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ai_disaster_recovery [2025/05/26 14:42] – [Core Components] eagleeyenebulaai_disaster_recovery [2025/05/26 14:44] (current) – [Best Practices] eagleeyenebula
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 In scenarios requiring persistent storage of checkpoints, the module can be extended with custom serialization. Here’s how to save checkpoints to disk: In scenarios requiring persistent storage of checkpoints, the module can be extended with custom serialization. Here’s how to save checkpoints to disk:
  
-```python+<code> 
 +python
 import pickle import pickle
 from ai_disaster_recovery import DisasterRecovery from ai_disaster_recovery import DisasterRecovery
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             logging.warning(f"Checkpoint file not found for step: {step_name}")             logging.warning(f"Checkpoint file not found for step: {step_name}")
             return None             return None
- +<code> 
-# Usage+# **Usage** 
 +<code>
 persistent_recovery = PersistentDisasterRecovery() persistent_recovery = PersistentDisasterRecovery()
- +</code> 
-# Save and rollback with disk persistence+# **Save and rollback with disk persistence** 
 +<code>
 persistent_recovery.save_checkpoint("step_3", {"data": [7, 8, 9]}) persistent_recovery.save_checkpoint("step_3", {"data": [7, 8, 9]})
 restored_data = persistent_recovery.rollback_to_checkpoint("step_3") restored_data = persistent_recovery.rollback_to_checkpoint("step_3")
 print(f"Restored data: {restored_data}") print(f"Restored data: {restored_data}")
-```+</code>
  
 **Expected Output:** **Expected Output:**
- 
- 
---- 
- 
 ===== Use Cases ===== ===== Use Cases =====
  
 1. **AI Model Training Pipelines**: 1. **AI Model Training Pipelines**:
-   - Save model state after every training epoch for fault recovery.+   * Save model state after every training epoch for fault recovery.
  
 2. **Data Processing Pipelines**: 2. **Data Processing Pipelines**:
-   - Save intermediate transformation results to prevent reprocessing from scratch in the event of failure.+   * Save intermediate transformation results to prevent reprocessing from scratch in the event of failure.
  
 3. **Workflow Management Systems**: 3. **Workflow Management Systems**:
-   - Use checkpoints to incrementally save the state of a multi-step workflow.+   * Use checkpoints to incrementally save the state of a multi-step workflow.
  
 4. **Debugging Complex Errors**: 4. **Debugging Complex Errors**:
-   - Rollback to a known-good state for error analysis and testing. +   * Rollback to a known-good state for error analysis and testing.
- +
----+
  
 ===== Best Practices ===== ===== Best Practices =====
  
 1. **Granular Checkpoints**: 1. **Granular Checkpoints**:
-   - Save checkpoints at critical pipeline steps (e.g., post-feature extraction, model training).+   * Save checkpoints at critical pipeline steps (e.g., post-feature extraction, model training).
  
 2. **Logging and Debugging**: 2. **Logging and Debugging**:
-   - Leverage logging to monitor checkpoint creation and rollback actions.+   * Leverage logging to monitor checkpoint creation and rollback actions.
  
 3. **Serialization**: 3. **Serialization**:
-   - Use serialization (e.g., `pickle`, `JSON`, or database) for persistent checkpoint management, especially in distributed systems.+   * Use serialization (e.g., **pickle**, **JSON**, or database) for persistent checkpoint management, especially in distributed systems.
  
 4. **Version Control**: 4. **Version Control**:
-   - Employ versioning for checkpoints to avoid overwriting critical recovery points.+   * Employ versioning for checkpoints to avoid overwriting critical recovery points.
  
 5. **Secure Recovery**: 5. **Secure Recovery**:
-   - When using external storage (e.g., cloud), ensure encryption to secure sensitive pipeline states. +   * When using external storage (e.g., cloud), ensure encryption to secure sensitive pipeline states.
- +
---- +
 ===== Conclusion ===== ===== Conclusion =====
  
ai_disaster_recovery.1748270530.txt.gz · Last modified: 2025/05/26 14:42 by eagleeyenebula