adding dotenv for credentials management

README.md

@@ -101,6 +101,17 @@ We'll start by setting up our cluster with the environment and compute configura
 
 </details>
 
+### Credentials
+```bash
+touch .env
+```
+```bash
+# Inside .env
+GITHUB_USERNAME="CHANGE_THIS_TO_YOUR_USERNAME"  # ← CHANGE THIS
+```bash
+source .env
+```
+
 ### Git setup
 
 Create a repository by following these instructions: [Create a new repository](https://github.com/new) → name it `Made-With-ML` → Toggle `Add a README file` (**very important** as this creates a `main` branch) → Click `Create repository` (scroll down)
@@ -109,7 +120,7 @@ Now we're ready to clone the repository that has all of our code:
 
 ```bash
 git clone https://github.com/GokuMohandas/Made-With-ML.git .
-git remote set-url origin https://github.com/GITHUB_USERNAME/Made-With-ML.git  # <-- CHANGE THIS to your username
+git remote set-url origin https://github.com/$GITHUB_USERNAME/Made-With-ML.git  # <-- CHANGE THIS to your username
 git checkout -b dev
 ```
 
@@ -317,15 +328,7 @@ python madewithml/predict.py predict \
   python madewithml/serve.py --run_id $RUN_ID
   ```
 
-  While the application is running, we can use it via cURL, Python, etc.:
-
-  ```bash
-  # via cURL
-  curl -X POST -H "Content-Type: application/json" -d '{
-    "title": "Transfer learning with transformers",
-    "description": "Using transformers for transfer learning on text classification tasks."
-  }' http://127.0.0.1:8000/predict
-  ```
+  Once the application is running, we can use it via cURL, Python, etc.:
 
   ```python
   # via Python
@@ -341,13 +344,6 @@ python madewithml/predict.py predict \
   ray stop  # shutdown
   ```
 
-```bash
-export HOLDOUT_LOC="https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/holdout.csv"
-curl -X POST -H "Content-Type: application/json" -d '{
-    "dataset_loc": "https://raw.githubusercontent.com/GokuMohandas/Made-With-ML/main/datasets/holdout.csv"
-  }' http://127.0.0.1:8000/evaluate
-```
-
 </details>
 
 <details open>
@@ -362,15 +358,7 @@ curl -X POST -H "Content-Type: application/json" -d '{
   python madewithml/serve.py --run_id $RUN_ID
   ```
 
-  While the application is running, we can use it via cURL, Python, etc.:
-
-  ```bash
-  # via cURL
-  curl -X POST -H "Content-Type: application/json" -d '{
-    "title": "Transfer learning with transformers",
-    "description": "Using transformers for transfer learning on text classification tasks."
-  }' http://127.0.0.1:8000/predict
-  ```
+  Once the application is running, we can use it via cURL, Python, etc.:
 
   ```python
   # via Python
@@ -399,7 +387,7 @@ export RUN_ID=$(python madewithml/predict.py get-best-run-id --experiment-name $
 pytest --run-id=$RUN_ID tests/model --verbose --disable-warnings
 
 # Coverage
-python3 -m pytest --cov madewithml --cov-report html
+python3 -m pytest tests/code --cov madewithml --cov-report html --disable-warnings
 ```
 
 ## Production

madewithml/__init__.py

@@ -0,0 +1,3 @@
+from dotenv import load_dotenv
+
+load_dotenv()

madewithml/config.py

@@ -1,5 +1,6 @@
 # config.py
 import logging
+import os
 import sys
 from pathlib import Path
 
@@ -10,9 +11,10 @@
 ROOT_DIR = Path(__file__).parent.parent.absolute()
 LOGS_DIR = Path(ROOT_DIR, "logs")
 LOGS_DIR.mkdir(parents=True, exist_ok=True)
+EFS_DIR = Path(f"/efs/shared_storage/madewithml/{os.environ.get('GITHUB_USERNAME', '')}")
 
 # Config MLflow
-MODEL_REGISTRY = Path("/tmp/mlflow")
+MODEL_REGISTRY = Path(f"{EFS_DIR}/mlflow")
 Path(MODEL_REGISTRY).mkdir(parents=True, exist_ok=True)
 MLFLOW_TRACKING_URI = "file://" + str(MODEL_REGISTRY.absolute())
 mlflow.set_tracking_uri(MLFLOW_TRACKING_URI)

madewithml/predict.py

@@ -125,7 +125,7 @@ def predict(
     # Load components
     best_checkpoint = get_best_checkpoint(run_id=run_id)
     predictor = TorchPredictor.from_checkpoint(best_checkpoint)
-    preprocessor = predictor.get_preprocessor()
+    # preprocessor = predictor.get_preprocessor()
 
     # Predict
     sample_df = pd.DataFrame([{"title": title, "description": description, "tag": "other"}])

madewithml/serve.py

@@ -1,4 +1,5 @@
 import argparse
+import os
 from http import HTTPStatus
 from typing import Dict
 
@@ -75,5 +76,5 @@ async def _predict(self, request: Request) -> Dict:
     parser.add_argument("--run_id", help="run ID to use for serving.")
     parser.add_argument("--threshold", type=float, default=0.9, help="threshold for `other` class.")
     args = parser.parse_args()
-    ray.init()
+    ray.init(runtime_env={"env_vars": {"GITHUB_USERNAME": os.environ["GITHUB_USERNAME"]}})
     serve.run(ModelDeployment.bind(run_id=args.run_id, threshold=args.threshold))

madewithml/train.py

@@ -1,5 +1,6 @@
 import datetime
 import json
+import os
 from typing import Tuple
 
 import numpy as np
@@ -23,7 +24,7 @@
 from typing_extensions import Annotated
 
 from madewithml import data, models, utils
-from madewithml.config import MLFLOW_TRACKING_URI, logger
+from madewithml.config import EFS_DIR, MLFLOW_TRACKING_URI, logger
 
 # Initialize Typer CLI app
 app = typer.Typer()
@@ -200,10 +201,7 @@ def train_model(
     )
 
     # Run config
-    run_config = RunConfig(
-        callbacks=[mlflow_callback],
-        checkpoint_config=checkpoint_config,
-    )
+    run_config = RunConfig(callbacks=[mlflow_callback], checkpoint_config=checkpoint_config, storage_path=EFS_DIR)
 
     # Dataset
     ds = data.load_data(dataset_loc=dataset_loc, num_samples=train_loop_config["num_samples"])
@@ -252,5 +250,5 @@ def train_model(
 if __name__ == "__main__":  # pragma: no cover, application
     if ray.is_initialized():
         ray.shutdown()
-    ray.init()
+    ray.init(runtime_env={"env_vars": {"GITHUB_USERNAME": os.environ["GITHUB_USERNAME"]}})
     app()

madewithml/tune.py

@@ -1,5 +1,6 @@
 import datetime
 import json
+import os
 
 import ray
 import typer
@@ -19,7 +20,7 @@
 from typing_extensions import Annotated
 
 from madewithml import data, train, utils
-from madewithml.config import MLFLOW_TRACKING_URI, logger
+from madewithml.config import EFS_DIR, MLFLOW_TRACKING_URI, logger
 
 # Initialize Typer CLI app
 app = typer.Typer()
@@ -117,10 +118,7 @@ def tune_models(
         experiment_name=experiment_name,
         save_artifact=True,
     )
-    run_config = RunConfig(
-        callbacks=[mlflow_callback],
-        checkpoint_config=checkpoint_config,
-    )
+    run_config = RunConfig(callbacks=[mlflow_callback], checkpoint_config=checkpoint_config, storage_path=EFS_DIR)
 
     # Hyperparameters to start with
     initial_params = json.loads(initial_params)
@@ -178,5 +176,5 @@ def tune_models(
 if __name__ == "__main__":  # pragma: no cover, application
     if ray.is_initialized():
         ray.shutdown()
-    ray.init()
+    ray.init(runtime_env={"env_vars": {"GITHUB_USERNAME": os.environ["GITHUB_USERNAME"]}})
     app()