Model Training: From Infrastructure to Evaluation, Debugging & Optimization
When
- April 30, 2025
Where
- Virtual
About This Event
Join Us for An Exclusive Technical Summit Where Leading Foundation-Model Researchers and Practitioners Converge to Tackle Real-World Challenges in Foundation Model Training.
This Immersive Event Bridges Theory and Practice, Offering Ai Researchers and Practitioners Training Foundation Models a Rare Opportunity to Exchange Battle-Tested Approaches for Infrastructure Scaling, Debugging Model Internals, Evaluation, and Optimization.
Focus Areas
1) Infrastructure Debugging & Monitoring
- Diagnosing Performance Bottlenecks in Multi-GPU/Multi-Node Setups
- Instrumenting Pipelines for Deep Observability (Profiling GPU Utilization, Data Flow, Etc.)
- Correlating Infrastructure Metrics with Model States (Loss, Gradients) in Real Time
- Failure Detection and Recovery Strategies in Distributed or HPC Environments
2) Model Internals & Debugging
- Techniques for Analyzing Attention and Activation Patterns (Layer-By-Layer Visualizations)
- Identifying and Fixing Gradient Issues (Vanishing, Exploding, Partial Inactivity)
- Debugging Architectural or Layer-Level Bottlenecks
- Leveraging Interpretability to Guide Early-Phase Debugging (During Pre-Training)
3) Evaluation
- Designing Targeted Test Sets and Adversarial Evaluations for Foundation Models
- Error Analysis Frameworks to Uncover Overlooked Failures or Biases
- Establishing Benchmarks for Generalization, Robustness, and Emergent Capabilities
- Integrating Evaluation Signals Back Into Hyperparameter Tuning and Model Iteration
4) Pre-Training Optimization
- Hyperparameter Optimization at Foundation-Model Scale (e.G., Population-Based Training)
- Data Pipeline Throughput (Streaming, Multi-Threaded I/O, Sharding)
- Memory-Saving Strategies for Large Context Windows (Activation Checkpointing, Gradient Sharding)
- Accelerating convergence (Curriculum Learning, Dynamic Batching, Advanced Scheduling)