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metadata>run_metadata #23

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merged 1 commit into from
Nov 16, 2023
Merged

metadata>run_metadata #23

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2 changes: 1 addition & 1 deletion README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -154,7 +154,7 @@ for step_name in step_names:
)
model: ClassifierMixin = hp_output.load()
# fetch metadata we attached earlier
metric = float(hp_output.metadata["metric"].value)
metric = float(hp_output.run_metadata["metric"].value)
if best_model is None or best_metric < metric:
best_model = model
```
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2 changes: 1 addition & 1 deletion template/steps/deployment/deployment_deploy.py
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Expand Up @@ -50,7 +50,7 @@ def deployment_deploy() -> (
deployment_service = mlflow_model_registry_deployer_step.entrypoint(
registry_model_name=model_version.name,
registry_model_version=model_version.get_model_artifact("model")
.metadata["model_registry_version"]
.run_metadata["model_registry_version"]
.value,
replace_existing=True,
)
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6 changes: 3 additions & 3 deletions ...teps/promotion/{% if metric_compare_promotion %}promote_with_metric_compare.py{% endif %}
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Expand Up @@ -74,11 +74,11 @@ def promote_with_metric_compare(
logger.info(f"Current model version was promoted to '{target_env}'.")

# Promote in Model Registry
latest_version_model_registry_number = latest_version.get_model_artifact("model").metadata["model_registry_version"].value
latest_version_model_registry_number = latest_version.get_model_artifact("model").run_metadata["model_registry_version"].value
if current_version_number is None:
current_version_model_registry_number = latest_version_model_registry_number
else:
current_version_model_registry_number = current_version.get_model_artifact("model").metadata["model_registry_version"].value
current_version_model_registry_number = current_version.get_model_artifact("model").run_metadata["model_registry_version"].value
promote_in_model_registry(
latest_version=latest_version_model_registry_number,
current_version=current_version_model_registry_number,
Expand All @@ -87,7 +87,7 @@ def promote_with_metric_compare(
)
promoted_version = latest_version_model_registry_number
else:
promoted_version = current_version.get_model_artifact("model").metadata["model_registry_version"].value
promoted_version = current_version.get_model_artifact("model").run_metadata["model_registry_version"].value

logger.info(
f"Current model version in `{target_env}` is `{promoted_version}` registered in Model Registry"
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4 changes: 2 additions & 2 deletions ...steps/promotion/{% if not metric_compare_promotion %}promote_latest_version.py{% endif %}
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Expand Up @@ -32,11 +32,11 @@ def promote_latest_version(
logger.info(f"Current model version was promoted to '{target_env}'.")

# Promote in Model Registry
latest_version_model_registry_number = latest_version.get_model_artifact("model").metadata["model_registry_version"].value
latest_version_model_registry_number = latest_version.get_model_artifact("model").run_metadata["model_registry_version"].value
if current_version.number is None:
current_version_model_registry_number = latest_version_model_registry_number
else:
current_version_model_registry_number = current_version.get_model_artifact("model").metadata["model_registry_version"].value
current_version_model_registry_number = current_version.get_model_artifact("model").run_metadata["model_registry_version"].value
promote_in_model_registry(
latest_version=latest_version_model_registry_number,
current_version=current_version_model_registry_number,
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2 changes: 1 addition & 1 deletion .../steps/{% if hyperparameters_tuning %}hp_tuning{% endif %}/hp_tuning_select_best_model.py
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Expand Up @@ -36,7 +36,7 @@ def hp_tuning_select_best_model(
)
model: ClassifierMixin = hp_output.load()
# fetch metadata we attached earlier
metric = float(hp_output.metadata["metric"].value)
metric = float(hp_output.run_metadata["metric"].value)
if best_model is None or best_metric < metric:
best_model = model
### YOUR CODE ENDS HERE ###
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