[Train][Data] Add heterogeneous cluster configurations for training ingest benchmarks #60458
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Add fixed-size and autoscaling configurations with CPU worker nodes for data loading alongside GPU worker nodes for training. Co-Authored-By: Claude Opus 4.5 <noreply@anthropic.com>
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The pull request introduces new benchmark configurations for heterogeneous clusters, including fixed-size and autoscaling variants. These new configurations will be valuable for benchmarking Ray Data operations alongside GPU worker nodes for training. However, there is a mismatch between the number of workers requested in the benchmark script and the maximum number of workers defined in the new cluster compute configurations.
Co-authored-by: Justin Yu <justinvyu@anyscale.com> Signed-off-by: Xinyuan <43737116+xinyuangui2@users.noreply.github.com>
Co-authored-by: Justin Yu <justinvyu@anyscale.com> Signed-off-by: Xinyuan <43737116+xinyuangui2@users.noreply.github.com>
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xinyuangui2
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Jan 24, 2026
Signed-off-by: Xinyuan <43737116+xinyuangui2@users.noreply.github.com>
Signed-off-by: Xinyuan <43737116+xinyuangui2@users.noreply.github.com>
release/train_tests/benchmark/compute_configs/heterogenous_fixed_size_gpu_4x4_aws.yaml
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Summary
Cluster Configurations
Fixed-size (
heterogenous_fixed_size_gpu_4x4_aws.yaml):Autoscaling (
heterogenous_autoscaling_gpu_4x4_aws.yaml):Release test