Data loading and model checkpointing are crucial to deep learning (especially distributed DL) workloads. Databricks Runtime offers different ways to support high performance data I/O:
- Databricks Runtime 6.3 ML (Unsupported) and above: Azure Databricks provides a high performance FUSE mount.
- Databricks Runtime 5.5 LTS ML: Azure Databricks provides
dbfs:/ml, a special folder that offers high-performance I/O for deep learning workloads, that maps to
file:/dbfs/mlon driver and worker nodes. Azure Databricks recommends saving data under
/dbfs/ml. This FUSE mount also alleviates the local file I/O API limitation in Databricks Runtime of supporting only files smaller than 2GB.