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Introduction

You can log any ML model metadata to Neptune and see it live in the Neptune UI.

In this guide, you will learn how to do basic model monitoring of your training process. You will:

• See the training progress by looking at learning curves for loss and accuracy

• Monitor hardware consumption during training across GPU/CPU/MEMORY

By the end of it, you will see your metrics, losses, and hardware live in Neptune!

Before you start

Make sure you meet the following prerequisites before starting:

• Have Python 3.6 or higher installed

• Have Tensorflow 2.x with Keras installed

• ​Have Neptune installed​

• ​Create a Neptune project​

Step 1: Create a basic training script

As an example, I’ll use a script that trains a Keras model on the MNIST dataset.

Create a file train.py and copy the script below.

train.py
from tensorflow import keras
​
PARAMS = {'epoch_nr': 100,
          'batch_size': 256,
          'lr': 0.005,
          'momentum': 0.4,
          'use_nesterov': True,
          'unit_nr': 256,
          'dropout': 0.05}
​
mnist = keras.datasets.mnist
(x_train, y_train),(x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
​
model = keras.models.Sequential([
  keras.layers.Flatten(),
  keras.layers.Dense(PARAMS['unit_nr'], activation=keras.activations.relu),
  keras.layers.Dropout(PARAMS['dropout']),
  keras.layers.Dense(10, activation=keras.activations.softmax)
])
​
optimizer = keras.optimizers.SGD(lr=PARAMS['lr'],
                                 momentum=PARAMS['momentum'],
                                 nesterov=PARAMS['use_nesterov'],)
​
model.compile(optimizer=optimizer,
              loss='sparse_categorical_crossentropy',
              metrics=['accuracy'])
​
model.fit(x_train, y_train,
          epochs=PARAMS['epoch_nr'],
          batch_size=PARAMS['batch_size'])

Run training to make sure that it works correctly.

python train.py

Step 2: Install psutil

To monitor hardware consumption in Neptune you need to have psutil installed.

pip install psutil

conda install -c anaconda psutil

Step 3: Connect Neptune to your script

At the top of your script add a snippet that connects it to Neptune:

import neptune.new as neptune
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run = neptune.init(api_token='<YOUR_API_TOKEN>',
                   project='<YOUR_PROJECT_NAME>') # your credentials

Executing this snippet will give you a link like this one: https://app.neptune.ai/o/common/org/quickstarts/e/QUI-28177 with common/quickstarts replaced by your_workspace/your_project_name, and QUI-28177replaced by your Run ID.

Step 4: Add logging for metrics and losses

To log a metric or loss during training you should use:

loss = ...
run["train/loss"].log(loss)

A few explanations here:

• "train/loss" is a path to a log in Neptune called Namespace where you can log metrics, losses and many other model building metadata ​

• You can have multiple log namespaces like 'train/acc', 'val/f1_score’, ‘train/log-loss’, ‘test/acc’).

• argument of the log() method is the actual value you want to log.

Typically during training, you log those metrics in a loop after every iteration. Call run["train/loss"].log(loss)at each step.

for i in range(epochs):
    ...
    run["train/loss"].log(loss)
    run["train/acc"].log(accuracy)

Many frameworks, like Keras, let you create a callback that is executed inside of the training loop.

Now that you know all this.

Steps for Keras

Create a Neptune callback.

class NeptuneMonitor(keras.callbacks.Callback):
     def on_epoch_end(self, epoch, logs=None):
          for metric_name, metric_value in logs.items():
              run["train/{}".format(metric_name)].log(metric_value)

Pass the callback to the model.fit() method:

model.fit(x_train, y_train,
           epochs=PARAMS['epoch_nr'],
           batch_size=PARAMS['batch_size'],
           callbacks=[NeptuneMonitor()])

Step 5: Stop logging

run.stop()

Step 6: Run your script and see results in Neptune

Run training script.

python train.py

If it worked correctly you should see:

• a link to Neptune run. Click on it and go to the app,

• metrics and losses in the Logs and Charts sections of the UI,

• hardware consumption and console logs in the Monitoring section of the UI.

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What's next?