Frequently Asked Questions¶

Additional FAQ questions can be found in the GitHub discussions.

Environments¶

How are QHub conda user environments created? Who creates them?¶

The environment specifications are located in qhub_config.yml in the deployment repo. These are served to the QHub deployment using conda-store. When you manage your environments in this way you get all of the benefits of environment versioning that QHub does under the hood, including future features such as convenient environment rollback and environment encapsulation in containers.

Anyone with access to the QHub deployment repo can add an environment, and there are no limits to the number of environments that can be included.

Do be careful of the yaml indentation as it will differ from a conda environment.yml

I need X package and it’s not in any available user environments - what do I do?¶

The proper solution is to add the package to the qhub_config.yml (See #1). If you don’t have access to the deployment repo, you will need to contact your QHub maintainer to get the required package. Just to note: you can do a user install for pip packages in a pinch (this is not recommended) but they will not be available to Dask workers.

I want to use Dask. What needs to be included in my user environment?¶

You will need to include the QHub Dask metapackage, e.g. qhub-dask==0.3.13. This will replace distributed, dask, ad dask-gateway.

Why can’t I just create my own local conda environment or edit the existing conda environments?¶

The version of conda-store used in QHub versions 0.3.11 and earlier is an alpha version. This version doesn’t support using local conda environments or editing pre-exising environments directly.

See the answer to #2 for information on how to modify environments properly. In the near future the QHub team will be adding support for user-defined environments via conda-store.

I have a local package - how can it be installed? Will it be available to my Dask workers?¶

If you’re using a setuptools package, you can install it into your local user environment using

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

If you’re using a flit package, you can install with

flit install -s

These will not be available to the Dask workers.

How to use .bashrc on QHub?¶

You can use .bashrc on QHub, but its important to note that only .bash_profile is sourced by default so you’ll need to be sure to source the .bashrc inside of the .bash_profile. Its important to note that if you set environment variables in this way, they WILL NOT be available inside of notebooks.

How do I use environment variables on dask workers (not loaded via a package)? This can be achieved through the UI:

import dask_gateway

gateway = dask_gateway.Gateway()
options = gateway.cluster_options()
options

Or programmatically:

env_vars = {
    "ENV_1": "VALUE_1",
    "ENV_2": "VALUE_2"
}
options.environment_vars = env_vars

This functionality is available in release 0.3.12 or later.

I can’t see the active conda env in the terminal. Set the changeps1 value in the conda config:
```
conda config --set changeps1 true
```
I want to use QHub server to compute a new pinned environment (which I’ll the serve via the qhub_config.yml). I understand it’s not recommended I use this environment for working. If you need to solve a conda env on a QHub server (not recommended, but there are valid usecases for this), you’ll need to specify the prefix. For example, conda env create -f env_test.yml --prefix /tmp/test-env where test-env will be the env name.

Using Dask Gateway

QHub Training Cluster