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This tempting but unsafe shortcut is not a sufficient remediation for NVIDIA Container Toolkit runtime integration: Assume launching any Ubuntu Docker container automatically gives it unrestricted access to host GPUs and drivers. Observed scenario: An operator enables container GPUs on one node yet a second worker lacks compatible runtime hooks. The governing mechanism is: The container runtime must map supported host GPU devices and libraries; ordinary Docker success is not GPU enablement. The proportionate action is: Install and configure the NVIDIA Container Toolkit and restrict GPU exposure to authorized workload allocations. A direct check is: Inspect toolkit runtime configuration, docker info, --gpus selection and in-container nvidia-smi. The acceptance condition is: The sample container sees only assigned GPUs and runs a compatible CUDA operation consistently across nodes.

This alternative is relevant to Docker GPU runtime device selection but does not directly resolve the current NVIDIA Container Toolkit runtime integration issue: Compare host and container GPU UUID lists, device requests, runtime logs and a targeted CUDA smoke test. Observed scenario: An operator enables container GPUs on one node yet a second worker lacks compatible runtime hooks.

This alternative is relevant to Base Command Manager head-node installation but does not directly resolve the current NVIDIA Container Toolkit runtime integration issue: Install the supported BCM head-node services and preserve configuration data before provisioning and accepting workers. Observed scenario: An operator enables container GPUs on one node yet a second worker lacks compatible runtime hooks.

This tempting but unsafe shortcut is not a sufficient remediation for NVIDIA Container Toolkit runtime integration: Assume launching any Ubuntu Docker container automatically gives it unrestricted access to host GPUs and drivers. A container receives every host GPU even though the job should access only one assigned accelerator.

This alternative is relevant to Docker GPU runtime device selection but does not directly resolve the current NVIDIA Container Toolkit runtime integration issue: Test --gpus or supported device-request selection and in-container identity against scheduler allocations. Observed scenario: A container receives every host GPU even though the job should access only one assigned accelerator.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current NVIDIA Container Toolkit runtime integration issue: Define explicit BCM node categories and managed overrides for shared hardware and service configuration. Observed scenario: A container receives every host GPU even though the job should access only one assigned accelerator.

This alternative is relevant to Pyxis integration with Slurm jobs but does not directly resolve the current NVIDIA Container Toolkit runtime integration issue: Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment. Observed scenario: A container receives every host GPU even though the job should access only one assigned accelerator.

That option concerns BCM high availability and management failover. The acceptance condition is: The sample container sees only assigned GPUs and runs a compatible CUDA operation consistently across nodes.

This tempting but unsafe shortcut is not a sufficient remediation for NVIDIA Container Toolkit runtime integration: Assume launching any Ubuntu Docker container automatically gives it unrestricted access to host GPUs and drivers. A container receives every host GPU even though the job should access only one assigned accelerator. The container runtime must map supported host GPU devices and libraries; ordinary Docker success is not GPU enablement.

This tempting but unsafe shortcut is not a sufficient remediation for NVIDIA Container Toolkit runtime integration: Assume launching any Ubuntu Docker container automatically gives it unrestricted access to host GPUs and drivers. Observed scenario: A container receives every host GPU even though the job should access only one assigned accelerator. The governing mechanism is: The container runtime must map supported host GPU devices and libraries; ordinary Docker success is not GPU enablement. The proportionate action is: Install and configure the NVIDIA Container Toolkit and restrict GPU exposure to authorized workload allocations.

This alternative is relevant to Pyxis integration with Slurm jobs but does not directly resolve the current NVIDIA Container Toolkit runtime integration issue: Inspect Slurm plugin configuration, Enroot availability, image identifiers, mount constraints and job logs. Observed scenario: A container receives every host GPU even though the job should access only one assigned accelerator.

This alternative is relevant to Base Command Manager head-node installation but does not directly resolve the current NVIDIA Container Toolkit runtime integration issue: Inspect BCM service health, database backups, node discovery, management interfaces and provisioning records. Observed scenario: A container receives every host GPU even though the job should access only one assigned accelerator.

This tempting but unsafe shortcut is not a sufficient remediation for NVIDIA Container Toolkit runtime integration: Assume launching any Ubuntu Docker container automatically gives it unrestricted access to host GPUs and drivers. Observed scenario: A container receives every host GPU even though the job should access only one assigned accelerator. The governing mechanism is: The container runtime must map supported host GPU devices and libraries; ordinary Docker success is not GPU enablement. The proportionate action is: Install and configure the NVIDIA Container Toolkit and restrict GPU exposure to authorized workload allocations. A direct check is: Inspect toolkit runtime configuration, docker info, --gpus selection and in-container nvidia-smi. The acceptance condition is: The sample container sees only assigned GPUs and runs a compatible CUDA operation consistently across nodes.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current NVIDIA Container Toolkit runtime integration issue: Correct GPU workers receive the intended profile without unrelated node categories being modified. Observed scenario: A container receives every host GPU even though the job should access only one assigned accelerator.

This alternative is relevant to Base Command Manager head-node installation but does not directly resolve the current NVIDIA Container Toolkit runtime integration issue: The management node reports correct cluster state and consistently deploys an approved image to intended nodes. Observed scenario: A container receives every host GPU even though the job should access only one assigned accelerator.

This alternative is relevant to NVIDIA Container Toolkit runtime integration but does not directly resolve the current Docker GPU runtime device selection issue: Install and configure the NVIDIA Container Toolkit and restrict GPU exposure to authorized workload allocations. Observed scenario: A host exposes eight GPUs but the container is supposed to use GPU 2 and instead sees all devices.

This tempting but unsafe shortcut is not a sufficient remediation for Docker GPU runtime device selection: Treat a set CUDA_VISIBLE_DEVICES string as a substitute for enforcing container device and driver mappings.

This alternative is relevant to Compute fabric interface bonding and RDMA prerequisites but does not directly resolve the current Docker GPU runtime device selection issue: Align supported interface or bond configuration, RDMA stack and fabric QoS with the cluster topology. Observed scenario: A host exposes eight GPUs but the container is supposed to use GPU 2 and instead sees all devices.

This tempting but unsafe shortcut is not a sufficient remediation for Docker GPU runtime device selection: Treat a set CUDA_VISIBLE_DEVICES string as a substitute for enforcing container device and driver mappings. Host GPU visibility and container GPU assignment are separate, and environment variables alone do not confer device access.

This alternative is relevant to Slurm GPU GRES enumeration and device mapping but does not directly resolve the current Docker GPU runtime device selection issue: Schedulers allocate declared GPU resources; a mismatch between configured GRES and detected devices causes wrong placement. Observed scenario: A host exposes eight GPUs but the container is supposed to use GPU 2 and instead sees all devices.

That option concerns BCM high availability and management failover. The acceptance condition is: The selected container can use exactly its authorized GPU resources while other devices remain inaccessible.

That option concerns Host SSH certificates keys and OS access segregation. Host GPU visibility and container GPU assignment are separate, and environment variables alone do not confer device access.

This tempting but unsafe shortcut is not a sufficient remediation for Docker GPU runtime device selection: Treat a set CUDA_VISIBLE_DEVICES string as a substitute for enforcing container device and driver mappings. A host exposes eight GPUs but the container is supposed to use GPU 2 and instead sees all devices.

This alternative is relevant to BCM image provisioning and PXE worker boot but does not directly resolve the current Docker GPU runtime device selection issue: Inspect DHCP leases, PXE requests, image revisions, BCM node mappings and host boot traces. Observed scenario: A host exposes eight GPUs but the container is supposed to use GPU 2 and instead sees all devices.

This alternative is relevant to BCM image provisioning and PXE worker boot but does not directly resolve the current Docker GPU runtime device selection issue: The intended worker reliably reboots into the approved OS and image with the right management identity. Observed scenario: A host exposes eight GPUs but the container is supposed to use GPU 2 and instead sees all devices.

This tempting but unsafe shortcut is not a sufficient remediation for Docker GPU runtime device selection: Treat a set CUDA_VISIBLE_DEVICES string as a substitute for enforcing container device and driver mappings. A host exposes eight GPUs but the container is supposed to use GPU 2 and instead sees all devices. Host GPU visibility and container GPU assignment are separate, and environment variables alone do not confer device access.

This alternative is relevant to BCM high availability and management failover but does not directly resolve the current Docker GPU runtime device selection issue: A planned primary outage transfers management to standby without losing cluster configuration integrity. Observed scenario: A host exposes eight GPUs but the container is supposed to use GPU 2 and instead sees all devices.

This alternative is relevant to Linux operating system and kernel module support but does not directly resolve the current Docker GPU runtime device selection issue: Use the supported OS, kernel and package combination and validate driver modules on a representative node before rollout. Observed scenario: A container launches with CUDA_VISIBLE_DEVICES inconsistent with the runtime's actual device mapping.

This tempting but unsafe shortcut is not a sufficient remediation for Docker GPU runtime device selection: Treat a set CUDA_VISIBLE_DEVICES string as a substitute for enforcing container device and driver mappings. A container launches with CUDA_VISIBLE_DEVICES inconsistent with the runtime's actual device mapping.

This alternative is relevant to Slurm GPU GRES enumeration and device mapping but does not directly resolve the current Docker GPU runtime device selection issue: Synchronize Slurm GRES inventory with hardware and MIG state and validate per-job device isolation. Observed scenario: A container launches with CUDA_VISIBLE_DEVICES inconsistent with the runtime's actual device mapping.

This alternative is relevant to Slurm GPU GRES enumeration and device mapping but does not directly resolve the current Docker GPU runtime device selection issue: Inspect slurmd discovery, gres configuration, CUDA_VISIBLE_DEVICES and simultaneous job placement. Observed scenario: A container launches with CUDA_VISIBLE_DEVICES inconsistent with the runtime's actual device mapping.

This tempting but unsafe shortcut is not a sufficient remediation for Docker GPU runtime device selection: Treat a set CUDA_VISIBLE_DEVICES string as a substitute for enforcing container device and driver mappings. A container launches with CUDA_VISIBLE_DEVICES inconsistent with the runtime's actual device mapping. Host GPU visibility and container GPU assignment are separate, and environment variables alone do not confer device access.

This alternative is relevant to Linux operating system and kernel module support but does not directly resolve the current Docker GPU runtime device selection issue: Use the supported OS, kernel and package combination and validate driver modules on a representative node before rollout. Observed scenario: A container launches with CUDA_VISIBLE_DEVICES inconsistent with the runtime's actual device mapping. The governing mechanism is: Host GPU visibility and container GPU assignment are separate, and environment variables alone do not confer device access.