Allentune
Hyperparameter Search for AllenNLP
Citation
If you use this repository for your research, please cite:
@inproceedings{showyourwork,
author = {Jesse Dodge and Suchin Gururangan and Dallas Card and Roy Schwartz and Noah A. Smith},
title = {Show Your Work: Improved Reporting of Experimental Results},
year = {2019},
booktitle = {Proceedings of EMNLP},
}
Run distributed, parallel hyperparameter search on GPUs or CPUs. See the associated paper.
This library was inspired by tuna, thanks to @ChristophAlt for the work!
To get started,
-
First install allentune with:
pip install git+git://github.com/allenai/allennlp@27ebcf6ba3e02afe341a5e62cb1a7d5c6906c0c9
Then, clone the
allentune
repository, cd into root folder, and runpip install --editable .
-
Then, make sure all tests pass:
pytest -v .
Now you can test your installation by running allentune -h
.
What does Allentune support?
This library is compatible with random and grid search algorithms via Raytune. Support for complex search schedulers (e.g. Hyperband, Median Stopping Rule, Population Based Training) is on the roadmap.
How does it work?
Allentune operates by combining a search_space
with an AllenNLP training config. The search_space
contains sampling strategies and bounds per hyperparameter. For each assignment, AllenTune sets the sampled hyperparameter values as environment variables and kicks off a job. The jobs are queued up and executed on a GPU/CPU when available. You can specify which and how many GPUs/CPUs you'd like AllenTune to use when doing hyperparameter search.
Setup base training config
See examples/classifier.jsonnet
as an example of a CNN-based classifier on the IMDB dataset. Crucially, the AllenNLP training config sets each hyperparameter value with the standard format std.extVar(HYPERPARAMETER_NAME)
, which allows jsonnet to instantiate the value with an environment variable.
Setup the Search space
See examples/search_space.json
as an example of search bounds applied to each hyperparameter of the CNN classifier.
There are a few sampling strategies currently supported:
choice
: choose an element in a specified set.integer
: choose a random integer within the specified bounds.uniform
: choose a random float using the uniform distribution within the specified bounds.loguniform
: choose a random float using the loguniform distribution within the specified bounds.
If you want to fix a particular hyperparameter, just set it as a constant in the search space file.
Run Hyperparameter Search
Example command for 30 samples of random search with a CNN classifier, on 4 GPUs:
allentune search \
--experiment-name classifier_search \
--num-cpus 56 \
--num-gpus 4 \
--cpus-per-trial 1 \
--gpus-per-trial 1 \
--search-space examples/search_space.json \
--num-samples 30 \
--base-config examples/classifier.jsonnet
To restrict the GPUs you run on, run the above command with CUDA_VISIBLE_DEVICES=xxx
.
Note: You can add the --include-package XXX
flag when using allentune on your custom library, just like you would with allennlp.
Search output
By default, allentune logs all search trials to a logs/
directory in your current directory. Each trial gets its own directory.
Generate a report from the search
To check progress on your search, or to check results when your search has completed, run allentune report
.
This command will generate a dataset of resulting hyperparameter assignments and training metrics, for further analysis:
allentune report \
--log-dir logs/classifier_search/ \
--performance-metric best_validation_accuracy \
--model cnn
This command will create a file results.jsonl
in logs/classifier_search
. Each line has the hyperparameter assignments and resulting training metrics from each experiment of your search.
allentune report
will also tell you the currently best performing model, and the path to its serialization directory.
Plot expected performance
Finally, you can plot expected performance as a function of hyperparameter assignments or training duration. For more information on how this plot is generated, check the associated paper.
allentune plot \
--data-name IMDB \
--subplot 1 1 \
--figsize 10 10 \
--result-file logs/classifier_search/results.jsonl \
--output-file classifier_performance.pdf \
--performance-metric-field best_validation_accuracy \
--performance-metric accuracy
Sample more hyperparameters until this curve converges to some expected validation performance!