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Installing FedML

FedML supports Linux, MacOS, Windows, and Android.

FedML Source Code Repository

https://github.com/FedML-AI/FedML

Installing with pip

pip install fedml

The default machine learning engine is PyTorch. FedML also supports TensorFlow, Jax, and MXNet. You can install related engine as follows:

pip install "fedml[MPI]"
pip install "fedml[tensorflow]"
pip install "fedml[jax]"
pip install "fedml[mxnet]"

For MPI installation, it’s used for local distributed simulation with MPI (https://mpi4py.readthedocs.io/en/stable/). On MacOS, the installation commands in conda environment is:

conda install mpi4py openmpi

About OpenMPI library installation for MPI, the reference is as follows: https://docs.open-mpi.org/en/v5.0.x/installing-open-mpi/quickstart.html For OpenMPI on MacOS, please review the following links: https://betterprogramming.pub/integrating-open-mpi-with-clion-on-apple-m1-76b7815c27f2 https://formulae.brew.sh/formula/open-mpi

The above commands work properly in Linux environment. For Windows/Mac OS (Intel)/Mac OS (M1), you may need to follow TensorFlow/Jax/MXNet official guidance to fix related installation issues.

Installing FedML with Anaconda

conda create --name fedml-pip python=3.8
conda activate fedml-pip
conda install --name fedml-pip pip
pip install fedml

(Note: please use python 3.8 if you met any compatability issues. Currently, we support 3.7, 3.8, 3.9, 3.10, 3.11. on MacOS and Python 3.11, if you meet any issues, please run the command: brew install autoconf automake libffi libtool pkg-config, then run the command to install fedml again: pip install fedml)

After installation, please use “pip list | grep fedml” to check whether fedml is installed.

Installing FedML from Debugging and Editable Mode

cd python
pip install -e ./

Installing FedML from Source

git clone https://github.com/FedML-AI/FedML.git && \
cd ./FedML/python && \
python setup.py install

If you want to run examples with tensforflow, jax or mxnet, you need to install optional dependencies:

git clone https://github.com/FedML-AI/FedML.git && \
cd ./FedML/python && \
python install -e '.[tensorflow]'
python install -e '.[jax]'
python install -e '.[mxnet]'

(Notes: Tensorflow example located in tf_mqtt_s3_fedavg_mnist_lr_example directory, Jax example location in jax_haiku_mqtt_s3_fedavg_mnist_lr_example directory)

If you need to install from a specific commit (normally used for the debugging/development phase), please follow commands below:

git clone https://github.com/FedML-AI/FedML.git && \
cd ./FedML && git checkout e798061d62560b03e049d514e7cc8f1a753fde6b && \
cd python && \
python setup.py install

Please change the above commit id to your own (you can find it at https://github.com/FedML-AI/FedML/commits/master)

Running FedML on Kubernetes

This tutorial will guide you to deploy your fedml client and server to target Kubernetes pods running on GPU/CPU physical nodes.

The entire workflow is as follows:

(k8s deployment file is located in: https://github.com/FedML-AI/FedML/tree/master/installation/install_on_k8s/fedml-edge-client-server)

  1. In the file fedml-edge-client-server/deployment-client.yml, modify the variable ACCOUNT_ID to your desired value

  2. Deploy the fedml client: kubectl apply -f ./fedml-edge-client-server/deployment-client.yml

  3. In the file fedml-edge-client-server/deployment-server.yml, modify the variable ACCOUNT_ID to your desired value

  4. Deploy the fedml server: kubectl apply -f ./fedml-edge-client-server/deployment-server.yml

  5. Login the FedML MLOps platform (https://open.fedml.ai), the above deployed client and server will be found in the edge devices

If you want to scale up or scal down the pods to your desired count, you may run the following command:

kubectl scale -n $YourNameSpace --replicas=$YourDesiredPodsCount deployment/fedml-client-deployment

kubectl scale -n $YourNameSpace --replicas=$YourDesiredPodsCount deployment/fedml-server-deployment

Q&A

  1. Q: How to scale up or scale down?
    A: Use the following commands:

kubectl scale -n $YourNameSpace --replicas=$YourDesiredPodsCount deployment/fedml-client-deployment

kubectl scale -n $YourNameSpace --replicas=$YourDesiredPodsCount deployment/fedml-server-deployment

  1. Q: FedML Client send online status to FedML Server via which protocol?
    A: Via MQTT

  2. Q: FedML Client send model, gradient parameters to FedML Server via which protocol?
    A: Use S3 protocol to store and exchange models and use MQTT to exchange messages between FedML Client and Server

  3. Q: Why do we need AWS S3?
    A: Use S3 protocol to store and exchange models.

Guidance for Windows Users

Please follow instructions at Windows Installation

Guidance for Raspberry Pi Users

Please follow instructions at Raspberry Pi Installation

Guidance for NVIDIA Jetson Devices

Please follow instructions at NVIDIA Jetson Device Installation

Testing if the installation succeeded

If the installation is successful, you will not see any issue when run import fedml.

(mnn37) chaoyanghe@Chaoyangs-MBP FedML-refactor % python
Python 3.7.7 (default, Mar 26 2020, 10:32:53) 
[Clang 4.0.1 (tags/RELEASE_401/final)] :: Anaconda, Inc. on darwin
Type "help", "copyright", "credits" or "license" for more information.
>>> import fedml
>>>

Installing FedML Android SDK/APP

Please follow the instructions at https://github.com/FedML-AI/FedML/java/README.md

Troubleshooting

If you met any issues during installation or have additional installation requirements, please post issues at https://github.com/FedML-AI/FedML/issues