Installing Cloudera Data Science Workbench 1.5.x Using Packages

Before you begin installing Cloudera Data Science Workbench, make sure you have completed the steps to configure your hosts and block devices.

Use the following steps to install Cloudera Data Science Workbench on the master host. Note that airgapped clusters and non-airgapped clusters use different files for installation.

1. Download the Cloudera Data Science Workbench repo file and/or RPM.

   Red Hat 7 Repository File	Link to RPM
   For non-airgapped installations, download this file and save it to /etc/yum.repos.d/: cloudera-cdsw.repo	For airgapped installations, download the Cloudera Data Science Workbench RPM file: Cloudera Data Science Workbench 1.5.0

2. Skip this step for airgapped installations. Add the Cloudera Public GPG repository key. This key verifies that you are downloading genuine packages.

   sudo rpm --import https://archive.cloudera.com/p/cdsw1/1.5.0/redhat7/yum/RPM-GPG-KEY-cloudera

3. Non-airgapped Installation - Install the latest RPM with the following command:

   sudo yum install cloudera-data-science-workbench

   Airgapped Installation - Copy the RPM downloaded in the previous step to the appropriate gateway host. Then, use the complete filename to install the package. For example:

   sudo yum install cloudera-data-science-workbench-1.5.0.12345.rpm

   For guidance on any warnings displayed during the installation process, see Understanding Installation Warnings.
4. Edit the configuration file at /etc/cdsw/config/cdsw.conf. The following table lists the configuration properties that can be configured in cdsw.conf.

   cdsw.conf Properties

   Properties	Description
   Required Configuration
   DOMAIN	Wildcard DNS domain configured to point to the master host. If the wildcard DNS entries are configured as cdsw.<company>.com and *.cdsw.<company>.com, then DOMAIN should be set to cdsw.<company>.com. Users' browsers should then contact the Cloudera Data Science Workbench web application at http://cdsw.<company>.com. This domain for DNS and is unrelated to Kerberos or LDAP domains.
   MASTER_IP	IPv4 address for the master host that is reachable from the worker hosts. Within an AWS VPC, MASTER_IP should be set to the internal IP address of the master host; for instance, if your hostname is ip-10-251-50-12.ec2.internal, set MASTER_IP to the corresponding IP address, 10.251.50.12.
   DISTRO	The Hadoop distribution installed on the cluster. Set this property to CDH.
   DOCKER_BLOCK_DEVICES	Block device(s) for Docker images (space separated if there are multiple). Use the full path to specify the image(s), for instance, /dev/xvde.
   JAVA_HOME	Path where Java is installed on the Cloudera Data Science Workbench hosts. The value for JAVA_HOME depends on whether you are using JDK or JRE. For example, if you're using JDK 1.8_162, set JAVA_HOME to /usr/java/jdk1.8.0_162. If you are only using JRE, set it to /usr/java/jdk1.8.0_162/jre. Note that Spark 2.2 (and higher) requires JDK 1.8. For more details on the specific versions of Oracle JDK recommended for CDH and Cloudera Manager clusters, see the Cloudera Product Compatibility Matrix - Supported JDK Versions.
   Optional Configuration
   APPLICATION_BLOCK_DEVICE	(Master Host Only) Configure a block device for application state. If this property is left blank, the filesystem mounted at /var/lib/cdsw on the master host will be used to store all user data. For production deployments, Cloudera strongly recommends you use this option with a dedicated SSD block device for the /var/lib/cdsw mount. (Not recommended) If set, Cloudera Data Science Workbench will format the provided block device as ext4, mount it to /var/lib/cdsw, and store all user data on it. This option has only been provided for proof-of-concept setups, and Cloudera is not responsible for any data loss. Use the full path to specify the mount point, for instance, /dev/xvdf.
   RESERVE_MASTER	Set this property to true to reserve the master host for Cloudera Data Science Workbench's internal components and services such as Livelog, the PostgreSQL database, and so on. User workloads will now run exclusively on worker hosts, while the master is reserved for internal application services. Note that this property is not yet available as a configuration property in Cloudera Manager. However, you can use an Advanced Configuration Snippet (Safety Valve) to configure this as described here: Reserving the Master Host for Internal CDSW Components.
   DISTRO_DIR	Path where the Hadoop distribution is installed on the Cloudera Data Science Workbench hosts. For CDH clusters, the default location of the parcel directory is /opt/cloudera/parcels. Specify this property only if you are using a non-default location.
   ANACONDA_DIR	Path where the Anaconda package is installed. On CDH clusters, Anaconda is installed as a parcel in Cloudera Manager. Therefore, this parameter does not apply and must be left blank.
   TLS_ENABLE	Enable and enforce HTTPS (TLS/SSL) for web access. Set to true to enable and enforce HTTPS access to the web application. You can also set this property to true to enable external TLS termination. For more details on TLS termination, see Enabling TLS/SSL for Cloudera Data Science Workbench.
   TLS_CERT TLS_KEY	Certificate and private key for internal TLS termination. Setting TLS_CERT and TLS_KEY will enable internal TLS termination. You must also set TLS_ENABLE to true above to enable and enforce internal termination. Set these only if you are not terminating TLS externally. Make sure you specify the full path to the certificate and key files, which must be in PEM format. For details on certificate requirements and enabling TLS termination, see Enabling TLS/SSL for Cloudera Data Science Workbench.
   HTTP_PROXY HTTPS_PROXY	If your deployment is behind an HTTP or HTTPS proxy, set the respective HTTP_PROXY or HTTPS_PROXY property in /etc/cdsw/config/cdsw.conf to the hostname of the proxy you are using. HTTP_PROXY="<http://proxy_host>:<proxy-port>" HTTPS_PROXY="<http://proxy_host>:<proxy_port>" If you are using an intermediate proxy such as Cntlm to handle NTLM authentication, add the Cntlm proxy address to the HTTP_PROXY or HTTPS_PROXY fields in cdsw.conf. HTTP_PROXY="http://localhost:3128" HTTPS_PROXY="http://localhost:3128" If the proxy server uses TLS encryption to handle connection requests, you will need to add the proxy's root CA certificate to your host's store of trusted certificates. This is because proxy servers typically sign their server certificate with their own root certificate. Therefore, any connection attempts will fail until the Cloudera Data Science Workbench host trusts the proxy's root CA certificate. If you do not have access to your proxy's root certificate, contact your Network / IT administrator. To enable trust, copy the proxy's root certificate to the trusted CA certificate store (ca-trust) on the Cloudera Data Science Workbench host. cp /tmp/<proxy-root-certificate>.crt /etc/pki/ca-trust/source/anchors/ Use the following command to rebuild the trusted certificate store. update-ca-trust extract
   ALL_PROXY	If a SOCKS proxy is in use, set to socks5://<host>:<port>/.
   NO_PROXY	Comma-separated list of hostnames that should be skipped from the proxy. Starting with version 1.4, if you have defined a proxy in the HTTP_PROXY(S) or ALL_PROXY properties, Cloudera Data Science Workbench automatically appends the following list of IP addresses to the NO_PROXY configuration. Note that this is the minimum required configuration for this field. This list includes 127.0.0.1, localhost, and any private Docker registries and HTTP services inside the firewall that Cloudera Data Science Workbench users might want to access from the engines. "127.0.0.1,localhost,100.66.0.1,100.66.0.2,100.66.0.3, 100.66.0.4,100.66.0.5,100.66.0.6,100.66.0.7,100.66.0.8,100.66.0.9, 100.66.0.10,100.66.0.11,100.66.0.12,100.66.0.13,100.66.0.14, 100.66.0.15,100.66.0.16,100.66.0.17,100.66.0.18,100.66.0.19, 100.66.0.20,100.66.0.21,100.66.0.22,100.66.0.23,100.66.0.24, 100.66.0.25,100.66.0.26,100.66.0.27,100.66.0.28,100.66.0.29, 100.66.0.30,100.66.0.31,100.66.0.32,100.66.0.33,100.66.0.34, 100.66.0.35,100.66.0.36,100.66.0.37,100.66.0.38,100.66.0.39, 100.66.0.40,100.66.0.41,100.66.0.42,100.66.0.43,100.66.0.44, 100.66.0.45,100.66.0.46,100.66.0.47,100.66.0.48,100.66.0.49, 100.66.0.50,100.77.0.10,100.77.0.128,100.77.0.129,100.77.0.130, 100.77.0.131,100.77.0.132,100.77.0.133,100.77.0.134,100.77.0.135, 100.77.0.136,100.77.0.137,100.77.0.138,100.77.0.139"
   NVIDIA_GPU_ENABLE	Set this property to true to enable GPU support for Cloudera Data Science Workbench workloads. When this property is enabled on a host is equipped with GPU hardware, the GPU(s) will be available for use by Cloudera Data Science Workbench hosts. If this property is set to true on a host that does not have GPU support, there will be no effect. By default, this property is set to false. For detailed instructions on how to enable GPU-based workloads on Cloudera Data Science Workbench, see Using NVIDIA GPUs for Cloudera Data Science Workbench Projects.
   NVIDIA_LIBRARY_PATH	Complete path to the NVIDIA driver libraries.

5. Initialize and start Cloudera Data Science Workbench.

   cdsw init

   The application will take a few minutes to bootstrap. You can watch the status of application installation and startup with watch cdsw status.

Cloudera Data Science Workbench supports adding and removing additional worker hosts at any time. Worker hosts allow you to transparently scale the number of concurrent workloads users can run.

Installation typically takes 30 minutes, although it might take an additional 60 minutes for the R, Python, and Scala engine to be available on all hosts.

The first account that you create becomes the site administrator. You may now use this account to create a new project and start using the workbench to run data science workloads. For a brief example, see Getting Started with the Cloudera Data Science Workbench.

As a site administrator, you can invite new users, monitor resource utilization, secure the deployment, and upload a license key for the product. For more details on these tasks, see the Administration and Security guides.

You can also start using the product by configuring your personal account and creating a new project. For a quickstart that walks you through creating a simple template project, see Getting Started with Cloudera Data Science Workbench. For more details on collaborating with teams, working on projects, and sharing results, see the Managing Cloudera Data Science Workbench Users.