Cloudera Data Science Workbench 1.7.2 Requirements and Supported Platforms

All cluster hosts must be managed by Cloudera Manager. Note that all Cloudera Data Science Workbench administrative tasks require root access to the cluster's gateway hosts where Cloudera Data Science Workbench is installed. Therefore, Cloudera Data Science Workbench does not support single-user mode installations.

Cloudera Data Science Workbench 1.7.2 is supported on the following operating systems. A gateway host that is dedicated to running Cloudera Data Science Workbench must use one of the following supported versions even if the remaining CDH hosts in your cluster are running any of the other operating systems supported by Cloudera Enterprise 5 or 6.

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.

For more specifics on the versions of Oracle JDK and OpenJDK recommended for CDH and Cloudera Manager clusters, and instructions on how to install the Java Cryptography Extension, see the Cloudera Product Compatibility Matrix - Supported JDK Versions.

• Enable IPv6 on all Cloudera Data Science Workbench gateway hosts. For instructions, refer the workaround provided here: Known Issue: CDSW cannot start sessions due to connection errors.
• All Cloudera Data Science Workbench gateway hosts must be part of the same datacenter and use the same network. Hosts from different data-centers or networks can result in unreliable performance.
• A wildcard subdomain such as *.cdsw.company.com must be configured. Wildcard subdomains are used to provide isolation for user-generated content.

  The wildcard DNS hostname configured for Cloudera Data Science Workbench must be resolvable from both, the CDSW cluster, and your browser.

• Disable all pre-existing iptables rules. While Kubernetes makes extensive use of iptables, it’s difficult to predict how pre-existing iptables rules will interact with the rules inserted by Kubernetes. Therefore, Cloudera recommends you to disable all pre-existing rules before you proceed with the installation.
  It is recommended to save the iptables and check whether the changes have been written to the /etc/sysconfig/iptables file before you disable them. If you disable the iptables without saving, then the settings can get erased upon system reboot.

  1. Save the iptables by running the following command:

     service iptables save

  2. Verify whether the changes have been written to the file by running the following command:

     ls -l /etc/sysconfig/iptables

  3. Disable the iptables by running the following commands:

     sudo iptables -P INPUT ACCEPT
     sudo iptables -P FORWARD ACCEPT
     sudo iptables -P OUTPUT ACCEPT
     sudo iptables -t nat -F
     sudo iptables -t mangle -F
     sudo iptables -F
     sudo iptables -X

• Cloudera Data Science Workbench sets the following sysctl options in /etc/sysctl.d/k8s.conf:
  • net.bridge.bridge-nf-call-iptables=1
  • net.bridge.bridge-nf-call-ip6tables=1
  • net.ipv4.ip_forward=1
  • net.ipv4.conf.default.forwarding=1
  Underlying components of Cloudera Data Science Workbench (Docker, Kubernetes, and NFS) require these options to work correctly. Make sure they are not overridden by high-priority configuration such as /etc/sysctl.conf.
• SELinux must either be disabled or run in permissive mode.
• Multi-homed networks are supported with Cloudera Data Science Workbench 1.2.2 (and higher). However, you will need to explicitly configure the private IP address of the worker nodes in the kubelet start script as follows:

  # vi /opt/cloudera/parcels/CDSW/scripts/start-kubelet-worker-standalone-core.sh
  88 kubelet_opts+=(--v=2)
  89 kubelet_opts+=(--node-ip=172.x.x.x)

• Firewall restrictions must be disabled across Cloudera Data Science Workbench and CDH/HDP cluster hosts. For more details on cluster communication, see Ports Required by Cloudera Data Science Workbench.
• Untrusted (non-sudo) SSH access to Cloudera Data Science Workbench hosts must be disabled to ensure a secure deployment.

  Cloudera Data Science Workbench assumes that users only access the gateway hosts through the web application. Untrusted users with SSH access to a Cloudera Data Science Workbench host can gain full access to the cluster, including access to other users' workloads.

• localhost must resolve to 127.0.0.1.
• Forward and reverse DNS lookup must be enabled for the Cloudera Data Science Workbench domain name and IP address (CDSW master host).
• Cloudera Data Science Workbench does not support DNS servers running on 127.0.0.1:53. This IP address resolves to the container localhost within Cloudera Data Science Workbench containers. As a workaround, use either a non-loopback address or a remote DNS server.
• All third-party security software (such as McAfee, Tanium, Symantec, etc.) must be disabled on CDSW hosts. Failure to do so can result in Cloudera Data Science Workbench failing randomly.

Cloudera Data Science Workbench does not support hosts or clusters that do not conform to these restrictions.

Cloudera Data Science Workbench hosts are added to your CDH cluster as gateway hosts. The recommended minimum hardware configuration for Cloudera Data Science Workbench gateway hosts is:

The default Cloudera Data Science Workbench engine currently includes Python 2.7.17 and Python 3.6.9. CDSW supports what comes bundled with the base image. To use PySpark with lambda functions that run within the CDH cluster, the Spark executors must have access to a matching version of Python. For many common operating systems, the default system Python will not match the minor release of Python included in Data Science Workbench.

To ensure that the Python versions match, Python can either be installed on every CDH host or made available per job run using Spark’s ability to distribute dependencies. Given the size of a typical isolated Python environment and the desire to avoid repeated uploads from gateway hosts, Cloudera recommends installing Python 2.7 and 3.6 on the cluster if you are using PySpark with lambda functions.

You can install Python 2.7 and 3.6 on the cluster using any method and set the corresponding PYSPARK_PYTHON environment variable in your project. Cloudera Data Science Workbench 1.3 (and higher) include a separate environment variable for Python 3 sessions called PYSPARK3_PYTHON. Python 2 sessions continue to use the default PYSPARK_PYTHON variable. This will allow you to run Python 2 and Python 3 sessions in parallel without either variable being overridden by the other.

For an example on distributing Python dependencies dynamically, see Example: Distributing Dependencies on a PySpark Cluster.

Cloudera Data Science Workbench only supports the versions of Docker and Kubernetes that are shipped with each release. Upgrading Docker or Kubernetes, or running on third-party Kubernetes clusters is not supported.

On AWS, Cloudera Data Science Workbench must be used with persistent/long-running Apache Hadoop clusters only.