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 choosen 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
/var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/venv/lib/python3.7/site-packages/gluoncv/__init__.py:40: UserWarning: Both mxnet==1.7.0 and torch==1.9.0+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. '
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
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 FIFO (standard) and
Hyperband scheduling, along with search by random sampling or Bayesian
optimization. These basic techniques are rendered surprisingly powerful
by AutoGluon’s support of asynchronous parallel execution.
Bayesian Optimization¶
Here is an example of using Bayesian Optimization using
autogluon.core.searcher.GPFIFOSearcher.
Bayesian Optimization fits a probabilistic surrogate model to estimate the function that relates each hyperparameter configuration to the resulting performance of a model trained under this hyperparameter configuration. Our implementation makes use of a Gaussian process surrogate model along with expected improvement as acquisition function. It has been developed specifically to support asynchronous parallel evaluations.
hyperparameters={'model': model, 'batch_size': batch_size, 'lr': lr, 'epochs': 2}
predictor = ImagePredictor()
predictor.fit(train_data, search_strategy='bayesopt', time_limit=60*10, hyperparameters=hyperparameters,
hyperparameter_tune_kwargs={'num_trials': 2})
print('Top-1 val acc: %.3f' % predictor.fit_summary()['valid_acc'])
Reset labels to [0, 1, 2, 3]
Randomly split train_data into train[720]/validation[80] splits.
The number of requested GPUs is greater than the number of available GPUs.Reduce the number to 1
Starting HPO experiments
0%| | 0/2 [00:00<?, ?it/s]
modified configs(<old> != <new>): {
root.train.early_stop_baseline 0.0 != -inf
root.train.early_stop_patience -1 != 10
root.train.rec_train_idx ~/.mxnet/datasets/imagenet/rec/train.idx != auto
root.train.lr 0.1 != 0.01
root.train.num_workers 4 != 0
root.train.rec_val_idx ~/.mxnet/datasets/imagenet/rec/val.idx != auto
root.train.rec_val ~/.mxnet/datasets/imagenet/rec/val.rec != auto
root.train.early_stop_max_value 1.0 != inf
root.train.batch_size 128 != 8
root.train.rec_train ~/.mxnet/datasets/imagenet/rec/train.rec != auto
root.train.data_dir ~/.mxnet/datasets/imagenet != auto
root.train.epochs 10 != 2
root.train.num_training_samples 1281167 != -1
root.img_cls.model resnet50_v1 != resnet18_v1b
root.valid.batch_size 128 != 8
root.valid.num_workers 4 != 0
}
Saved config to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/docs/_build/eval/tutorials/image_prediction/4538d171/.trial_0/config.yaml
Start training from [Epoch 0]
Epoch[0] Batch [49] Speed: 161.512299 samples/sec accuracy=0.427500 lr=0.010000
[Epoch 0] training: accuracy=0.501389
[Epoch 0] speed: 161 samples/sec time cost: 4.401932
[Epoch 0] validation: top1=0.712500 top5=1.000000
[Epoch 0] Current best top-1: 0.712500 vs previous -inf, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/docs/_build/eval/tutorials/image_prediction/4538d171/.trial_0/best_checkpoint.pkl
Epoch[1] Batch [49] Speed: 166.513435 samples/sec accuracy=0.655000 lr=0.010000
[Epoch 1] training: accuracy=0.638889
[Epoch 1] speed: 165 samples/sec time cost: 4.313575
[Epoch 1] validation: top1=0.800000 top5=1.000000
[Epoch 1] Current best top-1: 0.800000 vs previous 0.712500, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/docs/_build/eval/tutorials/image_prediction/4538d171/.trial_0/best_checkpoint.pkl
Applying the state from the best checkpoint...
modified configs(<old> != <new>): {
root.train.early_stop_baseline 0.0 != -inf
root.train.early_stop_patience -1 != 10
root.train.rec_train_idx ~/.mxnet/datasets/imagenet/rec/train.idx != auto
root.train.lr 0.1 != 0.01
root.train.num_workers 4 != 0
root.train.rec_val_idx ~/.mxnet/datasets/imagenet/rec/val.idx != auto
root.train.rec_val ~/.mxnet/datasets/imagenet/rec/val.rec != auto
root.train.early_stop_max_value 1.0 != inf
root.train.batch_size 128 != 8
root.train.rec_train ~/.mxnet/datasets/imagenet/rec/train.rec != auto
root.train.data_dir ~/.mxnet/datasets/imagenet != auto
root.train.epochs 10 != 2
root.train.num_training_samples 1281167 != -1
root.img_cls.model resnet50_v1 != mobilenetv3_small
root.valid.batch_size 128 != 8
root.valid.num_workers 4 != 0
}
Saved config to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/docs/_build/eval/tutorials/image_prediction/4538d171/.trial_1/config.yaml
Start training from [Epoch 0]
Epoch[0] Batch [49] Speed: 125.196592 samples/sec accuracy=0.290000 lr=0.010000
[Epoch 0] training: accuracy=0.370833
[Epoch 0] speed: 127 samples/sec time cost: 5.602776
[Epoch 0] validation: top1=0.625000 top5=1.000000
[Epoch 0] Current best top-1: 0.625000 vs previous -inf, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/docs/_build/eval/tutorials/image_prediction/4538d171/.trial_1/best_checkpoint.pkl
Epoch[1] Batch [49] Speed: 132.461615 samples/sec accuracy=0.517500 lr=0.010000
[Epoch 1] training: accuracy=0.544444
[Epoch 1] speed: 126 samples/sec time cost: 5.632095
[Epoch 1] validation: top1=0.762500 top5=1.000000
[Epoch 1] Current best top-1: 0.762500 vs previous 0.625000, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/docs/_build/eval/tutorials/image_prediction/4538d171/.trial_1/best_checkpoint.pkl
Applying the state from the best checkpoint...
modified configs(<old> != <new>): {
root.train.early_stop_baseline 0.0 != -inf
root.train.early_stop_patience -1 != 10
root.train.rec_train_idx ~/.mxnet/datasets/imagenet/rec/train.idx != auto
root.train.lr 0.1 != 0.01
root.train.num_workers 4 != 0
root.train.rec_val_idx ~/.mxnet/datasets/imagenet/rec/val.idx != auto
root.train.rec_val ~/.mxnet/datasets/imagenet/rec/val.rec != auto
root.train.early_stop_max_value 1.0 != inf
root.train.batch_size 128 != 8
root.train.rec_train ~/.mxnet/datasets/imagenet/rec/train.rec != auto
root.train.data_dir ~/.mxnet/datasets/imagenet != auto
root.train.epochs 10 != 2
root.train.num_training_samples 1281167 != -1
root.img_cls.model resnet50_v1 != resnet18_v1b
root.valid.batch_size 128 != 8
root.valid.num_workers 4 != 0
}
Saved config to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/docs/_build/eval/tutorials/image_prediction/4538d171/.trial_0/config.yaml
Start training from [Epoch 0]
Epoch[0] Batch [49] Speed: 167.750017 samples/sec accuracy=0.352500 lr=0.010000
[Epoch 0] training: accuracy=0.441667
[Epoch 0] speed: 165 samples/sec time cost: 4.295983
[Epoch 0] validation: top1=0.687500 top5=1.000000
[Epoch 0] Current best top-1: 0.687500 vs previous -inf, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/docs/_build/eval/tutorials/image_prediction/4538d171/.trial_0/best_checkpoint.pkl
Epoch[1] Batch [49] Speed: 168.216875 samples/sec accuracy=0.602500 lr=0.010000
[Epoch 1] training: accuracy=0.633333
[Epoch 1] speed: 164 samples/sec time cost: 4.322928
[Epoch 1] validation: top1=0.775000 top5=1.000000
[Epoch 1] Current best top-1: 0.775000 vs previous 0.687500, saved to /var/lib/jenkins/workspace/workspace/autogluon-tutorial-image-classification-v3/docs/_build/eval/tutorials/image_prediction/4538d171/.trial_0/best_checkpoint.pkl
Applying the state from the best checkpoint...
Finished, total runtime is 40.17 s
{ 'best_config': { 'estimator': <class 'gluoncv.auto.estimators.image_classification.image_classification.ImageClassificationEstimator'>,
'gpus': [0],
'img_cls': { 'batch_norm': False,
'last_gamma': False,
'model': 'resnet18_v1b',
'use_gn': False,
'use_pretrained': True,
'use_se': False},
'train': { 'batch_size': 8,
'crop_ratio': 0.875,
'data_dir': 'auto',
'dtype': 'float32',
'early_stop_baseline': -inf,
'early_stop_max_value': inf,
'early_stop_min_delta': 0.001,
'early_stop_patience': 10,
'epochs': 2,
'hard_weight': 0.5,
'input_size': 224,
'label_smoothing': False,
'log_interval': 50,
'lr': 0.01,
'lr_decay': 0.1,
'lr_decay_epoch': '40, 60',
'lr_decay_period': 0,
'lr_mode': 'step',
'mixup': False,
'mixup_alpha': 0.2,
'mixup_off_epoch': 0,
'mode': '',
'momentum': 0.9,
'no_wd': False,
'num_training_samples': -1,
'num_workers': 0,
'output_lr_mult': 0.1,
'pretrained_base': True,
'rec_train': 'auto',
'rec_train_idx': 'auto',
'rec_val': 'auto',
'rec_val_idx': 'auto',
'resume_epoch': 0,
'start_epoch': 0,
'teacher': None,
'temperature': 20,
'transfer_lr_mult': 0.01,
'use_rec': False,
'warmup_epochs': 0,
'warmup_lr': 0.0,
'wd': 0.0001},
'valid': {'batch_size': 8, 'num_workers': 0}},
'total_time': 40.165590047836304,
'train_acc': 0.6333333333333333,
'valid_acc': 0.775}
Top-1 val acc: 0.775
The BO searcher can be configured by search_options, see
autogluon.core.searcher.GPFIFOSearcher. Load the test dataset
and evaluate:
top1, top5 = predictor.evaluate(test_data)
print('Test acc on hold-out data:', top1)
Test acc on hold-out data: 0.75
Note that num_trials=2 above is only used to speed up the tutorial.
In normal practice, it is common to only use time_limit and drop
num_trials.
Hyperband Early Stopping¶
AutoGluon currently supports scheduling trials in serial order and with
early stopping (e.g., if the performance of the model early within
training already looks bad, the trial may be terminated early to free up
resources). Here is an example of using an early stopping scheduler
autogluon.core.scheduler.HyperbandScheduler.
scheduler_options is used to configure the scheduler. In this
example, we run Hyperband with a single bracket, and stop/go decisions
are made after 1 and 2 epochs (grace_period,
grace_period * reduction_factor):
hyperparameters.update({
'search_strategy': 'hyperband',
'grace_period': 1
})
The fit, evaluate and predict processes are exactly the
same, so we will skip training to save some time.
Bayesian Optimization and Hyperband¶
While Hyperband scheduling is normally driven by a random searcher, AutoGluon also provides Hyperband together with Bayesian optimization. The tuning of expensive DL models typically works best with this combination.
hyperparameters.update({
'search_strategy': 'bayesopt_hyperband',
'grace_period': 1
})
For a comparison of different search algorithms and scheduling
strategies, see Search Algorithms. For more options using fit, see
autogluon.vision.ImagePredictor.