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A Python framework that implements a commonly used boilerplate for machine learning experiments.

Project description

exp-base

expbase is a Python framework that implements a commonly used boilerplate for machine learning experiments. To that end, it takes care of preparing the usual experimental setup for you, and thus significantly reduces the amount of code you have to write in order to run your experiments. More precisely, expbase implements the following setup tasks:

  1. parse command-line args, and create a config object,
  2. prepare the results directory,
  3. store the used config as JSON file in the results directory,
  4. set up logging,
  5. launch the training procedure in a separate process, and
  6. wait for checkpoints to be created and launch the evaluation of each in a separate process as it is created.

Installation

The package expbase can be installed via pip as follows:

pip install expbase

How-To

Step 1: Define Your Config Class

expbase uses argmagiq to parse command-line args into a configuration object, which means that you have to define a config class that describes the user-defined configuration for running an experiment. If you should not be familiar with argmagiq, then please read about it here. To that end, your configuration class has to extend the class expbase.BaseConfig, which defines several configurations that are either used by expbase directly or required for all machine learning experiments. (Make sure to familiarize yourself with those.)

Step 2: Implement The TrainingExecutor And The EvaluationExecutor

To provide the code for training and evaluation of your models, you have to implement two classes, one extending expbase.TrainingExecutor and one extending expbase.EvaluationExecutor. Those are abstract classes that require you to implement the methods _run_training and _run_evaluation, respectively. When an experiment is launched, your TrainingExecutor is run in a separate process, and whenever a training checkpoint is delivered, your implementation of EvaluationExecutor is launched (in parallel) in another process in order to evaluate the same. In addition to this, both TrainingExecutor and EvaluationExecutor have a method _init, which may be overridden to run code that would usually go into __init__. The reason why we recommend using _init instead of __init__ is that instances might be moved between processes before _run_* is invoked, which occasionally results in issues, if certain objects are created in the constructor. However, _init is guaranteed to be invoked just before and in the same process as _run_*.

All created instances of TrainingExecutor and EvaluationExecutor are provided with an instance of your config class that has been populated based on the given command-lines args. Both classes store this config object in the attribute _conf, which you can use to access the user-defined configuration of an experiment from your implementations of _init and _run_*.

For additional details, please have a look at the docstrings of expbase.TrainingExecutor and expbase.EvaluationExecutor.

Step 3: Launch The Experiment

Once you defined all required classes, launching an experiment is as easy as creating an instance of expbase.Experiment, and invoking it's run method:

import expbase as xb

xb.Experiment(
        YourTrainingExecutorClass,
        YourEvaluationExecutorClass,
        YourConfigClass,
        app_name,
        app_description
).run()

In this code snippet, app_name and app_description are two strings that define the name of your application, which is used in its synopsis (the usage instruction at the beginning of the help), as well as a description of the same, which is displayed as part of the help text.

Delivering Checkpoints

As pointed out above already, evaluation is launched automatically (in a parallel process) whenever a checkpoint is delivered. In order to deliver a checkpoint, the class expbase.TrainingExecutor (which you are extending with your own training code) provides the method _deliver_ckpt, which expects a single str argument that allows for locating an according checkpoint file, usually a file path. As it depends on the particular experiment, you have to create the checkpoint file in your implementation of _run_training before you invoke _deliver_ckpt. The checkpoint string is then provided to the EvaluationExecutor that is launched to evaluate the according checkpoint and stored in the attribute _ckpt. Therefore, your own implementation of expbase.EvaluationExecutor. (more precisely, your _init and _run_evaluation) will find the checkpoint string stored in self._ckpt.

Running Tests

If you just want to run a test (without training), for example, to evaluate your model on the test partition, then you can use the command-line option --test, which causes expbase to launch only a single EvaluationExecutor. Notice that, in this case, the attribute _ckpt of your EvaluationExecutor will be None. Instead, however, you are required to manually specify a checkpoint when you run the application using the option --ckpt <CKPT>, which is then accessible via the config, i.e., self._conf.ckpt.

Results Directory

expbase creates a results directory for you, which is also used to store any files that are generated automatically (e.g., log files, config, etc.). By default, ./results/ is used as results directory, and can be adjusted by means of the arg --results-dir. Notice further that, for every training run, a subdirectory is created within the results dir, named according to when the experiment was conducted. For example, when you run an experiment with --results-dir ./fancy-results, then results will be stored in ./fancy-results/2020-06-22--10-11-28 (using the actual timestamp, of course). Please notice that the config object passed to TrainingExecutor and EvaluationExecutors contains the actual results directory including the particular subdirectory.

Finally, you can (and should) use a title to be appended to your result directories in order to make them easier to find. To that end, use the arg --title to define a label to be appended to the directory that your experiment writes results to. For example, --results-dir ./fancy-results --title project-ice-cream defines the results directory to be ./fancy-results/2020-06-22--10-11-28--project-ice-cream (again, using the actual timestamp, of course).

Config File

To make it easier to rerun a conducted experiment, expbase stores the configuration that was used to run one as a JSON file named config.json in the results directory. This file is produced by means of argmagiq, and can thus be used directly to launch the same experiment once again. For example, if you ran an experiment from the terminal using the command

$ python main.py --results-dir results/ --seed 12345 ...

then you can rerun it with the exact same configuration using

$ python main.py -- path/to/the/config.json

Notice that, for experiments with a lot of configuration, it might be easier to only use config files in the first place.

Logging

expbase uses the package streamtologger to automatically log all outputs produced via print("...") or by an exception, and stores them in three different log files in the results directory:

  1. experiment.log is a top-level log file used by the controller of the experiment,
  2. train.log captures outputs produced by the TrainingExecutor, and
  3. eval.log stores outputs produced by EvaluationExecutors.

If you, for some reason, wish you to change the names of those files, you can use the options --general-log, --training-log, and --evaluation-log to do so.

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