fix: improve metadata retrieval and default score handling in Validator#19
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nickcom007 wants to merge 1 commit intomainfrom
Open
fix: improve metadata retrieval and default score handling in Validator#19nickcom007 wants to merge 1 commit intomainfrom
nickcom007 wants to merge 1 commit intomainfrom
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WalkthroughThe code in the validator now fetches model metadata for each UID within the normalization loop, checks if metadata exists, and handles missing metadata by logging a warning and assigning a default normalized score. This prevents errors or invalid computations when metadata is absent during score normalization. Changes
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| # Get metadata for this UID | ||
| metadata = retrieve_model_metadata( | ||
| self.subtensor, self.config.netuid, self.metagraph.hotkeys[uid] | ||
| ) | ||
| if metadata is None: | ||
| bt.logging.warning(f"No metadata found for UID {uid}; defaulting score to 0") | ||
| normalized_score = constants.DEFAULT_SCORE | ||
| else: | ||
| normalized_score = compute_score( | ||
| scores_per_uid[uid], | ||
| competition.bench, | ||
| competition.minb, | ||
| competition.maxb, | ||
| competition.pow, | ||
| competition.bheight, | ||
| metadata.id.competition_id, | ||
| competition.id, | ||
| ) |
Contributor
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🛠️ Refactor suggestion
Optimize metadata retrieval to avoid duplicate network calls.
While the added metadata existence check and fallback handling is a good safety improvement, this implementation retrieves metadata twice for the same UID - once during evaluation (line 250) and again during normalization. This could impact performance and potentially lead to inconsistencies if metadata changes between calls.
Consider storing the metadata from the evaluation loop and reusing it during normalization:
# In the evaluation loop (around line 250), store metadata in a dictionary
metadata_per_uid = {}
# ... existing evaluation code ...
metadata = retrieve_model_metadata(
self.subtensor, self.config.netuid, self.metagraph.hotkeys[uid]
)
metadata_per_uid[uid] = metadata # Store for later use
# In the normalization loop, replace the new retrieval with stored metadata:
- # Get metadata for this UID
- metadata = retrieve_model_metadata(
- self.subtensor, self.config.netuid, self.metagraph.hotkeys[uid]
- )
+ # Use previously retrieved metadata
+ metadata = metadata_per_uid.get(uid)This approach would:
- Eliminate redundant network calls
- Ensure consistency between evaluation and normalization phases
- Maintain the same safety checks for missing metadata
Committable suggestion skipped: line range outside the PR's diff.
🤖 Prompt for AI Agents
In neurons/validator.py around lines 406 to 423, the code retrieves metadata
twice for the same UID, causing redundant network calls and potential
inconsistencies. To fix this, modify the evaluation loop to store the retrieved
metadata in a dictionary keyed by UID. Then, during normalization, reuse the
stored metadata from this dictionary instead of calling retrieve_model_metadata
again. This eliminates duplicate calls, ensures consistent metadata usage, and
retains the existing safety checks for missing metadata.
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