YUFENG GUO: Hi, my name is Yufeng Guo,
and today we're going to look at how
TensorFlow Enterprise, or TFE, builds on TensorFlow to enable
the seamless creation of production machine learning
applications with enterprise-grade support,
through the power of Google Cloud.
To learn about the benefits of TFE,
let's imagine a scenario where a data scientist visits an Ops
team to ask for help productionizing their machine
learning platform.
The question then becomes whether there
is a suitable platform for both experimentation and production.
TensorFlow Enterprise is ideal for this kind of situation.
Google Cloud has your solution to productionize
your TensorFlow applications.
As part of Google Cloud, TFE provides managed services,
cloud scale performance, and enterprise-grade support.
Let's start with managed services
and check back in to see how our data science and Ops teams
are doing.
The data science team needs development environments
with lots of compute power.
And in an on-prem, non-managed system,
the burden of setting them up often falls to the Ops team.
There are many challenges to operating this way.
Machines with GPUs are expensive and take a long time
to acquire.
Software installation and management
consumes significant time.
Additionally, there's the problem of over-provisioning.
This kind of non-managed ML platform
doesn't scale for enterprise-production projects.
One solution to these constraints
is to introduce a standardized ML
platform for the data scientists and Ops teams
to solve these problems.
Enter TensorFlow Enterprise on the Deep Learning VM, or DLVM.
The VM is a standardized VM image for machine learning
applications.
TFE currently supports TensorFlow 1.15 and two.
The machine comes pre-installed with the everyday tools
for data scientists, including CUDA drivers, NumPy, Sklearn,
Pandas, and of course, Jupyter.
Since it's a standardized software stack,
it's easily manageable by Ops engineers.
The DLVM is deployable to Google Cloud,
with Google Compute Engine or AI Platform Notebooks
in just a few minutes.
Also, there's no over-provisioning problem,
because you will only pay for the resources
you actually use, minute by minute.
Let's take a look at how you can actually create a GCE
instance with the DLVM image.
In the GCP console, head on over to the Compute Engines,
VM Instances page, and click Create.
Select Marketplace from the left-hand menu,
and type Deep Learning VM in the search bar.
Select the Deep Learning VM and click Launch.
In this menu, you can select your machine type
and if you want GPUs.
You can also select the TensorFlow Enterprise,
versions 1.15 and two.
For now, let's choose TensorFlow 1.15, without GPUs,
and just one CPU, a very simple image, and just click Deploy.
The manageability you just saw increases the portability
and scalability that you just can't get with local notebooks.
There's often a huge gap between proof of concept notebooks
and production machine learning code.
For example, notebooks are often particular
to the local environment of each data scientist.
But production code should be portable and shareable.
A single notebook instance can't serve 10 million users.
The Ops teams have to take on a lot of work
to scale and load balance to build a production system.
Proof of concepts are manually deployed,
whereas in production, the Ops team
needs to manage a reproducible end-to-end lifecycle.
And after all of that, continuous
monitoring and alerting still need
to be taken into consideration.
One way to fill the gap between experimentation and production
is Deep Learning Containers, or DLC,
with TensorFlow Enterprise.
DLC provides the exact same standardized ML software
stack as a DLVM, with support for TensorFlow 1.15 and two,
as well as lots of common data size libraries.
But now it's on a portable and scalable container image.
If your data science team is already using DLVMs,
then it's much easier for Ops engineers
to migrate their code to DLC, and deploy it
to Google Cloud Services, such as AI Platform and Kubernetes
Engine.
With Deep Learning Containers the Ops team
won't have to add 100,000 lines of code
to make a robust system.
For example, if you use AI Platform or a Kubernetes engine
to run the container, you automatically
get the benefits from the managed service,
such as container scheduling and lifecycle management, scaling
out to load balancing and failover, logging
and monitoring, troubleshooting, security, and more.
Now let's see how scale Cloud Performance of TensorFlow
Enterprise can benefit you.
In many cases, data scientists tend
to use smaller sample data sets for building
their machine learning models, and don't bother
handling the raw big data.
So it falls to the Ops team to design an ML pipeline that
can accommodate the 10 million rows from storage
without any I/O bottleneck, and continuously
and smoothly pre-process and train the model every day.
Because TensorFlow Enterprise provides TensorFlow runtimes
specifically designed for Google Cloud,
you can fully utilize the cloud scale performance of both Cloud
Storage and BigQuery with minimal changes
to your existing TensorFlow code.
Lastly, let's talk about enterprise-grade support.
The pace of AI and software versions is evolving rapidly,
and it's hard to keep up with new library versions always
coming out.
That is why TensorFlow Enterprise
includes long-term support.
For TensorFlow 1.15 and two, Google Cloud
will provide security patches and select bug fixes
for up to three years.
These versions will be available on Google Cloud,
and all patches and bug fixes will also
be available in the mainline TensorFlow repository.
TensorFlow Enterprise is available today
with support for TensorFlow 1.15 and two.
Speed up your software development,
and ensure the longevity and reliability
of your AI applications and services
with enterprise-grade support.
It's the only offering from the same team
that created TensorFlow.
Head over to g.co/cloud/TensorFlow to get
started today.
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