Add Summary to an Experiment Recorder

Summary helps in getting a glimpse of an experiment and helps in quickly comparing multiple experiments.

You can set the summary of an experiment using the Markov SDK.

Adding Summary to an existing experiment

from markov.api.recording.experiments.experiment_summary_api import ExperimentSummary

experiment_id = "paste-your-experiment-id-here"

summary = ExperimentSummary(experiment_id=experiment_id)

training_loss = 0.7
validation_loss = 0.9
training_accuracy = 0.88
validation_accuracy = 0.84
number_of_epochs = 25

summary.add_training_loss(value="{:.2f}".format(training_loss))
summary.add_validation_loss(value="{:.2f}".format(validation_loss))
summary.add_training_accuracy(value="{:.2f}".format(training_accuracy))
summary.add_validation_accuracy(value="{:.2f}".format(validation_accuracy))
summary.add_number_of_epochs(value="{}".format(number_of_epochs))

summary.add_custom(key="GPU", value="A100")

summary.set()

Adding Summary while recording a new experiment

import markov

# Your model definition 
# model = ...

# create recorder
recorder = markov.ExperimentRecorder(
    name="MarkovML experiment #1"
)
# The register method registers this recorder with the MarkovML backend. 
# You can record data with the backend only through a registered recorder.
recorder.register()

with recorder:
    for epoch in range(500):
        pred = model(x)  
        # Calculate and record loss
        loss = loss_function(pred, y)
        recorder.add_record({"loss": loss})
        
        # calculate and record accuracy
        accuracy = accuracy_function(pred, y)
        recorder.add_record({"accuracy": accuracy})
    
        # REST OF THE MODEL TRAINING CODE GOES HERE
            
    recorder.summary.add_training_loss(value=str(loss))
    recorder.summary.add_training_accuracy(value=str(accuracy))