Anaconda’s Data Science & AI Workbench provides detailed logs and monitoring information related to the Kubernetes services and containers it uses. Gravity-based installations can use the provided Operations Center and Kubernetes CLI to access this information, to help diagnose and debug errors that you or other users might encounter while using the platform.
Click the Private IP address of the Workbench master node, and select SSH login as root.
To display the current resource utilization of each node in the cluster, run this command:
kubectl top nodes --heapster-namespace=monitoring
This is actual resource utilization, not limits or requests.
To view utilization and requests for a particular node, run the kubectl describe node command against the IP address for the node (listed under NAME). For example:
kubectl describe node 172.31.25.175
To view the resource utilization per pod, run this command:
kubectl top pods --heapster-namespace=monitoring
To view the current status of all pods in the cluster, run kubectl get pods.
The following table summarizes common pod states:
To view information for a particular pod, run the kubectl describe pod command against the pod (listed under NAME). For example:
kubectl describe pod anaconda-session-89747d7fdb154b89b182d5eaa25b2e59-7f497db55wl9g
You can also use the Operations Center Logs to gain insights into pod behavior and troubleshoot issues. See Logging for more information.
If a user experiences issues within a Notebook session, have them send you the name of the pod associated with their project session. They can obtain this information by running the hostname command from within a Jupyter Notebook or terminal window.The hostname appears in the Notebook cell output:
Or from a terminal window:
You can then use the commands described above or the Operations Center’s Monitoring and Logs features to investigate the issue. See Monitoring sessions and deployments for more information.
As an Administrator, you can also use the Authentication Center to impersonate a user to try to reproduce the problem they are experiencing.
To access the Authentication Center:
Log in to Workbench, click Menu in the top right corner, then click the Administrative Console link in the bottom of the slideout menu.
Click Manage Users.
In the Manage menu on the left, click Users.
On the Lookup tab, click View all users to list every user in the system, or search the user database for all users that match the criteria you enter, based on their first name, last name, or email address.
Click Impersonate in the row of Actions for the user to display a table of all Applications this user has interacted with on the platform, including editor sessions and deployments.
Click the Anaconda Platform link to interact with Workbench as the user.
See Managing users for more information on managing users.
To troubleshoot issues with editor sessions, it helps to understand what happens behind the scenes.
When a user starts a session, Workbench launches the appropriate editor for them to work with their project files. In the background, the editor environment and other services are running in Docker containers.
To improve startup time for projects, the editor container includes conda environments for each of the project template environments provided by the platform. These environments are stored in /opt/continuum/anaconda/envs, along with any custom environments created during the editor session.
The project repository is cloned into /opt/continuum/project. (Only changes to files in this directory can be saved to the repository.)
The anaconda-project prepare command runs, scans the project’s anaconda-project.yml file for new packages and environments, and installs them into the running session.During this phase, you can monitor the progress by watching the output of /opt/continuum/preparing.When this process completes, the /opt/continuum/prepare.log is created.
Any changes made to the container image will be lost when the session stops, so any packages installed from the command line are available during the current session only. To persist package installs across sessions, they must be added to the project’s anaconda-project.yml file.
Helm rollback fails with a kubernetes-dashboard-csrf conflict
Running helm rollback anaconda-enterprise <REVISION> can fail with an error similar to:
Error: conflict occurred while applying object default/kubernetes-dashboard-csrf /v1, Kind=Secret: Apply failed with 1 conflict: conflict with "dashboard" using v1: .data.csrf
This occurs because the kubernetes-dashboard-csrf secret is managed by both the Helm chart and the Kubernetes Dashboard application, which writes its own CSRF token to the same secret at runtime. helm rollback reapplies the secret content captured at the time of the target revision, which is typically out of date by the time a rollback is needed, causing Kubernetes to reject the conflicting write. This affects Workbench chart versions prior to ae-helm-chart-1.1-364, on both K3s and BYOK8s installations.To resolve it:
Delete the conflicting secret and retry the rollback:
This is actual resource utilization, not limits or requests.
