plattli 0.7.2

Plättli is an opinionated dataformat for logging a series of metrics

Plättli

Readers and writers for the Plättli metric format. There is a fundamental issue in metric logging: reads are columnar (metrics), writes are rows (steps). Plättli solves this by making the format on disk columnar (like parquet) with an optional row-wise "hot log" (like jsonl) for recent writes.

It consists of one file per metric (raw homogeneous array or jsonl), plus a metrics manifest (plattli.json) that describes dtype and indices, a config.json with info about the run, and an optional hot.jsonl during live logging.

At some point I will take the time to write more details about it, but essentially it combines the best of parquet and jsonl while keeping everything very simple.

Install

pip install plattli

Requires Python 3.11+ (tested on 3.11-3.14).

CLI

A tool to convert jsonl (a common adhoc format) to plattli is provided, see

jsonl2plattli --help

By default it writes in-place as <run_dir>/metrics.plattli. With --outdir, it writes <run_name>.plattli into the output tree.

API

from plattli import CompactingWriter, DirectWriter

w = CompactingWriter("/experiments/123456", hotsize=200, config={"lr": 3e-4, "depth": 32})
w.write(loss=1.2)  # First write creates new metric, auto-guesses dtype (float32 here)
w.write(note="ok")  # strings work too. Writes are non-blocking.
w.end_step()  # Increments step by one. Flushes hot log.

w.write(loss=1.3)  # Next write appends
# Not every metric needs to be written every step.
w.write(accuracy=0.73)
w.end_step()

# Data is written ASAP, so almost nothing is lost on crash/preemption.
del w

# If we specify a start step and destination exists,
# existing metrics will be truncated to that and we continue from there.
w = CompactingWriter("/experiments/123456", step=1, hotsize=200, config={"lr": 3e-4, "depth": 32})
w.write(loss=1.1)

# You can also write json, btw (stored as jsonl).
w.write(prediction={"qid": "42096", "answer": "Yes"})

# When finishing cleanly, we can hindsight-optimize the data for faster consumption.
# This writes /experiments/123456/metrics.plattli and removes /experiments/123456/plattli.
w.finish()

# For fast local disks, write directly to columnar files:
d = DirectWriter("/experiments/123456", config={"lr": 3e-4, "depth": 32})
d.write(loss=1.2)
d.end_step()
d.finish()

Note: this library is meant to be called from a single thread. DirectWriter uses threads internally to be non-blocking, and CompactingWriter compacts in the background. Calling end_step from a different thread would lead to silently inconsistent data.

DirectWriter(outdir, step=0, write_threads=16, config="config.json", allow_resume_finalized=False)

• Prepares the writer to write under outdir/plattli, creating the dir and writing the config there.
• If outdir/plattli/plattli.json already exists, all metric files are truncated to step so you can resume a run and overwrite later data safely.
• If outdir/metrics.plattli exists, the constructor refuses to proceed unless allow_resume_finalized=True, which unzips into outdir/plattli and removes the zip.
• write_threads=0 disables background writes.
• config is a dict written to config.json, or a string path (resolved relative to outdir) to symlink config.json to (default: "config.json").
• If the target path does not exist, an empty config is written; pass None to force an empty config.

CompactingWriter(outdir, step=0, hotsize, config="config.json", allow_resume_finalized=False)

• Hot mode: writes rows to hot.jsonl and compacts them into columnar files in the background.
• hotsize must be > 0 and sets the compaction batch size: once the hot log reaches hotsize completed steps, the oldest hotsize rows are compacted at once.
• config follows the same rules as DirectWriter.
• allow_resume_finalized follows the same rules as DirectWriter.

DirectWriter.write(**metrics)

• Appends each metric at the current step.
• Auto-dtype rules:
  • array-like scalars -> use their dtype if supported
  • bool -> jsonl
  • float -> f32
  • int -> i64
  • explicit numpy types (eg np.float64) are taken as-is.
  • everything else -> jsonl
• Force a dtype by casting the value (for example: write(dim=np.float32(128))).
• Only scalar values are supported (including 0-d array-likes).
• Only standard dtypes are supported for now: no bf16, nvfp4, fp8; no complex/composite.

CompactingWriter.write(metrics=None, flush=False, **metrics)

• Appends each metric at the current step (pass a dict or kwargs).
• flush=True forces a hot.jsonl rewrite without advancing the step (use write(flush=True) to flush only).
• Uses the same auto-dtype rules and scalar restrictions as DirectWriter.write.

end_step()

• Increments step counter by one.
• DirectWriter waits for all previous step writes to finish and checks for errors.
• CompactingWriter flushes the hot row for the current step.

set_config(config)

• Replaces config.json with the provided json-dumpable config.

finish(optimize=True, zip=True)

• DirectWriter flushes writes; CompactingWriter compacts any remaining hot rows and removes hot.jsonl.
• Updates plattli.json.
• If optimize=True:
  • Tightens numeric dtypes (floats -> keep original float width, ints -> smallest fitting int/uint).
  • Converts monotonically spaced indices into {start, stop, step} and removes the .indices file.
  • Writes run_rows (max rows across metrics) into the manifest.
• If zip=True, zips the run folder to <outdir>/metrics.plattli (stored, not compressed).
• When zipping, outdir/plattli is removed after the zip is written.

Reader(path)

from plattli import Reader

with Reader("/experiments/123456") as r:
    print(r.metrics())
    print(r.rows("loss"), r.approx_max_rows(), r.when_exported())
    steps, values = r.metric("loss")
    step, value = r.metric("loss", idx=-1)

• Prefers metrics.plattli if present, otherwise reads the plattli/ directory.
• Keeps zip files open until close() (use a with block or call close() manually).
• List all available metric names with metrics().
• Read a metric with one of metric(name, idx=None) -> (indices, values), metric_indices(name), metric_values(name), which return numpy arrays.
• Some useful metadata: config() returns the attached config dict; when_exported() is a timestamp, rows(name) is the exact row count (not last step!) in the given metric, but because rows(name) can be a bit expensive for in-progress runs, approx_max_rows(faster=True) is a fast likely-correct estimate of the row count of the most-frequent metric.
• While the data format is simple, the reader code is a bit more complex because it tolerates corrupt tails, such that it's fine to read plattli's while they are being written.

Helpers

• plattli.is_run(path) -> whether the path is a plattli run (a correct folder structure, or a metrics.plattli zipfile).
• plattli.is_run_dir(path) -> whether the folder path contains plattli metrics (be it as subfolder or zipped).
• plattli.resolve_run_dir(path) -> resolved directory that contains plattli.json (returns either path or path/plattli), or None.

Data format

Each run directory contains a plattli/ folder, while the .plattli archive contains the same files at the top level:

run_dir/
  plattli/
    config.json
    plattli.json
    <metric>.indices
    <metric>.<dtype>   # or <metric>.jsonl
    hot.jsonl           # present during live logging if hotsize is enabled
  metrics.plattli

Manifest (plattli.json)

JSON object keyed by metric name, plus metadata keys like run_rows and when_exported:

{
  "loss": {"indices": "indices", "dtype": "f32"},
  "note": {"indices": "indices", "dtype": "jsonl"},
  "run_rows": 1234,
  "when_exported": "2026-01-03T12:34:56Z"
}

Fields:

• indices: "indices", a list of {start, stop, step} segments (canonical), or a single {start, stop, step} (legacy).
• dtype: one of f{32,64}, {i,u}{8,16,32,64}, or jsonl.
• run_rows: optional max rows across all metrics (written on finish only).
• when_exported: timestamp updated on manifest writes.

Indices (<metric>.indices)

Raw little-endian uint32 array. Each entry is the step value for that metric write. If optimize=True during finish(), the file may be removed and replaced by a list of {start, stop, step} segments (canonical) or a single {start, stop, step} (legacy) in the manifest.

Config (config.json)

Arbitrary JSON object (dict), written when a config is provided.

Values (<metric>.<dtype>)

Raw little-endian typed array. One scalar is appended per write call.

JSONL values (<metric>.jsonl)

One JSON value per line:

{"event":"start"}
{"event":"done"}

Metric names and subfolders

Metric names are used as file paths. A slash creates subfolders: detail/thing0 -> detail/thing0.f32. The metric name step is reserved.