Hey @steve2 ,  short answer: these look like TensorBoard event files, likely created by a library that briefly initialized a TensorBoard logger or writer during one of your training/serving runs; the sparse_logs folder naming and “manager stage: Model structure initialized” message strongly suggest a SparseML/Neural Magic integration was present at that time, which also commonly wires up a TensorBoard logger. Once that component stopped initializing, the files stopped appearing.

What the files are

• The files named events.out.tfevents.… are TensorBoard event logs written by TensorFlow, PyTorch’s SummaryWriter, PyTorch Lightning, Hugging Face Trainer, or frameworks that integrate a TensorBoard logger. TensorBoard recursively looks for “tfevents” files under a log directory, and the filenames include a timestamp, host, and process ID.
• On Databricks Runtime ML, TensorBoard is preinstalled and commonly used to monitor deep learning runs, which makes it easy for frameworks to emit these event files when a writer/logger is initialized.
• In PyTorch, simply creating a SummaryWriter is enough to create a tiny tfevents file even if no scalars are written; default logdir is “./runs”.

Why you might see them even if you didn’t explicitly enable TensorBoard

• Some frameworks auto-create a TensorBoard logger if TensorBoard is available. For example, SparseML integrations show code wiring a TensorBoardLogger into training (e.g., timm integration: TensorBoardLogger(log_path=output_dir)), which will generate “events.out.tfevents.*” in that directory even for short runs or initialization-only stages.
• The message you saw in sparse_logs—“manager stage: Model structure initialized”—is consistent with SparseML’s notion of a “ScheduledModifierManager” that applies recipe-driven sparsification steps during model setup/training. SparseML docs and examples reference this Manager as the component that modifies and finalizes model training loops, and those integrations frequently attach loggers (including TensorBoard).

Why they disappeared after deletion

• If the component that previously initialized a TensorBoard writer/logger is no longer invoked (e.g., a package removed, a logger disabled, or code path changed), new tfevents files won’t be created. On Databricks, as soon as a run stops using a TB writer, no new files appear.

Likely origin in your setup Based on your note:

• Folder name sparse_logs/ and the line “manager stage: Model structure initialized” are characteristic of Neural Magic’s SparseML/DeepSparse stack, which uses a “Manager” abstraction and commonly emits framework-stage messages and can include a TensorBoard logger alongside Python logging; this would explain both directories appearing around the same times.

How to confirm the source Try the following quick checks:

• Search your notebooks/jobs for any of these strings: “SummaryWriter”, “TensorBoardLogger”, “report_to='tensorboard'”, “SparseML”, “ScheduledModifierManager”, “deepsparse”. If any appear in code or dependencies used around Aug 30–Sep 2, that’s likely the origin.
• Check installed packages on the cluster image used then: pip list | grep -E 'sparseml|deepsparse|tensorboard|pytorch-lightning'. SparseML/DeepSparse presence would support the sparse_logs link.
• If you used PyTorch Lightning or Hugging Face Trainer, verify whether TensorBoard logging was implicitly enabled (PL’s TensorBoardLogger, or HF TrainingArguments(report_to=["tensorboard"])). Either can generate tfevents files even with brief runs.
• Verify that the tensorboard/ directory was set explicitly by your code/config. By default PyTorch writes to “./runs”; a custom path (like “tensorboard/”) is often set by a logger or training script.

Is this anything to worry about?

• Not typically. These are benign artifacts of a logger being initialized. If you don’t want them: Disable or remove the TensorBoard logger/writer in the relevant code path. Ensure frameworks don’t auto-wire TB loggers (e.g., adjust SparseML or Lightning logger configs).
• If you want to use TensorBoard, you can point TensorBoard to that directory and visualize metrics directly in Databricks notebooks or a separate tab.
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