Provisioning environments in Titan

Titan Tutorial #5: Defining deployments straight from a Jupyter Notebook

One of our most important objectives at Akoios is to make daily life easier for the Data Scientists.

```yaml
titan: v1
service:
image: scipy
machine:
cpu: 2
memory: 1024MB
command:
- pip install requirements.txt
```
  • We want the model to run in a scipy runtime.
  • Regarding hardware, we are requesting 2 cores and 1024MB of RAM memory.
  • Finally, we provide (if needed) a list of required dependencies for our model in the requirements.txt file.
  • cpu: Preferred number of cores.
  • memory: Desired memory in MB (NNNNMB). E.g. 1024MB, 2048MB…
  • command: Any arbitrary command.
$ titan deploy 

Wrap-up

In this post we have seen how Titan makes hardware and runtime provisioning as easy as it can get. Moreover, it is important to remark that arbitrary hardware limits and policies can be defined in every Titan installation to fit the needs of every company.

Next Tutorial

Don’t miss our next tutorial, where we see how to manage service versioning and deployments using Titan.

Foreword

Titan can help you to radically reduce and simplify the effort required to put AI/ML models into production, enabling Data Science teams to be agile, more productive and closer to the business impact of their developments.

Akoios: Frictionless solutions for modern data science.

Akoios