# Graphcore Feature Examples The code examples demonstrate features which will enable you to make the most of the IPU. They are part of the Developer resources provided by Graphcore: . Each of the examples contains its own README file with full instructions. ## Poplar Exchange data between host and IPU efficiently: - [Prefetching Callbacks](poplar/prefetch): A demonstration of prefetching data when a program runs several times. Demonstrate advanced features of Poplar: - [Advanced Features](poplar/advanced_example): An example demonstrating several advanced features of Poplar, including saving and restoring Poplar executables, moving I/O into separate Poplar programs, and using our PopLibs framework. ## TensorFlow 2 Debugging and analysis: - [Inspecting Tensors Using Custom Outfeed Layers and a Custom Optimizer](tensorflow2/inspecting_tensors): An example that shows how outfeed queues can be used to return activation and gradient tensors to the host for inspection. Use estimators: - [IPUEstimator](tensorflow2/ipu_estimator): An example showing how to use the IPUEstimator to train and evaluate a simple CNN. Specific layers: - [Embeddings](tensorflow2/embeddings): An example of a model with an embedding layer and an LSTM, trained on the IPU to predict the sentiment of an IMDB review. - [Recomputation Checkpoints](tensorflow2/recomputation_checkpoints): An example demonstrating the checkpointing of intermediate values to reduce live memory peaks with a simple Keras LSTM model. Efficiently use multiple IPUs and handle large models: - [Distributed Training and Inference](tensorflow2/popdist): This shows how to prepare a TensorFlow 2 application for distributed training and inference by using the PopDist API, and how to launch it with the PopRun distributed launcher. ## PyTorch Efficiently use multiple IPUs and handle large models: - [Distributed Training](pytorch/popdist): An example showing how to prepare a PyTorch application for distributed training and inference using the PopDist library, and how to launch it with the PopRun distributed launcher. Define custom operators: - [Using Custom Operators](pytorch/custom_op): An example showing how to create a PopART custom operator available to PopTorch and how to use it in a model. Specific layers: - [Octave Convolutions](pytorch/octconv): An example showing how to use Octave Convolutions in PopTorch training and inference models.