Collect metadata, metrics and artifacts from simulations, processing and AI/ML training tasks running on any platform, in real time.
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.
from simvue import Run
...
if __name__ == "__main__":
...
# Using a context manager means that the status will be set to completed automatically,
# and also means that if the code exits with an exception this will be reported to Simvue
with Run() as run:
# Specify a run name, metadata (dict), tags (list), description, folder
run.init('example-run-name',
{'learning_rate': 0.001, 'training_steps': 2000, 'batch_size': 32}, # Metadaata
['tensorflow'], # Tags
'This is a test.', # Description
'/Project-A/part1') # Folder full path
# Set folder details if necessary
run.set_folder_details('/Project-A/part1', # Folder full path
metadata={}, # Metadata
tags=['tensorflow'], # Tags
description='This is part 1 of a test') # Description
# Upload the code
run.save_file('training.py', 'code')
# Upload an input file
run.save_file('params.in', 'input')
# Add an alert (the alert definition will be created if necessary)
run.create_metric_threshold_alert(
name='loss-too-high', # Name
rule='is above', # Rule
metric='loss', # Metric
frequency=1, # Frequency
window=1, # Window
threshold=10, # Threshold
notification='email' # Notification type
)
...
while not converged:
...
# Send metrics inside main application loop
run.log_metrics({'loss': 0.5, 'density': 34.4})
...
# Upload an output file
run.save_file('output.cdf', 'output')
# If we weren't using a context manager we'd need to end the run
# run.close()Released under the terms of the Apache 2 license.