Work Report - 19 February 2026

1. Objective

Diagnose and resolve inference-time model loading failures for the AnanaCare pipeline, ensuring strict architectural parity and robust device management for production deployment.

2. Key Activities Completed

A. Debugged Device Handling Logic

Identified CUDA-related runtime error due to CPU-only PyTorch installation attempting GPU execution
Refactored device handling logic to make the system hardware-agnostic
Validated environment sync and hardware detection

B. Reverse Engineered Model Architecture

Encountered major state_dict mismatch while loading trained model checkpoint
Used Python REPL to manually load checkpoint, print parameter keys and tensor shapes
Reconstructed actual model structure through dimensional analysis
Identified: 1024-d base embedding, three categorical bias embeddings, concatenated 1039-d input, input projection layer, structured backbone, 20 attention modules, 20 regression task heads

C. Verified Input/Output Consistency

Traced data flow through backbone and task heads
Ensured input/output shapes matched checkpoint expectations

3. Technical Decisions Made Today

Strictly match inference architecture to training model definition
Avoid using strict=False for weight loading
Document device handling logic for hardware-agnostic deployment
Prepare to rebuild inference model for checkpoint compatibility

4. Key Learnings

Systematic diagnosis of model loading failures using state_dict inspection
Reverse engineering architectures from parameter names and tensor shapes
Importance of production-safe inference and robust device management
Risks of relying solely on checkpoint weights without clear documentation

5. Risks Identified & Mitigation

Risks:

Architecture mismatch between training and inference implementation
Original training model definition not directly referenced
Potential silent drift if input/output logic differs
Device handling errors in production environments

Mitigation:

Reverse engineered architecture from checkpoint weights
Refactored device handling for hardware-agnostic execution
Plan to rebuild inference model to match checkpoint structure
Documented all technical decisions and learnings

6. Skills Used

PyTorch, Deep Learning Debugging, Model Architecture Analysis, State Dict Inspection, Multi-Task Learning, Attention Mechanisms, Production ML Engineering

Technical Details

Troubleshooting Steps:

Used Python REPL to manually load and inspect checkpoint parameters
Printed all state_dict keys and tensor shapes for dimensional analysis
Compared layer names and shapes to reconstruct the original architecture
Identified device handling issues and refactored code for hardware-agnostic execution
Verified model input/output consistency by tracing data flow through the backbone and task heads

Key Insights:

Model architecture must be strictly matched between training and inference
Device management should be robust to avoid CUDA/CPU mismatches
Reverse engineering from checkpoint weights is feasible but time-consuming

Reflections

This experience highlighted the importance of maintaining clear documentation and version control for model architectures. Relying solely on checkpoint weights for reconstruction is risky and inefficient. I also realized the value of systematic debugging and the necessity of hardware-agnostic code for production environments.

Next Steps

Rebuild the inference model to exactly match the checkpoint structure
Validate the new model by loading the checkpoint and running test inference
Document the architecture and device handling logic for future reference
Collaborate with the team to ensure training and inference codebases remain synchronized

Future Goals

Automate architecture validation between training and inference
Develop robust logging and error reporting for model loading and device management
Explore ways to streamline checkpoint inspection and architecture reconstruction
Continue improving production ML engineering practices for reliability and scalability

What I worked on?

Work Summary * Today I focused on debugging and stabilizing the production inference pipeline for the AnanaCare model. After syncing the environment and running the application, I encountered a CUDA-related runtime error caused by a CPU-only PyTorch installation attempting GPU execution. I analyzed and corrected the device handling logic to make the system hardware-agnostic.

Learnings / Outcomes * Today I learned how to systematically diagnose model loading failures using state_dict inspection. I strengthened my understanding of how neural network architectures can be reconstructed from parameter names and tensor shapes. I also reinforced best practices for production-safe inference, especially regarding device management and strict architecture matching between training and deployment. Blockers / Risks The primary blocker was the architecture mismatch between the training model and the inference implementation. Since the original training model definition was not directly referenced, additional time was required to reverse engineer the architecture from checkpoint weights.

Skills

Skills Used * PyTorch, Deep Learning Debugging, Model Architecture Analysis, State Dict Inspection, Multi-Task Learning, Attention Mechanisms, Production ML Engineering

Today I learned how to systematically diagnose model loading failures using state_dict inspection. I strengthened my understanding of how neural network architectures can be reconstructed from parameter names and tensor shapes. I also reinforced best practices for production-safe inference, especially regarding device management and strict architecture matching between training and deployment.