New azure log refactor

[kirilklein]

…ndling

• Updated logging functions to utilize MLFLOW_CLIENT for metric, parameter, and image logging.
• Enhanced prefix handling for metrics and parameters to ensure proper naming conventions.
• Improved run handling logic to correctly traverse parent runs and build prefixes.
• Refactored function signatures for consistency and clarity.

Summary by CodeRabbit

• New Features

  • Improved logging functions for metrics, parameters, images, and figures, allowing for more flexible input formats and consistent prefixing of keys and artifact paths.
  • Enhanced MLflow run management with support for nested runs and run naming during job execution.
  • Added automatic logging of validation and test metrics, as well as training and validation losses, with configurable limits on the number of targets logged.

• Bug Fixes

  • Enhanced exception handling for external library imports.

• Refactor

  • Updated function signatures for logging utilities to accept dictionaries and objects directly, streamlining the logging process.

[coderabbitai]

Walkthrough

The changes refactor logging utilities for MLflow integration by improving exception handling, modifying function signatures, and ensuring consistent prefixing of keys and artifact paths. All logging functions now use the MLFLOW_CLIENT instance with explicit run IDs, and function parameters have been updated for clarity and consistency. Additionally, MLflow run initialization in the job runner was enhanced to support nested runs with explicit naming. A new block was added to log validation and test metrics during training in the causal trainer module.

Changes

Cohort / File(s)	Change Summary
MLflow Logging Utilities Refactor corebehrt/azure/util/log.py	Refactored MLflow logging utilities: improved exception handling for imports, changed parent run traversal logic, updated all logging functions to use MLFLOW_CLIENT with explicit run IDs, revised function signatures, and standardized prefix application.
MLflow Run Initialization Enhancement corebehrt/azure/util/job.py	Modified run_main to check MLflow availability and active run status before starting a run, enabling nested runs and setting run names explicitly during job execution.
Training Metrics Logging Addition corebehrt/modules/trainer/causal/trainer.py	Added MLflow logging of validation and test metrics, and training and validation losses within validate_and_log method, with configurable limits on the number of targets logged per epoch.

Estimated code review effort

🎯 3 (Moderate) | ⏱️ ~20 minutes

Possibly related PRs

• Merge/april1 #47: Directly related by changes to corebehrt/azure/util/log.py including get_run_and_prefix and logging function signatures and implementations using MLflow client instance.
• Logging #54: Related by modifications to logging function signatures and implementations in corebehrt/azure/util/log.py, focusing on simplifying log_param function signature and calls.

Poem

In the warren where logs abound,
A rabbit tweaks the code all around.
Prefixes hop to each metric and param,
With MLflow’s client, it’s never a jam.
No more generic excepts to fear—
Just tidy runs, all crystal clear!
🐇✨

Note

⚡️ Unit Test Generation is now available in beta!

Learn more here, or try it out under "Finishing Touches" below.

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Reviewing files that changed from the base of the PR and between 4e5013a and eeb5a4a.

📒 Files selected for processing (1)

• corebehrt/modules/trainer/causal/trainer.py (1 hunks)

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🔇 Additional comments (1)

corebehrt/modules/trainer/causal/trainer.py (1)

567-585: MLflow logging integration verified – no critical issues found.

• The run_log method is defined in corebehrt/modules/trainer/trainer.py (lines 481–485), so the logging calls will work as expected.
• As an optional enhancement, you may prioritize key metrics (e.g., AUC, loss, accuracy) before hitting the self.num_targets_to_log limit to ensure the most important metrics are always logged.
• You could also wrap the MLflow calls in a try/except block to guard against intermittent logging failures without interrupting training.

Optional metric-prioritization example:

 # Log all validation metrics
 if val_metrics:
-    for i, (metric_name, value) in enumerate(val_metrics.items()):
-        if i >= self.num_targets_to_log:
-            break
-        self.run_log(f"val_{metric_name}", value, step=epoch)
+    priority = [m for m in val_metrics if any(k in m for k in ['auc','loss','accuracy'])]
+    others   = [m for m in val_metrics if m not in priority]
+    count = 0
+    for name in priority + others:
+        if count >= self.num_targets_to_log:
+            break
+        self.run_log(f"val_{name}", val_metrics[name], step=epoch)
+        count += 1

Optional error handling example:

try:
    self.run_log("train_loss", avg_train_loss, step=epoch)
    if val_loss is not None:
        self.run_log("val_loss", val_loss, step=epoch)
except Exception as e:
    self.log(f"MLflow logging failed: {e}")

✨ Finishing Touches

• 📝 Generate Docstrings

🧪 Generate unit tests

• Create PR with unit tests
• Post copyable unit tests in a comment
• Commit unit tests in branch fix/log_azure_pipeline

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[kirilklein]

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