I think that the docker images sound like a good idea, especially because of setup difficulties, i can imagine that simply using an
image with preinstalled BLAS or MKL and GPU drivers would be much cleaner for beginner setup and execution than having people find and install all of these. That said someone have to setup these images, and preferably automate the building like done in Apache Airflow. Also, are you sure that the GPU would not be able to pass through to a docker instance on a Windows machine? https://www.tensorflow.org/install/docker?hl=nb ________________________________ From: Matthias Boehm <mboe...@gmail.com> Sent: Monday, May 10, 2021 11:49:14 AM To: dev@systemds.apache.org Subject: Re: [DISCUSS][SYSTEMDS-2974] Using SystemDS with Docker 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 >
