tb-like 0.1.1

A faster, columnar TensorBoard-style scalar viewer for many series and many runs.

tb_like

A faster, columnar TensorBoard-style scalar viewer — built for many series and many runs.

TensorBoard re-parses event files on demand and gets slow when a run has tens of thousands of scalar series across hundreds of experiments. tb_like instead converts TensorBoard event files into per-run Parquet once, then serves downsampled series on demand to a fast Plotly dashboard. New events are picked up incrementally in the background.

Why it's fast

• Convert once, read many. Each run's events.out.tfevents.* are parsed into a columnar Parquet file sorted by (tag, step), with row-group statistics so a query for a few tags only touches the matching row groups — even when a run has ~18k series.
• Incremental & idempotent. Ingestion tracks each event file's size and record count, so re-scans only parse new data. A background watcher keeps the cache in sync; parsing is parallelized across event files with joblib.
• Lazy, prioritized rendering. The dashboard renders charts only as they scroll into view, fetched through a priority queue (visible first, then neighbors, biased toward the scroll direction).
• LTTB downsampling keeps long curves cheap to draw without losing their shape.

Install

pip install tb-like
# or
uv tool install tb-like

Quick start

A "run" is a directory containing events.out.tfevents.* files. Point tb_like at a directory of runs and open the dashboard — that's it:

my_runs/
  run_a/  events.out.tfevents.*  config.yaml
  run_b/  events.out.tfevents.*
  ...

tblike my_runs --port 8000 --jobs 8
# open http://127.0.0.1:8000

The background watcher discovers runs under the folder, converts any that changed to Parquet (parsing event files across --jobs worker processes), keeps the cache in sync, and serves them — no separate build step. The cache lives in <runs_dir>/.tblike_cache by default (override with --cache-dir).

Dashboard features

• Hierarchical, searchable tag tree (regex filter) with smart grouping: path compression of a.b.c chains, numeric-enumeration collapsing (…expert_idx_∗), and layer indices kept as their own levels.
• Multi-run overlay, unified hover, EMA smoothing, log-y, step vs. relative-time x-axis, and outlier clipping by value percentiles.
• Collapsible per-group chart sections, resizable sidebar, and a one-click Refresh selected that re-ingests from disk and rebuilds the plots.

CLI

tblike <runs_dir> [--port P] [--host H] [--cache-dir D] [-j JOBS] [--no-watch]
                                            # the main command: serve + auto-ingest

Advanced / scriptable subcommands:

tblike convert RUN_DIR [RUN_ID] [-j JOBS]   # ingest one run into Parquet (one-off)
tblike scan                                 # one incremental ingest pass, no server

How it stores data

cache/<run_id>/
    data/seg-00000.parquet   # one immutable segment per ingest pass
    index.json               # tags, per-file ingest state, metadata
    meta.json                # tiny summary used for fast run listing

Reads union all segments and de-duplicate (tag, step) by latest wall_time.

License

MIT — see LICENSE.