Installation

Linux

You can install conda-store using conda :

conda install conda-store-server>=0.4.10

Once installed, start conda-store using :

conda-store-server --standalone

You can then access conda-store on port 5000 of the machine running it.

Kubernetes

The following will describe a local Kubernetes installation via minikube. The files required are in examples/kubernetes

minikube start --cpus 2 --memory 4096 --driver=docker

Now we deploy the conda-store components. Note that conda-store is compatible with any general s3 like provider and any general database via SQLAlchemy. Currently the docker image is build with support for PostgreSQL and SQLite. Consult the SQLAlchemy documentation on supporting your given database and then creating a custom docker image with your required database. Not all database engines were added to save on image size. Additionally You may not need to use MinIO and PostgreSQL deployments and use existing infrastructure. In the case of AWS this may mean using Amazon RDS and s3. Consult your cloud provider for compatible services. In general if it is supported by SQLAlchemy and there is a s3 compatible object store conda-store will work. kustomize is being used for the deployment which is part to the Kubernetes project itself.

kubectl apply -k examples/kubernetes

Make sure to change all the usernames and passwords for the deployment.

If your installation worked you should be able to port forward the conda-store web server.

kubectl port-forward service/conda-store-server 5000:5000

Then visit via your web browser http://localhost:5000

For additional configuration options see the administrative guide

A good test that conda-store is functioning properly is to apply the jupyterlab-conda-store pod as a quick test. It will cause conda-store to build an environment with JupyterLab and NumPy. This pod is not needed for running conda-store.

kubectl apply -f examples/kubernetes/test/jupyterlab-conda-store.yaml

If you instead mount a ReadWriteMany volume to the container conda-store-worker like nfs or Amazon EFS. You can mount the environments built via conda-store and use environments this way. Note that NFS can be significantly slower when it comes to creating environments (see performance docs).

Docker

To install on a local docker daemon there is an existing docker-compose.yaml for deployment. The example files required are in examples/docker

docker-compose up --build

Then visit via your web browser https://conda-store.localhost/conda-store. By default, you can log in with any username and use the password test, since we are using the DummyAuthenticator

Local Automated systemd Install

Not all environment are containerized and conda-store recognizes that. The goal of CONDA-STORE is to provide Conda environments in as many ways as possible so it SHOULD support non-contianerized environments. The example files required are in examples/ubuntu2004.

This example is not fully complete in that it does not install conda-store and get it running due to the conda-forge package.

If you would like to test it in a VM use the following. The following Vagrantfile is only compatible with libvirt.

vagrant up

However if you want to do a local deployment use

ansible-playbook -i <inventory> playbook.yaml