metrics | Capturing JVM and applicationlevel metrics | Analytics library

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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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

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quality kandi Quality

metrics has 0 bugs and 0 code smells.

metrics Quality

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metrics Quality

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security Security

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.

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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.

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build Reuse

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.

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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.

metrics Examples and Code Snippets

See all related Code Snippets

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

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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'

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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?

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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

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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

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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

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- 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

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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

See all related Code Snippets

Community Discussions

Trending Discussions on metrics

Microk8s dashboard using nginx-ingress via http not working (Error: `no matches for kind "Ingress" in version "extensions/v1beta1"`)
How to prevent Keras from computing metrics during training
Keras AttributeError: 'Sequential' object has no attribute 'predict_classes'
cannot import name '_registerMatType' from 'cv2.cv2'
How to measure energy usage in Xcode 13 / iOS15?
When recognizing hand gesture classes, I always get the same class in Keras
Spring Boot WebClient stops sending requests
Add Kubernetes scrape target to Prometheus instance that is NOT in Kubernetes
Saving model on Tensorflow 2.7.0 with data augmentation layer
get cloudfront usage report via aws cli

Trending Discussions on metrics

QUESTION

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

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

Reference : https://kubernetes.io/docs/concepts/services-networking/ingress/

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

QUESTION

How to prevent Keras from computing metrics during training

Asked 2022-Mar-23 at 09:20

I'm using Tensorflow/Keras 2.4.1 and I have a (unsupervised) custom metric that takes several of my model inputs as parameters such as:

model = build_model() # returns a tf.keras.Model object
my_metric = custom_metric(model.output, model.input[0], model.input[1])
model.add_metric(my_metric)
[...]
model.fit([...]) # training with fit

However, it happens that custom_metric is very expensive so I would like it to be computed during validation only. I found this answer but I hardly understand how I can adapt the solution to my metric that uses several model inputs as parameter since the update_state method doesn't seem flexible.

In my context, is there a way to avoid computing my metric during training, aside from writing my own training loop ? Also, I am very surprised we cannot natively specify to Tensorflow that some metrics should only be computed at validation time, is there a reason for that ?

In addition, since the model is trained to optimize the loss, and that the training dataset should not be used to evaluate a model, I don't even understand why, by default, Tensorflow computes metrics during training.

ANSWER

Answered 2022-Mar-16 at 13:52

I was able to use learning_phase but only in symbolic tensor mode (graph) mode:

So, at first we need to disable eager mode (this must be done right after importing tensorflow):

import tensorflow as tf
tf.compat.v1.disable_eager_execution()

Then you can create your metric using a symbolic if (backend.switch):

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

The method add_metric will ask for a name and an aggregation method, which you can set to "mean".

So, here is one example:

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)

Source https://stackoverflow.com/questions/71412499

QUESTION

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

Asked 2022-Mar-23 at 04:30

Im attempting to find model performance metrics (F1 score, accuracy, recall) following this guide https://machinelearningmastery.com/how-to-calculate-precision-recall-f1-and-more-for-deep-learning-models/

This exact code was working a few months ago but now returning all sorts of errors, very confusing since i havent changed one character of this code. Maybe a package update has changed things?

I fit the sequential model with model.fit, then used model.evaluate to find test accuracy. Now i am attempting to use model.predict_classes to make class predictions (model is a multi-class classifier). Code shown below:

model = Sequential()
model.add(Dense(24, input_dim=13, activation='relu'))
model.add(Dense(18, activation='relu'))
model.add(Dense(6, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])

-

history = model.fit(X_train, y_train, batch_size = 256, epochs = 10, verbose = 2, validation_split = 0.2)

-

score, acc = model.evaluate(X_test, y_test,verbose=2, batch_size= 256)
print('test accuracy:', acc)

-

yhat_classes = model.predict_classes(X_test)

last line returns error "AttributeError: 'Sequential' object has no attribute 'predict_classes'"

This exact code was working not long ago so struggling a bit, thanks for any help

ANSWER

Answered 2021-Aug-19 at 03:49

This function were removed in TensorFlow version 2.6. According to the keras in rstudio reference

update to

predict_x=model.predict(X_test) 
classes_x=np.argmax(predict_x,axis=1)

Or use TensorFlow 2.5 or later.

If you are using TensorFlow version 2.5, you will receive the following warning:

tensorflow\python\keras\engine\sequential.py:455: UserWarning: model.predict_classes() is deprecated and will be removed after 2021-01-01. Please use instead:* np.argmax(model.predict(x), axis=-1), if your model does multi-class classification (e.g. if it uses a softmax last-layer activation).* (model.predict(x) > 0.5).astype("int32"), if your model does binary classification (e.g. if it uses a sigmoid last-layer activation).

Source https://stackoverflow.com/questions/68836551

QUESTION

How to measure energy usage in Xcode 13 / iOS15?

Asked 2022-Feb-23 at 00:43

I'd like to confirm the battery usage of an app I am developing on iOS, specifically on Xcode 13 and iOS 15. (Note: This app previously showed no issues with battery usage on previous versions of iOS.)

Previously, it seems that there were two ways to gather energy usage information:

#1. On the device under Settings > Developer > Logging

As per Apple's documentation described in the section titled "Log Energy Usage Directly on an iOS Device".
However, on iOS15, I can't find any options for logging under Developer or anywhere under settings even when searching.

#2. Profiling via Instruments using the "Energy Log" template

As per the same documentation from Apple described in the section "Use the Energy Diagnostics Profiling Template".
While it is still available in Xcode 12, this template is missing in Xcode 13. Naturally, it's also not possible to profile an iOS15 device with Xcode 12.

Digging through the Xcode 13 release notes, I found the following:

Instruments no longer includes the Energy template; use metrics reporting in the Xcode Organizer instead. (74161279)

When I access the Organizer in Xcode (12 or 13), select an app and click "Energy" for all versions of the app, it shows the following:

Apple's documentation for "Analyzing the Performance of Your Shipping App" says:

"In some cases the pane shows “Insufficient usage data available,” because there may not be enough anonymized data reported by participating user devices. When this happens, try checking back in a few days."

Well over a year into production and having sufficient install numbers, I have a feeling that waiting a few days might not do much.

I would like to determine if this is a bug in my app or a bug in iOS15. How can energy usage data be gathered using Xcode 13 on iOS 15?

ANSWER

Answered 2022-Feb-23 at 00:43

After contacting Apple Developer Technical Support (DTS) regarding this issue, they provided me with the following guidance.

Regarding "insufficient usage data available" for energy logs accessible via the Xcode organizer:

DTS indicated that they do not publish the thresholds for active users and usage logs would be expected to be present if you have more that a few thousand active users consistently on each version of your app. If your app meets this criteria and still does not show energy logs, DTS recommended opening a bug report with them.

Regarding how to collect energy log data for your app:

DTS recommended using MetricKit to get daily metric payloads. Payloads are delivered to your app every 24 hours and it is then possible to consume them and send them off device.

The instantiation of this is vey basic and can be as simple as:

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
       }
   }
}

For a full list of metrics, see the MXMetricPayload class.

Regarding the two documentation links that I included above:

Apple asked me to open feedback tickets in order to get them updated. I opened both tickets (FB9665186, FB9665194) on September 30, 2021 and they are both still in an "Open" status as of today.

In the case of my app and the problem I had encountered, it turns out that battery drain was just a symptom and not the actual problem, so it wouldn't be useful to include any of those details here.

Source https://stackoverflow.com/questions/69354959

QUESTION

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

Asked 2022-Feb-22 at 13:49

When recognizing hand gesture classes, I always get the same class, although I tried changing the parameters and even passed the data without normalization:

df_train = pd.read_csv('train_dataset.csv')
df_train = df_train.drop(columns=['Unnamed: 0'], axis=1)
df_train = df_train.fillna(0)

x_train = df_train.drop(['y'], axis=1)
y_train = df_train['y']

x_train = x_train / 310

model = keras.models.Sequential([Dense(32, input_shape=(42,), activation='relu'),
                                Dense(64, activation='relu'),
                                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")

Here is a main code:

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

def mapper(val):
    return REV_CLASS_MAP[val]

if len(data[data.index(new_row)]) > 0:
    df = pd.DataFrame(data, columns=columns)
    df = df.fillna(0)
    df = df / 310
    pred = model.predict(df)
    move_code = np.argmax(pred[0])
    user_move_name = mapper(move_code)
    print(user_move_name)

Here is an example of input data:

56,172,72,169,88,155,100,144,111,139,78,120,81,94,82,77,82,62,66,120,62,104,62,124,64,136,54,122,50,110,52,130,55,139,43,126,40,114,42,129,45,137,0

What am I doing wrong and how to fix it? I noticed that in my data there are rows in which there is only one number. Could this be the cause of my problem? ⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀ P.S I am new to neural networks and keras.

New

Here is df_train before processing:

,x11,x21,x12,x22,x13,x23,x14,x24,x15,x25,x16,x26,x17,x27,x18,x28,x19,x29,x110,x210,x111,x211,x112,x212,x113,x213,114,214,115,x215,x116,x216,x117,x217,x118,x218,x119,x219,x120,x220,x121,x221,y
56,172,72,169,88,155,100,144,111,139,78,120,81,94,82,77,82,62,66,120,62,104,62,124,64,136,54,122,50,110,52,130,55,139,43,126,40,114,42,129,45,137,0
...
84,166,96,158,108,143,108,131,101,127,87,145,87,128,90,118,94,111,74,147,76,119,81,114,84,115,64,148,66,120,72,119,74,124,56,148,57,124,61,124,63,129,5

Here is df_train after processing:

     x11  x21  x12  x22  x13  x23  x14  ...  x119  x219  x120  x220  x121  x221    y
0     56  175   73  168   88  155  101  ...    42   113    44   130    47   138  0.0
1    172   72  169   88  155  100  144  ...   114    42   129    45   137     0  0.0
2    172   72  169   88  155  100  144  ...   114    42   129    45   137     0  0.0
3    174   74  167   89  155  101  144  ...   115    44   130    46   137     0  0.0
4    174   74  169   90  155  101  144  ...   114    44   128    46   136     0  0.0
..   ...  ...  ...  ...  ...  ...  ...  ...   ...   ...   ...   ...   ...   ...  ...
843  166   96  158  108  143  108  131  ...   124    61   124    63   129     5  0.0
844  166   94  158  105  145  104  132  ...   128    58   130    59   134     5  0.0
845  164   90  155  101  141  100  129  ...   126    55   129    57   134     5  0.0
846  158   88  152   99  140   96  128  ...   142    54   150    58   146     5  0.0
847  158   88  152   99  140   96  128  ...   142    54   150    58   146     5  0.0

⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀⠀

ANSWER

Answered 2022-Feb-17 at 18:48

All rows need the same data size, of course some values can be empty in csv.

feature1, feature2, feature3,y
aaa,bbb,3.0,2.0
bbb, ,4.1, 3.1

You need to impute empty values by using for example most frequent value for categorical values or median for numerical values. Predicted value cant be empty

Source https://stackoverflow.com/questions/71163462

QUESTION

Spring Boot WebClient stops sending requests

Asked 2022-Feb-18 at 14:42

I am running a Spring Boot app that uses WebClient for both non-blocking and blocking HTTP requests. After the app has run for some time, all outgoing HTTP requests seem to get stuck.

WebClient is used to send requests to multiple hosts, but as an example, here is how it is initialized and used to send requests to Telegram:

WebClientConfig:

    @Bean
    public ReactorClientHttpConnector httpClient() {
        HttpClient.create(ConnectionProvider.builder("connectionProvider").build())
                .option(ChannelOption.CONNECT_TIMEOUT_MILLIS, connectTimeout)
                .responseTimeout(Duration.ofMillis(responseTimeout));
        return new ReactorClientHttpConnector(httpClient);
    }

The same ReactorClientHttpConnector is used for all WebClients.

TelegramClient:

    @Autowired
    ReactorClientHttpConnector httpClient;

    WebClient webClient;

    RateLimiter rateLimiter;

    @PostConstruct
    public void init() {
        webClient = WebClient.builder()
                .clientConnector(httpClient)
                .baseUrl(telegramUrl)
                .build();

        rateLimiter = RateLimiter.of("telegram-rate-limiter",
                RateLimiterConfig.custom()
                        .limitRefreshPeriod(Duration.ofMinutes(1))
                        .limitForPeriod(20)
                        .build());
    }

    public void sendMessage(@PathVariable("token") String token, @RequestParam("chat_id") long chatId, @RequestParam("text") String message) {
        webClient.post().uri(String.format("/bot%s/sendMessage", token))
                .contentType(MediaType.APPLICATION_JSON)
                .body(BodyInserters.fromFormData("chat_id", String.valueOf(chatId))
                        .with("text", message))
                .retrieve()
                .bodyToMono(Void.class)
                .transformDeferred(RateLimiterOperator.of(rateLimiter))
                .block();
    }

The RateLimiter is used to ensure the number of requests does not exceed 20 a minute as specified in the Telegram API.

When the app is started, all requests are resolved normally as expected. But after some time has passed, all requests seem to get stuck. The amount of time needed for this to happen can vary from a few hours to a few days. It happens for all requests to different hosts and is easily noticeable when messages from the TelegramBot stops. Once the requests get stuck, they are stuck indefinitely and I have to restart the app to get it working again.

There are no exceptions in the log that seem to have caused this. Since I maintain a queue for my telegram messages, I can see the point in time when the requests stop when the number of messages in the queue steadily increases and when errors happen in the other processes that are waiting for the requests to resolve.

It does not seem like the requests are even sent out as the connect timeout and response timeout that I have set do not take effect.

I had previously also tried setting idle time to 0 but that did not solve the problem

    @Bean
    public ReactorClientHttpConnector httpClient() {
        HttpClient httpClient = HttpClient.create(ConnectionProvider.builder("connectionProvider").maxConnections(1000).maxIdleTime(Duration.ofSeconds(0)).build())
        HttpClient httpClient = HttpClient.newConnection()
                .option(ChannelOption.CONNECT_TIMEOUT_MILLIS, connectTimeout)
                .responseTimeout(Duration.ofMillis(responseTimeout));
        return new ReactorClientHttpConnector(httpClient);
    }

Update:

I enabled metrics and viewed it using micrometer when it got stuck. Interestingly, it shows that there is one connection for Telegram, but also shows no connections on idle, pending or active.

reactor_netty_connection_provider_idle_connections{id="-1268283746",name="connectionProvider",remote_address="api.telegram.org:443",} 0.0
reactor_netty_connection_provider_pending_connections{id="-1268283746",name="connectionProvider",remote_address="api.telegram.org:443",} 0.0
reactor_netty_connection_provider_active_connections{id="-1268283746",name="connectionProvider",remote_address="api.telegram.org:443",} 0.0
reactor_netty_connection_provider_total_connections{id="-1268283746",name="connectionProvider",remote_address="api.telegram.org:443",} 1.0

Could the issue be this missing connection?

Update 2:

I thought this might be related to this other issue: Closing Reactor Netty connection on error status codes

So I updated my HttpClient to this:

    @Bean
    public ReactorClientHttpConnector httpClient() {
        HttpClient httpClient = HttpClient.create(ConnectionProvider.builder("connectionProvider").metrics(true).build())
                .doAfterResponseSuccess((r, c) -> c.dispose())
                .option(ChannelOption.CONNECT_TIMEOUT_MILLIS, connectTimeout)
                .responseTimeout(Duration.ofMillis(responseTimeout));
        return new ReactorClientHttpConnector(httpClient);
    }

But all that seemed to do is accelerate the occurrence of the problem. Just like before, the active, pending and idle connections do not add up to the total connections. The total is always greater than the other 3 metrics added together.

Update 3: I did a thread dump when the issue happened. There were a total of 74 threads, so I don't think the app is running out of threads.

The dump for the Telegram thread:

"TelegramBot" #20 daemon prio=5 os_prio=0 cpu=14.65ms elapsed=47154.24s tid=0x00007f6b28e73000 nid=0x1c waiting on condition  [0x00007f6aed6fb000]
   java.lang.Thread.State: WAITING (parking)
        at jdk.internal.misc.Unsafe.park(java.base@11.0.13/Native Method)
        - parking to wait for  <0x00000000fa865c80> (a java.util.concurrent.CountDownLatch$Sync)
        at java.util.concurrent.locks.LockSupport.park(java.base@11.0.13/LockSupport.java:194)
        at java.util.concurrent.locks.AbstractQueuedSynchronizer.parkAndCheckInterrupt(java.base@11.0.13/AbstractQueuedSynchronizer.java:885)
        at java.util.concurrent.locks.AbstractQueuedSynchronizer.doAcquireSharedInterruptibly(java.base@11.0.13/AbstractQueuedSynchronizer.java:1039)
        at java.util.concurrent.locks.AbstractQueuedSynchronizer.acquireSharedInterruptibly(java.base@11.0.13/AbstractQueuedSynchronizer.java:1345)
        at java.util.concurrent.CountDownLatch.await(java.base@11.0.13/CountDownLatch.java:232)
        at reactor.core.publisher.BlockingSingleSubscriber.blockingGet(BlockingSingleSubscriber.java:87)
        at reactor.core.publisher.Mono.block(Mono.java:1707)
        at com.moon.arbitrage.cm.feign.TelegramClient.sendMessage(TelegramClient.java:59)
        at com.moon.arbitrage.cm.service.TelegramService.lambda$sendArbMessage$0(TelegramService.java:53)
        at com.moon.arbitrage.cm.service.TelegramService$$Lambda$1092/0x000000084070f840.run(Unknown Source)
        at com.moon.arbitrage.cm.service.TelegramService.task(TelegramService.java:82)
        at com.moon.arbitrage.cm.service.TelegramService$$Lambda$920/0x0000000840665040.run(Unknown Source)
        at java.lang.Thread.run(java.base@11.0.13/Thread.java:829)

   Locked ownable synchronizers:
        - None

The reactor worker threads:

"reactor-http-epoll-1" #15 daemon prio=5 os_prio=0 cpu=810.44ms elapsed=47157.07s tid=0x00007f6b281c4000 nid=0x17 runnable  [0x00007f6b0c46c000]
   java.lang.Thread.State: RUNNABLE
        at io.netty.channel.epoll.Native.epollWait0(Native Method)
        at io.netty.channel.epoll.Native.epollWait(Native.java:177)
        at io.netty.channel.epoll.EpollEventLoop.epollWait(EpollEventLoop.java:286)
        at io.netty.channel.epoll.EpollEventLoop.run(EpollEventLoop.java:351)
        at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:986)
        at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
        at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)
        at java.lang.Thread.run(java.base@11.0.13/Thread.java:829)

   Locked ownable synchronizers:
        - None

"reactor-http-epoll-2" #16 daemon prio=5 os_prio=0 cpu=1312.16ms elapsed=47157.07s tid=0x00007f6b281c5000 nid=0x18 waiting on condition  [0x00007f6b0c369000]
   java.lang.Thread.State: WAITING (parking)
        at jdk.internal.misc.Unsafe.park(java.base@11.0.13/Native Method)
        - parking to wait for  <0x00000000fa865948> (a java.util.concurrent.CompletableFuture$Signaller)
        at java.util.concurrent.locks.LockSupport.park(java.base@11.0.13/LockSupport.java:194)
        at java.util.concurrent.CompletableFuture$Signaller.block(java.base@11.0.13/CompletableFuture.java:1796)
        at java.util.concurrent.ForkJoinPool.managedBlock(java.base@11.0.13/ForkJoinPool.java:3128)
        at java.util.concurrent.CompletableFuture.waitingGet(java.base@11.0.13/CompletableFuture.java:1823)
        at java.util.concurrent.CompletableFuture.get(java.base@11.0.13/CompletableFuture.java:1998)
        at com.moon.arbitrage.cm.service.OrderService.reconcileOrder(OrderService.java:103)
        at com.moon.arbitrage.cm.service.BotService$BotTask.lambda$task$1(BotService.java:383)
        at com.moon.arbitrage.cm.service.BotService$BotTask$$Lambda$1161/0x00000008400af440.accept(Unknown Source)
        at reactor.core.publisher.MonoPeekTerminal$MonoTerminalPeekSubscriber.onNext(MonoPeekTerminal.java:171)
        at reactor.core.publisher.MonoPeekTerminal$MonoTerminalPeekSubscriber.onNext(MonoPeekTerminal.java:180)
        at reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1816)
        at reactor.core.publisher.MonoFlatMap$FlatMapInner.onNext(MonoFlatMap.java:249)
        at reactor.core.publisher.FluxOnErrorResume$ResumeSubscriber.onNext(FluxOnErrorResume.java:79)
        at reactor.core.publisher.FluxOnAssembly$OnAssemblySubscriber.onNext(FluxOnAssembly.java:539)
        at reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1816)
        at reactor.core.publisher.MonoFlatMap$FlatMapMain.onNext(MonoFlatMap.java:151)
        at reactor.core.publisher.FluxContextWrite$ContextWriteSubscriber.onNext(FluxContextWrite.java:107)
        at reactor.core.publisher.FluxMapFuseable$MapFuseableConditionalSubscriber.onNext(FluxMapFuseable.java:295)
        at reactor.core.publisher.FluxFilterFuseable$FilterFuseableConditionalSubscriber.onNext(FluxFilterFuseable.java:337)
        at reactor.core.publisher.Operators$MonoSubscriber.complete(Operators.java:1816)
        at reactor.core.publisher.MonoCollect$CollectSubscriber.onComplete(MonoCollect.java:159)
        at reactor.core.publisher.FluxMap$MapSubscriber.onComplete(FluxMap.java:142)
        at reactor.core.publisher.FluxPeek$PeekSubscriber.onComplete(FluxPeek.java:260)
        at reactor.core.publisher.FluxMap$MapSubscriber.onComplete(FluxMap.java:142)
        at reactor.netty.channel.FluxReceive.onInboundComplete(FluxReceive.java:400)
        at reactor.netty.channel.ChannelOperations.onInboundComplete(ChannelOperations.java:419)
        at reactor.netty.channel.ChannelOperations.terminate(ChannelOperations.java:473)
        at reactor.netty.http.client.HttpClientOperations.onInboundNext(HttpClientOperations.java:702)
        at reactor.netty.channel.ChannelOperationsHandler.channelRead(ChannelOperationsHandler.java:93)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:379)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:365)
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:357)
        at io.netty.handler.timeout.IdleStateHandler.channelRead(IdleStateHandler.java:286)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:379)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:365)
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:357)
        at io.netty.channel.CombinedChannelDuplexHandler$DelegatingChannelHandlerContext.fireChannelRead(CombinedChannelDuplexHandler.java:436)
        at io.netty.handler.codec.ByteToMessageDecoder.fireChannelRead(ByteToMessageDecoder.java:324)
        at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:296)
        at io.netty.channel.CombinedChannelDuplexHandler.channelRead(CombinedChannelDuplexHandler.java:251)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:379)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:365)
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:357)
        at io.netty.handler.ssl.SslHandler.unwrap(SslHandler.java:1372)
        at io.netty.handler.ssl.SslHandler.decodeJdkCompatible(SslHandler.java:1235)
        at io.netty.handler.ssl.SslHandler.decode(SslHandler.java:1284)
        at io.netty.handler.codec.ByteToMessageDecoder.decodeRemovalReentryProtection(ByteToMessageDecoder.java:507)
        at io.netty.handler.codec.ByteToMessageDecoder.callDecode(ByteToMessageDecoder.java:446)
        at io.netty.handler.codec.ByteToMessageDecoder.channelRead(ByteToMessageDecoder.java:276)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:379)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:365)
        at io.netty.channel.AbstractChannelHandlerContext.fireChannelRead(AbstractChannelHandlerContext.java:357)
        at io.netty.channel.DefaultChannelPipeline$HeadContext.channelRead(DefaultChannelPipeline.java:1410)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:379)
        at io.netty.channel.AbstractChannelHandlerContext.invokeChannelRead(AbstractChannelHandlerContext.java:365)
        at io.netty.channel.DefaultChannelPipeline.fireChannelRead(DefaultChannelPipeline.java:919)
        at io.netty.channel.epoll.AbstractEpollStreamChannel$EpollStreamUnsafe.epollInReady(AbstractEpollStreamChannel.java:795)
        at io.netty.channel.epoll.EpollEventLoop.processReady(EpollEventLoop.java:480)
        at io.netty.channel.epoll.EpollEventLoop.run(EpollEventLoop.java:378)
        at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:986)
        at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
        at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)
        at java.lang.Thread.run(java.base@11.0.13/Thread.java:829)

   Locked ownable synchronizers:
        - None

"reactor-http-epoll-3" #17 daemon prio=5 os_prio=0 cpu=171.84ms elapsed=47157.07s tid=0x00007f6b28beb000 nid=0x19 runnable  [0x00007f6b0c26a000]
   java.lang.Thread.State: RUNNABLE
        at io.netty.channel.epoll.Native.epollWait0(Native Method)
        at io.netty.channel.epoll.Native.epollWait(Native.java:177)
        at io.netty.channel.epoll.EpollEventLoop.epollWait(EpollEventLoop.java:281)
        at io.netty.channel.epoll.EpollEventLoop.run(EpollEventLoop.java:351)
        at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:986)
        at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
        at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)
        at java.lang.Thread.run(java.base@11.0.13/Thread.java:829)

   Locked ownable synchronizers:
        - None

"reactor-http-epoll-4" #18 daemon prio=5 os_prio=0 cpu=188.10ms elapsed=47157.07s tid=0x00007f6b28b7d800 nid=0x1a runnable  [0x00007f6b0c169000]
   java.lang.Thread.State: RUNNABLE
        at io.netty.channel.epoll.Native.epollWait0(Native Method)
        at io.netty.channel.epoll.Native.epollWait(Native.java:177)
        at io.netty.channel.epoll.EpollEventLoop.epollWait(EpollEventLoop.java:281)
        at io.netty.channel.epoll.EpollEventLoop.run(EpollEventLoop.java:351)
        at io.netty.util.concurrent.SingleThreadEventExecutor$4.run(SingleThreadEventExecutor.java:986)
        at io.netty.util.internal.ThreadExecutorMap$2.run(ThreadExecutorMap.java:74)
        at io.netty.util.concurrent.FastThreadLocalRunnable.run(FastThreadLocalRunnable.java:30)
        at java.lang.Thread.run(java.base@11.0.13/Thread.java:829)

   Locked ownable synchronizers:
        - None

Seems like one of them is blocked with another task (that isn't even from the Telegram service) but that shouldn't be an issue since the other three worker threads are runnable right?

ANSWER

Answered 2021-Dec-20 at 14:25

I would propose to take a look in the RateLimiter direction. Maybe it does not work as expected, depending on the number of requests your application does over time. From the Javadoc for Ratelimiter: "It is important to note that the number of permits requested never affects the throttling of the request itself ... but it affects the throttling of the next request. I.e., if an expensive task arrives at an idle RateLimiter, it will be granted immediately, but it is the next request that will experience extra throttling, thus paying for the cost of the expensive task." Also helpful might be this discussion: github or github

I could imaginge there is some throttling adding up or other effect in the RateLimiter, i would try to play around with it and make sure this thing really works the way you want. Alternatively, consider using Spring @Scheduled to read from your queue. You might want to spice it up using embedded JMS for further goodies (message persistence etc).

Source https://stackoverflow.com/questions/70357582

QUESTION

Saving model on Tensorflow 2.7.0 with data augmentation layer

Asked 2022-Feb-04 at 17:25

I am getting an error when trying to save a model with data augmentation layers with Tensorflow version 2.7.0.

Here is the code of data augmentation:

input_shape_rgb = (img_height, img_width, 3)
data_augmentation_rgb = tf.keras.Sequential(
  [ 
    layers.RandomFlip("horizontal"),
    layers.RandomFlip("vertical"),
    layers.RandomRotation(0.5),
    layers.RandomZoom(0.5),
    layers.RandomContrast(0.5),
    RandomColorDistortion(name='random_contrast_brightness/none'),
  ]
)

Now I build my model like this:

# Build the model
input_shape = (img_height, img_width, 3)

model = Sequential([
  layers.Input(input_shape),
  data_augmentation_rgb,
  layers.Rescaling((1./255)),

  layers.Conv2D(16, kernel_size, padding=padding, activation='relu', strides=1, 
     data_format='channels_last'),
  layers.MaxPooling2D(),
  layers.BatchNormalization(),

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

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

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

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

 model.compile(loss='categorical_crossentropy', optimizer='adam',metrics=metrics)
 model.summary()

Then after the training is done I just make:

model.save("./")

And I'm getting this error:

---------------------------------------------------------------------------
KeyError                                  Traceback (most recent call last)
<ipython-input-84-87d3f09f8bee> in <module>()
----> 1 model.save("./")


/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in 
 error_handler(*args, **kwargs)
 65     except Exception as e:  # pylint: disable=broad-except
 66       filtered_tb = _process_traceback_frames(e.__traceback__)
 ---> 67       raise e.with_traceback(filtered_tb) from None
 68     finally:
 69       del filtered_tb

 /usr/local/lib/python3.7/dist- 
 packages/tensorflow/python/saved_model/function_serialization.py in 
 serialize_concrete_function(concrete_function, node_ids, coder)
 66   except KeyError:
 67     raise KeyError(
 ---> 68         f"Failed to add concrete function '{concrete_function.name}' to 
 object-"
 69         f"based SavedModel as it captures tensor {capture!r} which is 
 unsupported"
 70         " or not reachable from root. "

 KeyError: "Failed to add concrete function 
 'b'__inference_sequential_46_layer_call_fn_662953'' to object-based SavedModel as it 
 captures tensor <tf.Tensor: shape=(), dtype=resource, value=<Resource Tensor>> which 
 is unsupported or not reachable from root. One reason could be that a stateful 
 object or a variable that the function depends on is not assigned to an attribute of 
 the serialized trackable object (see SaveTest.test_captures_unreachable_variable)."

I inspected the reason of getting this error by changing the architecture of my model and I just found that reason came from the data_augmentation layer since the RandomFlip and RandomRotation and others are changed from layers.experimental.prepocessing.RandomFlip to layers.RandomFlip, but still the error appears.

ANSWER

Answered 2022-Feb-04 at 17:25

This seems to be a bug in Tensorflow 2.7 when using model.save combined with the parameter save_format="tf", which is set by default. The layers RandomFlip, RandomRotation, RandomZoom, and RandomContrast are causing the problems, since they are not serializable. Interestingly, the Rescaling layer can be saved without any problems. A workaround would be to simply save your model with the older Keras H5 format model.save("test", save_format='h5'):

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')

Loading your model with your custom layer would look like this then:

model = tf.keras.models.load_model('test.h5', custom_objects={'RandomColorDistortion': RandomColorDistortion})

where RandomColorDistortion is the name of your custom layer.

Source https://stackoverflow.com/questions/69955838

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