Image Prediction - Search Space and Hyperparameter Optimization (HPO)

While the Image Prediction - Quick Start introduced basic usage of AutoGluon fit, evaluate, predict with default configurations, this tutorial dives into the various options that you can specify for more advanced control over the fitting process.

These options include:

  • Defining the search space of various hyperparameter values for the training of neural networks

  • Specifying how to search through your chosen hyperparameter space

  • Specifying how to schedule jobs to train a network under a particular hyperparameter configuration.

The advanced functionalities of AutoGluon enable you to use your external knowledge about your particular prediction problem and computing resources to guide the training process. If properly used, you may be able to achieve superior performance within less training time.

Tip: If you are new to AutoGluon, review Image Prediction - Quick Start to learn the basics of the AutoGluon API.

Since our task is to classify images, we will use AutoGluon to produce an ImagePredictor:

import autogluon.core as ag
from autogluon.vision import ImagePredictor, ImageDataset

/home/ci/opt/venv/lib/python3.8/site-packages/gluoncv/__init__.py:40: UserWarning: Both mxnet==1.9.1 and torch==1.12.1+cu102 are installed. You might encounter increased GPU memory footprint if both framework are used at the same time.
  warnings.warn(f'Both mxnet=={mx.__version__} and torch=={torch.__version__} are installed. '
INFO:torch.distributed.nn.jit.instantiator:Created a temporary directory at /tmp/tmpwkn_zq46
INFO:torch.distributed.nn.jit.instantiator:Writing /tmp/tmpwkn_zq46/_remote_module_non_scriptable.py
INFO:root:Generating grammar tables from /usr/lib/python3.8/lib2to3/Grammar.txt
INFO:root:Generating grammar tables from /usr/lib/python3.8/lib2to3/PatternGrammar.txt

Create AutoGluon Dataset

Let’s first create the dataset using the same subset of the Shopee-IET dataset as the Image Prediction - Quick Start tutorial. Recall that there’s no validation split in original data, a 90/10 train/validation split is automatically performed when fit with train_data.

train_data, _, test_data = ImageDataset.from_folders('https://autogluon.s3.amazonaws.com/datasets/shopee-iet.zip')

data/
├── test/
└── train/

Specify which Networks to Try

We start with specifying the pretrained neural network candidates. Given such a list, AutoGluon tries to train different networks from this list to identify the best-performing candidate. This is an example of a autogluon.core.space.Categorical search space, in which there are a limited number of values to choose from.

model = ag.Categorical('resnet18_v1b', 'mobilenetv3_small')

# you may choose more than 70+ available model in the model zoo provided by GluonCV:
model_list = ImagePredictor.list_models()

Specify the training hyper-parameters

Similarly, we can manually specify many crucial hyper-parameters, with specific value or search space (autogluon.core.space).

batch_size = 8
lr = ag.Categorical(1e-2, 1e-3)

Search Algorithms

In AutoGluon, autogluon.core.searcher supports different search strategies for both hyperparameter optimization and architecture search. Beyond simply specifying the space of hyperparameter configurations to search over, you can also tell AutoGluon what strategy it should employ to actually search through this space. This process of finding good hyperparameters from a given search space is commonly referred to as hyperparameter optimization (HPO) or hyperparameter tuning. autogluon.core.scheduler orchestrates how individual training jobs are scheduled. We currently support random search.