A high-throughput and memory-efficient inference and serving engine for LLMs
Mirrored from https://github.com/tduncan-lum/Nvidia-Optimization-Stack---VLLM.git
Synced Nov 14, 22:35 UTC
[Misc] Bump ray to 2.48.0 (#22123)
Signed-off-by: Rui Qiao <ruisearch42@gmail.com>
Rui Qiao committed
Aug 2, 2025 at 19:42 UTC
2ff46b882694cc3eb6cde48f6b9251ccbc5fdb04
4 files changed
+18
-10
requirements/cuda.txt
+1
-1
@@ -5,7 +5,7 @@ numba == 0.60.0; python_version == '3.9' # v0.61 doesn't support Python 3.9. Req
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numba == 0.61.2; python_version > '3.9'
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# Dependencies for NVIDIA GPUs
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-ray[cgraph]>=2.43.0, !=2.44.* # Ray Compiled Graph, required for pipeline parallelism in V1.
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+ray[cgraph]>=2.48.0 # Ray Compiled Graph, required for pipeline parallelism in V1.
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torch==2.7.1
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torchaudio==2.7.1
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# These must be updated alongside torch
requirements/nightly_torch_test.txt
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@@ -16,7 +16,7 @@ librosa # required for audio tests
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vocos # required for minicpmo_26 test
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peft
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pqdm
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-ray[cgraph,default]>=2.43.0, !=2.44.* # Ray Compiled Graph, required by pipeline parallelism tests
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+ray[cgraph,default]>=2.48.0 # Ray Compiled Graph, required by pipeline parallelism tests
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sentence-transformers # required for embedding tests
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soundfile # required for audio tests
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jiwer # required for audio tests
requirements/test.in
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-1
@@ -17,7 +17,7 @@ vector_quantize_pytorch # required for minicpmo_26 test
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vocos # required for minicpmo_26 test
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peft>=0.15.0 # required for phi-4-mm test
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pqdm
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-ray[cgraph,default]>=2.43.0, !=2.44.* # Ray Compiled Graph, required by pipeline parallelism tests
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+ray[cgraph,default]>=2.48.0 # Ray Compiled Graph, required by pipeline parallelism tests
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sentence-transformers # required for embedding tests
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soundfile # required for audio tests
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jiwer # required for audio tests
requirements/test.txt
+15
-7
@@ -22,9 +22,7 @@ aiohttp==3.10.11
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aiohttp-cors==0.8.1
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# via ray
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aiosignal==1.3.1
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- # via
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- # aiohttp
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- # ray
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+ # via aiohttp
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albucore==0.0.16
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# via terratorch
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albumentations==1.4.6
@@ -226,7 +224,6 @@ frozenlist==1.5.0
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# via
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# aiohttp
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# aiosignal
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- # ray
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fsspec==2024.9.0
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# via
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# datasets
@@ -603,10 +600,18 @@ opencv-python-headless==4.11.0.86
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opentelemetry-api==1.35.0
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# via
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# mlflow-skinny
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+ # opentelemetry-exporter-prometheus
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# opentelemetry-sdk
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# opentelemetry-semantic-conventions
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+opentelemetry-exporter-prometheus==0.56b0
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+ # via ray
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+opentelemetry-proto==1.36.0
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+ # via ray
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opentelemetry-sdk==1.35.0
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- # via mlflow-skinny
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+ # via
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+ # mlflow-skinny
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+ # opentelemetry-exporter-prometheus
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+ # ray
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opentelemetry-semantic-conventions==0.56b0
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# via opentelemetry-sdk
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packaging==24.2
@@ -697,7 +702,9 @@ pqdm==0.2.0
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pretrainedmodels==0.7.4
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# via segmentation-models-pytorch
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prometheus-client==0.22.0
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- # via ray
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+ # via
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+ # opentelemetry-exporter-prometheus
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+ # ray
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propcache==0.2.0
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# via yarl
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proto-plus==1.26.1
@@ -707,6 +714,7 @@ protobuf==5.28.3
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# google-api-core
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# googleapis-common-protos
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# mlflow-skinny
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+ # opentelemetry-proto
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# proto-plus
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# ray
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# tensorboardx
@@ -854,7 +862,7 @@ rasterio==1.4.3
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# rioxarray
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# terratorch
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# torchgeo
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-ray==2.43.0
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+ray==2.48.0
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# via -r requirements/test.in
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redis==5.2.0
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# via tensorizer