Question

Your team has a TensorFlow model that trains on a 4 TB dataset stored in Cloud Storage as TFRecord files. A single-node 8-GPU machine takes over 40 hours per training run, which is blocking your experimentation velocity. The model uses large embedding layers and dense transformer blocks, and the training loop is written with the Keras API. You need to dramatically reduce wall-clock training time while keeping code changes minimal and controlling cost. What is the most appropriate approach?

Accepted Answer

Refactor the training code to use tf.distribute.TPUStrategy and run on a Cloud TPU v-series Pod slice, using tf.data to stream the TFRecords from Cloud Storage.

Answer

Keep the single-node setup but switch the machine type to one with more vCPUs and add more Cloud Storage read parallelism to speed up data loading.

Answer

Migrate the workload to BigQuery ML so the training runs directly where a copy of the data can be loaded, avoiding GPU costs entirely.

Answer

Convert the model to scikit-learn and use joblib parallel backends across multiple CPU nodes to distribute the training.