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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.

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. The governing mechanism is: Host GPU visibility and container GPU assignment are separate, and environment variables alone do not confer device access.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current Docker GPU runtime device selection issue: Inspect category membership, inherited images, node overrides, resulting configuration and change impact. 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. 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. The proportionate action is: Test --gpus or supported device-request selection and in-container identity against scheduler allocations.

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. 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. The proportionate action is: Test --gpus or supported device-request selection and in-container identity against scheduler allocations. A direct check is: Compare host and container GPU UUID lists, device requests, runtime logs and a targeted CUDA smoke test. The acceptance condition is: The selected container can use exactly its authorized GPU resources while other devices remain inaccessible.

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: New and restarted nodes load the intended GPU and fabric drivers and pass required cluster smoke tests. Observed scenario: A container launches with CUDA_VISIBLE_DEVICES inconsistent with the runtime's actual device mapping.

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 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 team tests nvidia-smi on the host rather than proving GPU visibility inside the isolated container.

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 team tests nvidia-smi on the host rather than proving GPU visibility inside the isolated container.

That option concerns Base Command Manager head-node installation. Host GPU visibility and container GPU assignment are separate, and environment variables alone do not confer device access.

That option concerns BCM image provisioning and PXE worker boot. The acceptance condition is: The selected container can use exactly its authorized GPU resources while other devices remain inaccessible.

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 team tests nvidia-smi on the host rather than proving GPU visibility inside the isolated container.

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 team tests nvidia-smi on the host rather than proving GPU visibility inside the isolated container. 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. Observed scenario: A team tests nvidia-smi on the host rather than proving GPU visibility inside the isolated container. The governing mechanism is: Host GPU visibility and container GPU assignment are separate, and environment variables alone do not confer device access. The proportionate action is: Test --gpus or supported device-request selection and in-container identity against scheduler allocations.

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 team tests nvidia-smi on the host rather than proving GPU visibility inside the isolated container.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current Docker GPU runtime device selection issue: Inspect category membership, inherited images, node overrides, resulting configuration and change impact. Observed scenario: A team tests nvidia-smi on the host rather than proving GPU visibility inside the isolated container.

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. Observed scenario: A team tests nvidia-smi on the host rather than proving GPU visibility inside the isolated container. The governing mechanism is: Host GPU visibility and container GPU assignment are separate, and environment variables alone do not confer device access. The proportionate action is: Test --gpus or supported device-request selection and in-container identity against scheduler allocations. A direct check is: Compare host and container GPU UUID lists, device requests, runtime logs and a targeted CUDA smoke test. The acceptance condition is: The selected container can use exactly its authorized GPU resources while other devices remain inaccessible.

This alternative is relevant to NVIDIA Container Toolkit runtime integration but does not directly resolve the current Docker GPU runtime device selection issue: The sample container sees only assigned GPUs and runs a compatible CUDA operation consistently across nodes. Observed scenario: A team tests nvidia-smi on the host rather than proving GPU visibility inside the isolated container.

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 team tests nvidia-smi on the host rather than proving GPU visibility inside the isolated container.

This alternative is relevant to Compute fabric interface bonding and RDMA prerequisites but does not directly resolve the current Container dataset mounts and scratch isolation issue: Align supported interface or bond configuration, RDMA stack and fabric QoS with the cluster topology. Observed scenario: A training container starts but its expected dataset path points to an empty local directory instead of shared storage.

This alternative is relevant to Host SSH certificates keys and OS access segregation but does not directly resolve the current Container dataset mounts and scratch isolation issue: Enforce individual authenticated host access, network restrictions and tested key rotation with accountable emergency recovery. Observed scenario: A training container starts but its expected dataset path points to an empty local directory instead of shared storage.

This tempting but unsafe shortcut is not a sufficient remediation for Container dataset mounts and scratch isolation: Assume that copying a dataset pathname into a container guarantees the same shared files and access controls exist.

This tempting but unsafe shortcut is not a sufficient remediation for Container dataset mounts and scratch isolation: Assume that copying a dataset pathname into a container guarantees the same shared files and access controls exist. A GPU container can be computationally healthy while lacking authorized data paths or durable outputs.

This alternative is relevant to Host SSH certificates keys and OS access segregation but does not directly resolve the current Container dataset mounts and scratch isolation issue: Host administration requires least privilege and credential lifecycle control independently of GPU job permissions. Observed scenario: A training container starts but its expected dataset path points to an empty local directory instead of shared storage.

This alternative is relevant to BCM image provisioning and PXE worker boot but does not directly resolve the current Container dataset mounts and scratch isolation issue: Managed worker bring-up depends on the boot image and correct network identity, not just physical server power. Observed scenario: A training container starts but its expected dataset path points to an empty local directory instead of shared storage.

This alternative is relevant to Base Command Manager head-node installation but does not directly resolve the current Container dataset mounts and scratch isolation issue: Inspect BCM service health, database backups, node discovery, management interfaces and provisioning records. Observed scenario: A training container starts but its expected dataset path points to an empty local directory instead of shared storage.

This tempting but unsafe shortcut is not a sufficient remediation for Container dataset mounts and scratch isolation: Assume that copying a dataset pathname into a container guarantees the same shared files and access controls exist. A training container starts but its expected dataset path points to an empty local directory instead of shared storage.

This alternative is relevant to BCM high availability and management failover but does not directly resolve the current Container dataset mounts and scratch isolation issue: Check HA roles, sync status, virtual management endpoint, database replication and isolation tests. Observed scenario: A training container starts but its expected dataset path points to an empty local directory instead of shared storage.

This tempting but unsafe shortcut is not a sufficient remediation for Container dataset mounts and scratch isolation: Assume that copying a dataset pathname into a container guarantees the same shared files and access controls exist. A training container starts but its expected dataset path points to an empty local directory instead of shared storage. A GPU container can be computationally healthy while lacking authorized data paths or durable outputs.

That option concerns Linux operating system and kernel module support. A GPU container can be computationally healthy while lacking authorized data paths or durable outputs.

This alternative is relevant to Base Command Manager head-node installation but does not directly resolve the current Container dataset mounts and scratch isolation issue: Install the supported BCM head-node services and preserve configuration data before provisioning and accepting workers. Observed scenario: A training container starts but its expected dataset path points to an empty local directory instead of shared storage.