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Implement memory cleanup for ML models after channel matching operations

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SergeantPanda 2025-09-16 13:15:32 -05:00
parent c55dcfd26a
commit d6bb9e40b2
1 changed files with 30 additions and 0 deletions
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30
apps/channels/tasks.py
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@ -318,6 +318,12 @@ def match_channels_to_epg(channels_data, epg_data, region_code=None, use_ml=True
# No ML available or very low fuzzy score
logger.info(f"Channel {chan['id']} '{chan['name']}' => best fuzzy score={best_score} < {LOWER_FUZZY_THRESHOLD}, no ML fallback available")
# Clean up ML models from memory after matching (infrequent operation)
if _ml_model_cache['sentence_transformer'] is not None:
logger.info("Cleaning up ML models from memory")
_ml_model_cache['sentence_transformer'] = None
gc.collect()
return {
"channels_to_update": channels_to_update,
"matched_channels": matched_channels
@ -432,6 +438,11 @@ def match_epg_channels():
return f"Done. Matched {total_matched} channel(s)."
finally:
# Clean up ML models from memory after bulk matching
if _ml_model_cache['sentence_transformer'] is not None:
logger.info("Cleaning up ML models from memory")
_ml_model_cache['sentence_transformer'] = None
# Memory cleanup
gc.collect()
from core.utils import cleanup_memory
@ -529,6 +540,12 @@ def match_single_channel_epg(channel_id):
logger.info(success_msg)
# Clean up ML models from memory after single channel matching
if _ml_model_cache['sentence_transformer'] is not None:
logger.info("Cleaning up ML models from memory")
_ml_model_cache['sentence_transformer'] = None
gc.collect()
return {
"matched": True,
"message": success_msg,
@ -539,6 +556,12 @@ def match_single_channel_epg(channel_id):
return {"matched": False, "message": "Matched EPG data not found"}
# No match found
# Clean up ML models from memory after single channel matching
if _ml_model_cache['sentence_transformer'] is not None:
logger.info("Cleaning up ML models from memory")
_ml_model_cache['sentence_transformer'] = None
gc.collect()
return {
"matched": False,
"message": f"No suitable EPG match found for channel '{channel.name}'"
@ -546,6 +569,13 @@ def match_single_channel_epg(channel_id):
except Exception as e:
logger.error(f"Error in integrated single channel EPG matching: {e}", exc_info=True)
# Clean up ML models from memory even on error
if _ml_model_cache['sentence_transformer'] is not None:
logger.info("Cleaning up ML models from memory after error")
_ml_model_cache['sentence_transformer'] = None
gc.collect()
return {"matched": False, "message": f"Error during matching: {str(e)}"}