Oracle released a new tool, which is called GraphPipe, into open source in order to speed up real-world deployments of machine learning.
GraphPipe, which Oracle has put into open source via GitHub, was designed to standardize and clarify machine learning models in order to scale out services and applications to customers.
Vish Abrams, an Oracle architect for cloud development, and his deep learning team created GraphPipe, which is comprised of a set of libraries and tools. In a company blog post, Abrams, who previously served as chief technology officer of OpenStack startup Nebula and as principal engineer at Rackspace, said that there were three primary challenges that were stifling machine learning deployments.
The first challenge is there's no standard model for serving APIs, "so you are likely stuck with whatever your framework gives you," according to Abrams.
"Your business application will generally need a bespoke client just to talk to your deployed model," Abrams said. "And it's even worse if you are using multiple frameworks. If you want to create ensembles of models from multiple frameworks, you'll have to write custom code to combine them."
The second challenge is building server models can be extremely complicated. Out-of-the-box solutions are few and far between, and deployments get much less attention than training, according to Abrams.
"Try building a GPU version of TensorFlow-serving, for example," Abrams said. "You better be prepared to bang your head against it for a few days."
The last challenge is many of the existing frameworks for machine learning don't focus on performance, which means they can fall short in certain uses cases.
"We created GraphPipe to solve these three challenges," Abrams said. "It provides a standard, high-performance protocol for transmitting tensor data over the network, along with simple implementations of clients and servers that make deploying and querying machine learning models from any framework a breeze. GraphPipe's efficient servers can serve models built in TensorFlow, PyTorch, mxnet, CNTK, or caffe2."
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