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ai_disaster_recovery [2025/04/22 15:31] – created eagleeyenebulaai_disaster_recovery [2025/05/26 14:44] (current) – [Best Practices] eagleeyenebula
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 ====== AI Disaster Recovery ====== ====== AI Disaster Recovery ======
 +**[[https://autobotsolutions.com/god/templates/index.1.html|More Developers Docs]]**:
 The **AI Disaster Recovery Module** is an advanced framework for managing disaster recovery in AI pipelines. It provides functionalities for saving pipeline checkpoints, enabling rollback to previous states, and ensuring data integrity during the recovery process. This module is critical for systems where process continuity, resiliency, and fault-tolerance are paramount. The **AI Disaster Recovery Module** is an advanced framework for managing disaster recovery in AI pipelines. It provides functionalities for saving pipeline checkpoints, enabling rollback to previous states, and ensuring data integrity during the recovery process. This module is critical for systems where process continuity, resiliency, and fault-tolerance are paramount.
  
-Using a robust and extensible design, the module enables developers to save and retrieve checkpoints, allowing pipelines to gracefully recover from unexpected errors or failures in execution. This documentation provides an advanced guide, enhanced examples, and integration strategies for leveraging the module.+{{youtube>WpahNSask44?large}}
  
----+-------------------------------------------------------------
  
 +Using a robust and extensible design, the module enables developers to save and retrieve checkpoints, allowing pipelines to gracefully recover from unexpected errors or failures in execution. This documentation provides an advanced guide, enhanced examples, and integration strategies for leveraging the module.
 ===== Purpose ===== ===== Purpose =====
  
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 This system is a key component for pipelines requiring consistency, error handling, and disaster recovery. This system is a key component for pipelines requiring consistency, error handling, and disaster recovery.
- 
---- 
- 
 ===== Key Features ===== ===== Key Features =====
  
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 1. **Checkpoint Management**: 1. **Checkpoint Management**:
-   - Save the current state (`data`) of any pipeline step by associating it with a unique `step_name`. +   * Save the current state (**data**) of any pipeline step by associating it with a unique **step_name**. 
-   - Checkpoints are stored in an internal dictionary for immediate access.+   * Checkpoints are stored in an internal dictionary for immediate access.
  
 2. **Rollback Mechanism**: 2. **Rollback Mechanism**:
-   - Roll back to a previously saved state by specifying a `step_name`. +   * Roll back to a previously saved state by specifying a **step_name**. 
-   - Retrieve the corresponding state and reinitialize the pipeline from that step.+   * Retrieve the corresponding state and reinitialize the pipeline from that step.
  
 3. **Scalability**: 3. **Scalability**:
-   - Extend the module to integrate with external storage systems such as cloud storage, relational/NoSQL databases, or distributed caching layers for large-scale checkpoint management.+   * Extend the module to integrate with external storage systems such as cloud storage, **relational/NoSQL** databases, or distributed caching layers for large-scale checkpoint management.
  
 4. **Logging and Traceability**: 4. **Logging and Traceability**:
-   - Built-in logging to track when checkpoints are saved or rolled back, facilitating debugging and pipeline monitoring.+   * Built-in logging to track when checkpoints are saved or rolled back, facilitating debugging and pipeline monitoring.
  
 5. **Fault Isolation**: 5. **Fault Isolation**:
-   - Enables isolation of faults by restoring the last known good checkpoint, reducing the impact of pipeline errors.+   * Enables isolation of faults by restoring the last known good checkpoint, reducing the impact of pipeline errors.
  
 6. **Extensibility**: 6. **Extensibility**:
-   - Increase functionality by overriding methods to design customized recovery solutions (e.g., versioned checkpoints, distributed checkpointing). +   * Increase functionality by overriding methods to design customized recovery solutions (e.g., versioned checkpoints, distributed checkpointing).
- +
---- +
 ===== Architecture ===== ===== Architecture =====
  
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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.1745335894.txt.gz · Last modified: 2025/04/22 15:31 by eagleeyenebula