TLDR, building our DeepCell container from a base TensorFlow image is 50% faster to load and 60% smaller than using the Deep Learning container.
| Deep Learning image | Base TF image | Reduction | |
|---|---|---|---|
| Uncompressed | 19.5 GB | 7.2 GB | 63% |
| Compressed | 8.4 GB | 3.2 GB | 62% |
| Batch job load time | 6 min | 3 min | 50% |
This post covers how we rebuilt our container on the smaller base image; and why the Deep Learning container is so big to begin with. The long and short of it is that you pay a steep price to have so many development tools available, and you typically don’t need those for production tasks.