Learning Environment Installation

The MDAI Learning Environment is a full instance of the MdaiHub with all its clustered components, but running using Kind / Docker on your machine of choice. There are 2 ways to install the Learning Environment.

• A quick, cli-based install gets the MDAI sandbox up and running in minutes.

• A step-by-step install that let’s you see each of the components composing an MDAI cluster.

Prerequisites

Before you install the MDAI Leaning Environment, you’ll need a GitHub account to access resources from MyDecisive repos.

You’ll also need to install the following software.

• Docker
• Kubernetes (k8s)
• kubectl and kind
• Helm
• (Optional) k9s

Using the MDAI CLI

What is the MDAI “CLI”?

The MDAI CLI is an installation, command and control plane shell script used for managing your MDAI instances.

Setting up the MDAI CLI

Clone our mdai-labs repo

git clone https://github.com/DecisiveAI/mdai-labs.git

Create an mdai alias for the shell script

Recommended! You can create an alias in you .bashrc, .zshrc, or equivalent to run the command line. We will be using mdai to refer to the ./mdai/cli script in these docs.

In your .bashrc (or equivalent), add this to EOF

# Set this to the path of your local clone of mdai-labs
export MDAI_LABS_DIR="$HOME/path/to/mdai-labs"

# Set mdai alias
alias mdai="${MDAI_LABS_DIR%/}/cli/mdai.sh"

Make the MDAI script file executable.

cd path/to/mdai-labs
chmod +x ./cli/mdai.sh

You can now call mdai from your terminal and use it like you would any other CLI.

<!– mdai alias –>
mdai <command> [command flags]

<!-- alternative without alias -->
./cli/mdai.sh <command> [command flags]

Choose an Installation Method

mdai install --version 0.8.6 -f values/overrides_0.8.6.yaml

kubectl apply -f 0.8.6/k8s/otel_operator_rbac_patch.yaml

mdai --no-cert-manager install --version 0.8.6 -f values/overrides_0.8.6.yaml

kubectl apply -f 0.8.6/k8s/otel_operator_rbac_patch.yaml

mdai install_mdai --version 0.8.6 -f values/overrides_0.8.6.yaml

kubectl apply -f 0.8.6/k8s/otel_operator_rbac_patch.yaml

mdai --no-cert-manager install_mdai --version 0.8.6 -f values/overrides_0.8.6.yaml

kubectl apply -f 0.8.6/k8s/otel_operator_rbac_patch.yaml

kind create cluster --name mdai

kubectl apply -f https://github.com/cert-manager/cert-manager/releases/latest/download/cert-manager.yaml
  kubectl wait --for=condition=Established crd/certificates.cert-manager.io --timeout=60s
  kubectl wait --for=condition=Ready pod -l app.kubernetes.io/instance=cert-manager -n cert-manager --timeout=60s
  kubectl wait --for=condition=Available=True deploy -l app.kubernetes.io/instance=cert-manager -n cert-manager --timeout=60s

helm upgrade --install \
      mdai oci://ghcr.io/decisiveai/ \mdai-hub \
      --namespace mdai \
      --create-namespace \
      --version 0.8.6 \
      --set mdai-operator.manager.env.otelSdkDisabled=true \
      --set mdai-gateway.otelSdkDisabled=true \
      --set mdai-s3-logs-reader.enabled=false \
      --values values/overrides_0.8.6.yaml \
      --cleanup-on-fail

kubectl apply -f 0.8.6/k8s/otel_operator_rbac_patch.yaml

helm upgrade --install mdai-hub oci://ghcr.io/decisiveai/mdai-hub \
      --version 0.8.6 \
      --namespace mdai \
      --create-namespace \
      --set mdai-operator.manager.env.otelSdkDisabled=true \
      --set mdai-gateway.otelSdkDisabled=true \
      --set mdai-s3-logs-reader.enabled=false \
      --set opentelemetry-operator.admissionWebhooks.certManager.enabled=false \
      --set opentelemetry-operator.admissionWebhooks.autoGenerateCert.enabled=true \
      --set opentelemetry-operator.admissionWebhooks.autoGenerateCert.recreate=true \
      --set opentelemetry-operator.admissionWebhooks.autoGenerateCert.certPeriodDays=365 \
      --set mdai-operator.admissionWebhooks.certManager.enabled=false \
      --set mdai-operator.admissionWebhooks.autoGenerateCert.enabled=true \
      --set mdai-operator.admissionWebhooks.autoGenerateCert.recreate=true \
      --set mdai-operator.admissionWebhooks.autoGenerateCert.certPeriodDays=365 \
      --values values/overrides_0.8.6.yaml \
      --cleanup-on-fail

kubectl apply -f 0.8.6/k8s/otel_operator_rbac_patch.yaml

You’ll see a number of messages as the MDAI Learning Environment cluster components are installed. Once installation is complete, verify that the Learning Environment cluster’s pods are running.

kubectl get pods -n mdai

If your Learning Environment cluster is running, you’ll see output similar to the following.

NAME                                                READY   STATUS    RESTARTS     AGE
alertmanager-kube-prometheus-stack-alertmanager-0   2/2     Running   0            50s
kube-prometheus-stack-operator-686d7d855b-rfsth     1/1     Running   0            59s
mdai-event-hub-5575557f6d-w758s                     1/1     Running   0            59s
mdai-gateway-667c4c5fcc-cbd2z                       1/1     Running   0            59s
mdai-grafana-5ccdcf59c9-tlx5n                       3/3     Running   0            59s
mdai-kube-state-metrics-5bd8cdbccb-tls6s            1/1     Running   0            59s
mdai-nats-0                                         3/3     Running   0            59s
mdai-nats-1                                         3/3     Running   0            59s
mdai-nats-2                                         3/3     Running   0            59s
mdai-nats-box-fb7f9c849-kjtnk                       1/1     Running   0            59s
mdai-operator-controller-manager-7d7b4c9886-qmsjg   1/1     Running   0            59s
mdai-prometheus-node-exporter-fjmdh                 1/1     Running   0            59s
mdai-valkey-primary-0                               1/1     Running   0            59s
opentelemetry-operator-58cb5dbdbb-m8crb             1/1     Running   0            59s
prometheus-kube-prometheus-stack-prometheus-0       2/2     Running   0            50s

Explore MDAI Labs

When you’re done installing the MDAI Learning Environment, visit the Labs Catalog to explore and learn more.