AWS
A quick start guide for Featureform on AWS EKS.
This quickstart will walk through creating a few simple features, labels, and a training set using Postgres and Redis. We will use a transaction fraud training set.
- Python 3.7+
Install the Featureform SDK via Pip.
pip install featureform
You can follow our Minikube or Kubernetes deployment guide. This will walk through a simple AWS deployment of Featureform with our quick start Helm chart containing Postgres and Redis.
eksctl create cluster \
--name featureform \
--version 1.24 \
--region us-east-1 \
--nodegroup-name linux-nodes \
--nodes 1 \
--nodes-min 1 \
--nodes-max 4 \
--with-oidc \
--managed
Newer versions of
eksctl require you to separately add a Container Storage Interface (CSI) driver to support Persistent Volume Claims. For complete details on adding the Amazon EBS CSI driver to your EKS cluster, see Managing the Amazon EBS CSI driver as an Amazon EKS add-on; however, the below examples should allow for a simple deployment.eksctl create iamserviceaccount \
--name ebs-csi-controller-sa \
--namespace kube-system \
--cluster featureform \
--region us-east-1 \
--attach-policy-arn arn:aws:iam::aws:policy/service-role/AmazonEBSCSIDriverPolicy \
--approve \
--role-only \
--role-name AmazonEKS_EBS_CSI_DriverRole
To easily find the account ID you used to create the cluster, run:
aws sts get-caller-identity --query Account --output text
Then, to add the Amazon EBS CSI add-on, run:
eksctl create addon \
--name aws-ebs-csi-driver \
--cluster featureform \
--region us-east-1 \
--service-account-role-arn arn:aws:iam::<AWS ACCOUNT ID>:role/AmazonEKS_EBS_CSI_DriverRole \
--force
We'll be installing three Helm Charts: Featureform, the Quickstart Demo, and Certificate Manager.
First we need to add the Helm repositories.
helm repo add featureform https://storage.googleapis.com/featureform-helm/
helm repo add jetstack https://charts.jetstack.io
helm repo update
Prior to installing the Helm charts, export your
FEATUREFORM_HOST value:export FEATUREFORM_HOST=aws-eks-demo.featureform.com
Now we can install the Helm charts.
helm install certmgr jetstack/cert-manager \
--set installCRDs=true \
--version v1.8.0 \
--namespace cert-manager \
--create-namespace
helm install featureform featureform/featureform \
--set global.publicCert=true \
--set global.localCert=false \
--set global.hostname=$FEATUREFORM_HOST
helm install quickstart featureform/quickstart
The Quickstart helm chart creates a Postgres instance with preloaded data, as well as an empty Redis standalone instance. Now that they are deployed, we can write a config file in Python.
definitions.py
import featureform as ff
redis = ff.register_redis(
name = "redis-quickstart",
host="quickstart-redis", # The internal dns name for redis
port=6379,
description = "A Redis deployment we created for the Featureform quickstart"
)
postgres = ff.register_postgres(
name = "postgres-quickstart",
host="quickstart-postgres", # The internal dns name for postgres
port="5432",
user="postgres",
password="password",
database="postgres",
description = "A Postgres deployment we created for the Featureform quickstart"
)
Once we create our config file, we can apply it to our Featureform deployment.
featureform apply definitions.py
We will create a user profile for us, and set it as the default owner for all the following resource definitions.
definitions.py
ff.register_user("featureformer").make_default_owner()
Now we'll register our user fraud dataset in Featureform.
definitions.py
transactions = postgres.register_table(
name = "transactions",
variant = "kaggle",
description = "Fraud Dataset From Kaggle",
table = "Transactions", # This is the table's name in Postgres
)
Next, we'll define a SQL transformation on our dataset.
definitions.py
@postgres.sql_transformation(variant="quickstart")
def average_user_transaction():
"""the average transaction amount for a user """
return "SELECT CustomerID as user_id, avg(TransactionAmount) " \
"as avg_transaction_amt from {{transactions.kaggle}} GROUP BY user_id"
Next, we'll register a passenger entity to associate with a feature and label.
definitions.py
@ff.entity
class User:
# Register a column from our transformation as a feature
avg_transactions = ff.Feature(
average_user_transaction[["user_id", "avg_transaction_amt"]], # We can optional include the `timestamp_column` "timestamp" here
type=ff.Float32,
inference_store=redis,
)
# Register label from our base Transactions table
fraudulent = ff.Label(
transactions[["customerid", "isfraud"]], variant="quickstart", type=ff.Bool
)
Finally, we'll join together the feature and label into a training set.
definitions.py
ff.register_training_set(
"fraud_training", "quickstart",
label=("fraudulent", "quickstart"),
features=[("avg_transactions", "quickstart")],
)
Now that our definitions are complete, we can apply it to our Featureform instance.
featureform apply definitions.py
Once we have our training set and features registered, we can train our model.
import featureform as ff
client = ff.ServingClient()
dataset = client.training_set("fraud_training", "quickstart")
training_dataset = dataset.repeat(10).shuffle(1000).batch(8)
for row in training_dataset:
print(row.features(), row.label())
We can serve features in production once we deploy our trained model as well.
import featureform as ff
client = ff.ServingClient()
fpf = client.features([("avg_transactions", "quickstart")], {"user": "C1410926"})
# Run features through model
Last modified 5mo ago