Use Python for H2O

I wrote serveral blogs related to H2O topic as follows:
H2O vs Sparkling Water
Sparking Water Shell: Cloud size under 12 Exception
Access Sparkling Water via R Studio
Running H2O Cluster in Background and at Specific Port Number
Weird Ref-count mismatch Message from H2O

While both H2O Flow UI and R Studio can access H2O cluster started by Sparkling Water Shell, both lack the powerful functionality to manipulate data like Python does. In this blog, I am going to discuss how to use Python for H2O.

There are two main topics related to python for H2O. The first is to start a H2O cluster and another one is to access an existing H2O cluster using python.

1. Start H2O Cluster using pysparkling
I discussed start a H2O cluster using sparkling-shell in blog Sparking Water Shell: Cloud size under 12 Exception. For python, need to use pysparkling.

/opt/sparkling-water-2.2.2/bin/pysparkling \
--master yarn \
--conf spark.ext.h2o.cloud.name=WeidongH2O-Cluster \
--conf spark.ext.h2o.client.port.base=26000 \
--conf spark.executor.instances=6 \
--conf spark.executor.memory=12g \
--conf spark.driver.memory=8g \
--conf spark.yarn.executor.memoryOverhead=4g \
--conf spark.yarn.driver.memoryOverhead=4g \
--conf spark.scheduler.maxRegisteredResourcesWaitingTime=1000000 \
--conf spark.ext.h2o.fail.on.unsupported.spark.param=false \
--conf spark.dynamicAllocation.enabled=false \
--conf spark.sql.autoBroadcastJoinThreshold=-1 \
--conf spark.locality.wait=30000 \
--conf spark.yarn.queue=HighPool \
--conf spark.scheduler.minRegisteredResourcesRatio=1

Then run the following python commands.

from pysparkling import *
from pyspark import SparkContext
from pyspark.sql import SQLContext
import h2o
hc = H2OContext.getOrCreate(sc)

The following is the sample output.

[wzhou@enkbda1node05 sparkling-water-2.2.2]$ /opt/sparkling-water-2.2.2/bin/pysparkling \
> --master yarn \
> --conf spark.ext.h2o.cloud.name=WeidongH2O-Cluster \
> --conf spark.ext.h2o.client.port.base=26000 \
> --conf spark.executor.instances=6 \
> --conf spark.executor.memory=12g \
> --conf spark.driver.memory=8g \
> --conf spark.yarn.executor.memoryOverhead=4g \
> --conf spark.yarn.driver.memoryOverhead=4g \
> --conf spark.scheduler.maxRegisteredResourcesWaitingTime=1000000 \
> --conf spark.ext.h2o.fail.on.unsupported.spark.param=false \
> --conf spark.dynamicAllocation.enabled=false \
> --conf spark.sql.autoBroadcastJoinThreshold=-1 \
> --conf spark.locality.wait=30000 \
> --conf spark.yarn.queue=HighPool \
> --conf spark.scheduler.minRegisteredResourcesRatio=1
Python 2.7.13 |Anaconda 4.4.0 (64-bit)| (default, Dec 20 2016, 23:09:15)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
SLF4J: Class path contains multiple SLF4J bindings.
SLF4J: Found binding in [jar:file:/opt/anaconda2/lib/python2.7/site-packages/pyspark/jars/slf4j-log4j12-1.7.16.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: Found binding in [jar:file:/opt/cloudera/parcels/CDH-5.10.1-1.cdh5.10.1.p0.10/jars/slf4j-log4j12-1.7.5.jar!/org/slf4j/impl/StaticLoggerBinder.class]
SLF4J: See http://www.slf4j.org/codes.html#multiple_bindings for an explanation.
SLF4J: Actual binding is of type [org.slf4j.impl.Log4jLoggerFactory]
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
17/12/10 15:00:20 WARN util.Utils: Service 'SparkUI' could not bind on port 4040. Attempting port 4041.
17/12/10 15:00:21 WARN util.NativeCodeLoader: Unable to load native-hadoop library for your platform... using builtin-java classes where applicable
17/12/10 15:00:21 WARN shortcircuit.DomainSocketFactory: The short-circuit local reads feature cannot be used because libhadoop cannot be loaded.
17/12/10 15:00:24 WARN yarn.Client: Neither spark.yarn.jars nor spark.yarn.archive is set, falling back to uploading libraries under SPARK_HOME.
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.2.0
      /_/

Using Python version 2.7.13 (default, Dec 20 2016 23:09:15)
SparkSession available as 'spark'.
>>> from pysparkling import *
>>> from pyspark import SparkContext
>>> from pyspark.sql import SQLContext
>>> import h2o
>>> hc = H2OContext.getOrCreate(sc)
/opt/sparkling-water-2.2.2/py/build/dist/h2o_pysparkling_2.2-2.2.2.zip/pysparkling/context.py:111: UserWarning: Method H2OContext.getOrCreate with argument of type SparkContext is deprecated and parameter of type SparkSession is preferred.
17/12/10 15:01:45 WARN h2o.H2OContext: Method H2OContext.getOrCreate with an argument of type SparkContext is deprecated and parameter of type SparkSession is preferred.
Connecting to H2O server at http://192.168.10.41:26000... successful.
--------------------------  -------------------------------
H2O cluster uptime:         12 secs
H2O cluster version:        3.14.0.7
H2O cluster version age:    1 month and 21 days
H2O cluster name:           WeidongH2O-Cluster
H2O cluster total nodes:    6
H2O cluster free memory:    63.35 Gb
H2O cluster total cores:    192
H2O cluster allowed cores:  192
H2O cluster status:         accepting new members, healthy
H2O connection url:         http://192.168.10.41:26000
H2O connection proxy:
H2O internal security:      False
H2O API Extensions:         Algos, AutoML, Core V3, Core V4
Python version:             2.7.13 final
--------------------------  -------------------------------

Sparkling Water Context:
 * H2O name: WeidongH2O-Cluster
 * cluster size: 6
 * list of used nodes:
  (executorId, host, port)
  ------------------------
  (3,enkbda1node12.enkitec.com,26000)
  (1,enkbda1node13.enkitec.com,26000)
  (6,enkbda1node10.enkitec.com,26000)
  (5,enkbda1node09.enkitec.com,26000)
  (4,enkbda1node08.enkitec.com,26000)
  (2,enkbda1node11.enkitec.com,26000)
  ------------------------

  Open H2O Flow in browser: http://192.168.10.41:26000 (CMD + click in Mac OSX)


>>>
>>> h2o
<module 'h2o' from '/opt/sparkling-water-2.2.2/py/build/dist/h2o_pysparkling_2.2-2.2.2.zip/h2o/__init__.py'>
>>> h2o.cluster_status()
[WARNING] in <stdin> line 1:
    >>> ????
        ^^^^ Deprecated, use ``h2o.cluster().show_status(True)``.
--------------------------  -------------------------------
H2O cluster uptime:         14 secs
H2O cluster version:        3.14.0.7
H2O cluster version age:    1 month and 21 days
H2O cluster name:           WeidongH2O-Cluster
H2O cluster total nodes:    6
H2O cluster free memory:    63.35 Gb
H2O cluster total cores:    192
H2O cluster allowed cores:  192
H2O cluster status:         locked, healthy
H2O connection url:         http://192.168.10.41:26000
H2O connection proxy:
H2O internal security:      False
H2O API Extensions:         Algos, AutoML, Core V3, Core V4
Python version:             2.7.13 final
--------------------------  -------------------------------
Nodes info:     Node 1                                         Node 2                                         Node 3                                         Node 4                                         Node 5                                         Node 6
--------------  ---------------------------------------------  ---------------------------------------------  ---------------------------------------------  ---------------------------------------------  ---------------------------------------------  ---------------------------------------------
h2o             enkbda1node08.enkitec.com/192.168.10.44:26000  enkbda1node09.enkitec.com/192.168.10.45:26000  enkbda1node10.enkitec.com/192.168.10.46:26000  enkbda1node11.enkitec.com/192.168.10.47:26000  enkbda1node12.enkitec.com/192.168.10.48:26000  enkbda1node13.enkitec.com/192.168.10.49:26000
healthy         True                                           True                                           True                                           True                                           True                                           True
last_ping       1513108921427                                  1513108920821                                  1513108920602                                  1513108920876                                  1513108920718                                  1513108921149
num_cpus        32                                             32                                             32                                             32                                             32                                             32
sys_load        1.24                                           1.38                                           0.42                                           1.08                                           0.75                                           0.83
mem_value_size  0                                              0                                              0                                              0                                              0                                              0
free_mem        11339923456                                    11337239552                                    11339133952                                    11332368384                                    11331912704                                    11338491904
pojo_mem        113671168                                      116355072                                      114460672                                      121226240                                      121681920                                      115102720
swap_mem        0                                              0                                              0                                              0                                              0                                              0
free_disk       422764871680                                   389836439552                                   422211223552                                   418693251072                                   422237437952                                   422187106304
max_disk        491885953024                                   491885953024                                   491885953024                                   491885953024                                   491885953024                                   491885953024
pid             1172                                           801                                            15879                                          17866                                          28980                                          30818
num_keys        0                                              0                                              0                                              0                                              0                                              0
tcps_active     0                                              0                                              0                                              0                                              0                                              0
open_fds        441                                            441                                            441                                            441                                            441                                            441
rpcs_active     0                                              0                                              0                                              0                                              0                                              0

2. Accessing Existing H2O Cluster from a Edge Node
To access an existing H2O cluster from a edge node also requires to use pysparkling command. But you don’t have to specify a lot of parameters just like above step to start a H2O cluster. Running without any parameters is fine for the client purpose.

First start the pysparkling by running /opt/sparkling-water-2.2.2/bin/pysparkling command.

Once in the python prompt, run the following to connect to an existing cluster. The key is run h2o.init(ip=”enkbda1node05.enkitec.com”, port=26000). This command will connect to the existing H2O cluster.

from pysparkling import *
from pyspark import SparkContext
from pyspark.sql import SQLContext
import h2o

h2o.init(ip="enkbda1node05.enkitec.com", port=26000)
h2o.cluster_status()

Run some testing code there.

print("Importing hdfs data")
stock_data = h2o.import_file("hdfs://ENKBDA1-ns/user/wzhou/work/test/stock_price.txt")
stock_data

print("Spliting data")
train,test = stock_data.split_frame(ratios=[0.9])

h2o.ls()

The following is the sample output.

[wzhou@ham-lnx-vs-0086 ~]$ /opt/sparkling-water-2.2.2/bin/pysparkling
Python 2.7.13 |Anaconda 4.4.0 (64-bit)| (default, Dec 20 2016, 23:09:15)
[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)] on linux2
Type "help", "copyright", "credits" or "license" for more information.
Anaconda is brought to you by Continuum Analytics.
Please check out: http://continuum.io/thanks and https://anaconda.org
Setting default log level to "WARN".
To adjust logging level use sc.setLogLevel(newLevel). For SparkR, use setLogLevel(newLevel).
Welcome to
      ____              __
     / __/__  ___ _____/ /__
    _\ \/ _ \/ _ `/ __/  '_/
   /__ / .__/\_,_/_/ /_/\_\   version 2.2.0.cloudera1
      /_/

Using Python version 2.7.13 (default, Dec 20 2016 23:09:15)
SparkSession available as 'spark'.
>>> from pysparkling import *
>>> from pyspark import SparkContext
>>> from pyspark.sql import SQLContext
>>> import h2o
>>>
>>> h2o.init(ip="enkbda1node05.enkitec.com", port=26000)
Warning: connecting to remote server but falling back to local... Did you mean to use `h2o.connect()`?
Checking whether there is an H2O instance running at http://enkbda1node05.enkitec.com:26000. connected.
--------------------------  ---------------------------------------
H2O cluster uptime:         5 mins 43 secs
H2O cluster version:        3.14.0.7
H2O cluster version age:    1 month and 21 days
H2O cluster name:           WeidongH2O-Cluster
H2O cluster total nodes:    6
H2O cluster free memory:    58.10 Gb
H2O cluster total cores:    192
H2O cluster allowed cores:  192
H2O cluster status:         locked, healthy
H2O connection url:         http://enkbda1node05.enkitec.com:26000
H2O connection proxy:
H2O internal security:      False
H2O API Extensions:         Algos, AutoML, Core V3, Core V4
Python version:             2.7.13 final
--------------------------  ---------------------------------------
>>> h2o.cluster_status()
[WARNING] in <stdin> line 1:
    >>> ????
        ^^^^ Deprecated, use ``h2o.cluster().show_status(True)``.
--------------------------  ---------------------------------------
H2O cluster uptime:         5 mins 43 secs
H2O cluster version:        3.14.0.7
H2O cluster version age:    1 month and 21 days
H2O cluster name:           WeidongH2O-Cluster
H2O cluster total nodes:    6
H2O cluster free memory:    58.10 Gb
H2O cluster total cores:    192
H2O cluster allowed cores:  192
H2O cluster status:         locked, healthy
H2O connection url:         http://enkbda1node05.enkitec.com:26000
H2O connection proxy:
H2O internal security:      False
H2O API Extensions:         Algos, AutoML, Core V3, Core V4
Python version:             2.7.13 final
--------------------------  ---------------------------------------
Nodes info:     Node 1                                         Node 2                                         Node 3                                         Node 4                                         Node 5                                         Node 6
--------------  ---------------------------------------------  ---------------------------------------------  ---------------------------------------------  ---------------------------------------------  ---------------------------------------------  ---------------------------------------------
h2o             enkbda1node08.enkitec.com/192.168.10.44:26000  enkbda1node09.enkitec.com/192.168.10.45:26000  enkbda1node10.enkitec.com/192.168.10.46:26000  enkbda1node11.enkitec.com/192.168.10.47:26000  enkbda1node12.enkitec.com/192.168.10.48:26000  enkbda1node13.enkitec.com/192.168.10.49:26000
healthy         True                                           True                                           True                                           True                                           True                                           True
last_ping       1513109250525                                  1513109250725                                  1513109250400                                  1513109250218                                  1513109250709                                  1513109250536
num_cpus        32                                             32                                             32                                             32                                             32                                             32
sys_load        1.55                                           1.94                                           2.4                                            1.73                                           0.4                                            1.51
mem_value_size  0                                              0                                              0                                              0                                              0                                              0
free_mem        11339923456                                    10122031104                                    10076566528                                    10283636736                                    10280018944                                    10278896640
pojo_mem        113671168                                      1331563520                                     1377028096                                     1169957888                                     1173575680                                     1174697984
swap_mem        0                                              0                                              0                                              0                                              0                                              0
free_disk       422754385920                                   389839585280                                   422231146496                                   418692202496                                   422226952192                                   422176620544
max_disk        491885953024                                   491885953024                                   491885953024                                   491885953024                                   491885953024                                   491885953024
pid             1172                                           801                                            15879                                          17866                                          28980                                          30818
num_keys        0                                              0                                              0                                              0                                              0                                              0
tcps_active     0                                              0                                              0                                              0                                              0                                              0
open_fds        440                                            440                                            440                                            440                                            440                                            440
rpcs_active     0                                              0                                              0                                              0                                              0                                              0
>>> print("Importing hdfs data")
Importing hdfs data
>>> stock_data = h2o.import_file("hdfs://ENKBDA1-ns/user/wzhou/work/test/stock_price.txt")
Parse progress: | 100%

>>>
>>> stock_data
date                   close    volume    open    high     low
-------------------  -------  --------  ------  ------  ------
2016-09-23 00:00:00    24.05     56837   24.13  24.22   23.88
2016-09-22 00:00:00    24.1      56675   23.49  24.18   23.49
2016-09-21 00:00:00    23.38     70925   23.21  23.58   23.025
2016-09-20 00:00:00    23.07     35429   23.17  23.264  22.98
2016-09-19 00:00:00    23.12     34257   23.22  23.27   22.96
2016-09-16 00:00:00    23.16     83309   22.96  23.21   22.96
2016-09-15 00:00:00    23.01     43258   22.7   23.25   22.53
2016-09-14 00:00:00    22.69     33891   22.81  22.88   22.66
2016-09-13 00:00:00    22.81     59871   22.75  22.89   22.53
2016-09-12 00:00:00    22.85    109145   22.9   22.95   22.74

[65 rows x 6 columns]

>>> print("Spliting data")
Spliting data
>>> train,test = stock_data.split_frame(ratios=[0.9])
>>> h2o.ls()
                      key
0  py_1_sid_83d3_splitter
1         stock_price.hex

Let’s go to R Studio. We should see this H2O frame.

Let’s check it out from H2O Flow UI.

Cool, it’s there as well and we’re good here.

Advertisements

One thought on “Use Python for H2O

  1. Pingback: Parquet File Can not Be Read in Sparkling Water H2O | My Big Data World

Leave a Reply

Please log in using one of these methods to post your comment:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out /  Change )

Google photo

You are commenting using your Google account. Log Out /  Change )

Twitter picture

You are commenting using your Twitter account. Log Out /  Change )

Facebook photo

You are commenting using your Facebook account. Log Out /  Change )

Connecting to %s