GCP Deployment

Before You Start #

This guide assumes that:

You have already tried HPE Machine Learning Data Management locally and have some familiarity with Kubectl, Helm, Google Cloud SDK and jq
You have access to a Google Cloud account linked to an active billing account (gcloud alpha billing accounts list)

Configure Variables #

Configure the following variables.

Optional Variables for Scripting

PROJECT_NAME="pachyderm-0001"
SQL_ADMIN_PASSWORD="batteryhorsestaple"
BILLING_ACCOUNT_ID="000000-000000-000000" # see `gcloud alpha billing accounts list`
PRIVATE_KEY_ID=""
PRIVATE_KEY=""
CLIENT_ID=""

# This group of variables can be changed, but are sane defaults
GCP_REGION="us-central1"
GCP_ZONE="us-central1-a"
K8S_NAMESPACE="default"
CLUSTER_MACHINE_TYPE="n2-standard-2"
SQL_CPU="2"
SQL_MEM="7680MB"
LOGGING="SYSTEM"
PROJECT_ID=$(echo "pach-$(openssl rand -base64 32 | tr -dc 'a-z0-9-' | fold -w 2-26 | head -n 1)")

## optional name generator, useful if you are creating multiple clusters or testing.
adjective=("happy" "silly" "brave" "witty" "elegant" "fierce" "gentle" "clever" "vibrant" "charming")
color=("red" "blue" "green" "yellow" "purple" "orange" "pink" "black" "white" "gray")
animal=("cat" "dog" "elephant" "lion" "tiger" "panda" "giraffe" "zebra" "monkey" "bear")
NAME="${adjective[$RANDOM % ${#adjective[@]}]}-${color[$RANDOM % ${#color[@]}]}-${animal[$RANDOM % ${#animal[@]}]}"

# The following variables probably shouldn't be changed
CLUSTER_NAME="${NAME}-gke"
BUCKET_NAME="${NAME}-gcs"
LOKI_BUCKET_NAME="${NAME}-logs-gcs"
CLOUDSQL_INSTANCE_NAME="${NAME}-sql"
GSA_NAME="${NAME}-gsa"
LOKI_GSA_NAME="${NAME}-loki-gsa"
STATIC_IP_NAME="${NAME}-ip"

ROLE1="roles/cloudsql.client"
ROLE2="roles/storage.admin"
ROLE3="roles/storage.objectCreator"

SERVICE_ACCOUNT="${GSA_NAME}@${PROJECT_ID}.iam.gserviceaccount.com"
LOKI_SERVICE_ACCOUNT="${LOKI_GSA_NAME}@${PROJECT_ID}.iam.gserviceaccount.com"
PACH_WI="serviceAccount:${PROJECT_ID}.svc.id.goog[${K8S_NAMESPACE}/pachyderm]"
SIDECAR_WI="serviceAccount:${PROJECT_ID}.svc.id.goog[${K8S_NAMESPACE}/pachyderm-worker]"
CLOUDSQLAUTHPROXY_WI="serviceAccount:${PROJECT_ID}.svc.id.goog[${K8S_NAMESPACE}/k8s-cloudsql-auth-proxy]"

Save to an .env file.
Source them by inputting source .env into the terminal before starting the installation guide.

The following steps use a template to create a GKE cluster, a Cloud SQL instance, and a static IP address. The template also creates a service account for HPE Machine Learning Data Management and Loki, and grants the service account the necessary permissions to access the Cloud SQL instance and storage buckets. You do not have to use this template, but it’s a good outline for understanding how to create your own set up.

1. Create a New Project #

Create a new project (e.g.,pachyderm-quickstart-project). You can pre-define the project ID using between 6-30 characters, starting with a lowercase letter. This ID will be used to set up the cluster and will be referenced throughout this guide.
```
gcloud projects create ${PROJECT_ID} --name=${PROJECT_NAME} --set-as-default
gcloud alpha billing projects link ${PROJECT_ID} --billing-account=${BILLING_ACCOUNT_ID}
```

Enable the following APIs:

gcloud services enable container.googleapis.com
gcloud services enable sqladmin.googleapis.com
gcloud services enable compute.googleapis.com

2. Create a Static IP Address #

Create the static IP Address:

gcloud compute addresses create ${STATIC_IP_NAME} --region=${GCP_REGION}

Get the static IP address:

STATIC_IP_ADDR=$(gcloud compute addresses describe ${STATIC_IP_NAME} --region=${GCP_REGION} --format="json" --flatten="address" | jq -r '.[]')

3. Create a GKE Cluster #

Create a GKE cluster with the following command:

gcloud container clusters create ${CLUSTER_NAME} \
  --region=${GCP_REGION} \
  --machine-type=${CLUSTER_MACHINE_TYPE} \
  --workload-pool=${PROJECT_ID}.svc.id.goog \
  --enable-ip-alias \
  --create-subnetwork="" \
  --logging=${LOGGING} \
  --enable-dataplane-v2 \
  --enable-shielded-nodes \
  --release-channel="regular" \
  --workload-metadata="GKE_METADATA" \
  --enable-autorepair \
  --enable-autoupgrade \
  --disk-type="pd-ssd" \
  --image-type="COS_CONTAINERD"

Grant your user account the privileges needed for the helm install to work properly:

# By default, GKE clusters have RBAC enabled. To allow the 'helm install' to give the 'pachyderm' service account
# the requisite privileges via clusterrolebindings, you will need to grant *your user account* the privileges
# needed to create those clusterrolebindings.
#
# Note that this command is simple and concise, but gives your user account more privileges than necessary. See
# https://docs.pachyderm.io/en/latest/deploy-manage/deploy/rbac/ for the complete list of privileges that the
# Pachydermserviceaccount needs.
kubectl create clusterrolebinding cluster-admin-binding --clusterrole=cluster-admin --user=$(gcloud config get-value account)

Connect to the cluster:

gcloud container clusters get-credentials ${CLUSTER_NAME} --region=${GCP_REGION}

4. Create Storage Buckets #

gsutil mb -l ${GCP_REGION} gs://${BUCKET_NAME}
gsutil mb -l ${GCP_REGION} gs://${LOKI_BUCKET_NAME}

5. Create a Cloud SQL Instance #

Create a Cloud SQL instance with the following command:

gcloud sql instances create ${CLOUDSQL_INSTANCE_NAME} \
  --database-version=POSTGRES_14 \
  --cpu=${SQL_CPU} \
  --memory=${SQL_MEM} \
  --zone=${GCP_ZONE} \
  --availability-type=ZONAL \
  --storage-size=50GB \
  --storage-type=SSD \
  --storage-auto-increase \
  --root-password=${SQL_ADMIN_PASSWORD}

Create a databases for HPE Machine Learning Data Management and Dex:

gcloud sql databases create pachyderm -i ${CLOUDSQL_INSTANCE_NAME}
gcloud sql databases create dex -i ${CLOUDSQL_INSTANCE_NAME}

Get the Cloud SQL connection name:

CLOUDSQL_CONNECTION_NAME=$(gcloud sql instances describe ${CLOUDSQL_INSTANCE_NAME} --format=json | jq ."connectionName")

6. Create Service Accounts #

Create a service account for HPE Machine Learning Data Management and Loki.

gcloud iam service-accounts create ${GSA_NAME}

gcloud projects add-iam-policy-binding ${PROJECT_ID} \
    --member="serviceAccount:${SERVICE_ACCOUNT}" \
    --role="${ROLE1}"

gcloud projects add-iam-policy-binding ${PROJECT_ID} \
    --member="serviceAccount:${SERVICE_ACCOUNT}" \
    --role="${ROLE2}"

gcloud projects add-iam-policy-binding ${PROJECT_ID} \
    --member=serviceAccount:${SERVICE_ACCOUNT} \
    --role="${ROLE3}"

gcloud iam service-accounts add-iam-policy-binding ${SERVICE_ACCOUNT} \
    --role roles/iam.workloadIdentityUser \
    --member "${PACH_WI}"

gcloud iam service-accounts add-iam-policy-binding ${SERVICE_ACCOUNT} \
    --role roles/iam.workloadIdentityUser \
    --member "${SIDECAR_WI}"

gcloud iam service-accounts add-iam-policy-binding ${SERVICE_ACCOUNT} \
    --role roles/iam.workloadIdentityUser \
    --member "${CLOUDSQLAUTHPROXY_WI}"

gcloud iam service-accounts create ${LOKI_GSA_NAME}

gcloud iam service-accounts keys create "${LOKI_GSA_NAME}-key.json" --iam-account="${LOKI_SERVICE_ACCOUNT}"

gcloud projects add-iam-policy-binding ${PROJECT_ID} \
    --member="serviceAccount:${LOKI_SERVICE_ACCOUNT}" \
    --role="${ROLE2}"

gcloud projects add-iam-policy-binding ${PROJECT_ID} \
    --member="serviceAccount:${LOKI_SERVICE_ACCOUNT}" \
    --role="${ROLE3}"

7. Create a Loki Secret #

kubectl create secret generic loki-service-account --from-file="${LOKI_GSA_NAME}-key.json"

8. Build a Helm Values File #

Create a values.yaml file, inserting the variables we’ve created in the previous steps:

Values.yaml

cat <<EOF > ${NAME}.values.yaml
deployTarget: "GOOGLE"

pachd:
  enabled: true
  externalService:
    enabled: false
  image:
    tag: "2.10.0"
  lokiDeploy: true
  lokiLogging: true
  storage:
    google:
      bucket: "${BUCKET_NAME}"
  serviceAccount:
    additionalAnnotations:
      iam.gke.io/gcp-service-account: "${SERVICE_ACCOUNT}"
    create: true
    name: "pachyderm"
  worker:
    serviceAccount:
      additionalAnnotations:
        iam.gke.io/gcp-service-account: "${SERVICE_ACCOUNT}"
      create: true
      name: "pachyderm-worker"

cloudsqlAuthProxy:
  connectionName: ${CLOUDSQL_CONNECTION_NAME}
  serviceAccount: "${SERVICE_ACCOUNT}"
  enabled: true
  resources: 
    requests:
      memory: "500Mi"
      cpu: "250m"

postgresql:
  enabled: false

global:
  postgresql:
    postgresqlAuthType: "scram-sha-256"
    postgresqlHost: "cloudsql-auth-proxy.${K8S_NAMESPACE}.svc.cluster.local."
    postgresqlPort: "5432"
    postgresqlSSL: "disable"
    postgresqlUsername: "postgres"
    postgresqlPassword: "${SQL_ADMIN_PASSWORD}"

loki-stack:
  loki:
    env:
      - name: GOOGLE_APPLICATION_CREDENTIALS
        value: /etc/secrets/${LOKI_GSA_NAME}-key.json
    extraVolumes:
      - name: loki-service-account
        secret:
          secretName: loki-service-account
    extraVolumeMounts:
      - name: loki-service-account
        mountPath: /etc/secrets
    config:
      schema_config:
        configs:
          - from: 1989-11-09
            object_store: gcs
            store: boltdb-shipper
            schema: v11
            index:
              period: 24h
              prefix: loki_index_
            chunks:
              prefix: loki_chunks_
      storage_config:
        boltdb_shipper:
          active_index_directory: /data/loki/boltdb-shipper-active
          cache_location: /data/loki/boltdb-shipper-cache
          cache_ttl: 24h
          shared_store: gcs
        gcs:
          bucket_name: "${LOKI_BUCKET_NAME}"
  grafana:
    enabled: false

proxy:
  enabled: true
  storage:
    backend: "GOOGLE"
    storageURL: "gs://${BUCKET_NAME}"
    google: 
      cred: | 
        {
          "type": "service_account",
          "project_id": "${PROJECT_ID}",
          "private_key_id": "${PRIVATE_KEY_ID}",
          "private_key": "${PRIVATE_KEY}",
          "client_email": "${SERVICE_ACCOUNT}",
          "client_id": "${CLIENT_ID}",
          "auth_uri": "https://accounts.google.com/o/oauth2
          "token_uri": "https://oauth2.googleapis.com/token",
          "auth_provider_x509_cert_url": "https://www.googleapis.com/oauth2/v1/certs",
          "client_x509_cert_url": ""
        }

  service:  
    type: LoadBalancer
    loadBalancerIP: ${STATIC_IP_ADDR} 
    httpPort: 80  
    httpsPort: 443 
  tls: 
    enabled: false
EOF

Info

If your Postgres deployment requires SSL, you may need to update the parameters in the global section of your Helm Chart Values (HCVs)

Install using the following command:

helm repo add pachyderm https://helm.pachyderm.com
helm repo update
helm install pachyderm -f ./${NAME}.values.yaml pachyderm/pachyderm

9. Connect to Cluster #

You’ll need your organization’s cluster URL (proxy.host) value to connect.

Run the following command to get your cluster URL:

kubectl get services | grep pachyderm-proxy | awk '{print $4}'

Connect to your cluster:

pachctl connect grpc://<your-proxy.host-value>:80

pachctl connect grpcs://<your-proxy.host-value>:443

You can optionally run port-forward to connect to console in your dashboard at http://localhost:4000/.