Setup Distributed MXNet 🚀

Setting up distributed training with MXNet allows you to scale machine learning workloads across multiple devices or machines. Below is a step-by-step guide to get started.

Prerequisites 🛠️

• Python 3.6+ installed
• MXNet framework (via pip install mxnet)
• A distributed environment (e.g., multiple GPUs/TPUs or a cluster)

Installation Steps 📦

1. Install MXNet with distributed support:

   pip install mxnet --upgrade
   

2. Verify installation:
   Run a simple script to ensure MXNet is properly installed and configured.

Configuration Guide 🔧

• Set up communication backend: Use MPI or NCCL for multi-node training.
  Example:

  import mxnet as mx
  ctx = mx.gpu(0) if mx.context.num_gpus() > 0 else mx.cpu()
  

• Configure distributed training:
  Follow the MXNet distributed training guide for advanced settings.

Run Your First Distributed Job ⚡

1. Use mxnet.gluon.train to distribute training across devices.
2. Monitor progress with tools like TensorBoard or mxnet.log.

Related Resources 📚

• MXNet Official Documentation for detailed API references
• Distributed Training Tutorials for more examples

For visualizing distributed computing architectures, check out our [distributed_computing](/en/tutorials/distributed_computing) tutorial.