# TensorFlow 2 Distributed Training and Inference Example PopDist (Poplar Distributed Configuration) provides a set of APIs which can be used to write a distributed application. The application can then be launched on multiple instances through PopRun, our command line utility. This example contains a TensorFlow CNN with PopDist support, which can be launched on multiple instances using a PopRun command line. You can learn more about PopDist and PopRun in the [PopDist and PopRun User Guide](https://docs.graphcore.ai/projects/poprun-user-guide/en/latest/). ## File structure * `popdist_training.py` Example training script with PopDist support. * `popdist_inference.py` Example inference script with PopDist support. * `tests` Integration tests for this example. * `README.md` This file. ## How to use this example 1) Prepare the TensorFlow environment. Install the Poplar SDK following the instructions in the Getting Started guide for your IPU system. Make sure to source the `enable.sh` script for Poplar and activate a Python virtualenv with a TensorFlow 2 wheel from the Poplar SDK installed (use the version appropriate to your operating system and processor). 2) Run the script using PopRun. The number of instances and replicas are provided as command-line arguments. Example: ``` poprun --num-instances=2 --num-replicas=4 python3 popdist_training.py ``` 3) After training, run the inference script using PopRun. For inference, it is recommended that you do not use multiple replicas per instance. This is because you will likely become CPU bound by the data preparation done on the host (as each instance will only use a single CPU core). Example: ``` poprun --num-instances=4 --num-replicas=4 python3 popdist_inference.py ```