Artificial intelligence workload migration for planet-scale artificial intelligence infrastructure service

The disclosure herein describes platform-level migration for deep learning training (DLT) jobs from a checkpointed stated between a source node and a destination node. The checkpointing is performed through capturing GPU state (e.g., device state) and CPU state (e.g., host state). The GPU state includes GPU data (e.g., model parameters, optimizer state, etc.) that is located in the GPU and GPU context (e.g., the default stream in GPU, various handles created by libraries). Restoring the DLT job on the destination node involves resumption of processing of a destination GPU at the same checkpointed state.