Lift the MNIST example files.

examples/mnist/client.py

@@ -21,9 +21,11 @@ import datetime
 import logging
 import os
 
+import fire  # type: ignore
+
 import declearn
 import declearn.model.tensorflow
-import fire
+
 
 FILEDIR = os.path.dirname(__file__)
 
@@ -36,7 +38,6 @@ def run_client(
     serv_uri: str = "wss://localhost:8765",
     verbose: bool = True,
 ) -> None:
-
     """Instantiate and run a given client.
 
     Parameters
@@ -63,13 +64,7 @@ def run_client(
     declearn.utils.set_device_policy(gpu=False)
 
     # Set up logger and checkpointer
-    stamp = datetime.datetime.now()
-    stamp = stamp - datetime.timedelta(
-        minutes=stamp.minute % 5,
-        seconds=stamp.second,
-        microseconds=stamp.microsecond,
-    )
-    stamp = stamp.strftime("%y-%m-%d_%H-%M")
+    stamp = datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
     checkpoint = os.path.join(FILEDIR, f"result_{stamp}", client_name)
     logger = declearn.utils.get_logger(
         name=client_name,
@@ -124,11 +119,11 @@ def run_client(
 
 
 # This part should not be altered: it provides with an argument parser
-# for `python client.py`).
+# for `python client.py`.
 
 
 def main():
-    "fire-wrapped split data"
+    "Fire-wrapped `run_client`."
     fire.Fire(run_client)
 
 

examples/mnist/readme.md

@@ -5,16 +5,16 @@
 **We are going to train a common model between three simulated clients on the
 classic [MNIST dataset](http://yann.lecun.com/exdb/mnist/)**. The input of the
 model is a set of images of handwritten digits, and the model needs to
-determine which number between $0$ and $9$ each image corresponds to.
+determine to which digit between $0$ and $9$ each image corresponds.
 
 ## Setup
 
 To be able to experiment with this tutorial:
 
-* Clone the declearn repo, on the experimental branch:
+* Clone the declearn repo (you may specify a given release branch or tag):
 
 ```bash
-git clone -b experimental git@gitlab.inria.fr:magnet/declearn/declearn2.git declearn
+git clone git@gitlab.inria.fr:magnet/declearn/declearn2.git declearn
 ```
 
 * Create a dedicated virtual environment.
@@ -25,12 +25,14 @@ cd declearn && pip install .[websockets,tensorflow] && cd ..
 ```
 
 In an FL experiment, we consider your data as a given. So before running
-the experiment below, split the MNIST data using :
+the experiment below, download and split the MNIST data using:
 
 ```bash
-declearn-split --folder "examples/mnist" --n_shards 3 
+declearn-split --folder "examples/mnist" --n_shards 3
 ```
 
+You may add `--seed <some_number>` if you want to ensure reproducibility.
+
 ## Contents
 
 This script runs a FL experiment using MNIST. The folder is structured
@@ -60,14 +62,8 @@ details on what running the federated learning processes imply, see the last
 section.
 
 ```bash
-cd
-python run.py
-```
-
-Use :
-
-```bash
-python run.py  # note: python examples/heart-uci/run.py works as well
+cd declearn/examples/mnist/
+python run.py  # note: python declearn/examples/mnist/run.py works as well
 ```
 
 The `run.py` scripts collects the server and client routines defined under

examples/mnist/run.py

@@ -21,7 +21,7 @@ import glob
 import os
 import tempfile
 
-import fire
+import fire  # type: ignore
 
 from declearn.test_utils import generate_ssl_certificates, make_importable
 from declearn.utils import run_as_processes

examples/mnist/server.py

@@ -20,11 +20,12 @@
 import datetime
 import os
 
-import fire
+import fire  # type: ignore
 import tensorflow as tf  # type: ignore
 
 import declearn
 
+
 FILEDIR = os.path.dirname(os.path.abspath(__file__))
 DEFAULT_CERT = os.path.join(FILEDIR, "server-cert.pem")
 DEFAULT_PKEY = os.path.join(FILEDIR, "server-pkey.pem")
@@ -71,13 +72,7 @@ def run_server(
     )
 
     # Set up checkpointing and logging.
-    stamp = datetime.datetime.now()
-    stamp = stamp - datetime.timedelta(
-        minutes=stamp.minute % 5,
-        seconds=stamp.second,
-        microseconds=stamp.microsecond,
-    )
-    stamp = stamp.strftime("%y-%m-%d_%H-%M")
+    stamp = datetime.datetime.now().strftime("%y-%m-%d_%H-%M")
     checkpoint = os.path.join(FILEDIR, f"result_{stamp}", "server")
     # Set up a logger, records from which will go to a file.
     logger = declearn.utils.get_logger(
@@ -155,8 +150,12 @@ def run_server(
     )
 
     # Set up the experiment's hyper-parameters.
-    # Registration rules: wait for 10 seconds at registration.
-    register = declearn.main.config.RegisterConfig(timeout=10)
+    # Registration rules: wait for exactly `nb_clients`, at most 5 minutes.
+    register = declearn.main.config.RegisterConfig(
+        min_clients=nb_clients,
+        max_clients=nb_clients,
+        timeout=300,
+    )
     # Training rounds hyper-parameters. By default, 1 epoch / round.
     training = declearn.main.config.TrainingConfig(
         batch_size=32,
@@ -183,7 +182,7 @@ def run_server(
 
 
 def main():
-    "fire-wrapped split data"
+    "Fire-wrapped `run_server`."
     fire.Fire(run_server)