title: Why is H2O autoencoder so slow for one data set but not the other?
body: <p>When I run H2O autoencoder on two different data sets of about the same size (see below), I can finish one data set (A) within 5 minutes, but the other data set (B) is really slow. It takes >30 minutes to complete only 1% for data set B. I tried restarting R session and H2O a couple of times, but that didn’t help. There are about the same number of parameters (or coefficients) in the model for both data sets. </p>
  
  <p>Data set A: 4 * 1,000,000 in size (&lt;5 minutes)</p>
  
  <p>Data set B: 8 * 477,613 in size (very slow)</p>
  
  <p>The model below is used for both data sets:</p>
  
  <pre><code>model.dl = h2o.deeplearning(x = x, training_frame = data.hex, autoencoder = TRUE, activation = "Tanh", hidden = c(25,25,25), variable_importances = TRUE)
  </code></pre>
  
  <p>The memory of the H2O cluster is 15GB for both data sets. The same computer is used (OS X 10.14.6, 16 GB memory). Below is some information about the versions of H2O and R.</p>
  
  <pre><code>H2O cluster version:        3.30.0.1
  H2O cluster total nodes:    1
  H2O cluster total memory:   15.00 GB
  H2O cluster total cores:    16
  H2O cluster allowed cores:  16
  H2O cluster healthy:        TRUE
  R Version:                  R version 3.6.3 (2020-02-29)
  </code></pre>
  
  <p>Please let me know if there is any other information I can provide to get this issue resolved. </p>