Reduce multiple TensorBoard runs to new event (or CSV) files
Project description
Compute reduced statistics (mean
, std
, min
, max
, median
or any other numpy
operation) of multiple TensorBoard runs matching a directory glob pattern. This can be used after training multiple identical models to reduce the noise in their loss/accuracy/error curves e.g. when trying to establish a statistically significant improvement in training performance.
Requires PyTorch and TensorBoard. No TensorFlow installation required.
Installation
pip install tensorboard-reducer
Usage
Example:
tb-reducer -i 'glob_pattern/of_dirs_to_reduce*' -o basename_of_output_dir -r mean,std,min,max
tb-reducer
has the following flags:
-i/--indirs-glob
(required): Glob pattern of the run directories to reduce.-o/--outdir
(required): Name of the directory to save the new reduced run data. If--format
istb-events
, a separate directory will be created for each reduce op (mean
,std
, ...) post-fixed by the op's name (outdir-mean
,outdir-std
, ...). If--format
iscsv
, a single file will created andoutdir
must end with a.csv
extension.-r/--reduce-ops
(required): Comma-separated names of numpy reduction ops (mean
,std
,min
,max
, ...). Default ismean
. Each reduction is written to a separateoutdir
suffixed by its op name, e.g. ifoutdir='my-new-run
, the mean reduction will be written tomy-new-run-mean
.-f/--format
: Output format of reduced TensorBoard runs. One oftb-events
for regular TensorBoard event files orcsv
. Ifcsv
,-o/--outdir
must have.csv
extension and all reduction ops will be written to a single CSV file rather than separate directories for each reduce op. Usepandas.read_csv("path/to/file.csv", header=[0, 1], index_col=0)
to read data back into memory as a multi-index dataframe.-w/--overwrite
(optional): Whether to overwrite existingoutdir
s/CSV files.
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