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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.

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. Observed scenario: A multi-node Pyxis job pulls images independently and runs inconsistent image revisions. The governing mechanism is: Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment. The proportionate action is: Install supported Pyxis and Enroot plugin configuration on worker and controller paths and validate jobs end to end.

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 multi-node Pyxis job pulls images independently and runs inconsistent image revisions.

That option concerns Slurm controller partition setup. Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment.

This alternative is relevant to Slurm scheduling time limits fairness and preemption but does not directly resolve the current Pyxis integration with Slurm jobs issue: Jobs follow approved priority and maintenance tests do not unexpectedly preempt protected production work. 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. Observed scenario: A multi-node Pyxis job pulls images independently and runs inconsistent image revisions. The governing mechanism is: Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment. The proportionate action is: Install supported Pyxis and Enroot plugin configuration on worker and controller paths and validate jobs end to end. A direct check is: Inspect Slurm plugin configuration, Enroot availability, image identifiers, mount constraints and job logs. The acceptance condition is: A multi-node job starts the same approved container safely on each intended worker under the expected user ID.

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 multi-node Pyxis job pulls images independently and runs inconsistent image revisions.

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: An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting.

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. An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting.

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: Validate DHCP/PXE services, provisioned image and node identity mapping, then test clean node reboots. Observed scenario: An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting.

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. An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting. 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: Cluster managers coordinate images, nodes, services and configuration; worker OS boot cannot replace control-plane readiness. Observed scenario: An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting.

This alternative is relevant to Slurm scheduling time limits fairness and preemption but does not directly resolve the current Pyxis integration with Slurm jobs issue: Slurm scheduling fairness and maintenance isolation require deliberate policy beyond raw accelerator count. Observed scenario: An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting.

This alternative is relevant to Slurm scheduling time limits fairness and preemption but does not directly resolve the current Pyxis integration with Slurm jobs issue: Inspect partition time limits, QOS priorities, pending reasons, preemption settings and maintenance reservations. Observed scenario: An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting.

This alternative is relevant to NVIDIA Container Toolkit runtime integration but does not directly resolve the current Pyxis integration with Slurm jobs issue: Inspect toolkit runtime configuration, docker info, --gpus selection and in-container nvidia-smi.

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. Observed scenario: An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting. The governing mechanism is: Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment. The proportionate action is: Install supported Pyxis and Enroot plugin configuration on worker and controller paths and validate jobs end to end.

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: An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting.

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. Observed scenario: An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting. The governing mechanism is: Pyxis bridges Slurm scheduling and container execution; scheduling a node does not itself prepare its container environment. The proportionate action is: Install supported Pyxis and Enroot plugin configuration on worker and controller paths and validate jobs end to end. A direct check is: Inspect Slurm plugin configuration, Enroot availability, image identifiers, mount constraints and job logs. The acceptance condition is: A multi-node job starts the same approved container safely on each intended worker under the expected user ID.

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: New and restarted nodes load the intended GPU and fabric drivers and pass required cluster smoke tests. Observed scenario: An administrator permits privileged container mounts outside the approved data area through an unreviewed scheduler setting.

This tempting but unsafe shortcut is not a sufficient remediation for GPU driver modules and DKMS lifecycle: Assume nvidia-smi can use a new unsupported kernel module just because the user-space command remains installed.

This alternative is relevant to DOCA host networking and RDMA driver stack but does not directly resolve the current GPU driver modules and DKMS lifecycle issue: Use NVIDIA-supported OFED or DOCA-host and driver packages and verify host-DPU compatibility before promotion. Observed scenario: A CUDA example reports no GPU after an operator updates Linux headers but does not rebuild the NVIDIA module.

This alternative is relevant to Enroot container image and execution lifecycle but does not directly resolve the current GPU driver modules and DKMS lifecycle issue: Use approved Enroot images and versioned runtime dependencies with restricted mounts and a reproducible launch test. Observed scenario: A CUDA example reports no GPU after an operator updates Linux headers but does not rebuild the NVIDIA module.

That option concerns Linux operating system and kernel module support. The proportionate action is: Install a supported NVIDIA driver branch, manage kernel-module compatibility and verify driver load and CUDA enumeration.

This tempting but unsafe shortcut is not a sufficient remediation for GPU driver modules and DKMS lifecycle: Assume nvidia-smi can use a new unsupported kernel module just because the user-space command remains installed. CUDA user-space tools require a functioning compatible kernel driver; package presence alone is insufficient.

That option concerns BCM image provisioning and PXE worker boot. The proportionate action is: Install a supported NVIDIA driver branch, manage kernel-module compatibility and verify driver load and CUDA enumeration.

That option concerns Compute fabric interface bonding and RDMA prerequisites. CUDA user-space tools require a functioning compatible kernel driver; package presence alone is insufficient.

This alternative is relevant to BCM image provisioning and PXE worker boot but does not directly resolve the current GPU driver modules and DKMS lifecycle issue: Inspect DHCP leases, PXE requests, image revisions, BCM node mappings and host boot traces. Observed scenario: A CUDA example reports no GPU after an operator updates Linux headers but does not rebuild the NVIDIA module.

This tempting but unsafe shortcut is not a sufficient remediation for GPU driver modules and DKMS lifecycle: Assume nvidia-smi can use a new unsupported kernel module just because the user-space command remains installed. A CUDA example reports no GPU after an operator updates Linux headers but does not rebuild the NVIDIA module.

That option concerns BCM high availability and management failover. CUDA user-space tools require a functioning compatible kernel driver; package presence alone is insufficient.

This tempting but unsafe shortcut is not a sufficient remediation for GPU driver modules and DKMS lifecycle: Assume nvidia-smi can use a new unsupported kernel module just because the user-space command remains installed. A CUDA example reports no GPU after an operator updates Linux headers but does not rebuild the NVIDIA module. CUDA user-space tools require a functioning compatible kernel driver; package presence alone is insufficient.

This alternative is relevant to Host SSH certificates keys and OS access segregation but does not directly resolve the current GPU driver modules and DKMS lifecycle issue: Review authorized key and account policy, SSH server settings, firewall reachability and authentication logs. Observed scenario: A CUDA example reports no GPU after an operator updates Linux headers but does not rebuild the NVIDIA module.