R and H2OΒΆ

In order for H2O and R to work together, an instance of H2O must be running, and that instance of H2O must be specified in the R workspace. If the H2O instance is terminated the H2O package in R will no longer work because R will no longer be able to send information to H2O’s distributed analysis, and will no longer be able to get info mation back. Even if a new instance of H2O is started with the exact same IP and port number, users will need to reestablish the connection between H2O and R using the call h2o.init(), and will have to restart their H2O work session.

Updating the R Package aka. Avoid Version Mismatch!

H2O’s R package is now available for download on CRAN but typically the 0xdata team continues to push new releases faster than CRAN typically upload more recent packages. To avoid a version mismatch when upgrading or changing your version of H2O in R please run through the following steps :

  1. Close any Java instances up to kill any rogue H2O instances that hasn’t been properly shut down or terminated.

  2. Uninstall 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") }

  3. For Windows especially check to make sure there are no remanants of H2O in your personal R library.

  4. Download and/or install the H2O package version by following the instructions in our R user documentation.

  5. If you still run into trouble with h2o.init() try running in the terminal:

    $ java -Xmx1g -jar h2o.jar

  6. Go back to R and try running h2o.init() again. If the problem persist please contact us at support@0xdata.com.

How Do I Manage Dependencies in R?

The H2O R package utilizes other R packages (like lattice, and curl). From time to time R will fail to download from CRAN and give an error. In that case it’s best to get the binary from C RAN directly and install the package manually using the call:

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

You can tell if this is happening by looking 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 forked by R will inherit this limitation. To work around this, 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 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 import correctly but names() is not returning the column names.

The version of R you are using is outdated, update to at least R 3.0.

Why are string entries being converted into NAs during Parse?

At the moment columns with numeric values will have the string entries converted to NAs when the data is being ingested:

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 were meant to be 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 error: Column domain is too large to be represented as an enum : 10001>10000?

as.h2o like h2o.uploadFile uses a limited push method where the user initiates a request for information transfer; so it is recommended for bigger data files or files with more than 10000 enumerators in a column to save the file as a csv and import the data frame using h2o.importFile(localH2O, pathToData).