Slow logdir discovery on cloud filesystems due to level-by-level globbing

Summary

When using --logdir with a cloud filesystem path (e.g., gs://bucket/experiments/), TensorBoard's GetLogdirSubdirectories uses ListRecursivelyViaGlobbing which globs level by level (*, */*, */*/*, ...), listing every file at every directory depth. This is extremely slow when the directory tree contains many non-event files such as model checkpoints.

Steps to reproduce

1. Have a GCS directory structure like:
   复制

   gs://bucket/experiments/
     experiment-a/
       checkpoints/     # contains thousands of files
       tensorboard/     # contains a few tfevents files
     experiment-b/
       checkpoints/
       tensorboard/

2. Run: tensorboard --logdir gs://bucket/experiments/ --load_fast=false
3. Observe that the initial data loading takes ~100 seconds.

Expected behavior

TensorBoard should discover the event file directories in a few seconds.

Root cause

ListRecursivelyViaGlobbing iterates level by level, listing all files at each depth:

• Level 0: experiments/* → 2 entries
• Level 1: experiments/*/* → 4 entries
• Level 2: experiments/*/*/* → 27 entries
• ...
• Level 5: experiments/*/*/*/*/* → 10,963 entries (mostly checkpoint shards)

Most of these files are irrelevant checkpoint data. GCS does not support server-side pattern filtering, so each glob level requires listing all objects under the prefix.

Proposed fix

For cloud paths, replace the level-by-level globbing with a single targeted recursive glob **/*tfevents*. This still lists all objects under the prefix (GCS limitation), but does so in a single API call instead of multiple level-by-level calls, and avoids the overhead of re-listing at each level.

Benchmark on a real GCS directory with ~26,000 checkpoint files and 8 event files:

• Before: ~101 seconds
• After: ~13 seconds

Local filesystem paths are unaffected (they use ListRecursivelyViaWalking).

Environment

• TensorBoard 2.20.0
• macOS (Apple Silicon)
• Python 3.12
• Using gcsfs for GCS filesystem support (no TensorFlow installed)