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When you use min_ram or cpu_count resource hints for pipeline steps that don't require accelerators, Auto VM...

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When you use min_ram or cpu_count resource hints for pipeline steps that don't require accelerators, Auto VM Selection (Instance Flexibility) is enabled automatically. With Auto VM Selection, workers are provisioned from a curated list of machine types that meet your RAM and CPU requirements.

Data integration

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Also shipped Apr 7, 2026Google Cloud in April 2026 →

Chroma 1.5.6

Version: 1.5.6 Git ref: refs/tags/1.5.6 Build Date: 2026-04-07T03:01 PIP Package: chroma-1.5.6.tar.gz Github Container Registry Image: :1.5.6 DockerHub Image: :1.5.6 [DOC] add concrete example for query/get response [DOC] Make delete clearer [DOC] Add multimodal example [ENH]: S3 client config improvements [BUG] Tests for garbage collector panic on...

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Dataflow job builder now supports external Iceberg REST Catalogs as a source

Dataflow job builder now supports external Iceberg REST Catalogs as a source. You can now ingest data from external Apache Iceberg REST catalogs (IRC) directly into Lakehouse tables using Dataflow's job builder UI without writing code. For more information, see Import data from external Iceberg catalogs to Lakehouse using Dataflow .

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Dataflow now supports NVIDIA RTX Pro 6000 GPUs

Dataflow now supports NVIDIA RTX Pro 6000 GPUs. You can use this GPU model to run your Apache Beam pipelines on Dataflow. RTX Pro 6000 GPUs are recommended for large, medium, and small model inference workloads. To configure your workers with this GPU model, set the accelerator type to nvidia-rtx-pro-6000 .

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You can now use Hyperdisk Balanced disks for Dataflow worker VMs

You can now use Hyperdisk Balanced disks for Dataflow worker VMs. With Hyperdisk Balanced disks, you can provision IOPS and throughput independently of disk size by using the diskProvisionedIOPS and diskProvisionedThroughput pipeline options (Java SDK) or disk_provisioned_iops and disk_provisioned_throughput_mibps pipeline options (Python and Go SDKs).

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