Py36 drop: implement rasa telemetry improvement

[twerkmeister]

Description of Problem: With the python 3.6 drop, it seems possible to simplify the telemetry code according to this warning comment:

rasa/rasa/telemetry.py

Lines 680 to 749 in 3ac9530

	@async_generator.asynccontextmanager
	async def track_model_training(
	training_data: "TrainingDataImporter", model_type: Text, is_finetuning: bool = False
	) -> typing.AsyncGenerator[None, None]:
	"""Track a model training started.
	WARNING: since this is a generator, it can't use the ensure telemetry
	decorator. We need to manually add these checks here. This can be
	fixed as soon as we drop python 3.6 support.
	Args:
	training_data: Training data used for the training.
	model_type: Specifies the type of training, should be either "rasa", "core"
	or "nlu".
	is_finetuning: `True` if the model is trained by finetuning another model.
	"""
	if not initialize_telemetry():
	# telemetry reporting is disabled. we won't do any reporting
	yield # runs the training
	return # closes the async context
	config = await training_data.get_config()
	stories = await training_data.get_stories()
	nlu_data = await training_data.get_nlu_data()
	domain = await training_data.get_domain()
	count_conditional_responses = domain.count_conditional_response_variations()
	training_id = uuid.uuid4().hex
	# Make sure to update the example in docs/docs/telemetry/telemetry.mdx
	# if you change / add any properties
	_track(
	TRAINING_STARTED_EVENT,
	{
	"language": config.get("language"),
	"training_id": training_id,
	"type": model_type,
	"pipeline": config.get("pipeline"),
	"policies": config.get("policies"),
	"num_intent_examples": len(nlu_data.intent_examples),
	"num_entity_examples": len(nlu_data.entity_examples),
	"num_actions": len(domain.action_names_or_texts),
	# Old nomenclature from when 'responses' were still called
	# 'templates' in the domain
	"num_templates": len(domain.responses),
	"num_conditional_response_variations": count_conditional_responses,
	"num_slots": len(domain.slots),
	"num_forms": len(domain.forms),
	"num_intents": len(domain.intents),
	"num_entities": len(domain.entities),
	"num_story_steps": len(stories.story_steps),
	"num_lookup_tables": len(nlu_data.lookup_tables),
	"num_synonyms": len(nlu_data.entity_synonyms),
	"num_regexes": len(nlu_data.regex_features),
	"is_finetuning": is_finetuning,
	},
	)
	start = datetime.now()
	yield
	runtime = datetime.now() - start
	_track(
	TRAINING_COMPLETED_EVENT,
	{
	"training_id": training_id,
	"type": model_type,
	"runtime": int(runtime.total_seconds()),
	},
	)

Definition of Done:

• figure out what needs to be done - is it that the decorator can be used with python 3.7 or does something else needs changing?
• change is implemented
• change is tested

[sync-by-unito]

➤ Maxime Verger commented:

💡 Heads up! We're moving issues to Jira: https://rasa-open-source.atlassian.net/browse/OSS.

From now on, this Jira board is the place where you can browse (without an account) and create issues (you'll need a free Jira account for that). This GitHub issue has already been migrated to Jira and will be closed on January 9th, 2023. Do not forget to subscribe to the corresponding Jira issue!

➡️ More information in the forum: https://forum.rasa.com/t/migration-of-rasa-oss-issues-to-jira/56569.