Distributed Training Orchestration With Pmap And Pjit

via “multi-device-parallelization-with-pmap”

Google's numerical computing library — autodiff, JIT, vectorization, NumPy API for ML research.

Unique: JAX's pmap integrates with jit and grad — @jit @pmap @grad enables a single compiled function that computes gradients in parallel across devices with automatic all-reduce for gradient averaging. pmap is implemented as a tracer that replicates the function across devices and inserts collective communication primitives, enabling seamless composition with other transformations.

vs others: Simpler than explicit distributed training frameworks (Horovod, DeepSpeed) because it requires no manual communication code; more efficient than parameter servers because it uses collective operations and avoids centralized bottlenecks

via “multi-device parallelization via pmap with automatic sharding”

Differentiate, compile, and transform Numpy code.

Unique: JAX's pmap automatically generates sharded computation graphs and handles device placement, communication, and synchronization without explicit distributed code. The system integrates with XLA's collective operations (all-reduce, all-gather) and composes with JIT and grad. pmap is being superseded by pjit (jit with sharding annotations), which provides more flexible sharding patterns and better integration with the compiler.

vs others: Automatic device placement and communication with transparent composition to JIT and grad, whereas PyTorch's DistributedDataParallel requires explicit communication code and TensorFlow's tf.distribute requires graph construction changes.

Flax: A neural network library for JAX designed for flexibility

Unique: Provides distributed training patterns using JAX's pmap/pjit primitives that enable automatic device placement and communication without manual synchronization code, working seamlessly with Flax's functional training loops

vs others: More composable than PyTorch distributed training because device placement is explicit and integrated with JAX's compilation, and more flexible because pmap/pjit support both data and model parallelism without separate APIs

via “distributed training orchestration”