memory profiling

**Memory profiling** is the **analysis of allocation patterns, usage peaks, and fragmentation across model execution** - it helps prevent out-of-memory failures and reveals where memory pressure limits performance. **What Is Memory profiling?** - **Definition**: Tracking tensor allocation lifecycle, peak usage, cache behavior, and memory reuse dynamics. - **Key Signals**: High-water marks, fragmentation, retained tensors, and allocator churn frequency. - **Scope**: Covers activation memory, optimizer state, gradients, temporary buffers, and framework overhead. - **Failure Indicators**: Large free memory with small contiguous blocks, sudden spikes, and leaked references. **Why Memory profiling Matters** - **Stability**: Prevents intermittent OOM failures that break long-running training jobs. - **Batch Optimization**: Identifies safe headroom for larger batch sizes and higher throughput. - **Efficiency**: Exposes wasteful allocations that reduce effective model capacity. - **Debugging**: Helps isolate memory leaks caused by stale references or logging artifacts. - **Cost Control**: Better memory use can avoid unnecessary upgrades to larger GPU tiers. **How It Is Used in Practice** - **Profile Capture**: Collect per-step memory snapshots and allocator events during representative runs. - **Leak Investigation**: Trace persistent tensors back to owning modules or data structures. - **Mitigation**: Apply checkpointing, precision reduction, and in-place-safe patterns where appropriate. Memory profiling is **a critical reliability and scaling practice for deep learning systems** - understanding allocation behavior is essential for stable, high-utilization training.