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Last pushed: 2 months ago
Short Description
Image example for SciDB
Full Description

Streaming and Machine Learning Example

The Dockerfile provided here can be used to build a
Docker image which contains all the code necessary to follow the
examples in the
Machine Learning in SciDB
post. The Docker image can be built locally using:

$ docker build --tag rvernica/scidb-examples:stream-machine-learning .

Alternatively, an already built Docker image can be downloaded from
Docker Hub using:

$ docker pull rvernica/scidb-examples:stream-machine-learning

Once the Docker image is available a Docker container can be
started using:

$ docker run --tty --name scidb-example \
      rvernica/scidb-examples:stream-machine-learning

At start-up, the container starts a SciDB cluster with two
instances. The container contains all the code necessary to follow the
examples in the
Machine Learning in SciDB
post. The container does not contain any of the necessary data. The
data has to be downloaded separately from the Kaggle competition
page and copied into
the container:

$ docker exec scidb-example mkdir /kaggle
$ docker cp train.csv scidb-example:/kaggle/train.csv
$ docker cp test.csv scidb-example:/kaggle/test.csv

The code from the
Machine Learning in SciDB
post is available in /usr/local/src/stream-python/py_pkg/examples/4-machine-learning.py and can be run using:

$ docker exec scidb-example python \
      /usr/local/src/stream-python/py_pkg/examples/4-machine-learning.py
UserWarning: 2 type(s) promoted for null support. Precision loss may occur

The output file with predictions is available in the container in
/results.csv while all the intermediate arrays are available in
SciDB. For more details on how to use a Docker container please refer
to the Docker
Get Started tutorial.

Docker Pull Command
Owner
rvernica
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