Installing and Upgrading Cloudera Data Science Workbench 1.2.x Using Cloudera Manager
This topic describes how to install and upgrade Cloudera Data Science Workbench using Cloudera Manager.
Installing Cloudera Data Science Workbench 1.2.x Using Cloudera Manager
Use the following steps to install Cloudera Data Science Workbench using Cloudera Manager.
- Install Cloudera Distribution of Apache Spark 2
- Configure JAVA_HOME (Required for Spark 2.2)
- Download and Install the Cloudera Data Science Workbench CSD
- Install the Cloudera Data Science Workbench Parcel
- Add the Cloudera Data Science Workbench Service
- Create the Administrator Account
- Next Steps
Before you begin installing Cloudera Data Science Workbench, make sure you have completed the steps to secure your hosts, set up DNS subdomains, and configure block devices.
Install Cloudera Distribution of Apache Spark 2
If you have not already done so, install and configure the Cloudera Distribution of Apache Spark 2 parcel and CSD. For instructions, see Installing Cloudera Distribution of Apache Spark 2.
hdfs dfs -mkdir /user/<username> hdfs dfs -chown <username>:<username> /user/<username>
Configure JAVA_HOME (Required for Spark 2.2)
Spark 2.2 requires JDK 1.8. On CSD-based deployments, Cloudera Manager automatically detects the path and version of Java installed on Cloudera Data Science Workbench gateway hosts. However, if a host has both JDK 1.7 and JDK 1.8 installed, Cloudera Manager might choose to use JDK 1.7 over JDK 1.8. If you are using Spark 2.2, this will create a problem during the first run of the service because Spark 2.2 will not work with JDK 1.7. To work around this, configure Cloudera Manager to use JDK 1.8 on Cloudera Data Science Workbench gateway hosts. For instructions, see Configuring a Custom Java Home Location in Cloudera Manager.
To upgrade your entire CDH cluster to JDK 1.8, see Upgrading to Oracle JDK 1.8.
Download and Install the Cloudera Data Science Workbench CSD
- Download the Cloudera Data Science Workbench CSD.
Version Link to CSD Cloudera Data Science Workbench 1.2.2 CLOUDERA_DATA_SCIENCE_WORKBENCH-1.2.2.jar
- Log on to the Cloudera Manager Server host, and place the CSD file under /opt/cloudera/csd, which is the default location for CSD files. To configure a custom location for CSD files, refer the Cloudera Manager documentation at Configuring the Location of Custom Service Descriptor Files.
- Set the file ownership to cloudera-scm:cloudera-scm with permission 644.
- Restart the Cloudera Manager Server:
service cloudera-scm-server restart
- Log into the Cloudera Manager Admin Console and restart the Cloudera Management Service.
- Select .
- Select .
Install the Cloudera Data Science Workbench Parcel
- Log into the Cloudera Manager Admin Console.
- Click in the main navigation bar.
- Add the Cloudera Data Science Workbench parcel repository URL to Cloudera Manager. In this case, the location of the repository has already been included in the Cloudera Data Science
Workbench CSD. Therefore, the parcel should already be present and ready for downloading. If the parcel is already available, skip the rest of this step. If for some reason the parcel is not
available, perform the followings steps to add the remote parcel repository URL to Cloudera Manager.
- On the Parcels page, click Configuration.
- In the Remote Parcel Repository URLs list, click the addition symbol to open an additional row.
- Enter the path to the repository. Cloudera Data Science Workbench publishes placeholder parcels for other
operating systems as well. However, note that these do not work and have only been included to support mixed-OS clusters.
Version Remote Parcel Repository URL Cloudera Data Science Workbench 1.2.2 https://archive.cloudera.com/cdsw/1/parcels/1.2.2/
- Click Save Changes.
- Go to the page. The external parcel should now appear in the set of parcels available for download.
- Download the Cloudera Data Science Workbench parcel, distribute the parcel to the hosts in your cluster, and then activate the parcel. For more detailed information on each of these tasks, see Managing Parcels.
Add the Cloudera Data Science Workbench Service
To add the Cloudera Data Science Workbench service to your cluster:
- Log into the Cloudera Manager Admin Console.
- On the tab, click to the right of the cluster name and select Add a Service to launch the wizard. A list of services will be displayed.
- Select the Cloudera Data Science Workbench service and click Continue.
- Assign the Master and Worker roles to the gateway hosts. You must assign the Cloudera Data Science Workbench Master role to one gateway host , and optionally, assign the Worker role to
one or more gateway hosts.
There are two more role groups that fall under the Cloudera Data Science Workbench service: the Docker Daemon role, and the Application role.
The Docker Daemon role must be assigned to every Cloudera Data Science Workbench gateway host. On First Run, Cloudera Manager will assign this role to each Cloudera Data Science Workbench gateway host. However, if any more hosts are added or reassigned to Cloudera Data Science Workbench, you must explicitly assign the Docker Daemon role to them.
- On First Run, Cloudera Manager will assign the Application role to the host running the Cloudera Data Science Workbench Master role. The Application role is always assigned to the same host as the Master. Consequently, this role must never be assigned to a Worker host.
- Configure the following parameters and click Continue.
Wildcard DNS Domain
Wildcard DNS domain configured to point to the master node.
If the previously configured wildcard DNS entries are cdsw.<company>.com and *.cdsw.<company>.com, then this parameter should be set to cdsw.<company>.com. Users' browsers will then be able to contact the Cloudera Data Science Workbench web application at http://cdsw.<company>.com.
This domain for DNS only, and is unrelated to Kerberos or LDAP domains.
Master Node IPv4 Address
IPv4 address for the master node that is reachable from the worker nodes. By default, this field is left blank and Cloudera Manager uses the IPv4 address of the Master node.
Within an AWS VPC, set this parameter to the internal IP address of the master node; 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.
Install Required PackagesWhen this parameter is enabled, the Prepare Node command will install all the required package dependencies on First Run. If you choose to disable this property, you must manually install the following packages on all Cloudera Data Science Workbench gateway hosts.
nfs-utils libseccomp lvm2 bridge-utils libtool-ltdl iptables rsync policycoreutils-python selinux-policy-base selinux-policy-targeted ntp ebtables bind-utils nmap-ncat openssl e2fsprogs redhat-lsb-core
Docker Block Device
Block device(s) for Docker images. Use the full path to specify the image(s), for instance, /dev/xvde.
The Cloudera Data Science Workbench installer will format and mount Docker on each gateway host that is assigned the Docker Daemon role. Do not mount these block devices prior to installation.
- The wizard will now begin a First Run of the Cloudera Data Science Workbench service. This includes deploying client configuration for HDFS, YARN and Spark 2, installing the package dependencies on all hosts, and formatting the Docker block device. The wizard will also assign the Application role to the host running Master, and the Docker Daemon role to all the Cloudera Data Science Workbench gateway hosts.
- Once the First Run command has completed successfully, click Finish to go back to the Cloudera Manager home page.
Create the Administrator Account
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 and running 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 Cloudera Data Science Workbench User Guide.
Upgrading to the Latest Version of Cloudera Data Science Workbench 1.2.x Using Cloudera Manager
(Strongly Recommended) Safely stop Cloudera Data Science Workbench. To avoid running into the data loss issue described in TSB-346, run the cdsw_protect_stop_restart.sh script on the master node and follow the sequence of steps as instructed by the script.The script will first back up your project files to the specified target folder. It will then temporarily move your project files aside to protect against the data loss condition. At that point, it is safe to stop Cloudera Data Science Workbench. To stop Cloudera Data Science Workbench, run the following command on all Cloudera Data science Workbench nodes (master and workers):
After Cloudera Data Science Workbench has stopped, press enter to continue running the script as instructed. It will then move your project files back into place.
- (Strongly Recommended) On the master node, backup all your application data that is stored in the /var/lib/cdsw directory, and the configuration file at /etc/cdsw/config/cdsw.conf.
- Deactivate the existing Cloudera Data Science Workbench parcel. Go to the Cloudera Manager Admin Console. In the top navigation bar, click
Locate the current active CDSW parcel and click Deactivate.
- Install the latest version of Cloudera Data Science Workbench using the CSD and parcel. Note that when you are configuring role assignments for the Cloudera Data Science Workbench
service, the Master role must be assigned to the same node that was running as master prior to the upgrade.
For installation instructions, see Installing Cloudera Data Science Workbench 1.2.x Using Cloudera Manager. You might be able to skip the first few steps assuming you have the wildcard DNS domain and block devices already set up.
- Use your copy of the backup cdsw.conf created in Step 3 to recreate those settings in Cloudera Manager by configuring the corresponding properties under
the Cloudera Data Science Workbench service.
- Log into the Cloudera Manager Admin Console.
- Go to the Cloudera Data Science Workbench service.
- Click the Configuration tab.
- The following table lists all the cdsw.conf properties and their corresponding Cloudera Manager properties (in bold). Use the search box to bring up the properties you want to modify.
- Click Save Changes.
cdsw.conf Property Corresponding Cloudera Manager Property and Description
Enable TLS: Enable and enforce HTTPS (TLS/SSL) access to the web application (optional). Both internal and external termination are supported. To enable internal termination, you must also set the TLS Certificate for Internal Termination and TLS Key for Internal Termination parameters. If these parameters are not set, terminate TLS using an external proxy.
For more details on TLS termination, see Enabling TLS/SSL for Cloudera Data Science Workbench.
TLS Certificate for Internal Termination, TLS Key for Internal Termination
Complete path to the certificate and private key (in PEM format) to be used for internal TLS termination. Set these parameters only if you are not terminating TLS externally. You must also set the Enable TLS property to enable and enforce termination. The certificate must include both DOMAIN and *.DOMAIN as hostnames.
Self-signed certificates are not supported unless trusted fully by clients. Accepting an invalid certificate manually can cause connection failures for unknown subdomains.Set these only if you are not terminating TLS externally. For details on certificate requirements and enabling TLS termination, see Enabling TLS/SSL for Cloudera Data Science Workbench.
HTTP Proxy, HTTPS ProxyIf your deployment is behind an HTTP or HTTPS proxy, set the respective HTTP Proxy or HTTPS Proxy property to the hostname of the proxy you are using.
http://<proxy_host>:<proxy-port> or https://<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. That is, either http://localhost:3128 or https://localhost:3128 respectively.
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.
SOCKS Proxy: If a SOCKS proxy is in use, set this parameter to socks5://<host>:<port>/.
No Proxy: Comma-separated list of hostnames that should be skipped from the proxy.
These include 127.0.0.1, localhost, the value of MASTER_IP, and any private Docker registries and HTTP services inside the firewall that Cloudera Data Science Workbench users might want to access from the engines.At a minimum, Cloudera requires the following NO_PROXY configuration.
127.0.0.1,localhost,<MASTER_IP>,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, 100.77.0.129,100.77.0.130
Enable GPU Support: When this property is enabled, and the NVIDIA GPU Driver Library Path parameter is set, the GPUs installed on Cloudera Data Science Workbench nodes will be available for use in its workloads. By default, this parameter is disabled.
For instructions on how to enable GPU-based workloads on Cloudera Data Science Workbench, see Using GPUs for Cloudera Data Science Workbench Workloads.
NVIDIA GPU Driver Library Path: Complete path to the NVIDIA driver libraries. For instructions on how to create this directory, see Enable Docker NVIDIA Volumes on GPU Nodes.
- Cloudera Manager will prompt you to restart the service if needed.
- If the release you have just upgraded to includes a new version of the base engine image (see release
notes), you need to manually configure existing projects to use the new engine. Cloudera recommends you do so to take advantage of any new features and bug fixes included in the newly released
To upgrade a project to the new engine, go to the project's extended engines, you will need to modify them to use the new base engine image.page and select the new engine from the dropdown. If any of your projects are using custom