Convenience library for hyper-parameters management for deep learning development projects.

Pre-requisites

• Python 3.7 or higher

Why use hparams?

Hparams is a library mainly designed to make Deep learning development and experimentation easy to manage and track:

• Specify all run parameters (number of GPUs, model parameters, train parameters, etc) in one .cfg file.
• Hparams evaluates any expression used as "value" in the .cfg file. "value" can be any basic python object (floats, strings, lists, etc) or any python basic expression (1/2, max (3, 7), etc.) as long as the evaluation does not require any library importations or does not rely on other values from the .cfg.
• Hparams saves the configuration of previous runs for reproducibility, resuming training, etc.
• All hparams are saved by name, and re-using the same name will recall the old run instead of making a new one.
• The .cfg file is split into sections for readability, and all parameters in the file are accessible as class attributes in the codebase for convenience.
• The HParams object keeps a global state throughout all the scripts in the code, so it can be imported and used in all scripts.

Installation

pip install --upgrade git+https://github.com/Rayhane-mamah/hparams

How to use?

Config file basics

Hparams library parses a .cfg file (by default hparams.cfg) and makes its arguments accessible within a python project.

A basic hparams.cfg looks like follows:

[run]
name = some_run_name
some_other_run_param = some_value
...

[some_other_section]
some_other_param = some_other_value
...

Notes:

• run.name parameter is mandatory in any .cfg to be read by hparams library.
• Any argument in hparams.cfg can be accessed in code with hparams.section_name.argument_name.

Basic usage

In the entrypoint script of the project, instantiate the hparams object:

from hparams import HParams
...

# The object name of HParams IS NOT the same as the run.name.
# Signature: HParams(project_path (location of the .cfg file), hparams_filename (without extension), name (name of the hparams object))
hparams = HParams('.', name="some_object_name")
...
# Use some parameter from the hparams.cfg file
print(hparams.some_other_run_param)

The above command creates a global instance of the hparams file and remembers it by name. In any other script where one wants to use the hparams, it's possible to simple load the instantiated hparams by name:

from hparams import HParams
...

# Load an already instantiated HParams object.
hparams = HParams.get_hparams_by_name("some_object_name")
...
# Use some parameter from the hparams.cfg file
print(hparams.run.name)
print(hparams.some_other_section.some_other_param)

Instantiating an hparams object anywhere in the project saves the read .cfg file (by default hparams.cfg), makes a saved copy of the .cfg under logs-<run.name>/hparams-<run .name>.cfg. This saved copy is loaded in future runs instead of hparams.cfg as long as the run.name argument in hparams.cfg is kept the same. We use this copying process for safety of future execution, reproducibility and for mistakes minimization.

To restart an already existing run.name, then one should remove the saved copy:

rm -r logs-<run.name>

Beyond the default config file

It is also possible to instantiate the hparams object from a different .cfg file:

from hparams import HParams

# Do not define the extension in the hparams_filename
hparams = HParams('.', hparams_filename='some_other_cfg_name', name='some_object_name')

GCS backup

If your code is saving tensorboard files, model checkpoints or other files to GCS and you want hparams to also back itself up to a GCS bucket:

from hparams import HParams

# Define both the gcs project name and the gcs bucket path
hparams = HParams('.', gcs_backup_project='some_project_name', gcs_backup_bucket='some_bucket_path', name='some_name')

The GCS integration of hparams is a simple backup copy. Your local version is always the True one, and the gcs version is its replica.

The GCS backup also automatically appends the run name to the end of the gcs bucket path.