Remove local sandbox instructions from all examples.

scaleoutsystems · Jul 12, 2024 · 2922043 · 2922043
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diff --git a/examples/FedSimSiam/README.rst b/examples/FedSimSiam/README.rst
@@ -73,53 +73,4 @@ FedSimSiam's encoder, and evaluated on the feature embeddings of the test images
 
 This is a common method to track FedSimSiam's training progress, as FedSimSiam aims to minimize the distance between the embeddings of similar images.
 A high accuracy implies that the feature embeddings for images within the same class are indeed close to each other in the
-embedding space, i.e., FedSimSiam learned useful feature embeddings.
-
-
-Running FEDn in local development mode:
----------------------------------------
-
-Follow the steps above to install FEDn, generate 'package.tgz' and 'seed.tgz'.
-
-Start a pseudo-distributed FEDn network using docker-compose:
-.. code-block::
-
-   docker compose \
-    -f ../../docker-compose.yaml \
-    -f docker-compose.override.yaml \
-    up
-
-This starts up local services for MongoDB, Minio, the API Server, one Combiner and two clients. 
-You can verify the deployment using these urls: 
-
-- API Server: http://localhost:8092/get_controller_status
-- Minio: http://localhost:9000
-- Mongo Express: http://localhost:8081
-
-Upload the package and seed model to FEDn controller using the APIClient:
-
-.. code-block::
-
-   from fedn import APIClient
-   client = APIClient(host="localhost", port=8092)
-   client.set_active_package("package.tgz", helper="numpyhelper")
-   client.set_active_model("seed.npz")
-
-
-You can now start a training session with 100 rounds using the API client:
-
-.. code-block::
-
-   client.start_session(rounds=100)
-
-Clean up 
---------
-
-You can clean up by running
-
-.. code-block::
-
-   docker-compose \
-   -f ../../docker-compose.yaml \
-   -f docker-compose.override.yaml \
-   down -v
+embedding space, i.e., FedSimSiam learned useful feature embeddings.
diff --git a/examples/flower-client/README.rst b/examples/flower-client/README.rst
@@ -47,10 +47,10 @@ a FEDn network. Here you have two main options: using FEDn Studio
 (recommended for new users), or a self-managed pseudo-distributed deployment
 on your own machine. 
 
-If you are using FEDn Studio (recommended):
+Using FEDn Studio:
 -------------------------------------------
 
-Follow instructions here to register for Studio and start a project: https://fedn.readthedocs.io/en/stable/studio.html.
+Follow instructions here to register for Studio and start a project: https://fedn.readthedocs.io/en/stable/quickstart.html.
 
 In your Studio project: 
 
@@ -73,47 +73,13 @@ Or, if you prefer to use Docker (this might take a long time):
    -v $PWD/client.yaml:/app/client.yaml \
    -e CLIENT_NUMBER=0 \
    -e FEDN_PACKAGE_EXTRACT_DIR=package \
-   ghcr.io/scaleoutsystems/fedn/fedn:0.9.0 run client -in client.yaml --secure=True --force-ssl
-
-
-If you are running FEDn in local development mode:
---------------------------------------------------
-
-Deploy a FEDn network on local host (see `https://fedn.readthedocs.io/en/stable/quickstart.html#local-development-deployment-using-docker-compose`). 
-
-Use the FEDn API Client to initalize FEDn with the compute package and seed model: 
-
-.. code-block::
-
-   python init_fedn.py
-
-Create a file 'client.yaml' with the following content: 
-
-.. code-block::
-   
-   network_id: fedn-network
-   discover_host: api-server
-   discover_port: 8092
-   name: myclient
-
-Then start the client (using Docker)
-
-.. code-block::
-
-   docker run \
-   -v $PWD/client.yaml:/app/client.yaml \
-   --network=fedn_default \
-   -e CLIENT_NUMBER=0 \
-   -e FEDN_PACKAGE_EXTRACT_DIR=package \
-   ghcr.io/scaleoutsystems/fedn/fedn:0.9.0 run client -in client.yaml
-
+   ghcr.io/scaleoutsystems/fedn/fedn:0.11.1 run client -in client.yaml --secure=True --force-ssl
 
 Scaling to multiple clients
 ------------------------------------------------------------------
 
-To scale the experiment with additional clients on the same host, execute the run command
-again from another terminal. If running from another host, add another 'client.yaml', install 
-fedn, and execute the run command. In both cases inject a client number as an environment 
+To scale the experiment with additional clients on the same host, generate another 'client.yaml' and execute the run command
+again from another terminal. Inject a client number as an environment 
 varible which is used for distributing data (see 'flwr_task.py').
 
 For Unix Operating Systems:

diff --git a/examples/monai-2D-mednist/README.rst b/examples/monai-2D-mednist/README.rst
@@ -19,11 +19,6 @@ Using FEDn Studio:
 -  `Python 3.8, 3.9, 3.10 or 3.11 <https://www.python.org/downloads>`__
 -  `A FEDn Studio account <https://fedn.scaleoutsystems.com/signup>`__
 
-If using pseudo-distributed mode with docker-compose:
-
--  `Docker <https://docs.docker.com/get-docker>`__
--  `Docker Compose <https://docs.docker.com/compose/install>`__
-
 
 Creating the compute package and seed model
 -------------------------------------------
@@ -74,11 +69,10 @@ below command we divide the dataset into 10 parts.
     python prepare_data.py 10
 
 
-
 Using FEDn Studio
 -----------------
 
-Follow the guide here to set up your FEDn Studio project and learn how to connect clients (using token authentication): `Studio guide <https://fedn.readthedocs.io/en/stable/studio.html>`__.
+Follow the guide here to set up your FEDn Studio project and learn how to connect clients (using token authentication): `Studio guide <https://fedn.readthedocs.io/en/stable/quickstart.html>`__.
 On the step "Upload Files", upload 'package.tgz' and 'seed.npz' created above.
 
 Connecting clients:
@@ -110,83 +104,4 @@ For convenience, there is a Docker image hosted on ghrc.io with fedn preinstalle
      -e FEDN_DATA_PATH=/app/data/ \
      -e FEDN_CLIENT_SETTINGS_PATH=/app/client_settings.yaml \
      -e FEDN_DATA_SPLIT_INDEX=0 \
-     ghcr.io/scaleoutsystems/fedn/fedn:0.9.0 run client -in client.yaml --force-ssl --secure=True
-
-
-**NOTE: The following instructions are only for SDK-based client communication and for local development environments using Docker.**
-
-
-Local development mode using Docker/docker compose
---------------------------------------------------
-
-Follow the steps above to install FEDn, generate 'package.tgz' and 'seed.tgz'.
-
-Start a pseudo-distributed FEDn network using docker-compose:
-
-.. code-block::
-
-   docker compose \
-    -f ../../docker-compose.yaml \
-    -f docker-compose.override.yaml \
-    up
-
-This starts up local services for MongoDB, Minio, the API Server, one Combiner and two clients.
-You can verify the deployment using these urls:
-
-- API Server: http://localhost:8092/get_controller_status
-- Minio: http://localhost:9000
-- Mongo Express: http://localhost:8081
-
-Upload the package and seed model to FEDn controller using the APIClient. In Python:
-
-.. code-block::
-
-   from fedn import APIClient
-   client = APIClient(host="localhost", port=8092)
-   client.set_active_package("package.tgz", helper="numpyhelper")
-   client.set_active_model("seed.npz")
-
-You can now start a training session with 5 rounds (default):
-
-.. code-block::
-
-   client.start_session()
-
-Automate experimentation with several clients
-=============================================
-
-If you want to scale the number of clients, you can do so by modifying ``docker-compose.override.yaml``. For example,
-in order to run with 3 clients, change the environment variable ``FEDN_NUM_DATA_SPLITS`` to 3, and add one more client
-by copying ``client1``.
-
-
-Access message logs and validation data from MongoDB  
-====================================================
-
-You can access and download event logs and validation data via the API, and you can also as a developer obtain 
-the MongoDB backend data using pymongo or via the MongoExpress interface: 
-
-- http://localhost:8081/db/fedn-network/ 
-
-The credentials are as set in docker-compose.yaml in the root of the repository. 
-
-Access global models   
-====================
-
-You can obtain global model updates from the 'fedn-models' bucket in Minio: 
-
-- http://localhost:9000
-
-Reset the FEDn deployment   
-=========================
-
-To purge all data from a deployment incuding all session and round data, access the MongoExpress UI interface and 
-delete the entire ``fedn-network`` collection. Then restart all services. 
-
-Clean up
-========
-You can clean up by running 
-
-.. code-block::
-
-   docker-compose -f ../../docker-compose.yaml -f docker-compose.override.yaml down -v
+     ghcr.io/scaleoutsystems/fedn/fedn:0.11.1 run client -in client.yaml --force-ssl --secure=True