def _save_logs_to_mlflow(logs: Optional[skein.model.ApplicationLogs],
containers: Dict[str, Tuple[str, str]], n_try: int):
if not logs:
return
for key, logs in logs.items():
if key in containers:
task, status = containers[key]
filename = mlflow.format_key(f"{task}_{status}_{n_try}")
else:
filename = mlflow.format_key(f"{key}_{n_try}")
mlflow.save_text_to_mlflow(logs, filename)
def log_mlflow(self, n_try: int):
content = ""
for metric_name, value in self._asdict().items():
if isinstance(value, dict):
for k, v in value.items():
if v:
formatted_key = mlflow.format_key(f"{metric_name}_{k}_{n_try}")
content = content + f"{formatted_key}: {v.total_seconds()} secs\n"
else:
if value:
formatted_key = mlflow.format_key(f"{metric_name}_{n_try}")
content = content + f"{formatted_key}: {value.total_seconds()} secs\n"
mlflow.save_text_to_mlflow(content, "tf_yarn_duration_stats")