naw

naw logs machine-learning time-series metrics to a compressed file on disk, and gives you a CLI to read them back.

It is two things in one package:

a storage library (naw.rtsdb) that writes typed, columnar, compressed rows to a single .rtsdb file, and reads them back — including while the file is still being written;
a wandb-compatible façade (naw.wandb) so that code already instrumented with wandb.init() / run.log() keeps working with nothing but an import change.

Full documentation: https://mlacage.gitlabpages.inria.fr/naw

Installation

$ pip install naw          # or: uv add naw

naw requires Python 3.10 or newer and pulls in three small dependencies (argcomplete, pyyaml, tabulate) — no NumPy. The plot and heatmap subcommands render through uniplot and matplotlib, which are not installed by default; get them with the plot extra:

$ pip install "naw[plot]"   # or: uv add "naw[plot]"

Log a run

import naw.wandb as wandb

run = wandb.init(project="demo", config={"lr": 3e-4})
for step in range(1000):
    run.log({"loss": loss})
run.finish()

init() creates <dir>/wandb/<project>/<id>.rtsdb. A run that is never finished is still readable — you just lose the summary.

Read it back

Every subcommand takes the run filename as its first argument:

Command	Description
`naw metadata FILE`	Show the run's identity, configuration and final summary.
`naw metrics FILE`	List the metric columns present in the run and their storage types.
`naw watch FILE`	Print metric rows as they are written, then exit when the run closes.
`naw plot FILE -y METRIC`	Plot one metric against another (terminal, matplotlib, SVG, PNG or CSV).
`naw heatmap FILE -m METRIC`	Render a histogram metric as a 2-D density map over time.

$ naw plot wandb/demo/<run-id>.rtsdb -y loss --lines

How it compresses

Values are grouped by column rather than by row, and each column is encoded with a scheme suited to its type: integers as a delta against the previous row then LEB128 varint, floats as an XOR against the previous row's value then a varint over the resulting bit pattern, and float16/bfloat16 in 2 bytes. A step counter that increments by one costs a single byte per row.

The writer periodically emits a sync point carrying a schema index, so a reader that opens the file mid-run can seek to the last sync point instead of replaying the whole file. That is what makes naw watch a live tail rather than a repeated full parse.

Documentation

Get started — what naw is and when to reach for it.
Quickstart — log a run and read it back in five minutes.
CLI — every subcommand and flag.
Python API — the naw.rtsdb read and write path.
wandb compatibility — exactly which parts of the wandb API are covered.
File format — what is actually in an .rtsdb file.
Contributing — build, test and release.

References

The XOR-then-varint idea is the one introduced by Gorilla and refined by Chimp and Elf; naw uses varint framing rather than these papers' bit-level framing, which keeps decoding cheap and the format simple to reason about.

Gorilla: A fast, scalable, in-memory time series database
Chimp: efficient lossless floating point compression for time series databases
Elf: Erasing-Based Lossless Floating-Point Compression

License

MIT — see LICENSE.

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0.0.10

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naw 0.0.4

naw

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