114,879 research outputs found
Asynchronous Optimization Methods for Efficient Training of Deep Neural Networks with Guarantees
Asynchronous distributed algorithms are a popular way to reduce
synchronization costs in large-scale optimization, and in particular for neural
network training. However, for nonsmooth and nonconvex objectives, few
convergence guarantees exist beyond cases where closed-form proximal operator
solutions are available. As most popular contemporary deep neural networks lead
to nonsmooth and nonconvex objectives, there is now a pressing need for such
convergence guarantees. In this paper, we analyze for the first time the
convergence of stochastic asynchronous optimization for this general class of
objectives. In particular, we focus on stochastic subgradient methods allowing
for block variable partitioning, where the shared-memory-based model is
asynchronously updated by concurrent processes. To this end, we first introduce
a probabilistic model which captures key features of real asynchronous
scheduling between concurrent processes; under this model, we establish
convergence with probability one to an invariant set for stochastic subgradient
methods with momentum.
From the practical perspective, one issue with the family of methods we
consider is that it is not efficiently supported by machine learning
frameworks, as they mostly focus on distributed data-parallel strategies. To
address this, we propose a new implementation strategy for shared-memory based
training of deep neural networks, whereby concurrent parameter servers are
utilized to train a partitioned but shared model in single- and multi-GPU
settings. Based on this implementation, we achieve on average 1.2x speed-up in
comparison to state-of-the-art training methods for popular image
classification tasks without compromising accuracy
Distributed learning of CNNs on heterogeneous CPU/GPU architectures
Convolutional Neural Networks (CNNs) have shown to be powerful classification
tools in tasks that range from check reading to medical diagnosis, reaching
close to human perception, and in some cases surpassing it. However, the
problems to solve are becoming larger and more complex, which translates to
larger CNNs, leading to longer training times that not even the adoption of
Graphics Processing Units (GPUs) could keep up to. This problem is partially
solved by using more processing units and distributed training methods that are
offered by several frameworks dedicated to neural network training. However,
these techniques do not take full advantage of the possible parallelization
offered by CNNs and the cooperative use of heterogeneous devices with different
processing capabilities, clock speeds, memory size, among others. This paper
presents a new method for the parallel training of CNNs that can be considered
as a particular instantiation of model parallelism, where only the
convolutional layer is distributed. In fact, the convolutions processed during
training (forward and backward propagation included) represent from -\%
of global processing time. The paper analyzes the influence of network size,
bandwidth, batch size, number of devices, including their processing
capabilities, and other parameters. Results show that this technique is capable
of diminishing the training time without affecting the classification
performance for both CPUs and GPUs. For the CIFAR-10 dataset, using a CNN with
two convolutional layers, and and kernels, respectively, best
speedups achieve using four CPUs and with three GPUs.
Modern imaging datasets, larger and more complex than CIFAR-10 will certainly
require more than -\% of processing time calculating convolutions, and
speedups will tend to increase accordingly
Distributed computing methodology for training neural networks in an image-guided diagnostic application
Distributed computing is a process through which a set of computers connected by a network is used collectively to solve a single problem. In this paper, we propose a distributed computing methodology for training neural networks for the detection of lesions in colonoscopy. Our approach is based on partitioning the training set across multiple processors using a parallel virtual machine. In this way, interconnected computers of varied architectures can be used for the distributed evaluation of the error function and gradient values, and, thus, training neural networks utilizing various learning methods. The proposed methodology has large granularity and low synchronization, and has been implemented and tested. Our results indicate that the parallel virtual machine implementation of the training algorithms developed leads to considerable speedup, especially when large network architectures and training sets are used
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