Add Dockerfile for GCP CloudRun FeatureServer (#1887)

* Add `gcp_cloudrun` Dockerfile and resources

Signed-off-by: Judah Rand <17158624+judahrand@users.noreply.github.com>

* Remove unnecessary config

Signed-off-by: Judah Rand <17158624+judahrand@users.noreply.github.com>

* Sync CloudRun `app.py` with AWS Lambda

Signed-off-by: Judah Rand <17158624+judahrand@users.noreply.github.com>

sdk/python/feast/infra/feature_servers/gcp_cloudrun/Dockerfile

@@ -0,0 +1,28 @@
+FROM python:3.9-slim
+
+# Allow statements and log messages to immediately appear in the Knative logs
+ENV PYTHONUNBUFFERED True
+
+# Copy local code to the container image.
+ENV APP_HOME /app
+WORKDIR $APP_HOME
+
+# Copy app handler code
+COPY sdk/python/feast/infra/feature_servers/gcp_cloudrun/app.py ./app.py
+
+# Copy necessary parts of the Feast codebase
+COPY sdk/python ./sdk/python
+COPY protos ./protos
+COPY README.md ./README.md
+
+# Install production dependencies.
+RUN pip install --no-cache-dir \
+    -e 'sdk/python[gcp,redis]' \
+    -r ./sdk/python/feast/infra/feature_servers/gcp_cloudrun/requirements.txt
+
+# Run the web service on container startup. Here we use the gunicorn
+# webserver, with one worker process and 8 threads.
+# For environments with multiple CPU cores, increase the number of workers
+# to be equal to the cores available.
+# Timeout is set to 0 to disable the timeouts of the workers to allow Cloud Run to handle instance scaling.
+CMD exec gunicorn -k uvicorn.workers.UvicornWorker --bind :$PORT --workers 1 --threads 8 --timeout 0 app:app

sdk/python/feast/infra/feature_servers/gcp_cloudrun/app.py

@@ -0,0 +1,24 @@
+import base64
+import os
+import tempfile
+from pathlib import Path
+
+from feast import FeatureStore
+from feast.constants import FEATURE_STORE_YAML_ENV_NAME
+from feast.feature_server import get_app
+
+# Load RepoConfig
+config_base64 = os.environ[FEATURE_STORE_YAML_ENV_NAME]
+config_bytes = base64.b64decode(config_base64)
+
+# Create a new unique directory for writing feature_store.yaml
+repo_path = Path(tempfile.mkdtemp())
+
+with open(repo_path / "feature_store.yaml", "wb") as f:
+    f.write(config_bytes)
+
+# Initialize the feature store
+store = FeatureStore(repo_path=str(repo_path.resolve()))
+
+# Create the FastAPI app
+app = get_app(store)

sdk/python/feast/infra/feature_servers/gcp_cloudrun/config.py

@@ -0,0 +1,20 @@
+from pydantic import StrictBool
+from pydantic.typing import Literal
+
+from feast.repo_config import FeastConfigBaseModel
+
+
+class GcpCloudRunFeatureServerConfig(FeastConfigBaseModel):
+    """Feature server config for GCP CloudRun."""
+
+    type: Literal["gcp_cloudrun"] = "gcp_cloudrun"
+    """Feature server type selector."""
+
+    enabled: StrictBool = False
+    """Whether the feature server should be launched."""
+
+    public: StrictBool = True
+    """Whether the endpoint should be publicly accessible."""
+
+    auth: Literal["none", "api-key"] = "none"
+    """Authentication method for the endpoint."""

sdk/python/feast/infra/feature_servers/gcp_cloudrun/requirements.txt

@@ -0,0 +1 @@
+gunicorn

sdk/python/feast/repo_config.py

@@ -41,6 +41,7 @@
 
 FEATURE_SERVER_CONFIG_CLASS_FOR_TYPE = {
     "aws_lambda": "feast.infra.feature_servers.aws_lambda.config.AwsLambdaFeatureServerConfig",
+    "gcp_cloudrun": "feast.infra.feature_servers.gcp_cloudrun.config.GcpCloudRunFeatureServerConfig",
 }