superparams 0.0.6

Programmatic space search with a focus on flexibility

superparams

A high-level experiment manager.

• 📄 One Python file for your entire experiment, flexible and easily versionable.
• 💚 No boilerplate like parsing a text file with configuration variables, multiprocessing code, nor logging/saving results.
• ♻️ Easily re-run failed experiment settings.

Installation

pip install superparams

View on PyPi.

Usage

Superparams incentivises use of Python's built-in dataclass to specify both the parameters and the experiment-specific logic in one place. It bundles this with a bunch of quality-of-life improvements for managing your experiments.

# file: experiments/params.py

from dataclasses import dataclass
from superparams import Experiment, search

@dataclass
class Hyperparams(Experiment):

    steps         :int = 100
    batch_size    :int = search(16, 32)

    def run(self) -> dict:
        ''' Runs this setting of parameters (override this method)
            Auto-stores the returned dict in a parquet.
        '''

        results = {
            'total samples': self.batch_size * self.steps
        }
        print(results)

        # automatically save results in a parquet file by returning them 
        return results

    def format_results(self, results: pd.DataFrame) -> None | pd.DataFrame:
        ''' 
        Useful for plotting and post-processing, 
        optionally can return formatted dataframe to be saved 
        '''
        import plotly.express as px 
        fig = px.bar(results, x='index', y='total_samples)
        fig.show()

This constructs an iterator to grid-search the parameter settings, meaning you could add a snippet like the following to invoke your experiment from the terminal with python experiments/params.py.

# file: experiments/params.py

if __name__ == '__main__':
    for h in Hyperparams():
        results = h.run()
        print(f'Setting ({h.steps}, {h.batch_size}): {results}')
        # Setting (100, 16): {'total_samples': 1600}
        # Setting (100, 32): {'total_samples': 3200}

But we promised no boilerplate! Instead, you can invoke from the terminal, which handles result-caching for you, and enables easy multiprocessing.

experiment params.Hyperparams --num_proc 2

This will:

1. print a nice overview of the running experiments
2. store results and log under experiments/progress/params/Hyperparams
3. prompt you to resume interrupted/failed experiment settings
4. do the multiprocessing for you :)

[!WARNING] Python-native multiprocessing shares the Hyperparams data with each process by pickling it!. This is woefully inefficient, and poses a massive bottleneck if sharing >10MB data. Consider refactoring such that each run method instantiates this data itself.

In the future, I may do a refactor that shares the data more efficiently; but this is not trivial in Python. See https://docs.python.org/3/library/multiprocessing.shared_memory.html#module-multiprocessing.shared_memory

Flexibility

Dataclasses don't require Java-style repetitive constructors. To modify your hyperparameter combination, simply instantiate it as follows.

Hyperparams(batch_size=search(2,4,8))

Multiprocessing

You can run multiple settings on multiple processes.

params = Hyperparams()
params.run_all(num_proc = os.cpu_count() - 2)

Also note that Experiment objects have access to concurrency-related fields initialised by superparams. These are:

• rank: the process id of this experiment setting, i.e. rank in {0,1,2,3} if n_proc = 4.
• n_proc: parameter passed to the n_proc field.

Mutable Dataclass Attributes

Python throws a tantrum if you try to assign a mutable value to a dataclass:

@dataclass 
class Params(Experiment):
    iterable = [1,2,3]

# Error > you should use field(default_factory=lambda: [1,2,3])

This is ugly. Python does this to protect you in case you were to instantiate a second set of Params(), and modify the iterable. As it's a class attribute, you'd be modifying both instantiated Params objects.

I think this is stupid and limits the potential of dataclasses. For now, using iterable = search([1,2,3]) should work. In the future, I may rewrite the built-in dataclass to not follow this pattern to make it more explicit.

Note a similar thing is much more likely to happen in functions, where it is not guarded by python. E.g. in

def function(items = [1,2,3])
    print(items)
    items.append(4)

function() # [1,2,3]
function() # [1,2,3,4]

Further reference (python docs)

TODO

• nice overview of available experiments + improve progress reporting to work better across multiple processes.

• try merging .progress.lock lockfile with the .progress file, requires multiprocessing tests :).

• allow running multiple experiments defined in one file. I.e. experiment dataset runs all classes found in dataset. We should be able to do this by checking if the final component is a filename, or a directory name.

• allow relative imports in libraries imported by experiment.

• cli fn to run experiment exp.RQ1.

• Encapsulate current __main__ into a class, so the user can just add python # some/path/to/custom/experiments/__main__.py from superparams import entrypoint entrypoint()

• smarter experiment lookup: users may want to have a single file for all their experiments, or spread it into different folders.

  • experiment RQ1 runs all experiments in the file RQ1.py
  • experiment index.RQ1 runs the experiment RQ1 in the file index.py, or the file RQ1.py in the folder experiments/index.

• dataclasses improvements

• get rid of this annoying @dataclass annotation

• provide a value method to replace field pattern; do we assume immutability?

• check compatitibility with python=3.10, python=3.11.

• testing

• actual functional correctness tests

Alternatives

Any decent package should list viable alternatives. Here are some that I considered, but ended up building this package instead.

• wandb sweeps is best used for Bayesian hyperparameter search to optimise a DL model; but requires specifying settings in JSON files.
• ray tune enables SOTA algorithms like PBT (similar to genetic optimisation) and HyperBand/ASHA (large population with early stopping), and allows for relatively unsupervised optimisation by specifying a search space and objective in Python. It is also compatible with Keras Hyperopt and Pytorch Optuna.
• orion is similar to ray tune, but more or less a wrapper around an argument parser you need to set up yourself (so you have to specify everything in plain-text cli commands).
• hydra is probably most-similar in features to superparams, but relies on yaml for specification and doesn't collate results nicely into a pandas dataframe.

I think of superparams as more open-ended than ray-tune: there may not be a direct objective to optimise as the right objective is not yet established. And, by allowing everything to be specified in a single Python dataclass, you maintain flexibility by not assuming that the entire optimisation is a black-box. To me, it is valuable to be able to specify all parameters and logic in a single place, completely in lsp-understandable python; which also means everything can be version-tracked.