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woopwoop
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Rename batch to historical
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‎docs/administration/troubleshooting.md‎

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@@ -15,7 +15,7 @@ docker ps
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```text
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CONTAINER ID IMAGE COMMAND CREATED STATUS PORTS NAMES
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d7447205bced jupyter/datascience-notebook:latest "tini -g -- start-no…" 2 minutes ago Up 2 minutes 0.0.0.0:8888->8888/tcp feast_jupyter_1
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8e49dbe81b92 gcr.io/kf-feast/feast-serving:latest "java -Xms1024m -Xmx…" 2 minutes ago Up 5 seconds 0.0.0.0:6567->6567/tcp feast_batch_serving_1
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8e49dbe81b92 gcr.io/kf-feast/feast-serving:latest "java -Xms1024m -Xmx…" 2 minutes ago Up 5 seconds 0.0.0.0:6567->6567/tcp feast_historical_serving_1
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b859494bd33a gcr.io/kf-feast/feast-serving:latest "java -jar /opt/feas…" 2 minutes ago Up About a minute 0.0.0.0:6566->6566/tcp feast_online_serving_1
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5c4962811767 gcr.io/kf-feast/feast-core:latest "java -jar /opt/feas…" 2 minutes ago Up 2 minutes 0.0.0.0:6565->6565/tcp feast_core_1
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1ba7239e0ae0 confluentinc/cp-kafka:5.2.1 "/etc/confluent/dock…" 2 minutes ago Up 2 minutes 0.0.0.0:9092->9092/tcp, 0.0.0.0:9094->9094/tcp feast_kafka_1
@@ -60,7 +60,7 @@ You will probably need to connect using the hostnames of services and standard F
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```bash
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export FEAST_CORE_URL=core:6565
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export FEAST_ONLINE_SERVING_URL=online_serving:6566
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export FEAST_BATCH_SERVING_URL=batch_serving:6567
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export FEAST_historical_serving_URL=historical_serving:6567
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```
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### **Docker Compose \(from outside the docker cluster\)**
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```bash
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export FEAST_CORE_URL=localhost:6565
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export FEAST_ONLINE_SERVING_URL=localhost:6566
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export FEAST_BATCH_SERVING_URL=localhost:6567
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export FEAST_historical_serving_URL=localhost:6567
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```
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### **Google Kubernetes Engine \(GKE\)**
@@ -81,7 +81,7 @@ You will need to find the external IP of one of the nodes as well as the NodePor
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export FEAST_IP=$(kubectl describe nodes | grep ExternalIP | awk '{print $2}' | head -n 1)
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export FEAST_CORE_URL=${FEAST_IP}:32090
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export FEAST_ONLINE_SERVING_URL=${FEAST_IP}:32091
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export FEAST_BATCH_SERVING_URL=${FEAST_IP}:32092
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export FEAST_historical_serving_URL=${FEAST_IP}:32092
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```
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`netcat`, `telnet`, or even `curl` can be used to test whether all services are available and ports are open, but `grpc_cli` is the most powerful. It can be installed from [here](https://github.com/grpc/grpc/blob/master/doc/command_line_tool.md).
@@ -107,7 +107,7 @@ ListProjects
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### Testing Feast Batch Serving and Online Serving
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```bash
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grpc_cli ls ${FEAST_BATCH_SERVING_URL} feast.serving.ServingService
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grpc_cli ls ${FEAST_historical_serving_URL} feast.serving.ServingService
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```
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```text
@@ -145,7 +145,7 @@ In order to print the logs from these services, please run the commands below.
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```
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```text
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docker logs -f feast_batch_serving_1
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docker logs -f feast_historical_serving_1
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```
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```text

‎docs/getting-started/deploying-feast/docker-compose.md‎

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@@ -80,7 +80,7 @@ Configure the `.env` file based on your environment. At the very least you have
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| Parameter | Description |
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| :--- | :--- |
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| `FEAST_CORE_GCP_SERVICE_ACCOUNT_KEY` | This should be your service account file name, for example `key.json`. |
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| `FEAST_BATCH_SERVING_GCP_SERVICE_ACCOUNT_KEY` | This should be your service account file name, for example `key.json` |
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| `FEAST_historical_serving_GCP_SERVICE_ACCOUNT_KEY` | This should be your service account file name, for example `key.json` |
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| `FEAST_JUPYTER_GCP_SERVICE_ACCOUNT_KEY` | This should be your service account file name, for example `key.json` |
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### 3.3 Configure Historical Serving

‎docs/installation/gke.md‎

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@@ -80,7 +80,7 @@ For this guide we will use `NodePort` for exposing Feast services. In order to d
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export FEAST_IP=$(kubectl describe nodes | grep ExternalIP | awk '{print $2}' | head -n 1)
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export FEAST_CORE_URL=${FEAST_IP}:32090
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export FEAST_ONLINE_SERVING_URL=${FEAST_IP}:32091
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export FEAST_BATCH_SERVING_URL=${FEAST_IP}:32092
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export FEAST_historical_serving_URL=${FEAST_IP}:32092
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```
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Add firewall rules to open up ports on your Google Cloud Platform project:

‎examples/basic/basic.ipynb‎

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@@ -43,7 +43,7 @@
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"FEAST_ONLINE_SERVING_URL = os.getenv('FEAST_ONLINE_SERVING_URL', 'localhost:6566')\n",
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"\n",
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"# Feast Batch Serving allows for the retrieval of historical feature data\n",
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"FEAST_BATCH_SERVING_URL = os.getenv('FEAST_BATCH_SERVING_URL', 'localhost:6567')"
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"FEAST_historical_serving_URL = os.getenv('FEAST_historical_serving_URL', 'localhost:6567')"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"batch_client = Client(core_url=FEAST_CORE_URL, serving_url=FEAST_BATCH_SERVING_URL)"
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"batch_client = Client(core_url=FEAST_CORE_URL, serving_url=FEAST_historical_serving_URL)"
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]
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},
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{

‎examples/feast-xgboost-churn-prediction-tutorial/Telecom Customer Churn Prediction (with Feast and XGBoost).ipynb‎

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"FEAST_ONLINE_SERVING_URL = os.getenv('FEAST_ONLINE_SERVING_URL', 'localhost:6566')\n",
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"\n",
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"# Feast Batch Serving allows for the retrieval of historical feature data\n",
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"FEAST_BATCH_SERVING_URL = os.getenv('FEAST_BATCH_SERVING_URL', 'localhost:6567')"
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"FEAST_historical_serving_URL = os.getenv('FEAST_historical_serving_URL', 'localhost:6567')"
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]
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},
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{
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" def __init__(self, features, target, model_path=None):\n",
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" # Set up Feast clients to retrieve training and online serving data\n",
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" self._feast_online_client = Client(serving_url=os.environ['FEAST_ONLINE_SERVING_URL'])\n",
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" self._feast_batch_client = Client(serving_url=os.environ['FEAST_BATCH_SERVING_URL'],\n",
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" self._feast_batch_client = Client(serving_url=os.environ['FEAST_historical_serving_URL'],\n",
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" core_url=os.environ['FEAST_CORE_URL'])\n",
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" \n",
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" # Path to either save models after training or load models for serving\n",

‎infra/charts/feast/charts/feast-jupyter/templates/deployment.yaml‎

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@@ -38,7 +38,7 @@ spec:
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value: "{{ .Release.Name }}-feast-core:6565"
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- name: FEAST_ONLINE_SERVING_URL
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value: "{{ .Release.Name }}-feast-online-serving:6566"
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- name: FEAST_BATCH_SERVING_URL
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- name: FEAST_historical_serving_URL
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value: "{{ .Release.Name }}-feast-batch-serving:6566"
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{{- if .Values.gcpServiceAccount.enabled }}
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- name: GOOGLE_APPLICATION_CREDENTIALS

‎infra/charts/feast/templates/tests/test-feast-batch-serving.yaml‎

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@@ -70,14 +70,14 @@ spec:
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entity_rows_df = df.copy(deep=True).rename(columns={"datetime": "event_timestamp"})[["event_timestamp", "customer_id"]]
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pandavro.to_avro("entity_rows.avro", entity_rows_df)
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batch_serving_job = client.get_historical_features(
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historical_serving_job = client.get_historical_features(
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entity_rows="file://entity_rows.avro",
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feature_refs=[
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f"{project}/daily_transactions:1",
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f"{project}/total_transactions:1",
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]
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)
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result_df = batch_serving_job.to_dataframe()
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result_df = historical_serving_job.to_dataframe()
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print("Retrieved dataframe: ")
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print(result_df)

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