Fix: Rename example_repo.py to feature_definitions.py in templates

Author: Anshi Shrivastava

docs/getting-started/quickstart.md

@@ -97,7 +97,7 @@ Creating a new Feast repository in /home/Jovyan/my_project.
 Let's take a look at the resulting demo repo itself. It breaks down into
 
 * `data/` contains raw demo parquet data
-* `example_repo.py` contains demo feature definitions
+* `feature_definitions.py` contains demo feature definitions
 * `feature_store.yaml` contains a demo setup configuring where data sources are
 * `test_workflow.py` showcases how to run all key Feast commands, including defining, retrieving, and pushing features.
   You can run this with `python test_workflow.py`.
@@ -117,7 +117,7 @@ entity_key_serialization_version: 3
 ```
 {% endtab %}
 
-{% tab title="example_repo.py" %}
+{% tab title="feature_definitions.py" %}
 ```python
 # This is an example feature definition file
 
@@ -310,7 +310,7 @@ We'll walk through some snippets of code below and explain
 ### Step 4: Register feature definitions and deploy your feature store
 
 The `apply` command scans python files in the current directory for feature view/entity definitions, registers the 
-objects, and deploys infrastructure. In this example, it reads `example_repo.py` and sets up SQLite online store tables. Note that we had specified SQLite as the default online store by 
+objects, and deploys infrastructure. In this example, it reads `feature_definitions.py` and sets up SQLite online store tables. Note that we had specified SQLite as the default online store by 
 configuring `online_store` in `feature_store.yaml`.
 
 {% tabs %}
@@ -496,7 +496,7 @@ print(training_df.head())
 {% endtabs %}
 ### Step 6: Ingest batch features into your online store
 
-We now serialize the latest values of features since the beginning of time to prepare for serving. Note, `materialize_incremental` serializes all new features since the last `materialize` call, or since the time provided minus the `ttl` timedelta. In this case, this will be `CURRENT_TIME - 1 day` (`ttl` was set on the `FeatureView` instances in [feature_repo/feature_repo/example_repo.py](feature_repo/feature_repo/example_repo.py)).
+We now serialize the latest values of features since the beginning of time to prepare for serving. Note, `materialize_incremental` serializes all new features since the last `materialize` call, or since the time provided minus the `ttl` timedelta. In this case, this will be `CURRENT_TIME - 1 day` (`ttl` was set on the `FeatureView` instances in [feature_repo/feature_repo/feature_definitions.py](feature_repo/feature_repo/feature_definitions.py)).
 
 {% tabs %}
 {% tab title="Bash (with timestamp)" %}

docs/reference/online-stores/dragonfly.md

@@ -48,7 +48,7 @@ There are several options available to get Dragonfly up and running quickly. We
 
 `feast apply`
 
-The `apply` command scans python files in the current directory (`example_repo.py` in this case) for feature view/entity definitions, registers the objects, and deploys infrastructure.
+The `apply` command scans python files in the current directory (`feature_definitions.py` in this case) for feature view/entity definitions, registers the objects, and deploys infrastructure.
 You should see the following output:
 
 ```

examples/rhoai-quickstart/feast-demo-quickstart.ipynb

@@ -230,7 +230,7 @@
    "id": "cd23ce19-03fa-4353-86d3-261c7f514fd8",
    "metadata": {},
    "source": [
-    "File `data/driver_stats.parquet` is generated by the `feast init` command and it acts a historical information source to this example. We have defined this source in the [my_feast_project/feature_repo/example_repo.py](./my_feast_project/feature_repo/example_repo.py) file.\n",
+    "File `data/driver_stats.parquet` is generated by the `feast init` command and it acts a historical information source to this example. We have defined this source in the [my_feast_project/feature_repo/feature_definitions.py](./my_feast_project/feature_repo/feature_definitions.py) file.\n",
     "\n",
     "```python\n",
     "driver_stats_source = FileSource(\n",

sdk/python/feast/templates/aws/README.md

@@ -2,7 +2,7 @@
 A quick view of what's in this repository:
 
 * `data/` contains raw demo parquet data
-* `example_repo.py` contains demo feature definitions
+* `feature_definitions.py` contains demo feature definitions
 * `feature_store.yaml` contains a demo setup configuring where data sources are
 * `test_workflow.py` showcases how to run all key Feast commands, including defining, retrieving, and pushing features. 
 

sdk/python/feast/templates/aws/bootstrap.py

@@ -55,7 +55,7 @@ def bootstrap():
         )
 
     repo_path = pathlib.Path(__file__).parent.absolute() / "feature_repo"
-    example_py_file = repo_path / "example_repo.py"
+    example_py_file = repo_path / "feature_definitions.py"
     replace_str_in_file(example_py_file, "%REDSHIFT_DATABASE%", database)
 
     config_file = repo_path / "feature_store.yaml"

…mplates/aws/feature_repo/example_repo.py …/aws/feature_repo/feature_definitions.pysdk/python/feast/templates/aws/feature_repo/example_repo.py renamed to sdk/python/feast/templates/aws/feature_repo/feature_definitions.py sdk/python/feast/templates/aws/feature_repo/example_repo.py renamed to sdk/python/feast/templates/aws/feature_repo/feature_definitions.py

@@ -273,8 +273,8 @@ def bootstrap():
     driver_stats_path = data_path / "driver_stats.parquet"
     driver_df.to_parquet(path=str(driver_stats_path), allow_truncated_timestamps=True)
 
-    # example_repo.py
-    example_py_file = repo_path / "example_repo.py"
+    # feature_definitions.py
+    example_py_file = repo_path / "feature_definitions.py"
     replace_str_in_file(
         example_py_file, "%PARQUET_PATH%", str(driver_stats_path.relative_to(repo_path))
     )

sdk/python/feast/templates/cassandra/bootstrap.py

@@ -2,7 +2,7 @@
 A quick view of what's in this repository:
 
 * `data/` contains raw demo parquet data
-* `example_repo.py` contains demo feature definitions
+* `feature_definitions.py` contains demo feature definitions
 * `feature_store.yaml` contains a demo setup configuring where data sources are
 * `test_workflow.py` showcases how to run all key Feast commands, including defining, retrieving, and pushing features.