pipeline

**Pipeline Parallelism Deep Learning** is **a distributed training approach dividing neural networks into sequential stages across multiple devices, enabling concurrent execution of different stages** — Pipeline parallelism enables training of models too large for single devices through spatial decomposition exploiting pipeline depths. **Stage Partitioning** divides networks into stages based on number of devices, balancing computation load across stages, considering memory constraints. **Forward Pass Pipeline** executes different samples through different stages concurrently, sample 1 through stage 1, sample 2 through stage 1 while sample 1 processes stage 2. **Pipeline Bubble** represents idle time when stages wait for dependent computations, minimizing bubbles through careful batch scheduling. **Micro-batch Scheduling** divides mini-batches into micro-batches enabling finer-grained pipelining, trades communication overhead for reduced bubbles. **Gradient Computation** accumulates gradients from multiple micro-batches before updates, maintains convergence through careful learning rate adjustments. **Communication Optimization** overlaps gradient communication between stages with computation, implements gradient accumulation reducing synchronization frequency. **Re-computation vs Activation Storage** trades memory for recomputation, recomputing activations during backward pass avoiding storage. **Pipeline Parallelism Deep Learning** enables training models with parameters exceeding single-device memory.