Debug and scale a PyTorch training loop
You are presented with a PyTorch training script for a CIFAR-10 image classifier that exhibits one or more of the following critical issues:
- It does not converge (accuracy remains near random chance).
- It becomes unstable (loss values diverge to NaN or Inf).
- Its training speed is significantly slower than expected.
Your tasks are as follows:
1. Debugging a PyTorch Training Loop
Provide a systematic debugging checklist to identify the root cause of the observed issues. Your checklist should cover potential problems related to data, model architecture, loss function, optimizer configuration, device utilization, and numerical precision. For each category, suggest quick experiments you would conduct to diagnose the problem.
2. Scaling with Fully Sharded Data Parallel (FSDP)
Describe in detail how you would scale the same training process to leverage multiple GPUs using PyTorch's Fully Sharded Data Parallel (FSDP). Specifically address:
- The process of initializing distributed training.
- How to wrap the model effectively with FSDP.
- The mechanisms for handling optimizer states, gradients, and model parameters across devices.
- Strategies for implementing gradient accumulation and managing checkpointing within an FSDP setup.
3. Implementing Sparse All-Reduce
Discuss how you would implement or approximate a sparse matrix / sparse gradient all-reduce operation. Your discussion should cover:
- The communication patterns suitable for sparse data.
- The inherent tradeoffs when comparing sparse all-reduce against a dense all-reduce.
- The specific conditions or scenarios under which implementing a sparse all-reduce would be beneficial.
4. Custom Kernel Development
Under what specific conditions would you propose writing a custom CUDA or Triton kernel to improve training performance? Furthermore, explain your methodology for validating both the speedup achieved and the correctness of the custom kernel implementation.
AI interview prep
Core dilemma
Balancing extreme performance and scalability requirements for deep learning training with the complexities of distributed systems, numerical stability, and low-level hardware optimization, all while maintaining correctness and debuggability.
3 tradeoffs and 2 stress tests locked
Interview prep
Unlock the interviewer X-Ray rubric, tradeoff matrix, stress tests, clarifying questions, and key concepts.
Debug and scale a PyTorch training loop
You are presented with a PyTorch training script for a CIFAR-10 image classifier that exhibits one or more of the following critical issues:
- It does not converge (accuracy remains near random chance).
- It becomes unstable (loss values diverge to NaN or Inf).
- Its training speed is significantly slower than expected.
Your tasks are as follows:
1. Debugging a PyTorch Training Loop
Provide a systematic debugging checklist to identify the root cause of the observed issues. Your checklist should cover potential problems related to data, model architecture, loss function, optimizer configuration, device utilization, and numerical precision. For each category, suggest quick experiments you would conduct to diagnose the problem.
2. Scaling with Fully Sharded Data Parallel (FSDP)
Describe in detail how you would scale the same training process to leverage multiple GPUs using PyTorch's Fully Sharded Data Parallel (FSDP). Specifically address:
- The process of initializing distributed training.
- How to wrap the model effectively with FSDP.
- The mechanisms for handling optimizer states, gradients, and model parameters across devices.
- Strategies for implementing gradient accumulation and managing checkpointing within an FSDP setup.
3. Implementing Sparse All-Reduce
Discuss how you would implement or approximate a sparse matrix / sparse gradient all-reduce operation. Your discussion should cover:
- The communication patterns suitable for sparse data.
- The inherent tradeoffs when comparing sparse all-reduce against a dense all-reduce.
- The specific conditions or scenarios under which implementing a sparse all-reduce would be beneficial.
4. Custom Kernel Development
Under what specific conditions would you propose writing a custom CUDA or Triton kernel to improve training performance? Furthermore, explain your methodology for validating both the speedup achieved and the correctness of the custom kernel implementation.