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State-of-the-art deep learning systems such as TensorFlow and PyTorch tightlycouple the model with the underlying hardware. This coupling requires the userto modify application logic in order to run the same job across a different setof resources, thereby limiting the choice of hardware for a given workload andpotentially forcing the user to forgo more efficient hardware configurations.  We propose VirtualFlow, a system leveraging a novel abstraction calledvirtual node processing to decouple the model from the hardware. In each stepof training or inference, the batch of input data is split across virtual nodesinstead of hardware accelerators (e.g. GPUs and TPUs). Mapping multiple virtualnodes to each accelerator and processing them sequentially effectively timeslices the batch, thereby allowing users to reduce the memory requirement oftheir workloads and mimic large batch sizes on small clusters.  Using this technique, VirtualFlow enables many new use cases, such asreproducing training results across different hardware, resource elasticity,and heterogeneous training. In our evaluation, our implementation ofVirtualFlow for TensorFlow achieved strong convergence guarantees acrossdifferent hardware with out-of-the-box hyperparameters, up to 48% lower jobcompletion times with resource elasticity, and up to 42% higher throughput withheterogeneous training.

VirtualFlow: Decoupling Deep Learning Models from the Underlying Hardware