Question

You are training a TensorFlow image classification model on a single Vertex AI custom job with one NVIDIA A100 GPU. Training is slow, and monitoring shows GPU utilization averaging only 25%, with frequent idle periods. The dataset consists of millions of JPEG files stored in Cloud Storage and is read one file at a time inside a Python loop before being converted to tensors. What is the most effective change to improve training throughput?

Accepted Answer

Convert the images into sharded TFRecord files and build a tf.data pipeline using interleave, map with num_parallel_calls=tf.data.AUTOTUNE, batching, and prefetch.

Answer

Switch the training job from an A100 GPU to a TPU v3 pod to increase raw compute capacity.

Answer

Increase the batch size to the maximum the GPU memory allows so each step processes more images.

Answer

Enable mixed-precision training with float16 to reduce the per-step compute time on the GPU.