The tf. transformation can cache a dataset, either in memory or on local storage. Prefetch essentially creates a pipelined implementation for data augmentation function and train function so that they can operate in parallel.īased on above figures, prefetch seems to have a small improvement to performance when cache =False, but has a significant impact when cache =True. The prefetch transformation provides benefits any time there is an opportunity to overlap the work of a “producer” with the work of a “consumer.” Doing so reduces the step time to the maximum (as opposed to the sum) of the training and the time it takes to extract the data. While the model is executing training step s, the input pipeline is reading the data for step s+1. Prefetching overlaps the preprocessing and model execution of a training step. TF.Data Settings Detailed Analysis TF.Data Prefetch Setting However, with tf.data and albumentations, the first run itself shows a speedup of 130–200% and 180–6658% speedup in subsequent runs.
#KERAS DATA GENERATOR GENERATOR#
Percentage Speedup compared to Keras Generator + Keras AugmentationĪs shown, in the above figures, Keras sequence with albumentations gives around ~6% improvement compared to Keras Generator with Keras augmentation. Random Brightness with a range of +/-20%.The same augmentations were used for the performance measurement of Keras and Albumentations: MultiGPU Benchmark with 6 x NVIDIA 1080Ti 11GB will be added later. Raw Generator Performance with Augmentation Hardware SetupĬPU: AMD Ryzen 7 1800X - 8 Cores - 16 Logical Processors - 3600 MHz.
Primary reasons for choosing Albumentations: After breaking our head to integrate Keras augmentations without any luck, we finally decided to integrate Albumentations to tf.data pipeline. The performance was amazing but limited augmentations available were a serious problem. To deal with the problem, we started exploring migrating Keras generators to tf.data build input pipelines. Keras generators typically work on CPU and during multi-GPU training, the performance of training is hindered by Keras Generators. But as the training data grew, the limitations of Keras generators started coming into play. Keras Generators and their augmentations had made vision-based AI/ML model training a breeze.
#KERAS DATA GENERATOR UPDATE#
If you want to update the *_generator() function calls in your code the parameters of the function is exactly the same as before.Is it time to move from the Keras Generators? A generator or returning (inputs, targets) or (inputs, targets, sample_weights).Should return a tuple of either (inputs, targets) or (inputs, targets, sample_weights). A dict mapping input names to the corresponding array/tensors, if the model has named inputs.A TensorFlow tensor, or a list of tensors (in case the model has multiple inputs).A Numpy array (or array-like), or a list of arrays (in case the model has multiple inputs).
Therefore, all *_generator() function calls can now be replaced with their respective non-generator function calls: fit() instead of fit_generator(), evaluate() instead of evaluate_generator(), and predict() instead of predict_generator().įor example, the model.fit() function can take the following inputs ( source): This is because in tf.keras, as well as the latest version of multi-backend Keras, the model.fit() function can take generators as well. Please use Model.evaluate, which supports generators. Model.evaluate_generator (from .training ) is deprecated and will be removed in a future version.
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