To Reproduce Steps to reproduce the behavior:

1. ilab train --strategy lab-multiphase

Expected behavior

Screenshots

• 

Device Info (please complete the following information):

• Hardware Specs: IBM Cloud 8 H100 GPU instance
• OS Version: 
• InstructLab Version: 0.23.5
• Provide the output of these two commands:
  • "registry.stage.redhat.io/rhelai1/bootc-nvidia-rhel9:1.4.5-1746558530"

• •   Device 7: NVIDIA H100 80GB HBM3, compute capability 9.0, VMM: yes

Platform:

  sys.version: 3.11.7 (main, Jan  8 2025, 00:00:00) [GCC 11.4.1 20231218 (Red Hat 11.4.1-3)]

  sys.platform: linux

  os.name: posix

  platform.release: 5.14.0-427.55.1.el9_4.x86_64

  platform.machine: x86_64

  platform.node: ecosystem-qe-h100-de-145

  platform.python_version: 3.11.7

  os-release.ID: rhel

  os-release.VERSION_ID: 9.4

  os-release.PRETTY_NAME: Red Hat Enterprise Linux 9.4 (Plow)

  memory.total: 1763.83 GB

  memory.available: 1751.75 GB

  memory.used: 3.67 GB

 

InstructLab:

  instructlab.version: 0.23.5

  instructlab-dolomite.version: 0.2.0

  instructlab-eval.version: 0.5.1

  instructlab-quantize.version: 0.1.0

  instructlab-schema.version: 0.4.2

  instructlab-sdg.version: 0.7.3

  instructlab-training.version: 0.7.0

 

Torch:

  torch.version: 2.5.1

  torch.backends.cpu.capability: AVX512

  torch.version.cuda: 12.4

  torch.version.hip: None

  torch.cuda.available: True

  torch.backends.cuda.is_built: True

  torch.backends.mps.is_built: False

  torch.backends.mps.is_available: False

  torch.cuda.bf16: True

  torch.cuda.current.device: 0

  torch.cuda.0.name: NVIDIA H100 80GB HBM3

  torch.cuda.0.free: 78.6 GB

  torch.cuda.0.total: 79.1 GB

  torch.cuda.0.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.1.name: NVIDIA H100 80GB HBM3

  torch.cuda.1.free: 78.6 GB

  torch.cuda.1.total: 79.1 GB

  torch.cuda.1.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.2.name: NVIDIA H100 80GB HBM3

  torch.cuda.2.free: 78.6 GB

  torch.cuda.2.total: 79.1 GB

  torch.cuda.2.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.3.name: NVIDIA H100 80GB HBM3

  torch.cuda.3.free: 78.6 GB

  torch.cuda.3.total: 79.1 GB

  torch.cuda.3.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.4.name: NVIDIA H100 80GB HBM3

  torch.cuda.4.free: 78.6 GB

  torch.cuda.4.total: 79.1 GB

  torch.cuda.4.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.5.name: NVIDIA H100 80GB HBM3

  torch.cuda.5.free: 78.6 GB

  torch.cuda.5.total: 79.1 GB

  torch.cuda.5.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.6.name: NVIDIA H100 80GB HBM3

  torch.cuda.6.free: 78.6 GB

  torch.cuda.6.total: 79.1 GB

  torch.cuda.6.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)

  torch.cuda.7.name: NVIDIA H100 80GB HBM3

  torch.cuda.7.free: 78.6 GB

  torch.cuda.7.total: 79.1 GB

  torch.cuda.7.capability: 9.0 (see https://developer.nvidia.com/cuda-gpus#compute)

 

llama_cpp_python:

  llama_cpp_python.version: 0.3.2

  llama_cpp_python.supports_gpu_offload: True

Bug impact

• Please provide information on the impact of this bug to the end user.

Known workaround

• Please add any known workarounds.

Additional context

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