R FAQΒΆ

In order for H2O and R to work together, an H2O instance that is specified in the R workspace must be running. If the H2O instance is terminated, the H2O package in R will no longer work because R cannot send or receive information to or from H2O’s distributed analysis. Even if a new instance of H2O with the exact same IP and port number is started, users must re-establish the connection between H2O and R using the command h2o.init() and restart their H2O work session.

Updating the R Package to Avoid a Version Mismatch

To avoid a version mismatch when upgrading or changing your version of H2O in R, perform the following steps :

  1. Close all open Java instances to ensure all H2O instances have been properly shut down or terminated.

  2. Uninstall the previous version of H2O from R by running:

    if ("package:h2o" %in% search()) { detach("package:h2o", unload=TRUE) }
if ("h2o" %in% rownames(installed.packages())) { remove.packages("h2o") }

  1. For Windows, verify there are no H2O remnants in your personal R library.

  2. Download and/or install the H2O package version by following the instructions in Install H2O package in R.

  3. If you still run into trouble with h2o.init(), try running the following command in the terminal:

    $ java -Xmx1g -jar h2o.jar

  4. Try running h2o.init() in R again. If the problem persists, please contact us at support@h2o.ai.

How Do I Manage Dependencies in R?

The H2O R package utilizes other R packages (like lattice and curl). Get the binary from CRAN directly and install the package manually using the following command:

>install.packages("path/to/fpc/binary/file", repos = NULL, type = "binary")

Users may find this page on installing dependencies helpful:

http://stat.ethz.ch/R-manual/R-devel/library/utils/html/install.packages.html

Why is only one CPU being used when I start H2O from R?

Depending on your R installation, it may be configured to run with only one CPU by default. This is particularly common for Linux installations and can affect H2O when you use the h2o.init() function to start H2O from R.

To confirm this is the issue, look in /proc/<nnnnn>/status at the Cpus_allowed bitmask (where nnnnn is the PID of R).

(/proc/<nnnnn>/status: This configuration is BAD!)
Cpus_allowed:   00000001
Cpus_allowed_list:      0

If you see a bitmask with only one CPU allowed, then any H2O process called by R will inherit this limitation. As a workaround, set the following environment variable before starting R:

$ export OPENBLAS_MAIN_FREE=1
$ R

Now you should see something like the following in /proc/<nnnnn>/status

(/proc/<nnnnn>/status: This configuration is good.)
Cpus_allowed:   ffffffff
Cpus_allowed_list:      0-31

At this point, the h2o.init() function will start an H2O instance that can use more than one CPU.

Internal Server Error in R

brew install gnu-tar
cd /usr/bin
sudo ln -s /usr/local/opt/gnu-tar/libexec/gnubin/tar gnutar

The data imports correctly but names() is not returning the column names.

Your version of R is outdated - update to at least R 3.0.

Why are string entries being converted into NAs during Parse?

Currently, columns with numeric values will have the string entries converted to NAs when during data ingestion:

Data Frame in R              Data Frame in H2O
     V1  V2  V3  V4               V1  V2  V3  V4
1     1   6  11   A          1     1   6  11  NA
2     2   B   A   A          2     2  NA  NA  NA
3     3   A  13  18          3     3  NA  13  18
4     4   C  14  19          4     4  NA  14  19
5     5  10  15  20          5     5  10  15  20

If the numeric values in the column are intended as additional factor levels, then you can concatenate the values with a string and the column will parse as a enumerator column:

     V1  V2  V3  V4
1     1  i6 i11   A
2     2   B   A   A
3     3   A i13 i18
4     4   C i14 i19
5     5 i10 i15 i20

Why does as.h2o(localH2O, data) generate the following error: Column domain is too large to be represented as an enum : 10001>10000?

Like h2o.uploadFile, as.h2o uses a limited push method, where the user initiates a request for information transfer. For bigger data files or files with more than 10000 enumerators in a column, we recommend saving the file as a .csv and import the data frame using h2o.importFile(localH2O, pathToData).