Quick Start: TF Watcher

What if you could monitor your Machine Learning jobs on Colab, Kaggle, AzureML, or pretty much anywhere on your mobile phones, so you could monitor your models on the fly. Even while taking a walk🚶in < 5 lines of code!

Consider giving a star⭐ to the project if you like it.

In this example we’ll use the Fashion MNIST dataset to serve as a quickstart.

Setup

We start off by installing TF Watcher from PyPI.

[1]:

!pip install tf-watcher

Collecting tf-watcher
  Downloading tf_watcher-0.1.1-py3-none-any.whl (22 kB)
Collecting pyrebase4~=4.5.0
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Successfully built gcloud
Installing collected packages: deprecated, cryptography, jwcrypto, grpcio, tensorflow-estimator, requests-toolbelt, python-jwt, pycryptodome, keras-nightly, gcloud, tensorflow, pyrebase4, tf-watcher
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    Uninstalling tensorflow-2.6.0:
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Successfully installed cryptography-3.4.7 deprecated-1.2.12 gcloud-0.18.3 grpcio-1.34.1 jwcrypto-1.0 keras-nightly-2.5.0.dev2021032900 pycryptodome-3.10.1 pyrebase4-4.5.0 python-jwt-3.3.0 requests-toolbelt-0.9.1 tensorflow-2.5.1 tensorflow-estimator-2.5.0 tf-watcher-0.1.1

[2]:

import tensorflow as tf
from tensorflow import keras
import tfwatcher

Get the data

[3]:

# Load example MNIST data and pre-process it
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data()

Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
11493376/11490434 [==============================] - 0s 0us/step

Some simple pre-processing

[4]:

x_train = x_train.reshape(-1, 784).astype("float32") / 255.0
x_test = x_test.reshape(-1, 784).astype("float32") / 255.0

# Limit the data to 1000 samples
x_train = x_train[:1000]
y_train = y_train[:1000]
x_test = x_test[:1000]
y_test = y_test[:1000]

Create a simple model

Note the metrics argument in model.compile, specify any additional metrics you want to log from here.

[5]:

# Define the Keras model to add callbacks to
def get_model():
    model = keras.Sequential()
    model.add(keras.layers.Dense(1, input_dim=784))
    model.compile(
        optimizer=keras.optimizers.RMSprop(learning_rate=0.1),
        loss="mean_squared_error",
        metrics=["accuracy"],
    )
    return model

Use TF Watcher while training

We use the EpochEnd class from TF Watcher to specify we are interested in operating in the epoch level
We pass in schedule as 1 to monitor after every epoch. You could pass in this as 3 to monitor after every 3 epochs or also pass in a list of the specific epoch number you want to monitor on

[6]:

MonitorCallback = tfwatcher.callbacks.EpochEnd(schedule = 1)

Use this ID to monitor training for this session: ybhzyxK

We will now use this ID on https://www.tfwatcher.tech to monitor our model

[7]:

model = get_model()
history = model.fit(
    x_train,
    y_train,
    batch_size=128,
    epochs=100,
    validation_split=0.5,
    callbacks = [MonitorCallback]
)

Epoch 1/100
4/4 [==============================] - 1s 58ms/step - loss: 220.0279 - accuracy: 0.1400 - val_loss: 7.1787 - val_accuracy: 0.1000
Epoch 2/100
4/4 [==============================] - 0s 9ms/step - loss: 5.8449 - accuracy: 0.1400 - val_loss: 5.6531 - val_accuracy: 0.1240
Epoch 3/100
4/4 [==============================] - 0s 10ms/step - loss: 4.3850 - accuracy: 0.1580 - val_loss: 4.7036 - val_accuracy: 0.1160
Epoch 4/100
4/4 [==============================] - 0s 9ms/step - loss: 3.6359 - accuracy: 0.1700 - val_loss: 5.1514 - val_accuracy: 0.1040
Epoch 5/100
4/4 [==============================] - 0s 10ms/step - loss: 22.7958 - accuracy: 0.1500 - val_loss: 94.3385 - val_accuracy: 0.1000
Epoch 6/100
4/4 [==============================] - 0s 11ms/step - loss: 66.1149 - accuracy: 0.1160 - val_loss: 38.9990 - val_accuracy: 0.1000
Epoch 7/100
4/4 [==============================] - 0s 10ms/step - loss: 23.7571 - accuracy: 0.1280 - val_loss: 16.4039 - val_accuracy: 0.1000
Epoch 8/100
4/4 [==============================] - 0s 9ms/step - loss: 16.1609 - accuracy: 0.1080 - val_loss: 31.8129 - val_accuracy: 0.1000
Epoch 9/100
4/4 [==============================] - 0s 10ms/step - loss: 36.0135 - accuracy: 0.1200 - val_loss: 47.4181 - val_accuracy: 0.1000
Epoch 10/100
4/4 [==============================] - 0s 10ms/step - loss: 38.0579 - accuracy: 0.1220 - val_loss: 24.0960 - val_accuracy: 0.1000
Epoch 11/100
4/4 [==============================] - 0s 10ms/step - loss: 23.2553 - accuracy: 0.1040 - val_loss: 24.5393 - val_accuracy: 0.1000
Epoch 12/100
4/4 [==============================] - 0s 9ms/step - loss: 25.9761 - accuracy: 0.1180 - val_loss: 43.4903 - val_accuracy: 0.1000
Epoch 13/100
4/4 [==============================] - 0s 9ms/step - loss: 36.0534 - accuracy: 0.1320 - val_loss: 27.3967 - val_accuracy: 0.1000
Epoch 14/100
4/4 [==============================] - 0s 9ms/step - loss: 23.6804 - accuracy: 0.1200 - val_loss: 30.0495 - val_accuracy: 0.1000
Epoch 15/100
4/4 [==============================] - 0s 9ms/step - loss: 34.2381 - accuracy: 0.1220 - val_loss: 41.0987 - val_accuracy: 0.1000
Epoch 16/100
4/4 [==============================] - 0s 9ms/step - loss: 28.6988 - accuracy: 0.1220 - val_loss: 38.2743 - val_accuracy: 0.1000
Epoch 17/100
4/4 [==============================] - 0s 9ms/step - loss: 29.1761 - accuracy: 0.1180 - val_loss: 38.2930 - val_accuracy: 0.1000
Epoch 18/100
4/4 [==============================] - 0s 11ms/step - loss: 29.0422 - accuracy: 0.1320 - val_loss: 33.8774 - val_accuracy: 0.1000
Epoch 19/100
4/4 [==============================] - 0s 10ms/step - loss: 25.2365 - accuracy: 0.1360 - val_loss: 36.8045 - val_accuracy: 0.1000
Epoch 20/100
4/4 [==============================] - 0s 9ms/step - loss: 33.1871 - accuracy: 0.1080 - val_loss: 35.7805 - val_accuracy: 0.1000
Epoch 21/100
4/4 [==============================] - 0s 15ms/step - loss: 30.1454 - accuracy: 0.1140 - val_loss: 29.5536 - val_accuracy: 0.1000
Epoch 22/100
4/4 [==============================] - 0s 11ms/step - loss: 24.0112 - accuracy: 0.1120 - val_loss: 30.0866 - val_accuracy: 0.1000
Epoch 23/100
4/4 [==============================] - 0s 10ms/step - loss: 34.7015 - accuracy: 0.1040 - val_loss: 29.4295 - val_accuracy: 0.1000
Epoch 24/100
4/4 [==============================] - 0s 10ms/step - loss: 24.5739 - accuracy: 0.1340 - val_loss: 23.0243 - val_accuracy: 0.1000
Epoch 25/100
4/4 [==============================] - 0s 10ms/step - loss: 20.7962 - accuracy: 0.1360 - val_loss: 50.8549 - val_accuracy: 0.1000
Epoch 26/100
4/4 [==============================] - 0s 15ms/step - loss: 41.6597 - accuracy: 0.1160 - val_loss: 32.8466 - val_accuracy: 0.1000
Epoch 27/100
4/4 [==============================] - 0s 14ms/step - loss: 26.6706 - accuracy: 0.1260 - val_loss: 31.4648 - val_accuracy: 0.1000
Epoch 28/100
4/4 [==============================] - 0s 12ms/step - loss: 28.9898 - accuracy: 0.1060 - val_loss: 34.4348 - val_accuracy: 0.1000
Epoch 29/100
4/4 [==============================] - 0s 10ms/step - loss: 32.6081 - accuracy: 0.1180 - val_loss: 37.6328 - val_accuracy: 0.1000
Epoch 30/100
4/4 [==============================] - 0s 10ms/step - loss: 26.6324 - accuracy: 0.1340 - val_loss: 26.2719 - val_accuracy: 0.1000
Epoch 31/100
4/4 [==============================] - 0s 12ms/step - loss: 17.5867 - accuracy: 0.1200 - val_loss: 32.1177 - val_accuracy: 0.1000
Epoch 32/100
4/4 [==============================] - 0s 10ms/step - loss: 34.4390 - accuracy: 0.1240 - val_loss: 46.8234 - val_accuracy: 0.1000
Epoch 33/100
4/4 [==============================] - 0s 15ms/step - loss: 35.8227 - accuracy: 0.1020 - val_loss: 24.2219 - val_accuracy: 0.1000
Epoch 34/100
4/4 [==============================] - 0s 10ms/step - loss: 19.7204 - accuracy: 0.1160 - val_loss: 32.0298 - val_accuracy: 0.1000
Epoch 35/100
4/4 [==============================] - 0s 12ms/step - loss: 34.6682 - accuracy: 0.1300 - val_loss: 43.5553 - val_accuracy: 0.1000
Epoch 36/100
4/4 [==============================] - 0s 10ms/step - loss: 27.7967 - accuracy: 0.1360 - val_loss: 35.5064 - val_accuracy: 0.1000
Epoch 37/100
4/4 [==============================] - 0s 10ms/step - loss: 24.1768 - accuracy: 0.1060 - val_loss: 33.8834 - val_accuracy: 0.1000
Epoch 38/100
4/4 [==============================] - 0s 13ms/step - loss: 35.0535 - accuracy: 0.0980 - val_loss: 33.7792 - val_accuracy: 0.1000
Epoch 39/100
4/4 [==============================] - 0s 9ms/step - loss: 27.3413 - accuracy: 0.1040 - val_loss: 26.7047 - val_accuracy: 0.1000
Epoch 40/100
4/4 [==============================] - 0s 9ms/step - loss: 26.9778 - accuracy: 0.1180 - val_loss: 34.2578 - val_accuracy: 0.1000
Epoch 41/100
4/4 [==============================] - 0s 10ms/step - loss: 28.9345 - accuracy: 0.0940 - val_loss: 38.9659 - val_accuracy: 0.1000
Epoch 42/100
4/4 [==============================] - 0s 11ms/step - loss: 31.8423 - accuracy: 0.1180 - val_loss: 33.2312 - val_accuracy: 0.1000
Epoch 43/100
4/4 [==============================] - 0s 10ms/step - loss: 27.6944 - accuracy: 0.1100 - val_loss: 23.8519 - val_accuracy: 0.1000
Epoch 44/100
4/4 [==============================] - 0s 9ms/step - loss: 21.8561 - accuracy: 0.1160 - val_loss: 37.2674 - val_accuracy: 0.1000
Epoch 45/100
4/4 [==============================] - 0s 10ms/step - loss: 28.3689 - accuracy: 0.1200 - val_loss: 50.9949 - val_accuracy: 0.1000
Epoch 46/100
4/4 [==============================] - 0s 11ms/step - loss: 37.0924 - accuracy: 0.1120 - val_loss: 37.8075 - val_accuracy: 0.1000
Epoch 47/100
4/4 [==============================] - 0s 15ms/step - loss: 22.4427 - accuracy: 0.1220 - val_loss: 31.0246 - val_accuracy: 0.1000
Epoch 48/100
4/4 [==============================] - 0s 16ms/step - loss: 30.8454 - accuracy: 0.1160 - val_loss: 40.3705 - val_accuracy: 0.1000
Epoch 49/100
4/4 [==============================] - 0s 12ms/step - loss: 30.1953 - accuracy: 0.1080 - val_loss: 39.0889 - val_accuracy: 0.1000
Epoch 50/100
4/4 [==============================] - 0s 10ms/step - loss: 19.4889 - accuracy: 0.1080 - val_loss: 34.6825 - val_accuracy: 0.1000
Epoch 51/100
4/4 [==============================] - 0s 10ms/step - loss: 34.8757 - accuracy: 0.1300 - val_loss: 49.4359 - val_accuracy: 0.1000
Epoch 52/100
4/4 [==============================] - 0s 10ms/step - loss: 34.9451 - accuracy: 0.1140 - val_loss: 31.5136 - val_accuracy: 0.1000
Epoch 53/100
4/4 [==============================] - 0s 10ms/step - loss: 22.9106 - accuracy: 0.1380 - val_loss: 26.9605 - val_accuracy: 0.1000
Epoch 54/100
4/4 [==============================] - 0s 10ms/step - loss: 26.2229 - accuracy: 0.1040 - val_loss: 38.6458 - val_accuracy: 0.1000
Epoch 55/100
4/4 [==============================] - 0s 10ms/step - loss: 26.1122 - accuracy: 0.1140 - val_loss: 45.1972 - val_accuracy: 0.1000
Epoch 56/100
4/4 [==============================] - 0s 10ms/step - loss: 36.5613 - accuracy: 0.1280 - val_loss: 33.8477 - val_accuracy: 0.1000
Epoch 57/100
4/4 [==============================] - 0s 10ms/step - loss: 26.9645 - accuracy: 0.1020 - val_loss: 29.3765 - val_accuracy: 0.1000
Epoch 58/100
4/4 [==============================] - 0s 9ms/step - loss: 23.7523 - accuracy: 0.1140 - val_loss: 38.5822 - val_accuracy: 0.1000
Epoch 59/100
4/4 [==============================] - 0s 10ms/step - loss: 30.7082 - accuracy: 0.1440 - val_loss: 40.9264 - val_accuracy: 0.1000
Epoch 60/100
4/4 [==============================] - 0s 15ms/step - loss: 31.7553 - accuracy: 0.1200 - val_loss: 28.5086 - val_accuracy: 0.1000
Epoch 61/100
4/4 [==============================] - 0s 10ms/step - loss: 27.3221 - accuracy: 0.1240 - val_loss: 32.6517 - val_accuracy: 0.1000
Epoch 62/100
4/4 [==============================] - 0s 10ms/step - loss: 27.3470 - accuracy: 0.1200 - val_loss: 39.8263 - val_accuracy: 0.1000
Epoch 63/100
4/4 [==============================] - 0s 10ms/step - loss: 26.0612 - accuracy: 0.1240 - val_loss: 34.5444 - val_accuracy: 0.1000
Epoch 64/100
4/4 [==============================] - 0s 11ms/step - loss: 34.7778 - accuracy: 0.1200 - val_loss: 44.2647 - val_accuracy: 0.1000
Epoch 65/100
4/4 [==============================] - 0s 12ms/step - loss: 34.8926 - accuracy: 0.1080 - val_loss: 34.5197 - val_accuracy: 0.1000
Epoch 66/100
4/4 [==============================] - 0s 11ms/step - loss: 24.3221 - accuracy: 0.1220 - val_loss: 30.9930 - val_accuracy: 0.1000
Epoch 67/100
4/4 [==============================] - 0s 10ms/step - loss: 27.4201 - accuracy: 0.1160 - val_loss: 37.4984 - val_accuracy: 0.1000
Epoch 68/100
4/4 [==============================] - 0s 10ms/step - loss: 28.9322 - accuracy: 0.1280 - val_loss: 36.6450 - val_accuracy: 0.1000
Epoch 69/100
4/4 [==============================] - 0s 14ms/step - loss: 28.5764 - accuracy: 0.1320 - val_loss: 34.3183 - val_accuracy: 0.1000
Epoch 70/100
4/4 [==============================] - 0s 16ms/step - loss: 23.6287 - accuracy: 0.1200 - val_loss: 40.6597 - val_accuracy: 0.1000
Epoch 71/100
4/4 [==============================] - 0s 10ms/step - loss: 30.6631 - accuracy: 0.1160 - val_loss: 43.1053 - val_accuracy: 0.1000
Epoch 72/100
4/4 [==============================] - 0s 10ms/step - loss: 31.3124 - accuracy: 0.1160 - val_loss: 31.0489 - val_accuracy: 0.1000
Epoch 73/100
4/4 [==============================] - 0s 10ms/step - loss: 24.3881 - accuracy: 0.1100 - val_loss: 36.0015 - val_accuracy: 0.1000
Epoch 74/100
4/4 [==============================] - 0s 12ms/step - loss: 32.8749 - accuracy: 0.1060 - val_loss: 31.0803 - val_accuracy: 0.1000
Epoch 75/100
4/4 [==============================] - 0s 11ms/step - loss: 23.9160 - accuracy: 0.1160 - val_loss: 32.9105 - val_accuracy: 0.1000
Epoch 76/100
4/4 [==============================] - 0s 11ms/step - loss: 31.2406 - accuracy: 0.1140 - val_loss: 34.7558 - val_accuracy: 0.1000
Epoch 77/100
4/4 [==============================] - 0s 10ms/step - loss: 26.3004 - accuracy: 0.1220 - val_loss: 40.3014 - val_accuracy: 0.1000
Epoch 78/100
4/4 [==============================] - 0s 9ms/step - loss: 37.0920 - accuracy: 0.1340 - val_loss: 27.9852 - val_accuracy: 0.1000
Epoch 79/100
4/4 [==============================] - 0s 10ms/step - loss: 20.3414 - accuracy: 0.1140 - val_loss: 27.2020 - val_accuracy: 0.1000
Epoch 80/100
4/4 [==============================] - 0s 9ms/step - loss: 29.3138 - accuracy: 0.1340 - val_loss: 47.7526 - val_accuracy: 0.1000
Epoch 81/100
4/4 [==============================] - 0s 9ms/step - loss: 34.6481 - accuracy: 0.1320 - val_loss: 32.0029 - val_accuracy: 0.1000
Epoch 82/100
4/4 [==============================] - 0s 10ms/step - loss: 19.7531 - accuracy: 0.1260 - val_loss: 23.1984 - val_accuracy: 0.1000
Epoch 83/100
4/4 [==============================] - 0s 10ms/step - loss: 27.6197 - accuracy: 0.1200 - val_loss: 44.1223 - val_accuracy: 0.1000
Epoch 84/100
4/4 [==============================] - 0s 14ms/step - loss: 42.5353 - accuracy: 0.1200 - val_loss: 33.2591 - val_accuracy: 0.1000
Epoch 85/100
4/4 [==============================] - 0s 11ms/step - loss: 19.8397 - accuracy: 0.1200 - val_loss: 18.8762 - val_accuracy: 0.1000
Epoch 86/100
4/4 [==============================] - 0s 15ms/step - loss: 19.0276 - accuracy: 0.1200 - val_loss: 39.6742 - val_accuracy: 0.1000
Epoch 87/100
4/4 [==============================] - 0s 11ms/step - loss: 45.4169 - accuracy: 0.1160 - val_loss: 33.8931 - val_accuracy: 0.1000
Epoch 88/100
4/4 [==============================] - 0s 12ms/step - loss: 26.4559 - accuracy: 0.1100 - val_loss: 16.6738 - val_accuracy: 0.1040
Epoch 89/100
4/4 [==============================] - 0s 10ms/step - loss: 12.4969 - accuracy: 0.1260 - val_loss: 33.8079 - val_accuracy: 0.1000
Epoch 90/100
4/4 [==============================] - 0s 16ms/step - loss: 38.4502 - accuracy: 0.1000 - val_loss: 49.4086 - val_accuracy: 0.1000
Epoch 91/100
4/4 [==============================] - 0s 14ms/step - loss: 29.9915 - accuracy: 0.1280 - val_loss: 36.8529 - val_accuracy: 0.1000
Epoch 92/100
4/4 [==============================] - 0s 14ms/step - loss: 25.7386 - accuracy: 0.1040 - val_loss: 31.6561 - val_accuracy: 0.1000
Epoch 93/100
4/4 [==============================] - 0s 10ms/step - loss: 27.4582 - accuracy: 0.1160 - val_loss: 50.1114 - val_accuracy: 0.1000
Epoch 94/100
4/4 [==============================] - 0s 9ms/step - loss: 36.5150 - accuracy: 0.0920 - val_loss: 28.7904 - val_accuracy: 0.1000
Epoch 95/100
4/4 [==============================] - 0s 9ms/step - loss: 19.6982 - accuracy: 0.1280 - val_loss: 35.2172 - val_accuracy: 0.1000
Epoch 96/100
4/4 [==============================] - 0s 10ms/step - loss: 31.5606 - accuracy: 0.1000 - val_loss: 39.6383 - val_accuracy: 0.1000
Epoch 97/100
4/4 [==============================] - 0s 9ms/step - loss: 32.0419 - accuracy: 0.1100 - val_loss: 37.4634 - val_accuracy: 0.1000
Epoch 98/100
4/4 [==============================] - 0s 10ms/step - loss: 25.6015 - accuracy: 0.1280 - val_loss: 23.4898 - val_accuracy: 0.1000
Epoch 99/100
4/4 [==============================] - 0s 10ms/step - loss: 22.4975 - accuracy: 0.1100 - val_loss: 41.4927 - val_accuracy: 0.1000
Epoch 100/100
4/4 [==============================] - 0s 10ms/step - loss: 38.1962 - accuracy: 0.1000 - val_loss: 33.3583 - val_accuracy: 0.1000