The tensorflow_mnist.py example demonstrates the integration of ClearML into code which uses TensorFlow and Keras to trains a neural network on the Keras built-in MNIST handwritten digits dataset. It builds a TensorFlow Keras model, and trains and tests it with the following:
Loss objective function - tf.keras.metrics.SparseCategoricalCrossentropy
Accuracy metric - tf.keras.metrics.SparseCategoricalAccuracy
When the script runs, it creates an experiment named
Tensorflow v2 mnist with summaries, which is associated with the
The loss and accuracy metric scalar plots appear in RESULTS > SCALARS, along with the resource utilization plots, which are titled :monitor: machine.
ClearML automatically logs TensorFlow Definitions. They appear in CONFIGURATIONS > HYPER PARAMETERS > TF_DEFINE.
All console output appears in RESULTS > LOG.
Model artifacts associated with the experiment appear in the experiment info panel (in the EXPERIMENTS tab), and in the model info panel (in the MODELS tab).
The experiment info panel shows model tracking, including the model name and design (in this case, no design was stored).
The model info panel contains the model details, including the model design (which is also in the experiment info panel), the label enumeration, model URL, framework, and snapshot locations.