---
type: concept
x-llmwiki:
  schemaVersion: "0.1"
  contentHash: 168efe747ad383cdb7a71355eeab37cd22ec9325e8c4ea8a27a8f60979d279c2
  pageDirectory: concepts
  sources:
    - deep-learning-using-tensorflow-with-horovodrunner-for-mnist-databricks-on-aws.md
  confidence: 0.8
  provenanceState: extracted
  freshnessStatus: unverified
  aliases:
    - data-preparation-for-distributed-training
    - DPFDT
  citations:
    - file: deep-learning-using-tensorflow-with-horovodrunner-for-mnist-databricks-on-aws.md
title: Data preparation for distributed training
description: The step of preparing data before running distributed training notebooks with HorovodRunner on Databricks.
tags:
  - data-preparation
  - distributed-training
  - databricks
timestamp: "2026-06-19T18:19:14.676Z"
---

Here is the wiki page for "Data Preparation for Distributed Training", written based solely on the provided source material.

---

## Data Preparation for Distributed Training

**Data Preparation for Distributed Training** refers to the process of organizing, partitioning, and loading datasets so they can be efficiently consumed by multiple workers (e.g., GPUs or nodes) during a distributed deep learning job. Proper data preparation is essential for avoiding bottlenecks, ensuring balanced workloads, and maximizing training throughput.

### Role in the Training Workflow

Before launching a distributed training job — such as the [HorovodRunner TensorFlow and Keras MNIST example](/concepts/mnist-tensorflow-keras-example.md) on Databricks — practitioners must first prepare their data for distributed training. ^[deep-learning-using-tensorflow-with-horovodrunner-for-mnist-databricks-on-aws.md]

This preparation step typically involves:
- **Sharding the dataset** across workers so that each worker processes a distinct subset of the data per epoch.
- **Loading data in parallel** using libraries like TensorFlow `tf.data` or PyTorch `DataLoader` with appropriate prefetching and interleaving.
- **Normalizing and preprocessing** features to ensure consistent input formatting across all workers.
- **Distributing metadata** (e.g., label mappings, vocabulary) so that all workers share the same transformation logic.

### Common Strategies

| Strategy | Description |
|----------|-------------|
| **Data parallelism** | Each worker holds a full copy of the model and processes a different mini-batch; gradients are averaged across workers. Data preparation must ensure each worker receives a unique slice of the global dataset. |
| **TFRecord / Parquet files** | Storing data in a columnar, sharded format (e.g., TFRecords for TensorFlow, Parquet for Spark) enables efficient reading across many nodes without file contention. |
| **Input pipelines with caching** | Workers cache preprocessed data in memory or on local SSDs to reduce I/O waits during training. |
| **Distributed shuffle** | Using a global shuffle pattern (e.g., via a random file list or distributed sharding) prevents data ordering biases from affecting convergence. |

### Integration with Databricks

On Databricks, data preparation for distributed training often leverages Apache Spark to load, transform, and write data to cloud storage (e.g., DBFS, S3, ADLS). The resulting shards are then read by the training framework (e.g., TensorFlow, PyTorch, or Horovod) using distributed file system connectors. The [HorovodRunner](/concepts/horovodrunner.md) notebook example recommends completing data preparation before executing the distributed training cell. ^[deep-learning-using-tensorflow-with-horovodrunner-for-mnist-databricks-on-aws.md]

### Related Concepts

- [Distributed Training](/concepts/workload-yaml-for-distributed-training.md)
- [HorovodRunner](/concepts/horovodrunner.md)
- Data Loading Best Practices
- Spark for Data Engineering
- GPU Memory Optimization

## Sources

- deep-learning-using-tensorflow-with-horovodrunner-for-mnist-databricks-on-aws.md

# Citations

1. [deep-learning-using-tensorflow-with-horovodrunner-for-mnist-databricks-on-aws.md](/references/deep-learning-using-tensorflow-with-horovodrunner-for-mnist-databricks-on-aws-06d44e07.md)
