shopty 0.0.3

shopty is a tool for tuning hyperparameters on your computer or slurm-managed clusters.

shopty

Simple Hyperparameter OPTimization in pYthon

Install from source (recommended)

git clone https://github.com/colligant/shopty
# optional: pip install flit
cd shopty && flit install

Install via pip

pip install shopty

hyperband on a slurm cluster

shopty hyperband --config_file my_config.yaml --supervisor slurm

run 20 random hyperparameter configs each for 100 iterations

shopty random --config_file my_config.yaml --supervisor slurm --max_iter 100 --n_experiments 20

A non-cli example is here.

What is the purpose of this tool?

Lots of other hyperparameter tuning libraries (at least the ones I've found, anyways) require modifying a bunch of source code and make assumptions about your deployment environment.

shopty is a simple library to tune hyperparameters either on your personal computer or a slurm-managed cluster that requires minimal code changes and uses a simple config file to do hyperparameter sweeps.

Design

The Supervisor classes in shopty spawn (if on CPU) or submit (if on slurm) different experiments, each with their own set of hyperparameters. Submissions are done within python by creating a bash or sbatch file and submitting it via subprocess.call.

Each experiment writes a "results.txt" after its finished to a unique directory. The Supervisor class detects when each experiment is done and reads the "results.txt" file for the outcome of the experiment that wrote it.

Source code modifications

See a simple example here. A neural network example is here.

Supervisors communicate with experiments via environment variables. Your custom training code must know how to deal with some shopty-specific use cases. In particular, it must a) run the code for max_iter iterations, b) reload the training state from a checkpoint file, and c) write the result post-training to a results file. The max_iter variable is an experiment-specific environment variable, as is the checkpoint file's name and the results file's name.

I've already written the code for this for pytorch lightning (PTL). I highly recommend using PTL, as it does a lot of useful things for you under the hood.

How to define hyperparameters and slurm directives

We use a .yaml file to define hyperparameters for training models as well as other commands you want to run to set up the training environment. The .yaml file must have the following structure:

project_name: 'your_project_name'
run_command: "python3 my_cool_script.py"
project_dir: "~/deep_thought/"
monitor: "max"
poll_interval: 10

hparams:
  learning_rate:
    begin: -10
    end: -1
    random: True
    log: True
  your_custom_hparam:
    begin: 1
    end: 5
    step: 1 
  another_custom_hparam:
    begin: 1
    end: 5
    random: True
  
statics:
  a_static_hparam: 1e-10

slurm_directives:
  - "--partition=gpu"
  - "--gres=gpu:1"

environment_commands:
  - "conda activate my_env"

run_command

The run_command is how shopty runs your program. Generated hyperparameters are passed in to the run_command via the command line in no particular order. For example, if you want to tune the learning rate of the model in my_cool_script.py, my_cool_script.py must accept a --learning_rate <learning_rate> argument.

Notice how the hparams header has two levels of indentation: one for the name of hyperparameter, and the next for the beginning and end of the range over which to sample from. There are three required elements for each hparam: begin, end, and <random or step>. The hyperparameter can either be sampled randomly between the interval [begin, end) or iterated over from begin to end with step step. Binary variables can be added to the project with

hparams:
  binary_indicator:
    begin: 0
    end: 2
    step: 1

Static variables can be added via

statics:
    my_static_var: 10
    # or, if you need to specify a type:
    my_other_static_var:
        val: 100.0
        type: 'float'

Slurm directives

Slurm scripts have headers that specify what resources a program will use (#SBATCH statements). Add these to each experiment by editing the slurm_directives section of the yaml file. They will be added as #SBATCH statements in each slurm submission script.

Environment commands

These are arbitrary commands that you want to run before the run_command is called in the generated script.