Oracle open sources Graphpipe to standardize machine learning model deployment

INSUBCONTINENT EXCLUSIVE:
Oracle, a company not exactly known for having the best relationship with the open source community, is releasing a new open source tool
today called Graphpipe, which is designed to simplify and standardize the deployment of machine learning models. The tool consists of a set
of libraries and tools for following the standard. Vish Abrams, whose background includes helping develop OpenStack at NASA and later
helping launch Nebula, an OpenStack startup in 2011, is leading the project
He says as his team dug into the machine learning workflow, they found a gap
While teams spend lots of energy developing a machine learning model, it hard to actually deploy the model for customers to use
That where Graphpipe comes in. He points out that it common with newer technologies like machine learning for people to get caught up in the
hype
Even though the development process keeps improving, he says that people often don''t think about deployment. Graphpipe is what grown out of
our attempt to really improve deployment stories for machine learning models, and to create an open standard around having a way of doing
that to improve the space,& Abrams told TechCrunch. As Oracle dug into this, they identified three main problems
For starters, there is no standard way to serve APIs, leaving you to use whatever your framework provides
Next, there is no standard deployment mechanism, which leaves developers to build custom ones every time
Finally, they found existing methods leave performance as an afterthought, which in machine learning could be a major problem. We created
Graphpipe to solve these three challenges
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,& Abrams wrote in a blog post
announcing the release of Graphpipe. The company decided to make this a standard and to open source it to try and move machine learning
model deployment forward
&Graphpipe sits on that intersection between solving a business problems and pushing the state of the art forward, and I think personally,
the best way to do that is by have an open source approach
Often, if you&re trying to standardize something without going for the open source bits, what you end up with is a bunch of competing
technologies,& he said. Abrams acknowledged the tension that has existed between Oracle and the open source community over the years, but
says they have been working to change the perception recently with contributions to Kubernetes and the Oracle Functions Project as examples
Ultimately he says, if the technology is interesting enough, people will give it a chance, regardless of who is putting it out there
And of course, once it out there, if a community builds around it, they will adapt and change it as open source projects tend to do
Abrams hopes that happens. We care more about the standard becoming quite broadly adopted, than we do about our particular implementation of
it because that makes it easier for everyone
It really up to the community decide that this is valuable and interesting.& he said. Graphpipe is available starting today on the Oracle
GitHub page.