fix: fixing the call to train.py directly in python

else we have issues with the versions

use_case_examples/deployment/breast_cancer/README.md

@@ -8,14 +8,54 @@ To run this example on AWS you will also need to have the AWS CLI properly setup
 To do so please refer to [AWS documentation](https://docs.aws.amazon.com/cli/latest/userguide/cli-configure-quickstart.html).
 One can also run this example locally using Docker, or just by running the scripts locally.
 
-1. To train your model you can use `train.py`, or `train_with_docker.sh` to use Docker (recommended way).
+#### On the developer machine:
+
+1. To train your model you can
+      - use `train_with_docker.sh` to use Docker (recommended way),
+      - or, only if you know what you're doing and will manage synchronisation between versions, use `python train.py`
+
    This will train a model and [serialize the FHE circuit](../../../docs/guides/client_server.md) in a new folder called `./dev`.
-1. Once that's done you can use the script provided in Concrete ML in `use_case_examples/deployment/server/`, use `deploy_to_docker.py`.
 
-- `python use_case_examples/deployment/server/deploy_to_docker.py --path-to-model ./dev`
+#### On the server machine:
+
+1. Copy the './dev' directory from the developer machine.
+1. Launch the server via:
+
+```
+python use_case_examples/deployment/server/deploy_to_docker.py --path-to-model ./dev
+```
+
+This will let a server open on Port 5000.
+
+#### On the client machine:
+
+##### If you go for a Docker part on the client side:
+
+1. Launch the `build_docker_client_image.py` to build a client Docker image.
+1. Run the client with `client.sh` script. This will run the container in interactive mode.
+1. Then, in this Docker, you can launch the client script to interact with the server:
+
+```
+URL="<my_url>" python client.py
+```
+
+where `<my_url>` is the content of the `url.txt` file (if you don't set URL, the default is `0.0.0.0`; this defines the IP to use when running server in Docker on localhost).
+
+#### If you go for client side done in Python:
+
+1. Prepare the client side:
+
+```
+python3.8 -m venv .venvclient
+source .venvclient/bin/activate
+python build_docker_client_image.py
+
+pip install -r client_requirements.txt
+```
+1. Run the client script:
 
-3. Once that's done you can launch the `build_docker_client_image.py` script to build a client Docker image.
-1. You can then run the client by using the `client.sh` script. This will run the container in interactive mode.
-   To interact with the server you can launch the `client.py` script using `URL="<my_url>" python client.py` where `<my_url>` is the content of the `url.txt` file (default is `0.0.0.0`, ip to use when running server in Docker on localhost).
+```
+URL="http://localhost:8888" python client.py
+```
 
-And here it is you deployed a Concrete ML model and ran an inference using Fully Homormophic Encryption.
+And here it is! Whether you use Docker or Python for the client side, you deployed a Concrete ML model and ran an inference using Fully Homormophic Encryption. In particular, you can see that the FHE predictions are correct.

use_case_examples/deployment/breast_cancer/client.py

@@ -21,7 +21,7 @@
 
 from concrete.ml.deployment import FHEModelClient
 
-URL = os.environ.get("URL", f"http://localhost:5000")
+URL = os.environ.get("URL", f"http://localhost:8888")
 STATUS_OK = 200
 ROOT = Path(__file__).parent / "client"
 ROOT.mkdir(exist_ok=True)
@@ -105,4 +105,13 @@
         encrypted_result = result.content
         decrypted_prediction = client.deserialize_decrypt_dequantize(encrypted_result)[0]
         decrypted_predictions.append(decrypted_prediction)
-    print(decrypted_predictions)
+    print(f"Decrypted predictions are: {decrypted_predictions}")
+
+    decrypted_predictions_classes = numpy.array(decrypted_predictions).argmax(axis=1)
+    print(f"Decrypted prediction classes are: {decrypted_predictions_classes}")
+
+    # Let's check the results and compare them against the clear model
+    clear_prediction_classes = y[0:10]
+    accuracy = (clear_prediction_classes == decrypted_predictions_classes).mean()
+    print(f"Accuracy between FHE prediction and expected results is: {accuracy*100:.0f}%")
+

use_case_examples/deployment/breast_cancer/client_requirements.txt

@@ -1,3 +1,6 @@
 grequests
 requests
 tqdm
+numpy
+scikit-learn
+concrete-ml

use_case_examples/deployment/breast_cancer/train.py

@@ -20,4 +20,4 @@
     model.fit(X_train, y_train)
     model.compile(X_train)
     dev = FHEModelDev("./dev", model)
-    dev.save()
+    dev.save(via_mlir=True)

use_case_examples/deployment/server/deploy_to_docker.py

@@ -97,11 +97,13 @@ def main(path_to_model: Path, image_name: str):
     if args.only_build:
         return
 
+    PORT_TO_CHOOSE=8888
+
     # Run newly created Docker server
     try:
         with open("./url.txt", mode="w", encoding="utf-8") as file:
-            file.write("http://localhost:5000")
-        subprocess.check_output(f"docker run -p 5000:5000 {image_name}", shell=True)
+            file.write(f"http://localhost:{PORT_TO_CHOOSE}")
+        subprocess.check_output(f"docker run -p {PORT_TO_CHOOSE}:5000 {image_name}", shell=True)
     except KeyboardInterrupt:
         message = "Terminate container? (y/n) "
         shutdown_instance = input(message).lower()