Project Checklist: Engagement Prediction & Viral Likelihood Classifier

0. Compiling First CUDA Program

• 0.a. Install CUDA Toolkit
• 0.b. Write a Simple CUDA Program
  • Create a basic .cu file containing host and device code.
• 0.c. Compile the Program
  • Use nvcc to compile the .cu file into an executable.
• 0.d. Run and Verify
  • Execute the compiled program and ensure it produces the expected output.

1. Data Preprocessing

• 1.a. Data Loading and Integration

  • 1.a.1. Load Posts Data
    • Read the Posts dataset from the source (e.g., CSV, database).
    • Handle scientific notation in Post ID (e.g., 7.18825E+18) by converting to string or appropriate numeric type.
  • 1.a.2. Load Users Data
    • Read the Users dataset from the source.
    • Ensure consistency in User IDs and Usernames between Posts and Users datasets.
  • 1.a.3. Merge Posts with User Metadata
    • Use Username as the foreign key to join Posts with Users.
    • Verify the integrity of the merged dataset (no missing or mismatched entries).

• 1.b. Handling Missing and Inconsistent Data

  • Identify missing values in both Posts and Users datasets.
  • Decide on strategies to handle missing data (e.g., imputation, removal).
  • Normalize inconsistent formats (e.g., date formats in Created At).

• 1.c. Feature Engineering

  • 1.c.1. Temporal Features
    • Extract features from Created At (e.g., hour of day, day of week, month).
    • Calculate time since account creation if such data is available.
  • 1.c.2. Text Features
    • Clean Description text (remove special characters, emojis, etc.).
    • Tokenize Description and Hashtags.
    • Generate text embeddings (e.g., using Word2Vec, GloVe) or use pre-trained models for richer representations.
  • 1.c.3. Audio Features
    • Extract features from the Song field (e.g., song metadata like genre, tempo).
    • If raw audio files are available, extract audio features (e.g., MFCCs) using CUDA.
  • 1.c.4. Video Features
    • Utilize Length (seconds) as a feature.
    • If video frames are available:
      • Extract key frames.
      • Resize and preprocess frames using CUDA kernels.
      • Extract visual features using CNNs.
  • 1.c.5. Engagement Metrics
    • Normalize engagement metrics (Plays, Comments, Diggs, Shares) as needed.
    • Create target variables:
      • Engagement Score: Composite metric combining Plays, Comments, Diggs, Shares.
      • Viral Likelihood: Binary classification based on predefined thresholds of engagement metrics.

• 1.d. Handling Categorical and Numerical Data

  • 1.d.1. Numerical Features
    • Normalize/Standardize numerical features (Length, Follower Count, Following Count, Total Likes, Total Videos).
    • Implement CUDA kernels for scaling operations.
  • 1.d.2. Categorical Features
    • One-Hot Encode Hashtags.
    • Implement embedding lookups for categorical features using CUDA.
    • Handle sparsity and memory constraints for high-cardinality categories.

2. Feature Extraction

• 2.a. Text Feature Extraction
  • Implement CUDA-based tokenization for Description and Hashtags.
  • Build vocabulary index mapping tokens to unique IDs.
  • Convert tokens to numerical IDs with padding/truncating for sequences.
• 2.b. Audio Feature Extraction
  • If raw audio is available, implement CUDA kernels for MFCC extraction.
  • Otherwise, utilize song metadata features.
• 2.c. Video Feature Extraction
  • Implement CUDA-based frame extraction and preprocessing.
  • Develop CNNs in CUDA to extract visual features from video frames.
• 2.d. User Metadata Encoding
  • Embed categorical user features (if any) using embedding layers in CUDA.
  • Normalize numerical user features and integrate into feature vectors.

3. Model Architecture Design

• 3.a. Define Separate Encoders for Each Modality
  • 3.a.1. Text Encoder
    • Implement RNN (e.g., LSTM) or Transformer blocks in CUDA for processing Description and Hashtags.
  • 3.a.2. Audio Encoder
    • Implement fully connected layers or CNNs in CUDA for audio features.
  • 3.a.3. Video Encoder
    • Utilize the CNN implementation to process visual features from videos.
  • 3.a.4. User Metadata Encoder
    • Combine embeddings and dense layers for user-related features.
• 3.b. Feature Fusion
  • Implement CUDA kernels to concatenate feature vectors from text, audio, video, and user encoders.
  • Integrate attention mechanisms to dynamically weigh the importance of different modalities.
  • Optionally, design Multimodal Transformers to handle integrated features.
• 3.c. Prediction Layers
  • 3.c.1. Engagement Prediction (Regression)
    • Implement fully connected layers ending with a linear activation in CUDA.
  • 3.c.2. Viral Likelihood (Classification)
    • Implement dense layers with sigmoid (binary) or softmax (multi-class) activations in CUDA.

4. Training the Model

• 4.a. Prepare Training and Validation Sets
  • Split the dataset into training, validation, and test sets.
  • Ensure balanced representation of high and low engagement posts.
  • Implement stratified sampling to maintain class distribution.
• 4.b. Address Data Bias
  • 4.b.1. Balanced Sampling
    • Apply undersampling for high-engagement posts or oversampling for low-engagement posts.
  • 4.b.2. Class Weighting
    • Assign higher weights to low-engagement classes in the loss function.
  • 4.b.3. Data Augmentation
    • Generate synthetic low-engagement examples through perturbations.
• 4.c. Implement Forward Pass
  • Ensure efficient data flow through all model layers using CUDA streams.
  • Implement CUDA kernels for activation functions (ReLU, sigmoid, softmax).
• 4.d. Define Loss Functions
  • 4.d.1. Regression Loss
    • Implement Mean Squared Error (MSE) and Mean Absolute Error (MAE) in CUDA.
  • 4.d.2. Classification Loss
    • Implement Binary Cross-Entropy and Categorical Cross-Entropy in CUDA.
• 4.e. Implement Backward Pass (Gradient Calculation)
  • Manually implement backpropagation for each layer using CUDA.
  • Allocate memory for gradients and ensure accurate computation.
• 4.f. Optimization Algorithms
  • 4.f.1. Adam Optimizer
    • Implement Adam optimizer with moment estimates in CUDA.
  • 4.f.2. Optional Optimizers
    • Implement SGD with momentum or RMSprop in CUDA.
• 4.g. Parameter Updates
  • Use CUDA kernels to update weights based on gradients and optimizer rules.
  • Implement learning rate scheduling (step decay, cosine annealing) in CUDA.
• 4.h. Regularization Techniques
  • 4.h.1. Dropout
    • Implement dropout layers by randomly zeroing activations during training.
  • 4.h.2. L2 Regularization
    • Incorporate L2 penalty terms into loss functions and update rules.
• 4.i. Training Loop Development
  • Structure the training loop to iterate over epochs and batches.
  • Manage data loading, preprocessing, and synchronization within the loop.
  • Implement checkpointing to save model weights periodically.
• 4.j. Parallelism and Optimization
  • Utilize multiple CUDA streams to overlap computation and data transfers.
  • Optimize memory usage by reusing buffers and minimizing host-device transfers.

5. Model Evaluation and Validation

• 5.a. Define Evaluation Metrics
  • For Regression:
    • Mean Squared Error (MSE)
    • Mean Absolute Error (MAE)
  • For Classification:
    • Accuracy
    • Precision, Recall, F1-Score
    • AUC-ROC
  • Use Confusion Matrix to assess class-wise performance.
• 5.b. Validate Model Performance
  • Evaluate on validation set after each epoch.
  • Monitor for overfitting or underfitting.
  • Adjust hyperparameters based on validation performance.
• 5.c. Address Model Bias
  • Ensure the model performs well across both high and low engagement posts.
  • Analyze misclassifications to identify patterns of bias.

6. Inference Pipeline Development

• 6.a. Model Deployment Pipeline
  • 6.a.1. Data Preprocessing
    • Implement the same preprocessing steps used during training for new data.
  • 6.a.2. Feature Extraction
    • Use trained encoders to extract features from incoming posts.
  • 6.a.3. Forward Pass
    • Run the forward pass through the network to obtain predictions.
  • 6.a.4. Post-processing
    • Apply activation functions to interpret outputs for engagement metrics or viral likelihood.
• 6.b. Optimization for Speed and Memory
  • 6.b.1. Model Pruning
    • Remove less important weights to reduce model size.
  • 6.b.2. Quantization
    • Convert weights to lower precision (e.g., FP16) to speed up inference.
  • 6.b.3. Batch Inference
    • Process multiple inputs simultaneously to maximize GPU utilization.

7. Utilities and Infrastructure

• 7.a. Data Management
  • 7.a.1. Efficient Storage
    • Store preprocessed data in GPU-friendly formats (e.g., binary blobs) to speed up loading.
  • 7.a.2. Shuffling and Batching
    • Implement efficient data shuffling mechanisms.
    • Implement batching mechanisms to ensure training robustness.
• 7.b. Logging and Monitoring
  • 7.b.1. Performance Metrics
    • Track loss, accuracy, and other relevant metrics during training using CUDA-compatible logging.
  • 7.b.2. Visualization
    • Prepare data logs for external visualization tools (e.g., TensorBoard).
• 7.c. Debugging and Validation
  • 7.c.1. Unit Tests
    • Implement tests for each CUDA kernel to ensure correctness.
  • 7.c.2. Validation Set
    • Use a separate dataset to validate model performance during training.
• 7.d. Checkpointing and Model Saving
  • 7.d.1. State Saving
    • Implement mechanisms to save model weights and optimizer states to disk.
  • 7.d.2. Recovery
    • Ensure the ability to resume training from saved checkpoints.

8. Implementation Tips

• 8.a. Modular Design
  • Structure your code into modules (e.g., data loader, preprocessors, encoders, model, optimizers) to manage complexity.
• 8.b. Performance Profiling
  • Use CUDA profiling tools (e.g., nvprof, Nsight) to identify and optimize bottlenecks.
• 8.c. Memory Optimization
  • Minimize global memory accesses.
  • Utilize shared memory for faster computations where applicable.
• 8.d. Parallelism
  • Maximize parallel execution by ensuring that CUDA kernels are optimized for the GPU architecture.

9. Testing and Validation

• 9.a. Small-Scale Testing
  • Implement and test each component on a smaller subset of data to ensure functionality.
• 9.b. Integration Testing
  • Gradually integrate components, testing the entire pipeline to identify and fix issues.
• 9.c. Performance Benchmarking
  • Compare model predictions against known metrics to validate accuracy.
  • Benchmark inference speed and memory usage.

10. Deployment Considerations

• 10.a. Scalability
  • Ensure that the implementation can handle scaling beyond the current dataset size (e.g., more posts, users).
• 10.b. Resource Management
  • Efficiently manage GPU resources to prevent memory overflows.
  • Ensure optimal GPU utilization during both training and inference.
• 10.c. Maintenance
  • Document code thoroughly for future reference and updates.
  • Implement version control (e.g., Git) to manage changes effectively.

11. Addressing Data Bias and Generalization

• 11.a. Analyze Current Data Distribution
  • Assess the proportion of high vs. low engagement posts in your dataset.
  • Define thresholds for "extremely engaging" vs. "unpopular" posts.
• 11.b. Incorporate Low-Engagement Data
  • Collect additional data on posts with lower engagement metrics.
  • Use data augmentation to create synthetic low-engagement examples.
• 11.c. Implement Balanced Training Strategies
  • Apply stratified sampling or class weighting during the training process.
  • Adjust loss functions to account for class imbalance.
• 11.d. Continuous Monitoring and Updating
  • Implement feedback loops to continuously monitor model performance.
  • Regularly update the model with new data reflecting current engagement trends.

12. Final Model Evaluation and Deployment

• 12.a. Final Evaluation on Test Set
  • Assess model performance using the test dataset.
  • Ensure that evaluation metrics meet the desired thresholds.
• 12.b. Deploy the Model
  • Integrate the model into the desired production environment.
  • Ensure that the inference pipeline is optimized for real-time predictions if required.
• 12.c. Post-Deployment Monitoring
  • Continuously monitor model performance in the production environment.
  • Implement mechanisms to retrain or update the model as new data becomes available.

Summary

• Compiling First CUDA Program: Install CUDA Toolkit, set up environment variables, write and compile a simple CUDA program, run and debug it to ensure the CUDA environment is correctly configured.
• Data Preprocessing: Load, clean, and engineer features from Posts and Users datasets, ensuring integration and consistency.
• Feature Extraction: Extract and process text, audio, video, and numerical features tailored to the dataset specifics.
• Model Architecture: Design a multimodal neural network with separate encoders for each data modality and effective feature fusion strategies.
• Training: Implement balanced training strategies to mitigate data bias, develop forward and backward pass mechanisms, and optimize the model using CUDA.
• Evaluation: Rigorously evaluate the model using comprehensive metrics to ensure accuracy and generalization across engagement levels.
• Inference: Develop an optimized pipeline for deploying the trained model to make real-time predictions on new data.
• Utilities and Infrastructure: Build robust systems for data management, logging, checkpointing, and ensure efficient resource utilization.
• Testing and Validation: Conduct thorough testing at both component and integrated levels to ensure model reliability and performance.
• Deployment: Prepare and deploy the model for scalable and efficient use, with ongoing maintenance and updates to handle evolving data trends.
• Addressing Bias: Implement strategies to balance the training data and ensure the model generalizes well across different engagement levels.