Alien4Cloud® is an advanced cloud management tool that aims at simplifying the composition (both visual and text), management (e.g. source storing/versioning/editing), handling, and deployment (deploy, undeploy, update) of cloud topologies described using the TOSCA specification language. Alien4Cloud® supports users (with different roles) who can utilize either basic TOSCA template components or full TOSCA template topologies to deploy their own cloud infrastructures.
The DEEP Hybrid DataCloud project integrates Alien4Cloud® as the main component that allows our users to interact with the rest of the infrastructure. Figure F1 shows a custom infrastructure loaded from an existing topology template file that represents a small Kubernetes® cluster running the DEEPPaaS application. You can find more details about DEEP and the work being done at http://www.deep-hybrid-datacloud.eu. Diagram D1 depicts the role of the cloud management tool in the project. Alien4Cloud® is already a usable part of the project, please be sure you check our public demos on Youtube to see it in action.
Since Alien4Cloud® allows functionality extension by the means of plugins, we developed one freely available on Github that connects it with the INDIGO PaaS Orchestrator used in DEEP. The Github repository doesn’t contain only the code for the orchestrator plugin, it also includes the Dockerfile necessary to create a Docker image (prebuilt at https://hub.docker.com/r/indigodatacloud/alien4cloud-deep) that includes all the components needed for deployment and use in DEEP:
First part: Running a module locally for prediction
Deep Learning is nowadays at the forefront of Artificial Intelligence, shaping tools that are being used to achieve very high levels of accuracy in many different research fields. Training a Deep Learning model is a very complex and computationally intensive task requiring the user to have a full setup involving a certain hardware, the adequate drivers, dedicated software and enough memory and storage resources. Very often the Deep Learning practitioner is not a computing expert, and want all of this technology as accessible and transparent as possible to be able to just focus on creating a new model or applying a prebuild one to some data.
With the DEEP-HybridDataCloud solutions you will be able to start working from the very first moment!
The DEEP-HybridDataCloud project offers a framework for
all users, and not just for a few experts, enabling the transparent training,
sharing and serving of Deep Learning models both locally or on hybrid cloud
The DEEP Open Catalog (https://marketplace.deep-hybrid-datacloud.eu/, also known as “marketplace”) provides the
universal point of entry to all services offered by DEEP. Its offers several
options for users of all levels to get acquainted with DEEP:
Users can browse the DEEP Open Catalog, download a
certain model and apply it to some local or remote data for
can also browse the DEEP Open Catalog, download a model and do some training
using their own data easily changing with the parameters of the training.
Advanced Users can
do all of the above. In addition, they will work on more complex tasks, that
include larger amounts of data.
DEEP-HybridDataCloud solution is based on Docker containers packaging already
all the tools needed to deploy and run the Deep Learning models in the most
transparent way. No need to worry about compatibility problems,
everything has already been tested and encapsulated so that the user has a
fully working model in just a few minutes.
To make things even easier, we have
developed an API allowing the user to
interact with the model directly from the web browser. It is possible to
perform inference, train or check the model metadata just with a simple click!
Let’s see how all this work!
In this post we will show how to download and use
one of the available models from the DEEP Open Catalog in our local machine.
These instructions will assume the user is running on linux but the docker
containers can run on any platform.
First we browse the catalog and click on the model
we are interested in among the many that are already in place. Once we click on
the model of our choice we will see something similar to this:
In this case we have selected a module classifying
plant images according to their species using a convolutional neural network
architecture developed in Tensorflow. Under the name of each of the modules in
the DEEP Open Catalog we find some useful links:
to the GitHub repository including the model source code
to the Docker Hub repository of the docker containing all the needed software
configured and ready to use
case this is a pretrained model, a link to the original dataset used for the
Before starting we need to have
either docker or udocker installed in our computer. We will be using udocker
since it allows to run docker container without requiring root privileges. To
install udocker you can just follow this very simple instructions:
git clone https://github.com/indigo-dc/udocker
pip install .
We can now just follow the
instructions on the right part of the module page and type the following
udocker run -p 5000:5000 deephdc/deep-oc-plants-classification-tf
This will download (pull) the
docker container from Docker Hub and run it on our local machine. The run methods includes the option -p 5000:5000 which maps the port 5000 from our local
server into the port 5000 in the container.
We have now the DEEP API running
on our localhost!
You can go to your preferred web
browser and enter localhost:5000 in the address bar. This will open the DEEP as
a Service API endpoint. It looks like this:
As you can see in the image different
methods can be chosen. You can either return the list of loaded models (in this
case we are just running the plant classification example) or the metadata of
your models. You can also do some prediction on some plant image of your
interest or even train the classification neural network on a completely new
dataset. All this directly from your web browser.
Let’s now try out the
prediction method. We can either use a local file or the URL to some online
plant image to perform the classification. For this example we will use a
locally stored image.
We click on Select
File and browse our file system for the image we are interested in. In this
case we will use the image of a rose. If you want to reproduce this example you
can find the image here.
Now that we have selected the image we can click on Execute. The first time we perform a prediction with a given model the process takes a little while since the Tensorflow environment must be initialized. Afterwards, the prediction will be extremely quick (less than one second in many cases).
The prediction for our roses gives us the following output:
The result shows us the 5 most
probable species. The most probable one is the Rosa Chinensis with a
probability of 80%. Our module has predicted correctly! Together with the
prediction we can find a link pointing to Wikipedia to check the species.
The output is given in JSON format that can be very easily
integrated with any other application needing to access the results.
In this example we have seen how to use one of the DEEP-HybridDatacloud modules running a Deep Learning model in just a few simple steps on our local machine.
If you want more detail, you can find the full documentation here.
In next posts we will see how to train a model using the DEEP API and how to run on a cloud system. Stay tunned!