Getting Started

Getting Started: Quick Start


$ python3 -m pip install nvflare

Clone NVFLARE repo to get examples, switch main branch (latest stable branch)

$ git clone
$ cd NVFlare
$ git switch 2.4

Note on branches:

  • The main branch is the default (unstable) development branch

  • The 2.1, 2.2, 2.3, and 2.4 etc. branches are the branches for each major release and minor patches

Quick Start with Simulator

Making sure the NVFLARE environment is set up correctly following Installation, you can run an example application with The FL Simulator using the following script:

nvflare simulator -w /tmp/nvflare/hello-numpy-sag -n 2 -t 2 examples/hello-world/hello-numpy-sag/jobs/hello-numpy-sag

Now you can watch the simulator run two clients (n=2) with two threads (t=2) and logs are saved in the /tmp/nvflare/hello-numpy-sag workspace.

Getting Started Guide

This Getting Started guide is geared towards new users of NVIDIA FLARE and walks through installation, the FL Simulator, and a simple “hello world” application.

Once you’re familiar with the platform, the Example Applications are a great next step. These examples introduce some of the key concepts of the platform and showcase the integration of popular libraries and frameworks like Numpy, Pytorch, Tensorflow, and MONAI.

Any FLARE application used with the FL Simulator can also be run in a real-world, distributed FL deployment. The Real-World FL section describes some of the considerations and tools used for establishing a secure, distributed FL workflow.



The server and client versions of nvflare must match, we do not support cross-version compatibility.

Supported Operating Systems

  • Linux

  • OSX (Note: some optional dependencies are not compatible, such as tenseal and openmined.psi)

Python Version

NVIDIA FLARE requires Python 3.8+.

Install NVIDIA FLARE in a virtual environment

It is highly recommended to install NVIDIA FLARE in a virtual environment if you are not using Containerized Deployment with Docker. This guide briefly describes how to create a virtual environment with venv.

Virtual Environments and Packages

Python’s official document explains the main idea about virtual environments. The module used to create and manage virtual environments is called venv. You can find more information there. We only describe a few necessary steps for a virtual environment for NVIDIA FLARE.

Depending on your OS and the Python distribution, you may need to install the Python’s venv package separately. For example, in Ubuntu 20.04, you need to run the following commands to continue creating a virtual environment with venv. Note that in newer versions of Ubuntu, you may need to make sure you are using Python 3.8 and not a newer version.

$ sudo apt update
$ sudo apt-get install python3-venv

Once venv is installed, you can use it to create a virtual environment with:

$ python3 -m venv nvflare-env

This will create the nvflare-env directory in current working directory if it doesn’t exist, and also create directories inside it containing a copy of the Python interpreter, the standard library, and various supporting files.

Activate the virtualenv by running the following command:

$ source nvflare-env/bin/activate

You may find that the pip and setuptools versions in the venv need updating:

(nvflare-env) $ python3 -m pip install -U pip
(nvflare-env) $ python3 -m pip install -U setuptools

Install Stable Release

Stable releases are available on NVIDIA FLARE PyPI:

$ python3 -m pip install nvflare


In addition to the dependencies included when installing nvflare, many of our example applications have additional packages that must be installed. Make sure to install from any requirement.txt files before running the examples. See nvflare/app_opt for modules and components with optional dependencies.

Containerized Deployment with Docker

Running NVIDIA FLARE in a Docker container is sometimes a convenient way to ensure a uniform OS and software environment across client and server systems. This can be used as an alternative to the bare-metal Python virtual environment described above and will use a similar installation to simplify transitioning between a bare metal and containerized environment.

To get started with a containerized deployment, you will first need to install a supported container runtime and the NVIDIA Container Toolkit to enable support for GPUs. System requirements and instructions for this can be found in the NVIDIA Container Toolkit Install Guide.

A simple Dockerfile is used to capture the base requirements and dependencies. In this case, we’re building an environment that will support PyTorch-based workflows, in particular the Hello PyTorch example. The base for this build is the NGC PyTorch container. On this base image, we will install the necessary dependencies and clone the NVIDIA FLARE GitHub source code into the root workspace directory.

Let’s first create a folder called build and then create a file inside named Dockerfile:

mkdir build
cd build
touch Dockerfile

Using any text editor to edit the Dockerfile and paste the following:



RUN python3 -m pip install -U pip
RUN python3 -m pip install -U setuptools
RUN python3 -m pip install nvflare

WORKDIR /workspace/
RUN git clone --branch ${NVF_BRANCH} --single-branch NVFlare

We can then build the new container by running docker build in the directory containing this Dockerfile, for example tagging it nvflare-pt:

docker build -t nvflare-pt . -f Dockerfile

This will result in a docker image, nvflare-pt:latest. You can run this container with Docker, in this example mounting a local my-workspace directory into the container for use as a persistent workspace:

mkdir my-workspace
docker run --rm -it --gpus all \
    --ipc=host --ulimit memlock=-1 --ulimit stack=67108864 \
    -w $(pwd -P)/my-workspace:/workspace/my-workspace \

Once the container is running, you can also exec into the container, for example if you need another terminal to start additional FLARE clients. First find the CONTAINER ID using docker ps, and then use that ID to exec into the container:

docker ps  # use the CONTAINER ID in the output
docker exec -it <CONTAINER ID> /bin/bash

This container can be used to run the FL Simulator or any FL server or client. When using the FL Simulator (described in the next section), you can simply mount in any directories needed for your FLARE application code, and run the Simulator within the Docker container with all dependencies installed.

Ways to Run NVFLARE

NVFLARE can currently support running with the FL Simulator, POC mode, or Production mode.

FL Simulator is lightweight and uses threads to simulate different clients. The code used for the simulator can be directly used in production mode.

POC mode is a quick way to get set up to run locally on one machine. The FL server and each client run on different processes or dockers.

Production mode is secure with TLS certificates - depending the choice the deployment, you can further choose:

Using non-HA, secure, local mode (all clients and server running on the same host), production mode is very similar to POC mode except it is secure.

Which mode should I choose for running NVFLARE?

  • For a quick research run, use the FL Simulator

  • For simulating real cases within the same machine, use POC or production (local, non-HA, secure) mode. POC has convenient nvflare poc commands for ease of use.

  • For all other cases, use production mode.

The FL Simulator

After installing the nvflare pip package, you have access to the NVFlare CLI including the FL Simulator. The Simulator allows you to start a FLARE server and any number of connected clients on your local workstation or laptop, and to quickly deploy an application for testing and debugging.

Basic usage for the FL Simulator is available with nvflare simulator -h:

$ nvflare simulator -h
usage: nvflare simulator [-h] [-w WORKSPACE] [-n N_CLIENTS] [-c CLIENTS] [-t THREADS] [-gpu GPU] [-m MAX_CLIENTS] job_folder

positional arguments:

optional arguments:
  -h, --help            show this help message and exit
  -w WORKSPACE, --workspace WORKSPACE
                        WORKSPACE folder
  -n N_CLIENTS, --n_clients N_CLIENTS
                        number of clients
  -c CLIENTS, --clients CLIENTS
                        client names list
  -t THREADS, --threads THREADS
                        number of parallel running clients
  -gpu GPU, --gpu GPU   list of GPU Device Ids, comma separated
  -m MAX_CLIENTS, --max_clients MAX_CLIENTS
                        max number of clients

Before we get into the Simulator, we’ll walk through a few additional setup steps in the next section required to run an example application.

Running an example application

Any of the Example Applications can be used with the FL Simulator. We’ll demonstrate the steps here using the hello-pt example.

First, we need to clone the NVFlare repo to get the source code for the examples:

$ git clone

Please make sure to switch to the correct branch that matches the NVFlare library version you installed.

$ git switch [nvflare version]

We can then copy the necessary files (the exercise code in the examples directory of the NVFlare repository) to a working directory:

mkdir simulator-example
cp -rf NVFlare/examples/hello-world/hello-pt simulator-example/

The hello-pt application requires a few dependencies to be installed. As in the installation section, we can install these in the Python virtual environment by running:

source nvflare-env/bin/activate
python3 -m pip install -r simulator-example/requirements.txt

If using the Dockerfile above to run in a container, these dependencies have already been installed.

Next, we can create a workspace for the Simulator to use for outputs of the application run, and launch the simulator using simulator-example/hello-pt/jobs/hello-pt as the input job directory. In this example, we’ll run on two clients using two threads:

mkdir simulator-example/workspace
nvflare simulator -w simulator-example/workspace -n 2 -t 2 simulator-example/hello-pt/jobs/hello-pt

Now you will see output streaming from the server and client processes as they execute the federated application. Once the run completes, your workspace directory will contain the input application configuration and codes, logs of the output, site and global models, cross-site validation results.

$ tree -L 3 simulator-example/workspace/
├── audit.log
├── local
      └── log.config
    ├── simulate_job
      ├── app_server
         ├── config
         ├── custom
      ├── app_site-1
         ├── audit.log
         ├── config
         ├── custom
         └── log.txt
      ├── app_site-2
         ├── audit.log
         ├── config
         ├── custom
         └── log.txt
      ├── cross_site_val
         ├── cross_val_results.json
         ├── model_shareables
         └── result_shareables
      ├── log.txt
      ├── models
      └── tb_events
          ├── site-1
          └── site-2
    └── startup

Now that we’ve explored an example application with the FL Simulator, we can look at what it takes to bring this type of application to a secure, distributed deployment in the Real World Federated Learning section.

Setting Up the Application Environment in POC Mode

To get started with a proof of concept (POC) setup after Installation, run this command to generate a poc folder with an overseer, server, two clients, and one admin client:

$ nvflare poc prepare -n 2

For more details, see Proof Of Concept (POC) Command.

Starting the Application Environment in POC Mode

Once you are ready to start the FL system, you can run the following command to start the server and client systems and an admin console:

nvflare poc start

To start the server and client systems without an admin console:

nvflare poc start -ex

For more details, see Proof Of Concept (POC) Command.