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pytorch: add PyTorch 2.12.1 DLC images (CUDA + CPU, EC2 + SageMaker) (#6322)
* pytorch: add PyTorch 2.12.1 DLC images (CUDA + CPU, EC2 + SageMaker) Add the 2.12 minor version alongside 2.11: - docker/pytorch/2.12/: Dockerfiles + pyproject/uv.lock for cuda and cpu - 4 image configs (ec2/sagemaker x cuda/cpu) - 4 caller workflows (PR cuda/cpu, autorelease ec2/sagemaker) - register both autorelease workflows in release-schedule.yml Version pins: torch 2.12.1, torchvision 0.27.1. torchaudio stays at 2.11.0 (its final release — no 2.12 exists upstream); it has no torch pin and resolves cleanly against torch 2.12.1. CUDA index unchanged (cu130); remaining GPU pins mirror 2.11. Autorelease crons use free Mon/Wed slots (22:00 ec2, 23:00 sagemaker). * pytorch: consolidate per-version Dockerfiles into one shared file per device The CUDA and CPU Dockerfiles were byte-identical across PyTorch minor versions except for ARG defaults (TORCH_VERSION, FRAMEWORK_SHORT_VERSION) that CI always overrides. Every version-specific input is already located via context-root-relative COPY of docker/pytorch/${FRAMEWORK_SHORT_VERSION}/..., and FRAMEWORK_SHORT_VERSION is derived at build time by the pytorch_runtime pre_build hook — so the Dockerfile path no longer needs to encode the version. - Move 2.12 Dockerfiles to docker/pytorch/Dockerfile.{cuda,cpu}; delete the 2.11 copies (byte-identical aside from ARG defaults) - Repoint all 8 image configs' build.dockerfile at the shared files - Repoint the 2.11 + 2.12 PR path filters at the shared files - Per-version pin dirs (2.x/{cuda,cpu}/{pyproject.toml,uv.lock}) unchanged — they remain the per-version source of truth Verified: a build with context=. and -f the shared Dockerfile, differing only by --build-arg FRAMEWORK_SHORT_VERSION, resolves the correct version's pyproject (2.11 -> torch 2.11.0, 2.12 -> torch 2.12.1). A change to the shared Dockerfile now triggers PR builds for every live version.
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‎.github/config/image/pytorch/2.11-ec2-cpu.yml‎

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prod_image: "pytorch:2.11-cpu-amzn2023"
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build:
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dockerfile: "docker/pytorch/2.11/Dockerfile.cpu"
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dockerfile: "docker/pytorch/Dockerfile.cpu"
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target: "runtime"
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python_version: "3.12"
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torch_version: "2.11.0"

‎.github/config/image/pytorch/2.11-ec2-cuda.yml‎

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prod_image: "pytorch:2.11-cu130-amzn2023"
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build:
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dockerfile: "docker/pytorch/2.11/Dockerfile.cuda"
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dockerfile: "docker/pytorch/Dockerfile.cuda"
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target: "runtime"
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python_version: "3.12"
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cuda_version: "13.0.2"

‎.github/config/image/pytorch/2.11-sagemaker-cpu.yml‎

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prod_image: "pytorch:2.11-cpu-amzn2023-sagemaker"
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build:
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dockerfile: "docker/pytorch/2.11/Dockerfile.cpu"
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dockerfile: "docker/pytorch/Dockerfile.cpu"
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target: "sagemaker"
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python_version: "3.12"
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torch_version: "2.11.0"

‎.github/config/image/pytorch/2.11-sagemaker-cuda.yml‎

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prod_image: "pytorch:2.11-cu130-amzn2023-sagemaker"
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build:
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dockerfile: "docker/pytorch/2.11/Dockerfile.cuda"
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dockerfile: "docker/pytorch/Dockerfile.cuda"
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target: "sagemaker"
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python_version: "3.12"
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cuda_version: "13.0.2"

‎.github/config/image/pytorch/2.12-ec2-cpu.yml‎

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image:
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name: "pytorch-2.12-ec2-cpu"
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description: "PyTorch 2.12 CPU training for EC2 instances"
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metadata:
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framework: "pytorch_runtime"
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framework_version: "2.12.1"
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os_version: "amzn2023"
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customer_type: "ec2"
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arch_type: "x86"
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device_type: "cpu"
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job_type: "training"
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prod_image: "pytorch:2.12-cpu-amzn2023"
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build:
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dockerfile: "docker/pytorch/Dockerfile.cpu"
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target: "runtime"
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python_version: "3.12"
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torch_version: "2.12.1"
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torchvision_version: "0.27.1"
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torchaudio_version: "2.11.0"
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max_jobs: "8"
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dlc_major_version: "1"
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dlc_minor_version: "0"
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release:
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release: true
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force_release: false
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public_registry: true
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private_registry: true
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enable_soci: true
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environment: "production"

‎.github/config/image/pytorch/2.12-ec2-cuda.yml‎

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image:
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name: "pytorch-2.12-ec2-cuda"
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description: "PyTorch 2.12 CUDA training for EC2 instances"
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metadata:
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framework: "pytorch_runtime"
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framework_version: "2.12.1"
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os_version: "amzn2023"
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customer_type: "ec2"
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arch_type: "x86"
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device_type: "gpu"
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job_type: "training"
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prod_image: "pytorch:2.12-cu130-amzn2023"
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build:
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dockerfile: "docker/pytorch/Dockerfile.cuda"
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target: "runtime"
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python_version: "3.12"
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cuda_version: "13.0.2"
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torch_version: "2.12.1"
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torchvision_version: "0.27.1"
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torchaudio_version: "2.11.0"
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flash_attn_version: "2.8.3"
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deepspeed_version: "0.18.8"
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transformer_engine_version: "2.12.0"
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nccl_version: "2.26.2"
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efa_version: "1.47.0"
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gdrcopy_version: "2.4.4"
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max_jobs: "8"
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dlc_major_version: "1"
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dlc_minor_version: "0"
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release:
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release: true
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force_release: false
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public_registry: true
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private_registry: true
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enable_soci: true
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environment: "production"

‎.github/config/image/pytorch/2.12-sagemaker-cpu.yml‎

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image:
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name: "pytorch-2.12-sagemaker-cpu"
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description: "PyTorch 2.12 CPU training for SageMaker"
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metadata:
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framework: "pytorch_runtime"
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framework_version: "2.12.1"
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os_version: "amzn2023"
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customer_type: "sagemaker"
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platform: "sagemaker"
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arch_type: "x86"
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device_type: "cpu"
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job_type: "training"
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prod_image: "pytorch:2.12-cpu-amzn2023-sagemaker"
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build:
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dockerfile: "docker/pytorch/Dockerfile.cpu"
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target: "sagemaker"
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python_version: "3.12"
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torch_version: "2.12.1"
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torchvision_version: "0.27.1"
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torchaudio_version: "2.11.0"
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max_jobs: "8"
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dlc_major_version: "1"
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dlc_minor_version: "0"
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release:
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release: true
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force_release: false
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public_registry: true
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private_registry: true
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enable_soci: true
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environment: "production"

‎.github/config/image/pytorch/2.12-sagemaker-cuda.yml‎

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image:
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name: "pytorch-2.12-sagemaker-cuda"
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description: "PyTorch 2.12 CUDA training for SageMaker"
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metadata:
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framework: "pytorch_runtime"
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framework_version: "2.12.1"
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os_version: "amzn2023"
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customer_type: "sagemaker"
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platform: "sagemaker"
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arch_type: "x86"
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device_type: "gpu"
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job_type: "training"
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prod_image: "pytorch:2.12-cu130-amzn2023-sagemaker"
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build:
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dockerfile: "docker/pytorch/Dockerfile.cuda"
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target: "sagemaker"
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python_version: "3.12"
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cuda_version: "13.0.2"
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torch_version: "2.12.1"
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torchvision_version: "0.27.1"
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torchaudio_version: "2.11.0"
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flash_attn_version: "2.8.3"
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deepspeed_version: "0.18.8"
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transformer_engine_version: "2.12.0"
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nccl_version: "2.26.2"
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efa_version: "1.47.0"
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gdrcopy_version: "2.4.4"
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max_jobs: "8"
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dlc_major_version: "1"
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dlc_minor_version: "0"
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release:
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release: true
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force_release: false
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public_registry: true
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private_registry: true
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enable_soci: true
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environment: "production"

‎.github/release-schedule.yml‎

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workflow: vllm.autorelease-sagemaker-ubuntu.yml
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- cron: "00 22 * * 1,3"
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workflow: pytorch.autorelease-2.12-ec2.yml
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gpu: light
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- cron: "30 22 * * 1,3"
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workflow: base.autorelease-cu130.yml
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gpu: minimal
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- cron: "00 23 * * 1,3"
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workflow: pytorch.autorelease-2.12-sagemaker.yml
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gpu: light
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# Tuesday / Thursday
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- cron: "00 16 * * 2,4"
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workflow: sglang.autorelease-ec2-amzn2023.yml

‎.github/workflows/pytorch.autorelease-2.12-ec2.yml‎

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name: "Auto Release - PyTorch 2.12 EC2"
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on:
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schedule:
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- cron: '00 22 * * 1,3'
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workflow_dispatch:
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concurrency:
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group: ${{ github.workflow }}
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cancel-in-progress: false
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permissions:
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contents: read
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jobs:
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discover:
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runs-on: ubuntu-latest
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outputs:
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configs: ${{ steps.discover.outputs.configs }}
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steps:
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- uses: actions/checkout@v6
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- id: discover
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uses: ./.github/actions/discover-configs
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with:
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pattern: ".github/config/image/pytorch/2.12-ec2-*.yml"
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pipeline:
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needs: [discover]
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strategy:
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fail-fast: false
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matrix:
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include: ${{ fromJson(needs.discover.outputs.configs) }}
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uses: ./.github/workflows/pytorch.pipeline.yml
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with:
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config-file: ${{ matrix.config_file }}
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tag-suffix: ${{ github.run_id }}
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run-multi-gpu-test: false
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run-multi-node-test: false
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release: true
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secrets: inherit

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