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ai_disaster_recovery [2025/05/26 14:38] – [Key Features] eagleeyenebulaai_disaster_recovery [2025/05/26 14:44] (current) – [Best Practices] eagleeyenebula
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 ==== Core Components ==== ==== Core Components ====
  
-1. **`save_checkpoint(step_name, data)`**: +1. **save_checkpoint(step_name, data)**: 
-   - Saves the current state of the pipeline for the specified step. +   * Saves the current state of the pipeline for the specified step. 
-   - Logs the operation to ensure visibility in execution traces.+   * Logs the operation to ensure visibility in execution traces.
        
-2. **`rollback_to_checkpoint(step_name)`**: +2. **rollback_to_checkpoint(step_name)**: 
-   - Retrieves the saved state for the specified step name. +   * Retrieves the saved state for the specified step name. 
-   - Allows the pipeline to resume execution from the last known good state.+   * Allows the pipeline to resume execution from the last known good state.
  
-3. **Checkpoints Store (`self.checkpoints`)**: +3. **Checkpoints Store (self.checkpoints)**: 
-   - Maintains the in-memory storage for all pipeline checkpoints. +   * Maintains the in-memory storage for all pipeline checkpoints. 
-   - Key: `step_name` (uniquely identifies the pipeline step). +   * Key: **step_name** (uniquely identifies the pipeline step). 
-   - Value: Serialized state (`data`) to restore the pipeline.+   * Value: Serialized state (**data**) to restore the pipeline.
  
 ==== Class Definition ==== ==== Class Definition ====
  
-```python+<code> 
 +python
 import logging import logging
  
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         logging.info(f"Rolling back to checkpoint: {step_name}")         logging.info(f"Rolling back to checkpoint: {step_name}")
         return self.checkpoints.get(step_name, None)         return self.checkpoints.get(step_name, None)
-``` +</code>
- +
----+
  
 ===== Usage Examples ===== ===== Usage Examples =====
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 The following example demonstrates how to save pipeline checkpoints and perform a rollback: The following example demonstrates how to save pipeline checkpoints and perform a rollback:
  
-```python+<code> 
 +python
 from ai_disaster_recovery import DisasterRecovery from ai_disaster_recovery import DisasterRecovery
- +</code> 
-# Initialize the recovery manager+# **Initialize the recovery manager** 
 +<code>
 recovery_manager = DisasterRecovery() recovery_manager = DisasterRecovery()
- +</code> 
-# Save checkpoints for pipeline steps+# **Save checkpoints for pipeline steps** 
 +<code>
 recovery_manager.save_checkpoint("step_1", {"data": [1, 2, 3]}) recovery_manager.save_checkpoint("step_1", {"data": [1, 2, 3]})
 recovery_manager.save_checkpoint("step_2", {"data": [4, 5, 6]}) recovery_manager.save_checkpoint("step_2", {"data": [4, 5, 6]})
- +</code> 
-# Rollback to a checkpoint+# **Rollback to a checkpoint** 
 +<code>
 state_step_2 = recovery_manager.rollback_to_checkpoint("step_2") state_step_2 = recovery_manager.rollback_to_checkpoint("step_2")
 print(f"Restored state for step_2: {state_step_2}") print(f"Restored state for step_2: {state_step_2}")
- +</code> 
-# Rollback to an undefined checkpoint+# **Rollback to an undefined checkpoint** 
 +<code>
 state_invalid = recovery_manager.rollback_to_checkpoint("missing_step") state_invalid = recovery_manager.rollback_to_checkpoint("missing_step")
 print(f"Restored state for missing step: {state_invalid}") print(f"Restored state for missing step: {state_invalid}")
-```+</code>
  
 **Expected Output:** **Expected Output:**
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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.1748270295.txt.gz · Last modified: 2025/05/26 14:38 by eagleeyenebula