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Frequently asked questions

Why is the NEBARI_KUBECONFIG file in /tmp?​

Nebari regenerates this file on every run. This means it will be removed by the operating system during its cleanup process, but running the nebari deploy command again as a Nebari administrator will update/create a NEBARI_KUBECONFIG file for you.

How are conda user environments created? Who creates them?​

Conda-store manages all environments on the Nebari. It allows users to create private environments in their own namespace, or shared environments under a group namespace. For more details check out the doc on creating environments in Nebari via conda-store.

Additionally, there is a legacy approach which is still available in Nebari. Administrators can create global environments by specifying the environment in nebari-config.yml. Environments specified in this way will be made available for all users and services under the nebari-git namespace on conda-store.

What should be included in the environment if I want to use Dask?​

There are drop-in replacements for distributed, dask, and dask-gateway with the correct pinned versions available via the Nebari Dask metapackage. Example: nebari-dask==||nebari_VERSION||.

How can I install a package locally? Will this package be available to Dask workers?​

If you want to install a package locally, we suggest following the guide for developing local packages.

It's important to note that packages installed this way aren't available to the Dask workers. See our Dask tutorial for more information.

Can I modify the .bashrc file on Nebari?​

Nebari automatically creates and manages .bashrc and .profile. Therefore, end users do not have write permissions to modify this file. However, by default Nebari will source .bash_profile. Users may use this file to populate environment variables or set up alias, etc. However, there are some important things to note:

  • The .bash_profile is sourced after the .bashrc - be aware of the implications, one of which is that you will lose changes to the prompt syntax. To avoid this, you can always source the .bashrc inside the .bash_profile.
  • JupyterLab kernels do not source .bash_profile but the Jupyter terminal does.
  • The VS Code terminal does not source .bash_profile by default.

What if I can't see the active conda environment in the terminal?​

Set the changeps1 value in the conda config:

conda config --set changeps1 true

The conda config is located in the /home/{user}/.condarc file. You can change the conda config with a text editor (for example: nano, which is included in Nebari by default), and the changes will be applied on saving the file.

How do I clean up old environment builds in conda-store?​

You may find that the pods hosting your environment get full over time, prompting you to clear them out. As an admin, you can delete environments completely (including all builds of the environment) in the conda-store UI. Go to the environment, click Edit and then click Delete.

If you'd like to retain the latest version of an environment and only remove specific builds, you'll need to navigate to the conda-store admin page located at <nebari-domain/conda-store/admin>. Click on the environment you'd like to clean up. At the bottom of the page, there is a list of each environment build, each with it's own "delete" button.

How do I use preemptible and spot instances on Nebari?​

A preemptible or spot VM is an instance that you can create and run at a much lower price than normal instances. Azure and Google Cloud platform use the term preemptible, while AWS uses the term spot. However, the cloud provider might stop these instances if it requires access to those resources for other tasks. Preemptible instances are excess Cloud Provider's capacity, so their availability varies with usage.

Usage​

Google Cloud Platform​

The preemptible flag in the Nebari config file defines the preemptible instances.

google_cloud_platform:
  project: project-name
  region: us-central1
  zone: us-central1-c
  availability_zones:
  - us-central1-c
  kubernetes_version: 1.18.16-gke.502
  node_groups:
# ...
    preemptible-instance-group:
      preemptible: true
      instance: "e2-standard-8"
      min_nodes: 0
      max_nodes: 10
Amazon Web Services​

Spot instances aren't supported at this moment.

Azure​

Preemptible instances aren't supported at this moment.

Why doesn't my code recognize the GPU(s) on Nebari?​

First be sure you chose a GPU-enabled server when you selected a profile. Next, if you're using PyTorch, see Using GPUs on Nebari. If it's still not working for you, be sure your environment includes a GPU-specific version of either PyTorch or TensorFlow, i.e. pytorch-gpu or tensorflow-gpu. Also note that tensorflow>=2 includes both CPU and GPU capabilities, but if the GPU is still not recognized by the library, try removing tensorflow from your environment and adding tensorflow-gpu instead.

How do I migrate from Qhub to Nebari?​

Nebari was previously called QHub. If your Qhub version lives in the 0.4.x series, you can migrate to Nebari by following the migration guide. If you're using a version of Qhub that lives in the 0.3.x series, you will need to upgrade to 0.4.x first as the user group management is different between the two versions. For more information, see the deprecation notice in the Nebari release note.

Why is there duplication in names of environments?​

The default Dask environment is named nebari-git-nebari-git-dask, with nebari-git duplicated.

nebari-git is the name of the namespace. Namespaces are a concept in conda-store, however conda itself does not recognize it.

It is possible to use conda-store to create an environment with the name "dask" in two different namespaces. But because conda doesn't understand namespaces, conda won't be able to differentiate between them. To avoid this, we prepend the namespace's name into the environment building on conda-store.

Next, nb_conda_kernels with nb-conda-store-kernels are the packages that we use to transform conda environments into runnable kernels in JupyterLab (that's why we require that all environments have ipykernel).

The issue is that nb_conda_kernels insists the following path: /a/path/to/global/datascience-env, which corresponds to global-datascience-env being the name that users see while datascience-env is what conda sees.

Hence, to make things unique we've named things as /a/path/to/global/global-datascience-env. This makes conda see the env as global-datascience-env, but nb_conda_kernel now displays it as global-global-datascience-env.

We have discussed contributing a PR to nb_conda_kernels, but the project has not accepted community PRs in over 3 years, so we don't currently have the motivation to do this.

If you have potential solutions or can help us move forward with updates to the nb_conda_kernels, please reach out to us on our discussion forum!

Why does my VS Code server continue to run even after I've been idle for a long time?​

Nebari automatically shuts down servers when users are idle, as described in Nebari's documentation for the idle culler settings. This functionality currently applies only to JupyterLab servers. A VS Code instance, however, runs on Code Server, which isn't managed by the idle culler. VS Code, and other non-JupyterLab services, will not be automatically shut down.

note

Until this issue is addressed, we recommend manually shutting down your VS Code server when it is not in use.