1. Preparation

In the following sections, we'll guide you through the process of setting up and using FedLearn, from installation to advanced features. Whether you're a data scientist, machine learning engineer, or decision-maker in a data-driven organization, FedLearn provides the tools and framework to advance your AI initiatives while prioritizing data privacy and security.

This introduction provides a comprehensive overview of FedLearn, its workings, features, benefits, and potential applications. It sets the stage for the more detailed technical sections that follow in the documentation.

System Requirements

To run FedLearn effectively, your system should meet the following requirements:

• Operating System:

  • Linux (Ubuntu 18.04 or later)

  • MacOS (10.14 or later)

  • Windows 10

• Python: Version 3.7 or higher

• RAM: Minimum 8GB, 16GB or more recommended for larger models

• Storage: At least 30GB of free disk space

• Network: Stable internet connection for collaboration mode

GPU Requirements (Optional)

FedLearn supports two main types of GPU acceleration: NVIDIA CUDA and Google's Tensor Processing Units (TPUs). Choose the option that best fits your hardware and requirements.

NVIDIA CUDA

If you're using NVIDIA GPUs:

• CUDA-capable GPU: NVIDIA GPU with compute capability 3.5 or higher

• CUDA Toolkit: Version 10.1 or later (11.8 recommended for optimal performance)

• cuDNN: Version 7.6 or later

• NVIDIA GPU Drivers: Compatible with your CUDA Toolkit version

To verify your GPU is CUDA-capable:

bash

lspci | grep -i nvidia

To check CUDA version:

bash

nvcc --version

Tensor Processing Units (TPUs)

If you're using Google Cloud TPUs:

• TPU Hardware: Access to a TPU device or TPU Pod

• TensorFlow: Version 2.3.0 or later with TPU support

• Google Cloud Account: Active account with TPU quota

• Cloud Storage Bucket: For storing model checkpoints and data

To check TPU availability in TensorFlow:

python

import tensorflow as tf
print(tf.config.list_physical_devices('TPU'))