Add reference to querying by RepoConfig

adchia · adchia · commit 9b7b2e1891e3 · 2022-05-16T10:48:20.000-04:00
Signed-off-by: Danny Chiao &lt;danny@tecton.ai&gt;
diff --git a/module_0/README.md b/module_0/README.md
@@ -441,7 +441,7 @@ We don't need to do anything new here since data scientists will be doing many o
 
 There are two ways data scientists can use Feast:
 - Use Feast primarily as a way of pulling production ready features. 
-  - See the `client/` folder for an example of how users can pull features by only having a `feature_store.yaml` 
+  - See the `client/` or `client_no_yaml` folders for examples of how users can pull features by only having a `feature_store.yaml` or instantiating a `RepoConfig` object
   - This is **not recommended** since data scientists cannot register feature services to indicate they depend on certain features in production. 
 - **[Recommended]** Have a local copy of the feature repository (e.g. `git clone`) and author / iterate / re-use features. 
   - Data scientist can:
diff --git a/module_0/client_no_yaml/test_fetch.py b/module_0/client_no_yaml/test_fetch.py
@@ -0,0 +1,25 @@
+from feast import FeatureStore, RepoConfig
+from feast.repo_config import RegistryConfig
+
+# TODO: replace with your bucket
+repo_config = RepoConfig(
+    registry=RegistryConfig(path="s3://feast-workshop-danny/registry.pb"),
+    project="feast_demo_aws",
+    provider="aws",
+    offline_store="file",  # Could also be the OfflineStoreConfig e.g. FileOfflineStoreConfig
+    online_store="null",  # Could also be the OnlineStoreConfig e.g. RedisOnlineStoreConfig
+)
+store = FeatureStore(config=repo_config)
+
+import pandas as pd
+
+# Get the latest feature values for unique entities
+entity_df = pd.DataFrame.from_dict({"driver_id": [1001, 1002, 1003, 1004, 1005],})
+entity_df["event_timestamp"] = pd.to_datetime("now", utc=True)
+training_df = store.get_historical_features(
+    entity_df=entity_df, features=store.get_feature_service("model_v2"),
+).to_df()
+
+# Make batch predictions
+# predictions = model.predict(training_df)
+print(training_df)
