Create a notebook in Watson Studio Local
To create a notebook in Watson Studio Local, set up a project, create the notebook file, and use the notebook interface to develop your notebook.
- Create the notebook file
- Create the SparkContext
- Set Watson Studio Local Spark resources
- Analyze data in the notebook
Create the notebook file
To create or get a notebook file to add to the project:
- From your project assets view, click the add notebook link.
- In the Create Notebook window, specify the method to use to create your notebook.
- You can create a blank notebook, upload a notebook file from your file system, or upload a notebook file from a URL. The notebook you create or select must be a .ipynb file.
- Specify the rest of the details for your notebook.
- Click Create Notebook.
Alternatively, you can copy a sample notebook from the community page. The sample notebooks are based on real-world scenarios and contain many useful examples of computations and visualizations that you can adapt to your analysis needs. To work with a copy of the sample notebook, click the Open Notebook icon () and specify your project and the Spark service for the notebook.
For information about the notebook interface, see parts of a notebook.
Create the SparkContext
A SparkContext is required if you want to analyze data using Spark. The SparkContext can either point to the Spark running in cluster mode or Spark running in local mode within the user environment.
By default, SparkContext is not set up for R notebooks. Watson Studio Local users can modify one of the following templates to create a SparkContext setup for their R notebooks:
- sparklyr library
- For Python 2.7:
library(sparklyr) library(dplyr) sc <- spark_connect(master = "spark://spark-master-svc:7077")
- For Python 3.5:
library(sparklyr) library(dplyr) sc <- spark_connect(master = "spark://spark-master221-svc:7077")
- SparkR library
- For Python 2.7, use
master="spark://spark-master-svc:7077". For Python 3.5, use
library(SparkR) sc <- sparkR.session(master="spark://spark-master-svc:7077", appName="notebook-R", enableHiveSupport=FALSE, sparkEnvir=list( spark.port.maxRetries="100", spark.dynamicAllocation.enabled="true", spark.shuffle.service.enabled="true", spark.dynamicAllocation.executorIdleTimeout="300", spark.executor.memory="4g", spark.cores.max="2", spark.dynamicAllocation.initialExecutors="1", spark.driver.extraJavaOptions="-Djavax.net.ssl.trustStore=/user-home/_global_/security/customer-truststores/cacerts", spark.executor.extraJavaOptions="-Djavax.net.ssl.trustStore=/user-home/_global_/security/customer-truststores/cacerts" ) )
Set Watson Studio Local Spark resources
Based on the user cases, you might need to change the resources allocated for the Spark application. The default settings of Watson Studio Local Spark are as follows:
|Parameter||Watson Studio Local Defaults||Meaning|
|spark.cores.max||3||The maximum amount of CPU cores to request for the application from across the cluster (not from each machine).|
|spark.dynamicAllocation.initialExecutors||3||Initial number of executors to run.|
|spark.executor.cores||1||The number of cores to use on each executor.|
|spark.executor.memory||4g||Amount of memory to use per executor process.|
By default, Watson Studio Local uses three Spark workers on the compute nodes. If you add more compute nodes, one additional Spark worker will be started on each added compute node.
To change the resources for the Spark application in the notebook:
First, stop the pre-created
sc and then create a new spark context with the
proper resource configuration. Python example:
sc.stop() from pyspark import SparkConf, SparkContext conf = (SparkConf() .set("spark.cores.max", "15") .set("spark.dynamicAllocation.initialExecutors", "3") .set("spark.executor.cores", "5") .set("spark.executor.memory", "6g")) sc=SparkContext(conf=conf)
Then you can verify the new settings by running the following command in a cell using the new
for item in sorted(sc._conf.getAll()): print(item)
Note that the resource settings also apply to running notebooks for scheduling jobs.
See Spark Configuration for more information.
Analyze data in the notebook
Now you're ready for the real work to begin!
Typically, you'll install any necessary libraries, load the data, and then start analyzing it. You and your collaborators can prepare the data, visualize data, make predictions, make prescriptive recommendations, and more.
%autosavemagic command in the cell, for example,
[*]) even though it has actually finished.