●Change8

Migrating to Ray ray-2.56.0

Version ray-2.56.0 introduces 1 breaking change. This guide details how to update your code.

Released: 6/29/2026

1
Breaking Changes
3
Migration Steps
25
Affected Symbols

⚠️ Check Your Code

If you use any of these symbols, you need to read this guide:

Dataset.mix()MixOperatorParquetDatasourceV2Dataset.map_batchesread_parquetiter_batchesfilter(expr=...)DataIterator.to_torchConcurrencyCapBackpressurePolicyray.io/gpu-domainAutoscaler v2ConsistentHashRouterCapacityQueueRouterAggregateFnV2HashAggregateCheckpointConfigfsspec-S3ZipOperatorwrite_parquetShuffleStrategy.GPU_SHUFFLEDatasetStatDATA_ENABLE_OP_RESOURCE_RESERVATIONPyFileSystempyarrow.dataset.write_datasetread_tfrecords

Breaking Changes

●Issue #1

The subcluster label key used for Ray Data multi-dataset support has been renamed from `__subcluster__` to `ray-subcluster`. Update any scripts or configurations relying on the old label key.

Migration Steps

  1. 1
    If you were relying on the deprecated `concurrency=` parameter in `filter(expr=...)`, replace it with the `compute=` parameter.
  2. 2
    Update any code using `DataIterator.to_torch()` to use alternative methods or update to use PyArrow directly.
  3. 3
    Update any configuration or code relying on the subcluster label key `__subcluster__` to use `ray-subcluster` instead.

Release Summary

This release focuses heavily on Ray Data stability, introducing features like multi-dataset support, automatic batch sizing, and improved memory management. Ray Serve received significant LLM performance enhancements via re-architecture and new routing policies, while Ray Core introduced GPU-domain-aware placement groups and initial Kubernetes in-place pod resizing support.

Need More Details?

View the full release notes and all changes for Ray ray-2.56.0.

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