simvue 0.0.3

Simulation tracking

Simvue Python client

Collects metadata, metrics and files from simulations in real time.

Configuration

The service URL and token can be defined as environment variables:

export SIMVUE_URL=...
export SIMVUE_TOKEN=...

or a file simvue.ini 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.

Usage example

from simvue import Simvue

...

# Using a context manager means that the status will be set to completed automatically
with Simvue() 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('training.py', 'code')

    # Upload an input file
    run.save('params.in', 'input')

    # Add an alert (the alert definition will be created if necessary)
    run.add_alert('loss-too-high',      # Name
                  'is above',           # Type
                  'loss',               # Metric
                  1,                    # Frequency
                  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('output.cdf', 'output')
    
    # If we weren't using a context manager we'd need to end the run
    # run.close()