Dask simple tutorial and usage in a kubernetes cluster.

1. for analytics
Nico Liberato Candio

2. WHY DASK
DASK IN ACTION
RESULTS TABLE OF
CONTENTS
01
02
03
04 REFERENCES

3. 03
WHY DASK
01

4. Parallel computing
library that scales
the existing Python
ecosystem : NumPy,
Pandas , Scikit
PARALLELIZATION FLEXIBLE RESPONSIVE
Task scheduling
interface for more
custom workloads
and integration.
Single machine or
distributed
workloads across
GCP clusters
Runs resiliently up
to 1000s cores.
Horizontal and
vertical scaling.

5. 03
DASK IN ACTION
02

6. 6
Example:
1. Dask in GKE cluster
2. Models deployment (Helm)
3. Faster training and
workload parallelization
4. Scale via Kubernetes or
Dask (threads/workers)
Demo: simple tree
summation computation
with Dask installed in our
cluster.

7. Example: parallelization in GKE.

8. 03
CONCLUSIONS
03

9. WHY DASK IS
GOOD FOR US
OPEN SOURCE
Our setup is on GKE
with a single Dask
cluster deployed
with Helm. Open
Source solution
configurable for all
the ML projects.
SCALING
We scale the number
of workers (threads)
dividing the
workload in multiple
units. Horizontal or
vertical scaling via
dashboard.
SPEED
We speed up the
computation
achieving faster
times in training
models

10. Next?
Creation of Dask clusters
by computation context,
saving costs .
Storage and dataframes
parallelization refactoring
the model
implementation.

11. Questions?
nicoliberatoc@gmail.com
+353 089 44 207 64
THANKS!

12. CREDITS
This is where you give credit to the ones who are part of this
project.
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