Question

A data science team has prototyped a deep neural network in TensorFlow/Keras for a tabular churn prediction problem. Training on a single n1-standard-16 CPU instance takes 14 hours per run, which is blocking their experimentation. The model uses dense layers with large matrix multiplications, a batch size of 4096, and a dataset of 20 million rows stored as sharded TFRecords in Cloud Storage. Profiling shows the CPU is compute-bound (near 100% utilization) during the training step, not I/O bound. What is the most effective change to reduce per-run training time while minimizing code changes?

Accepted Answer

Switch the training job to a single Vertex AI custom training node with an attached NVIDIA GPU (e.g., a2-highgpu-1g), since dense matrix multiplications benefit from GPU acceleration.

Answer

Increase the CPU machine to n1-standard-64 to provide more cores for the matrix multiplication workload.

Answer

Convert the model to run on a TPU v3 Pod slice with 32 cores to maximize parallelism.

Answer

Reduce the batch size to 256 so that each training step completes faster and the epoch finishes sooner.