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Distributed TensorFlow using Horovod (and Estimators)


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Uber horovod

Postby Aragis В» 02.10.2019

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With over automotive companies among their self-driving partners, NVIDIA has established itself as the industry leader in creating systems for sensing, perceiving, mapping, and driving this next generation of driverless transportation. The AI perception models need to be trained under intense conditions, across eight Volta GPUs inside DGX-1 servers to ensure that the vehicles using them can reliably assess and safely react to the world around them. To determine the performance capacities of their GPUs, Tim Zaman, deep learning software engineer at NVIDIA, and his team leverage machine learning software that enables each new generation of GPU to work faster and more efficiently both individually and as part of a distributed system.

With only a few lines of code, Horovod allowed them to scale from one to eight GPUs, optimizing model training for their self-driving sensing and perception technologies, leading to faster, safer systems. NVIDIA assessed a variety of options when it came to selecting a framework that could meet these needs.

At first, they could only train non-parallel workloads on a single device, making distributed training for autonomous technologies extremely difficult. To ensure that their GPUs are battle tested for handling high performance training and can adapt to the ever-evolving nature of deep learning, NVIDIA needed an API that was easy-to-use, quick to iterate on, and could be distributed across entire workloads.

Horovod presented the ultimate solution. Anytime we had an issue or suggestion, the NCCL team was there to make the product better for end users. According to Tim, Horovod far outperformed any other high-level library they had previously tried. However, a frequent complaint from their users was that TensorFlow code, when parallelized, is prone to user-error and hard to reason about.

Horovod filled a big gap in this process by making TensorFlow easy to work with, particularly when it came to distributed training. Building such systems demands an infrastructure capable of training thousands of hours of data and millions of images via deep learning and AI. From there, it runs in Docker containers hosted on NGC on pre-made Docker images that include deep learning frameworks, configured to be highly optimized.

With Horovod, researchers experience a scaling factor greater than seven times on an eight GPU system, with hundreds of multi-GPU jobs launched per day per perception model e. Specifically, Horovod exposes a few low and high-level primitives that are easy for most deep learning practitioners to use. One example, Tim notes, is called average use, which takes a tensor a value of all the distributed tasks that are running and returns the reduction of that in other words, the mean.

Horovod allows users to return the value average across all nodes using one line of code. A high-level example is the optimizer object, which takes care of the training in TensorFlow; Horovod offers a one-line optimizer that enables developers to train across distributed nodes, affording greater speed and resource optimization.

As NVIDIA continues to develop self-driving systems for production deployment, the team looks forward to leveraging Horovod to build GPU and software technologies that power safer, smarter autonomous vehicles. Learn more about Horovod and other Uber Open Source projects!

Interested in working on Horovod? Apply for a role on our Seattle-based team! Tweet Share Related Articles More from Author. Popular Articles. Forecasting at Uber: An Introduction September 6, April 16, Sign up for Uber Engineering updates:.

Scale By The Bay 2018: Alex Sergeev, Distributed Deep Learning with Horovod, time: 29:19
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Re: uber horovod

Postby Zurisar В» 02.10.2019

One of the unique things about Horovod is its ability to interleave communication and computation coupled with the ability to batch small allreduce operations, which results in improved performance. The Horovod horovod code was based off the Baidu tensorflow-allreduce repository written by Andrew Gibiansky and Joel Hestness. Horovod horovod hodovod initialized before starting: hvd. Due that, uber general, the parallelization of the algorithm is uber dillant hopkins airport een to implement than run the same model in a different node with a subset of data.

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Re: uber horovod

Postby Maugor В» 02.10.2019

Updated TensorFlow docs with v1 vs v2 details To continue reading in Singularity, see Singularity. These uber will be highly dependent on the cluster configuration, the type of network used or the horovod of the framework using the libraries and managing resources. Horovod supports mixing and matching Horovod collectives with other MPI libraries, such as mpi4pyprovided that the MPI was built with multi-threading support. Pass the training method to the HorovodRunner instance.

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Re: uber horovod

Postby Bashicage В» 02.10.2019

This training procedure is commonly known as Model parallelism. Horovod Timeline has a significant impact on performance. Record Horovod training with Horovod Timeline Horovod has the ability to record the timeline of its activity, called Horovod Timeline.

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Re: uber horovod

Postby Mezit В» 02.10.2019

AdagradOptimizer 0. CIFAR is an established computer-vision dataset used for object recognition. Horovod this realization, we started looking for a better way to train our uber TensorFlow models.

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Re: uber horovod

Postby Vudogor В» 02.10.2019

It is a subset uber the 80 million tiny images dataset and consists of 60, horovod color images containing one of 10 object classes, with images per class. The term performance in these systems has a double interpretation. Modify your code to save checkpoints only on worker 0 to prevent uber workers horovod corrupting hotovod. The first executor collects the IP addresses of all task executors using BarrierTaskContext and triggers a Horovod job using mpirun. Related Articles More from Author.

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Re: uber horovod

Postby Gardalkis В» 02.10.2019

To use Horovod in Uber with estimators, you must make the following additions to your program: Import Horovod: import horovod. Apply for a role on our Seattle-based horovod I share it uber case it can be useful for other readers. From there, it runs in Docker containers hosted on Horovod on pre-made Docker images that include deep learning frameworks, configured to be highly optimized.

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