.. _hello_cross_val: Hello Cross-Site Validation =========================== Before You Start ---------------- Before jumping into this guide, make sure you have an environment with `NVIDIA FLARE `_ installed. You can follow :ref:`getting_started` on the general concept of setting up a Python virtual environment (the recommended environment) and how to install NVIDIA FLARE. Prerequisite ------------- This example builds on the :doc:`Hello Scatter and Gather ` example based on the :class:`ScatterAndGather` workflow. Please make sure you go through it completely as the concepts are heavily tied. Introduction ------------- This tutorial is meant to solely demonstrate how the NVIDIA FLARE system works, without introducing any actual deep learning concepts. Through this exercise, you will learn how to use NVIDIA FLARE with numpy to perform cross site validation after training. The training process is explained in the :doc:`Hello Scatter and Gather ` example. Using simplified weights and metrics, you will be able to clearly see how NVIDIA FLARE performs validation across different sites with little extra work. The setup of this exercise consists of one **server** and two **clients**. The server side model starting with weights ``[[1, 2, 3], [4, 5, 6], [7, 8, 9]]``. Cross site validation consists of the following steps: - During the initial phase of training with the :class:`ScatterAndGather` workflow, NPTrainer saves the local model to disk for the clients. - The :class:`CrossSiteModelEval` workflow gets the client models with the ``submit_model`` task. - The ``validate`` task is broadcast to the all participating clients with the model shareable containing the model data, and results from the ``validate`` task are saved. During this exercise, we will see how NVIDIA FLARE takes care of most of the above steps with little work from the user. We will be working with the ``hello-numpy-cross-val`` application in the examples folder. Custom FL applications can contain the folders: #. **custom**: contains the custom components (``np_trainer.py``, ``np_model_persistor.py``, ``np_validator.py``, ``np_model_locator``, ``np_formatter``) #. **config**: contains client and server configurations (``config_fed_client.json``, ``config_fed_server.json``) #. **resources**: contains the logger config (``log_config.json``) Let's get started. First clone the repo, if you haven't already: .. code-block:: shell $ git clone https://github.com/NVIDIA/NVFlare.git Remember to activate your NVIDIA FLARE Python virtual environment from the installation guide. Ensure numpy is installed. .. code-block:: shell (nvflare-env) $ python3 -m pip install numpy Now that you have all your dependencies installed, let's implement the Federated Learning system. Training -------------------------------- In the :doc:`Hello Scatter and Gather ` example, we implemented the ``NPTrainer`` object. In this example, we use the same ``NPTrainer`` but extend it to process the ``submit_model`` task to work with the :class:`CrossSiteModelEval` workflow to get the client models. The code in ``np_trainer.py`` saves the model to disk after each step of training in the model. Note that the server also produces a global model. The :class:`CrossSiteModelEval` workflow submits the server model for evaluation after the client models. Implementing the Validator -------------------------- The validator is an Executor that is called for validating the models received from the server during the :class:`CrossSiteModelEval` workflow. These models could be from other clients or models generated on server. .. literalinclude:: ../../nvflare/app_common/np/np_validator.py :language: python :lines: 15- :lineno-start: 15 :linenos: :caption: np_validator.py The validator is an Executor and implements the **execute** function which receives a Shareable. It handles the ``validate`` task by performing a calculation to find the sum divided by the max of the data and adding a ``random_epsilon`` before returning the results packaged with a DXO into a Shareable. .. note:: Note that in our hello-examples, we are demonstrating Federated Learning using data that does not have to do with deep learning. NVIDIA FLARE can be used with any data packaged inside a :ref:`Shareable ` object (subclasses ``dict``), and :ref:`DXO ` is recommended as a way to manage that data in a standard way. Cross site validation! ---------------------- We can run it using NVFlare simulator .. code-block:: bash python3 job_train_and_cse.py During the first phase, the model will be trained. During the second phase, cross site validation will happen. The workflow on the client will change to :class:`CrossSiteModelEval` as it enters this second phase. During cross site model evaluation, every client validates other clients' models and server models (if present). This can produce a lot of results. All the results will be kept in the job's workspace when it is completed. Understanding the Output ^^^^^^^^^^^^^^^^^^^^^^^^ You can find the running logs and results inside the simulator's workspace: .. code-block:: bash ls /tmp/nvflare/jobs/workdir/ server/ site-1/ site-2/ startup/ The cross site validation results: .. code-block:: bash cat /tmp/nvflare/jobs/workdir/server/simulate_job/cross_site_val/cross_val_results.json Congratulations! You've successfully run your numpy federated learning system with cross site validation. The full source code for this exercise can be found in :github_nvflare_link:`examples/hello-world/hello-numpy-cross-val `. Previous Versions of Hello Cross-Site Validation ------------------------------------------------ - `hello-numpy-cross-val for 2.0 `_ - `hello-numpy-cross-val for 2.1 `_ - `hello-numpy-cross-val for 2.2 `_ - `hello-numpy-cross-val for 2.3 `_ - `hello-numpy-cross-val for 2.4 `_