def test_get_items_from_string() -> None:
"""
Check items correctly extracted from string.
"""
assert ["i", "p"] == common_util.get_items_from_string("i, ,p")
assert ["i", "p"] == common_util.get_items_from_string("i- -p", separator="-")
assert ["i", " ", " p"] == common_util.get_items_from_string("i, , p", remove_blanks=False)
assert ["i", "p"] == common_util.get_items_from_string("i, , p")
assert [] == common_util.get_items_from_string("")
def monitor(monitor_config: AMLTensorBoardMonitorConfig,
azure_config: AzureConfig) -> None:
"""
Starts TensorBoard monitoring as per the provided arguments.
:param monitor_config: The config containing information on which runs that need be monitored.
:param azure_config: An AzureConfig object with secrets/keys to access the workspace.
"""
# Fetch AzureML workspace and the experiment runs in it
workspace = azure_config.get_workspace()
if monitor_config.run_ids is not None:
if len(monitor_config.run_ids) == 0:
print("At least one run_recovery_id must be given for monitoring.")
sys.exit(1)
exp_runs = [
azure_util.fetch_run(workspace, run_id)
for run_id in monitor_config.run_ids
]
else:
if monitor_config.experiment_name not in workspace.experiments:
print(f"The experiment: {monitor_config.experiment_name} doesn't "
f"exist in the {monitor_config.workspace_name} workspace.")
sys.exit(1)
experiment = Experiment(workspace, monitor_config.experiment_name)
filters = common_util.get_items_from_string(
monitor_config.run_status) if monitor_config.run_status else []
exp_runs = azure_util.fetch_runs(experiment, filters)
if len(exp_runs) == 0:
_msg = "No runs to monitor"
if monitor_config.run_status:
_msg += f"with status [{monitor_config.run_status}]."
print(_msg)
sys.exit(1)
# Start TensorBoard on executing machine
ts = Tensorboard(exp_runs,
local_root=str(monitor_config.local_root),
port=monitor_config.port)
print(
"=============================================================================="
)
for run in exp_runs:
print(f"Run URL: {run.get_portal_url()}")
print("TensorBoard URL: ")
ts.start()
print(
"==============================================================================\n\n"
)
input("Press Enter to close TensorBoard...")
ts.stop()