Strange training results: why is a batch size of 1 more efficient than larger batch sizes, despite using a GPU/TPU?

• seed_rl

  8 760 0.0 Python

  Discontinued SEED RL: Scalable and Efficient Deep-RL with Accelerated Central Inference. Implements IMPALA and R2D2 algorithms in TF2 with SEED's architecture.

  

I'm currently doing some tests in preparation for my first real bit of training. I'm using Google Cloud AI Platform to train, and am trying to find the optimal machine setup. It's a work in progress, but here's a table I'm putting together to get a sense of the efficiency of each setup. On the left you'll see the accelerator type, ordered from least to most expensive. Here you'll also find the number of accelerator's used, the cost per hour, and the batch size. To the right are the average time it took to complete an entire training iteration and how long it took to complete the minimization step. You'll notice that the values are almost identical for each setup; I'm using Google Research's SEED RL, so I thought to record both values since I'm not sure exactly everything that happens between iterations. Turns out it's not much. There's also a calculation of the the time it takes to complete a single "step" (aka, a single observation from a single environment), as well as the average cost per step.

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