tDistributed

tdistributed is a distributed training framework designed to coordinate scalable machine learning workloads across multi-node, multi-GPU clusters. It provides mechanisms for data parallelism, model parallelism, and pipeline parallelism, and supports multiple communication backends including NCCL, MPI, and Gloo. The framework aims to offer both synchronous and asynchronous execution models, elastic scaling, and fault tolerance to accommodate dynamic clusters.

Architecture and operation: A central scheduler or controller assigns work to a pool of worker processes that run on compute nodes. Data is partitioned among workers, while gradients or parameters are synchronized through collective communication operations such as all-reduce or parameter-server-style updates. tdistributed includes adapters to integrate with major ML frameworks (for example PyTorch, TensorFlow) so that models can be trained with the framework’s distributed runtime without extensive code changes. It also offers a pluggable backend layer allowing users to choose communication and scheduling strategies.

Features: Elastic resource management that permits nodes to join or leave the training run; automatic device placement and mixed-precision support; checkpointing and resume, experiment tracking, and built-in monitoring. It provides fault tolerance through state replication and periodic checkpoints and supports reproducible runs through deterministic seeds.

Usage and ecosystem: Typical usage involves defining a cluster specification, choosing a parallelism strategy, and launching a distributed training session via a command-line interface or Python API. The ecosystem includes connectors to common data sources, model libraries, and monitoring tools, along with documentation and tutorials.

See also: Horovod, Ray, TensorFlow tf.distribute, PyTorch Distributed, Dask.