this based on ingress version and K8s version you are running.

This is for version 1.19 in K8s and will work in 1.22 also

Example :

apiVersion: networking.k8s.io/v1
kind: Ingress
metadata:
  name: minimal-ingress
  annotations:
    nginx.ingress.kubernetes.io/rewrite-target: /
spec:
  rules:
  - http:
      paths:
      - path: /testpath
        pathType: Prefix
        backend:
          service:
            name: test
            port:
              number: 80

Source https://stackoverflow.com/questions/69517855

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

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