This plugin allows you to easily add Simvue tracking and monitoring functionality to the training and testing of ML models built using TensorFlow.
This package provides a custom TensorVue callback, which inherits from TensorFlow's Callback class. This will do the following when training, testing or validating a model:
- Uploads the Python script creating the model as a Code Artifact
- Uploads the model config as an Input Artifact
- Uploads parameters about the model as Metadata
- Uploads the Training Accuracy and Loss after each batch to an Epoch runUploads the Training and Validation Accuracy and Loss after each Epoch to the Simulation run
- Uploads model checkpoints after each Epoch to the corresponding Epoch run as Output Artifacts(if enabled by the user)
- Uploads the final model to the Simulation run as an Output Artifact
To install and use this plugin, first create a virtual environment:
python -m venv venv
Then activate it:
source venv/bin/activate
And then use pip to install this module:
pip install simvue-tensorflow
The service URL and token can be defined as environment variables:
export SIMVUE_URL=...
export SIMVUE_TOKEN=...or a file simvue.toml can be created containing:
[server]
url = "..."
token = "..."The exact contents of both of the above options can be obtained directly by clicking the Create new run button on the web UI. Note that the environment variables have preference over the config file.
import tensorflow as tf
from tensorflow import keras
import numpy
import matplotlib.pyplot as plt
# Firstly we import our Tensorflow integration:
import simvue_tensorflow.plugin as sv_tf
# Load the training and test data
(img_train, label_train), (img_test, label_test) = keras.datasets.fashion_mnist.load_data()
# Normalize pixel values between 0 and 1
img_train = img_train.astype('float32') / 255.0
img_test = img_test.astype('float32') / 255.0
# Create a basic model
model = keras.Sequential()
model.add(keras.layers.Flatten(input_shape=(28, 28)))
model.add(keras.layers.Dense(32, activation='relu'))
model.add(keras.layers.Dense(10))
model.compile(optimizer=keras.optimizers.Adam(learning_rate=0.01),
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
# At the most basic level, all we need to do is initialize our callback, providing a run name
tensorvue = sv_tf.TensorVue("recognising_clothes_basic")
# Train the model.
model.fit(
img_train,
label_train,
epochs=5,
validation_split=0.2,
# Add the tensorvue class as a callback
callbacks=[tensorvue,]
)
# That's it! Check your Simvue dashboard and you should see:
# - A 'simulation' run, which summarises the overall training performance
# - A number of 'epoch' runs, which show the training performed in each epoch
# You can also use the TensorVue callback to record results from model.evaluate
# Above we do it all in one step during the fitting, but you can also do it afterwards:
results = model.evaluate(
img_test,
label_test,
# Add the tensorvue class as a callback
callbacks=[tensorvue,]
)Released under the terms of the Apache 2 license.
To reference Simvue, please use the information outlined in this citation file.