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This alternative is relevant to GPU driver modules and DKMS lifecycle but does not directly resolve the current Enroot container image and execution lifecycle issue: Install a supported NVIDIA driver branch, manage kernel-module compatibility and verify driver load and CUDA enumeration. Observed scenario: An image archive extracts locally but its expected CUDA libraries are absent at runtime.

This alternative is relevant to Container dataset mounts and scratch isolation but does not directly resolve the current Enroot container image and execution lifecycle issue: Inspect bind mounts, UID and GID mapping, effective write permissions, persistent volumes and sample checkpoint recovery. Observed scenario: An image archive extracts locally but its expected CUDA libraries are absent at runtime.

This tempting but unsafe shortcut is not a sufficient remediation for Enroot container image and execution lifecycle: Assume any Docker-compatible image automatically includes the correct NVIDIA runtime integration on every worker. Observed scenario: An image archive extracts locally but its expected CUDA libraries are absent at runtime. The governing mechanism is: Container packaging improves portability only when image provenance and host-driver compatibility are controlled. The proportionate action is: Use approved Enroot images and versioned runtime dependencies with restricted mounts and a reproducible launch test. A direct check is: Inspect image digests, Enroot startup logs, mounted directories, runtime libraries and test-job output. The acceptance condition is: An authorized user launches the pinned container and reproduces the intended GPU workload safely.

This alternative is relevant to BCM high availability and management failover but does not directly resolve the current Enroot container image and execution lifecycle issue: Configure supported BCM HA peers, synchronized services and planned failover tests with clear recovery boundaries. Observed scenario: A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run.

This tempting but unsafe shortcut is not a sufficient remediation for Enroot container image and execution lifecycle: Assume any Docker-compatible image automatically includes the correct NVIDIA runtime integration on every worker. A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run.

This alternative is relevant to NVIDIA Container Toolkit runtime integration but does not directly resolve the current Enroot container image and execution lifecycle issue: Install and configure the NVIDIA Container Toolkit and restrict GPU exposure to authorized workload allocations. Observed scenario: A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run.

This tempting but unsafe shortcut is not a sufficient remediation for Enroot container image and execution lifecycle: Assume any Docker-compatible image automatically includes the correct NVIDIA runtime integration on every worker. A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run. Container packaging improves portability only when image provenance and host-driver compatibility are controlled.

This alternative is relevant to NVIDIA Container Toolkit runtime integration but does not directly resolve the current Enroot container image and execution lifecycle issue: The container runtime must map supported host GPU devices and libraries; ordinary Docker success is not GPU enablement. Observed scenario: A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run.

That option concerns Base Command Manager head-node installation. The proportionate action is: Use approved Enroot images and versioned runtime dependencies with restricted mounts and a reproducible launch test.

This tempting but unsafe shortcut is not a sufficient remediation for Enroot container image and execution lifecycle: Assume any Docker-compatible image automatically includes the correct NVIDIA runtime integration on every worker. Observed scenario: A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run. The governing mechanism is: Container packaging improves portability only when image provenance and host-driver compatibility are controlled. The proportionate action is: Use approved Enroot images and versioned runtime dependencies with restricted mounts and a reproducible launch test.

This alternative is relevant to NVIDIA Container Toolkit runtime integration but does not directly resolve the current Enroot container image and execution lifecycle issue: Inspect toolkit runtime configuration, docker info, --gpus selection and in-container nvidia-smi. Observed scenario: A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run.

This alternative is relevant to Base Command Manager head-node installation but does not directly resolve the current Enroot container image and execution lifecycle issue: Inspect BCM service health, database backups, node discovery, management interfaces and provisioning records. Observed scenario: A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run.

This alternative is relevant to Container dataset mounts and scratch isolation but does not directly resolve the current Enroot container image and execution lifecycle issue: The intended dataset is readable, restricted files remain protected and checkpoints survive container replacement. Observed scenario: A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run.

This tempting but unsafe shortcut is not a sufficient remediation for Enroot container image and execution lifecycle: Assume any Docker-compatible image automatically includes the correct NVIDIA runtime integration on every worker. Observed scenario: A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run. The governing mechanism is: Container packaging improves portability only when image provenance and host-driver compatibility are controlled. The proportionate action is: Use approved Enroot images and versioned runtime dependencies with restricted mounts and a reproducible launch test. A direct check is: Inspect image digests, Enroot startup logs, mounted directories, runtime libraries and test-job output. The acceptance condition is: An authorized user launches the pinned container and reproduces the intended GPU workload safely.

This alternative is relevant to BCM high availability and management failover but does not directly resolve the current Enroot container image and execution lifecycle issue: A planned primary outage transfers management to standby without losing cluster configuration integrity. Observed scenario: A cluster team stores mutable model dependencies inside each job directory and cannot reproduce a prior run.

This tempting but unsafe shortcut is not a sufficient remediation for Pyxis integration with Slurm jobs: Treat a successful srun allocation as proof that every scheduled node can instantiate the specified container image.

This alternative is relevant to Slurm GPU GRES enumeration and device mapping but does not directly resolve the current Pyxis integration with Slurm jobs issue: Synchronize Slurm GRES inventory with hardware and MIG state and validate per-job device isolation. Observed scenario: A Slurm submission includes container directives but nodes lack the Pyxis plugin and the job fails at launch.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current Pyxis integration with Slurm jobs issue: Define explicit BCM node categories and managed overrides for shared hardware and service configuration. Observed scenario: A Slurm submission includes container directives but nodes lack the Pyxis plugin and the job fails at launch.

This alternative is relevant to BCM image provisioning and PXE worker boot but does not directly resolve the current Pyxis integration with Slurm jobs issue: Managed worker bring-up depends on the boot image and correct network identity, not just physical server power. Observed scenario: A Slurm submission includes container directives but nodes lack the Pyxis plugin and the job fails at launch.

That option concerns BCM high availability and management failover. Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment.

This tempting but unsafe shortcut is not a sufficient remediation for Pyxis integration with Slurm jobs: Treat a successful srun allocation as proof that every scheduled node can instantiate the specified container image. Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment.

This alternative is relevant to Base Command Manager head-node installation but does not directly resolve the current Pyxis integration with Slurm jobs issue: Inspect BCM service health, database backups, node discovery, management interfaces and provisioning records. Observed scenario: A Slurm submission includes container directives but nodes lack the Pyxis plugin and the job fails at launch.

This tempting but unsafe shortcut is not a sufficient remediation for Pyxis integration with Slurm jobs: Treat a successful srun allocation as proof that every scheduled node can instantiate the specified container image. A Slurm submission includes container directives but nodes lack the Pyxis plugin and the job fails at launch.

This alternative is relevant to BCM image provisioning and PXE worker boot but does not directly resolve the current Pyxis integration with Slurm jobs issue: Inspect DHCP leases, PXE requests, image revisions, BCM node mappings and host boot traces. Observed scenario: A Slurm submission includes container directives but nodes lack the Pyxis plugin and the job fails at launch.

This alternative is relevant to Base Command Manager head-node installation but does not directly resolve the current Pyxis integration with Slurm jobs issue: The management node reports correct cluster state and consistently deploys an approved image to intended nodes. Observed scenario: A Slurm submission includes container directives but nodes lack the Pyxis plugin and the job fails at launch.

This tempting but unsafe shortcut is not a sufficient remediation for Pyxis integration with Slurm jobs: Treat a successful srun allocation as proof that every scheduled node can instantiate the specified container image. A Slurm submission includes container directives but nodes lack the Pyxis plugin and the job fails at launch. Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment.

This alternative is relevant to BCM node categories and role inheritance but does not directly resolve the current Pyxis integration with Slurm jobs issue: Correct GPU workers receive the intended profile without unrelated node categories being modified. Observed scenario: A Slurm submission includes container directives but nodes lack the Pyxis plugin and the job fails at launch.

This alternative is relevant to Slurm GPU GRES enumeration and device mapping but does not directly resolve the current Pyxis integration with Slurm jobs issue: Synchronize Slurm GRES inventory with hardware and MIG state and validate per-job device isolation. Observed scenario: A multi-node Pyxis job pulls images independently and runs inconsistent image revisions.

This tempting but unsafe shortcut is not a sufficient remediation for Pyxis integration with Slurm jobs: Treat a successful srun allocation as proof that every scheduled node can instantiate the specified container image. A multi-node Pyxis job pulls images independently and runs inconsistent image revisions.

That option concerns NVIDIA Container Toolkit runtime integration. Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment.

This tempting but unsafe shortcut is not a sufficient remediation for Pyxis integration with Slurm jobs: Treat a successful srun allocation as proof that every scheduled node can instantiate the specified container image. A multi-node Pyxis job pulls images independently and runs inconsistent image revisions. Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment.

This alternative is relevant to Slurm GPU GRES enumeration and device mapping but does not directly resolve the current Pyxis integration with Slurm jobs issue: Inspect slurmd discovery, gres configuration, CUDA_VISIBLE_DEVICES and simultaneous job placement. Observed scenario: A multi-node Pyxis job pulls images independently and runs inconsistent image revisions.

This alternative is relevant to Linux operating system and kernel module support but does not directly resolve the current Pyxis integration with Slurm jobs issue: Use the supported OS, kernel and package combination and validate driver modules on a representative node before rollout. Observed scenario: A multi-node Pyxis job pulls images independently and runs inconsistent image revisions.