Re: does "Deep Learning Pipelines" scale out linearly?

Hello Andy, regarding your question, this will depend a lot on the specific task:  - for tasks that are "easy" to distribute such as inference (scoring), hyper-parameter tuning or cross-validation, these tasks will take full advantage of the cluster and the performance should improve more or less

Re: does "Deep Learning Pipelines" scale out linearly?

For that package specifically it’s best to see if they have a mailing list and if not perhaps ask on github issues. Having said that perhaps the folks involved in that package will reply here too. On Wed, 22 Nov 2017 at 20:03, Andy Davidson wrote: > I am starting

does "Deep Learning Pipelines" scale out linearly?

I am starting a new deep learning project currently we do all of our work on a single machine using a combination of Keras and Tensor flow. https://databricks.github.io/spark-deep-learning/site/index.html looks very promising. Any idea how performance is likely to improve as I add machines to my