3,658 research outputs found
Characterizing Deep-Learning I/O Workloads in TensorFlow
The performance of Deep-Learning (DL) computing frameworks rely on the
performance of data ingestion and checkpointing. In fact, during the training,
a considerable high number of relatively small files are first loaded and
pre-processed on CPUs and then moved to accelerator for computation. In
addition, checkpointing and restart operations are carried out to allow DL
computing frameworks to restart quickly from a checkpoint. Because of this, I/O
affects the performance of DL applications. In this work, we characterize the
I/O performance and scaling of TensorFlow, an open-source programming framework
developed by Google and specifically designed for solving DL problems. To
measure TensorFlow I/O performance, we first design a micro-benchmark to
measure TensorFlow reads, and then use a TensorFlow mini-application based on
AlexNet to measure the performance cost of I/O and checkpointing in TensorFlow.
To improve the checkpointing performance, we design and implement a burst
buffer. We find that increasing the number of threads increases TensorFlow
bandwidth by a maximum of 2.3x and 7.8x on our benchmark environments. The use
of the tensorFlow prefetcher results in a complete overlap of computation on
accelerator and input pipeline on CPU eliminating the effective cost of I/O on
the overall performance. The use of a burst buffer to checkpoint to a fast
small capacity storage and copy asynchronously the checkpoints to a slower
large capacity storage resulted in a performance improvement of 2.6x with
respect to checkpointing directly to slower storage on our benchmark
environment.Comment: Accepted for publication at pdsw-DISCS 201
Inefficiency of K-FAC for Large Batch Size Training
In stochastic optimization, using large batch sizes during training can
leverage parallel resources to produce faster wall-clock training times per
training epoch. However, for both training loss and testing error, recent
results analyzing large batch Stochastic Gradient Descent (SGD) have found
sharp diminishing returns, beyond a certain critical batch size. In the hopes
of addressing this, it has been suggested that the Kronecker-Factored
Approximate Curvature (\mbox{K-FAC}) method allows for greater scalability to
large batch sizes, for non-convex machine learning problems such as neural
network optimization, as well as greater robustness to variation in model
hyperparameters. Here, we perform a detailed empirical analysis of large batch
size training %of these two hypotheses, for both \mbox{K-FAC} and SGD,
evaluating performance in terms of both wall-clock time and aggregate
computational cost. Our main results are twofold: first, we find that both
\mbox{K-FAC} and SGD doesn't have ideal scalability behavior beyond a certain
batch size, and that \mbox{K-FAC} does not exhibit improved large-batch
scalability behavior, as compared to SGD; and second, we find that
\mbox{K-FAC}, in addition to requiring more hyperparameters to tune, suffers
from similar hyperparameter sensitivity behavior as does SGD. We discuss
extensive results using ResNet and AlexNet on \mbox{CIFAR-10} and SVHN,
respectively, as well as more general implications of our findings
Robust Data Pruning under Label Noise via Maximizing Re-labeling Accuracy
Data pruning, which aims to downsize a large training set into a small
informative subset, is crucial for reducing the enormous computational costs of
modern deep learning. Though large-scale data collections invariably contain
annotation noise and numerous robust learning methods have been developed, data
pruning for the noise-robust learning scenario has received little attention.
With state-of-the-art Re-labeling methods that self-correct erroneous labels
while training, it is challenging to identify which subset induces the most
accurate re-labeling of erroneous labels in the entire training set. In this
paper, we formalize the problem of data pruning with re-labeling. We first show
that the likelihood of a training example being correctly re-labeled is
proportional to the prediction confidence of its neighborhood in the subset.
Therefore, we propose a novel data pruning algorithm, Prune4Rel, that finds a
subset maximizing the total neighborhood confidence of all training examples,
thereby maximizing the re-labeling accuracy and generalization performance.
Extensive experiments on four real and one synthetic noisy datasets show that
\algname{} outperforms the baselines with Re-labeling models by up to 9.1% as
well as those with a standard model by up to 21.6%
Semi-Supervised Generation with Cluster-aware Generative Models
Deep generative models trained with large amounts of unlabelled data have
proven to be powerful within the domain of unsupervised learning. Many real
life data sets contain a small amount of labelled data points, that are
typically disregarded when training generative models. We propose the
Cluster-aware Generative Model, that uses unlabelled information to infer a
latent representation that models the natural clustering of the data, and
additional labelled data points to refine this clustering. The generative
performances of the model significantly improve when labelled information is
exploited, obtaining a log-likelihood of -79.38 nats on permutation invariant
MNIST, while also achieving competitive semi-supervised classification
accuracies. The model can also be trained fully unsupervised, and still improve
the log-likelihood performance with respect to related methods
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