personally I don't see a good use case for such docker images. We use
docker for the test setup including installed R packages, but other than
that try to keep the setup as lean as possible (which is important given
the growing number of APIs and deployment environments).
What is "the burden of individual component installation" - isn't it a
git clone and mvn package?
Regards,
Matthias
On 5/10/2021 4:30 AM, Janardhan wrote:
Hi all,
We already have docker support for testing, the same files
can be utilized for a public image (with CPU, GPU and Jupyter).
Use cases:
1. Invoke dml or python scripts right inside container
2. Prototype algorithm/pipelines without the burden of individual
component installation.
3. Working with GPU (only Linux supported)
Anyone would want to volunteer for the implementation of the same,
the intended docker support is documented at [1].
How to implement:
1. Build image tags for release channels (latest, v1.2.0, dev).
2. For GPU follow the instructions in [1]. Can be implemented later.
[1] https://github.com/apache/systemds/pull/1271
Thank you,
Janardhan
