Dotscience installation on Google Cloud Platform
Start by cloning our open-source terraform repo:
git clone https://github.com/dotmesh-io/dotscience-tf cd dotscience-tf
Choose the Google Cloud Platform version:
Set up Terraform
Init your terraform, this will ensure the gcloud plugin is installed:
Ensure you are authenticated to GCP:
gcloud auth application-default login
(or use another supported authentication mechanism, like a GCP service account key file)
inputs.tfvars in your favorite text editor, and put the following in it:
project = "<your google project id>" admin_password = "<your choice of password>" grafana_admin_password = "<your choice of password>" license_key = "<your license key from licensing.dotscience.com>" hub_ingress_cidr = "0.0.0.0/0" ssh_access_cidr = "0.0.0.0/0" letsencrypt_mode = "production" hub_volume_size = 100
Get your license key from our Licensing service.
Now deploy the Dotscience stack:
terraform apply -var-file inputs.tfvars
It should print out the hostname you can access your stack on!
Wait a few minutes for the hub to set itself up. (To observe progress,
tail -f /var/log/syslog on the VM, and look out for
INFO startup-script log lines – the startup script is run via cloud-init).
Now log in and do some data science!
Runners - where training happens
Runners are where model training happens, such as within Jupyter notebooks or via
Managed runners are VMs which are be auto-provisioned when users create them through Dotscience.
They are by default are an
They will be destroyed automatically when idle to save money.
You can also attach non-managed runners, such as on-prem physical hardware, which can include GPUs.
Simply go to menu (top-right) in the app and click Runners, and Add New Runner, and you’ll be given a
docker run command to execute on your runner (e.g. DGX server).
Deployers - where inference and monitoring happens
Deployers are where models run.
You can also attach non-managed deployers, such as an on-prem Kubernetes cluster.
Simply go to menu (top-right) in the app and click Deployers, and Add New Deployer, and you’ll be given a
kubectl apply command to execute on your Kubernetes cluster.
We plan to improve the GCP Terraform stack in the following ways:
- Pre-configure the stack with standard GCP instance types as runner profiles.
- Support auto-provisioned GPU runners.
- Automatically deploy a GKE cluster as a managed deployer and install the monitoring stack on it.
- Document how to set up your own domain and hostname in DNS and having Let’s Encrypt work for it.