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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 Nebari conda user environments created? Who creates them?​

The short answer: there are currently two ways of creating environments, as we are in the process of migrating Nebari to conda-store, and so which way depends on your use-case.

The longer answer:

  • For global environments, you can specify the environment in nebari-config.yml, and it will be made available for all users and services.
  • By comparison, creating the environments through conda-store will provide more granular control over certain settings and permissions.

As Nebari and conda-store mature, the intent is to migrate exclusively to conda-store for environment creation and management.

What if I need to install package X, and it's not available in the environment?​

You can add the package to the nebari_config.yml. If you don't have access to the deployment repo, you'll need to contact your Nebari administrator to include the required package.

What's included in the conda 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||.

What packages are needed in your environment to create a dashboard?​

When deploying an app with JHub App Launcher, you need to have the following in your environment:

  • jhub-apps package
  • packages corresponding to the dashboard framework (for example, panel, gradio, etc.)
  • any other libraries required for the analysis in the dashboard creation script/notebook

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

⚠️caution

We strongly recommend installing packages by adding them through the conda-store UI. If you're developing a package and need to install the package through pip, conda, or similar, the following approach may be used.

If you are using a setuptools package, you can install it into your local user environment by:

pip install --no-build-isolation --user -e .

If you're using a flit package, you can install it through the following command:

flit install -s

If the package requires build tools like gcc and cmake, remember that you can create a conda environment through the conda-store UI that includes the build tools, then just activate the environment and install the package locally.

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?​

Regular Nebari users do not have write permissions to modify the .bashrc file.

Nebari automatically creates and manages .bashrc and .profile, so if the intent of using the .bashrc file is to populate environment variables in bash scripts or similar, you can source the file in any scripts you create by including the following line in your scripts:

source ~/.bashrc

You can use .bashrc on Nebari, but it's important to note that by default Nebari sources .bash_profile. You should double-check to source the .bashrc inside the .bash_profile. Also, note that if you set environment variables in this way, these variables aren't available inside the notebooks.

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 or delete the conda-store pod, if I need to?​

You may find that the pods hosting your environment get full over time, prompting you to clear them out. To delete old builds of your environment on conda-store, click the "delete" button in the conda-store UI.

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, and Digital Ocean doesn't support these types of instances. 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.

Digital Ocean​

Digital Ocean doesn't support these type of instances.

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 PyTorch best practices. 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.

Why can't I run a notebook with Jupyter-Scheduler? Why do Argo Workflows fail to run?​

If your notebook job has status Failed, click on the job name to get a more detailed error message. For example, you might see an error similar to this one:

Server returned status code 403 with message: 'workflows.argoproj.io is forbidden: User "system:serviceaccount:dev:argo-viewer" cannot create resource "workflows" in API group "argoproj.io" in the namespace "dev"

If so, you need to configure groups. In addition to that, papermill needs to be installed in your environment.

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.

ℹ️note

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