bash-5.1# cat ilab-config-show time="2025-06-11T14:37:42Z" level=warning msg="The input device is not a TTY. The --tty and --interactive flags might not work properly" # Chat configuration section. chat:   # Predefined setting or environment that influences the behavior and responses of   # the chat assistant. Each context is associated with a specific prompt that   # guides the assistant on how to respond to user inputs. Available contexts:   # default, cli_helper.   # Default: default   context: default   # Directory where chat logs are stored.   # Default: /var/home/azureuser/.local/share/instructlab/chatlogs   logs_dir: /var/home/azureuser/.local/share/instructlab/chatlogs   # The maximum number of tokens that can be generated in the chat completion. Be   # aware that larger values use more memory.   # Default: None   max_tokens:   # Model to be used for chatting with.   # Default: /var/home/azureuser/.cache/instructlab/models/granite-7b-lab-Q4_K_M.gguf   model: /var/home/azureuser/.cache/instructlab/models/granite-3.1-8b-lab-v2.1   # Filepath of a dialog session file.   # Default: None   session:   # Controls the randomness of the model's responses. Lower values make the output   # more deterministic, while higher values produce more random results.   # Default: 1.0   temperature: 1.0   # Enable vim keybindings for chat.   # Default: False   vi_mode: false   # Renders vertical overflow if enabled, displays ellipses otherwise.   # Default: True   visible_overflow: true # Evaluate configuration section. evaluate:   # Base taxonomy branch   # Default: None   base_branch:   # Base model to compare with 'model' for mt_bench_branch and mmlu_branch.   # Default: instructlab/granite-7b-lab   base_model: /var/home/azureuser/.cache/instructlab/models/granite-3.1-8b-starter-v2.1   # Taxonomy branch containing custom skills/knowledge that should be used for   # evaluation runs.   # Default: None   branch:   # Settings to run DK-Bench against a file of user created questions, reference   # answers, and responses. If responses are not provided they are generated from a   # model   dk_bench:     # File with questions and reference answers used for evaluation during DK-Bench.     # The file must be valid a '.jsonl' file with the fields 'user_input' and     # 'reference' in each entry     # Default: None     input_questions:     # Judge model for DK-Bench.     # Default: gpt-4o     judge_model: gpt-4o     # Directory where DK-Bench evaluation results are stored.     # Default: /var/home/azureuser/.local/share/instructlab/internal/eval_data/dk_bench     output_dir: /var/home/azureuser/.local/share/instructlab/internal/eval_data/dk_bench     # Comma-separated list of file formats for results of the DK-Bench evaluation. Ex:     # 'csv,jsonl'. Valid options in the list are csv, jsonl, and xlsx. If this option     # is not provided the results are written as a .jsonl file     # Default: jsonl     output_file_formats: jsonl   # Number of GPUs to use for running evaluation.   # Default: None   gpus: 1   # MMLU benchmarking settings   mmlu:     # Batch size for evaluation. Valid values are a positive integer or 'auto' to     # select the largest batch size that will fit in memory.     # Default: auto     batch_size: auto     # Number of question-answer pairs provided in the context preceding the question     # used for evaluation.     # Default: 5     few_shots: 5   # Settings to run MMLU against a branch of taxonomy containing custom   # skills/knowledge used for training.   mmlu_branch:     # Directory where custom MMLU tasks are stored.     # Default: /var/home/azureuser/.local/share/instructlab/datasets     tasks_dir: /var/home/azureuser/.local/share/instructlab/datasets   # Model to be evaluated   # Default: None   model:   # Multi-turn benchmarking settings for skills.   mt_bench:     # Judge model for MT-Bench.     # Default: prometheus-eval/prometheus-8x7b-v2.0     judge_model: /var/home/azureuser/.cache/instructlab/models/prometheus-8x7b-v2-0     # Number of workers to use for evaluation with mt_bench or mt_bench_branch. Must     # be a positive integer or 'auto'.     # Default: auto     max_workers: auto     # Directory where MT-Bench evaluation results are stored.     # Default: /var/home/azureuser/.local/share/instructlab/internal/eval_data/mt_bench     output_dir: /var/home/azureuser/.local/share/instructlab/internal/eval_data/mt_bench   # Settings to run MT-Bench against a branch of taxonomy containing custom   # skills/knowledge used for training   mt_bench_branch:     # Judge model for MT-Bench-Branch.     # Default: prometheus-eval/prometheus-8x7b-v2.0     judge_model: /var/home/azureuser/.cache/instructlab/models/prometheus-8x7b-v2-0     # Directory where MT-Bench-Branch evaluation results are stored.     # Default: /var/home/azureuser/.local/share/instructlab/internal/eval_data/mt_bench_branch     output_dir: /var/home/azureuser/.local/share/instructlab/internal/eval_data/mt_bench_branch     # Path to where base taxonomy is stored.     # Default: /var/home/azureuser/.local/share/instructlab/taxonomy     taxonomy_path: /var/home/azureuser/.local/share/instructlab/taxonomy   # System prompt for model getting responses during DK-Bench.   # Default: None   system_prompt:   # Temperature for model getting responses during DK-Bench. Temperature controls   # the randomness of the model's responses. Lower values make the output more   # deterministic, while higher values produce more random results.   # Default: 0.0   temperature: 0.0 # General configuration section. general:   # Debug level for logging.   # Default: 0   debug_level: 0   # Log format. https://docs.python.org/3/library/logging.html#logrecord-attributes   # Default: %(levelname)s %(asctime)s %(name)s:%(lineno)d: %(message)s   log_format: '%(levelname)s %(asctime)s %(name)s:%(lineno)d: %(message)s'   # Log level for logging.   # Default: INFO   log_level: INFO   # ID of the student model to be used for training.   # Default: None   student_model_id: granite-3.1-starter-v2   # ID of the teacher model to be used for data generation.   # Default: None   teacher_model_id:   # Use legacy IBM Granite chat template (default uses 3.0 Instruct template)   # Default: False   use_legacy_tmpl: false # Generate configuration section. generate:   # Number of Batches to send for generation on each core.   # Default: 8   batch_size: 256   # Maximum number of words per chunk.   # Default: 1000   chunk_word_count: 1000   # The maximum amount of tokens for the model to generate during knowledge   # generation. A lower number yields less data but a faster SDG run. It is   # reccomended to use this on consumer hardware   # Default: 4096   max_num_tokens: 4096   # Teacher model that will be used to synthetically generate training data.   # Default: /var/home/azureuser/.cache/instructlab/models/mistral-7b-instruct-v0.2.Q4_K_M.gguf   model: /var/home/azureuser/.cache/instructlab/models/mixtral-8x7b-instruct-v0-1   # Number of CPU cores to use for generation.   # Default: 10   num_cpus: 2   # Number of instructions to use   # Default: -1   # Deprecated: see 'sdg_scale_factor' instead   num_instructions: -1   # Directory where generated datasets are stored.   # Default: /var/home/azureuser/.local/share/instructlab/datasets   output_dir: /var/home/azureuser/.local/share/instructlab/datasets   # Data generation pipeline to use. Available: 'simple', 'full', or a valid path to   # a directory of pipeline workflow YAML files. Note that 'full' requires a larger   # teacher model, Mixtral-8x7b.   # Default: full   pipeline: /usr/share/instructlab/sdg/pipelines/agentic   # The total number of instructions to be generated.   # Default: 30   sdg_scale_factor: 30   # Branch of taxonomy used to calculate diff against.   # Default: origin/main   taxonomy_base: empty   # Directory where taxonomy is stored and accessed from.   # Default: /var/home/azureuser/.local/share/instructlab/taxonomy   taxonomy_path: /var/home/azureuser/.local/share/instructlab/taxonomy   # Teacher configuration   teacher:     # Serving backend to use to host the model.     # Default: None     # Examples:     #   - vllm     #   - llama-cpp     backend: vllm     # Chat template to supply to the model. Possible values: 'auto'(default),     # 'tokenizer', a path to a jinja2 file.     # Default: None     # Examples:     #   - auto     #   - tokenizer     #   - A filesystem path expressing the location of a custom template     chat_template: tokenizer     # llama-cpp serving settings.     llama_cpp:       # Number of model layers to offload to GPU. -1 means all layers.       # Default: -1       gpu_layers: -1       # Large Language Model Family       # Default: ''       # Examples:       #   - granite       #   - mixtral       llm_family: ''       # Maximum number of tokens that can be processed by the model.       # Default: 4096       max_ctx_size: 4096     # Directory where model to be served is stored.     # Default: /var/home/azureuser/.cache/instructlab/models/granite-7b-lab-Q4_K_M.gguf     model_path: /var/home/azureuser/.cache/instructlab/models/mixtral-8x7b-instruct-v0-1     # Server configuration including host and port.     # Default: host='127.0.0.1' port=8000 backend_type='' current_max_ctx_size=4096     server:       # Backend Instance Type       # Default: ''       # Examples:       #   - llama-cpp       #   - vllm       backend_type: ''       # Maximum number of tokens that can be processed by the currently served model.       # Default: 4096       current_max_ctx_size: 4096       # Host to serve on.       # Default: 127.0.0.1       host: 127.0.0.1       # Port to serve on.       # Default: 8000       port: 8000     # vLLM serving settings.     vllm:       # Number of GPUs to use.       # Default: None       gpus: 1       # Large Language Model Family       # Default: ''       # Examples:       #   - granite       #   - mixtral       llm_family: mixtral       # Maximum number of attempts to start the vLLM server.       # Default: 120       max_startup_attempts: 120       vllm_args:         - --max-num-seqs         - '512'         - --enable-lora         - --enable-prefix-caching         - --max-lora-rank         - '64'         - --dtype         - bfloat16         - --lora-dtype         - bfloat16         - --fully-sharded-loras         - --lora-modules         - skill-classifier-v3-clm=/var/home/azureuser/.cache/instructlab/models/skills-adapter-v3         - text-classifier-knowledge-v3-clm=/var/home/azureuser/.cache/instructlab/models/knowledge-adapter-v3 # Metadata pertaining to the specifics of the system which the Configuration is # meant to be applied to. metadata:   # Manufacturer, Family, and SKU of the system CPU, ex: Apple M3 Max   # Default: None   cpu_info:   # Amount of GPUs on the system, ex: 8   # Default: None   gpu_count: 8   # Family of the system GPU, ex: H100   # Default: None   gpu_family: MI300X   # Manufacturer of the system GPU, ex: Nvidia   # Default: None   gpu_manufacturer: AMD   # Specific SKU related information about the given GPU, ex: PCIe, NVL   # Default: None   gpu_sku: models:   -   # Family the model belongs to.     family: llama   # Internal ID referring to a particular model from the list.     id: llama-3.3   # Path to where the model can be found. Can either be a HF reference or local   # filepath.     path: /var/home/azureuser/.cache/instructlab/models/meta-llama/Llama-3.3-70B-Instruct   # The initial message used to prompt the conversation with this model. E.g. "You   # are a helfpul AI assistant..."   # Default: None     system_prompt:   -   # Family the model belongs to.     family: granite   # Internal ID referring to a particular model from the list.     id: granite-3.1-starter-v2   # Path to where the model can be found. Can either be a HF reference or local   # filepath.     path: /var/home/azureuser/.cache/instructlab/models/granite-3.1-8b-starter-v2.1   # The initial message used to prompt the conversation with this model. E.g. "You   # are a helfpul AI assistant..."   # Default: None     system_prompt: You are a Red Hat® Instruct Model, an AI language model developed       by Red Hat and IBM Research based on the granite-3.1-8b-base model. Your primary       role is to serve as a chat assistant. # RAG configuration section. rag:   # RAG convert configuration section.   convert:     # Directory where converted documents are stored.     # Default: /var/home/azureuser/.local/share/instructlab/converted_documents     output_dir: /var/home/azureuser/.local/share/instructlab/converted_documents     # Branch of taxonomy used to calculate diff against.     # Default: origin/main     taxonomy_base: origin/main     # Directory where taxonomy is stored and accessed from.     # Default: /var/home/azureuser/.local/share/instructlab/taxonomy     taxonomy_path: /var/home/azureuser/.local/share/instructlab/taxonomy   # Document store configuration for RAG.   document_store:     # Document store collection name.     # Default: ilab     collection_name: ilab     # Document store service URI.     # Default: /var/home/azureuser/.local/share/instructlab/embeddings.db     uri: /var/home/azureuser/.local/share/instructlab/embeddings.db   # Embedding model configuration for RAG   embedding_model:     # Embedding model to use for RAG.     # Default: /var/home/azureuser/.cache/instructlab/models/ibm-granite/granite-embedding-125m-english     embedding_model_path:       /var/home/azureuser/.cache/instructlab/models/ibm-granite/granite-embedding-125m-english   # Flag for enabling RAG functionality.   # Default: False   enabled: false   # Retrieval configuration parameters for RAG   retriever:     # The maximum number of documents to retrieve.     # Default: 3     top_k: 3 # Serve configuration section. serve:   # Serving backend to use to host the model.   # Default: None   # Examples:   #   - vllm   #   - llama-cpp   backend: vllm   # Chat template to supply to the model. Possible values: 'auto'(default),   # 'tokenizer', a path to a jinja2 file.   # Default: None   # Examples:   #   - auto   #   - tokenizer   #   - A filesystem path expressing the location of a custom template   chat_template: auto   # llama-cpp serving settings.   llama_cpp:     # Number of model layers to offload to GPU. -1 means all layers.     # Default: -1     gpu_layers: -1     # Large Language Model Family     # Default: ''     # Examples:     #   - granite     #   - mixtral     llm_family: ''     # Maximum number of tokens that can be processed by the model.     # Default: 4096     max_ctx_size: 4096   # Directory where model to be served is stored.   # Default: /var/home/azureuser/.cache/instructlab/models/granite-7b-lab-Q4_K_M.gguf   model_path: /var/home/azureuser/.cache/instructlab/models/granite-3.1-8b-lab-v2.1   # Server configuration including host and port.   # Default: host='127.0.0.1' port=8000 backend_type='' current_max_ctx_size=4096   server:     # Backend Instance Type     # Default: ''     # Examples:     #   - llama-cpp     #   - vllm     backend_type: vllm     # Maximum number of tokens that can be processed by the currently served model.     # Default: 4096     current_max_ctx_size: 4096     # Host to serve on.     # Default: 127.0.0.1     host: 127.0.0.1     # Port to serve on.     # Default: 8000     port: 8000   # vLLM serving settings.   vllm:     # Number of GPUs to use.     # Default: None     gpus: 8     # Large Language Model Family     # Default: ''     # Examples:     #   - granite     #   - mixtral     llm_family: ''     # Maximum number of attempts to start the vLLM server.     # Default: 120     max_startup_attempts: 120     vllm_args:       - --tensor-parallel-size       - '8' # Train configuration section. train:   # Additional arguments to pass to the training script. These arguments are passed   # as key-value pairs to the training script.   # Default: {}   additional_args:     learning_rate: 6e-6     lora_alpha: 32     lora_dropout: 0.1     warmup_steps: 25     use_dolomite: false   # Save a checkpoint at the end of each epoch.   # Default: True   checkpoint_at_epoch: true   # Directory where periodic training checkpoints are stored.   # Default: /var/home/azureuser/.local/share/instructlab/checkpoints   ckpt_output_dir: /var/home/azureuser/.local/share/instructlab/checkpoints   # Directory where the processed training data is stored (post   # filtering/tokenization/masking).   # Default: /var/home/azureuser/.local/share/instructlab/internal   data_output_dir: /var/home/azureuser/.local/share/instructlab/internal   # For the training library (pipelines 'full' or 'accelerated'), this must specify   # the path to the dataset '.jsonl' file. For legacy training (pipeline 'simple'),   # this specifies the path to the directory.   # Default: /var/home/azureuser/.local/share/instructlab/datasets   data_path: /var/home/azureuser/.local/share/instructlab/datasets   # Allow CPU offload for deepspeed optimizer.   # Default: False   deepspeed_cpu_offload_optimizer: false   # PyTorch device to use. Use 'cpu' for 'simple' and 'full' training on Linux. Use   # 'mps' for 'full' training on MacOS Metal Performance Shader. Use 'cuda' for   # Nvidia CUDA / AMD ROCm GPUs. Use 'hpu' for Intel Gaudi GPUs.   # Default: cpu   # Examples:   #   - cpu   #   - mps   #   - cuda   #   - hpu   device: cuda   # Whether or not we should disable the use of flash-attention during training.   # This is useful when using older GPUs.   # Default: False   disable_flash_attn: false   # Pick a distributed training backend framework for GPU accelerated full fine-   # tuning.   # Default: fsdp   distributed_backend: fsdp   # The number of samples in a batch that the model should see before its parameters   # are updated.   # Default: 64   effective_batch_size: 128   # Allow CPU offload for FSDP optimizer.   # Default: False   fsdp_cpu_offload_optimizer: false   # Boolean to indicate if the model being trained is a padding-free transformer   # model such as Granite.   # Default: False   is_padding_free: false   # The data type for quantization in LoRA training. Valid options are 'None' and   # 'nf4'.   # Default: nf4   # Examples:   #   - nf4   lora_quantize_dtype:   # Rank of low rank matrices to be used during training.   # Default: 0   lora_rank: 0   # Maximum tokens per gpu for each batch that will be handled in a single step. If   # running into out-of-memory errors, this value can be lowered but not below the   # `max_seq_len`.   # Default: 5000   max_batch_len: 120000   # Maximum sequence length to be included in the training set. Samples exceeding   # this length will be dropped.   # Default: 4096   max_seq_len: 10000   # Directory where the model to be trained is stored.   # Default: instructlab/granite-7b-lab   model_path: /var/home/azureuser/.cache/instructlab/models/granite-3.1-8b-starter-v2.1   # Number of GPUs to use for training. This value is not supported in legacy   # training or MacOS.   # Default: 1   nproc_per_node: 8   # Number of epochs to run training for.   # Default: 10   num_epochs: 8   # Base directory for organization of end-to-end intermediate outputs.   # Default: /var/home/azureuser/.local/share/instructlab/phased   phased_base_dir: /var/home/azureuser/.local/share/instructlab/phased   # Judge model path for phased MT-Bench evaluation.   # Default: /var/home/azureuser/.cache/instructlab/models/prometheus-eval/prometheus-8x7b-v2.0   phased_mt_bench_judge: /var/home/azureuser/.cache/instructlab/models/prometheus-8x7b-v2-0   # Phased phase1 effective batch size.   # Default: 128   phased_phase1_effective_batch_size: 128   # Learning rate for phase1 knowledge training.   # Default: 2e-05   phased_phase1_learning_rate: 2e-05   # Number of epochs to run training for during phase1 (experimentally optimal   # number is 7).   # Default: 7   phased_phase1_num_epochs: 7   # Number of samples the model should see before saving a checkpoint during phase1.   # Disabled when set to 0.   # Default: 0   phased_phase1_samples_per_save: 0   # Phased phase2 effective batch size.   # Default: 3840   phased_phase2_effective_batch_size: 3840   # Learning rate for phase2 skills training.   # Default: 6e-06   phased_phase2_learning_rate: 6e-06   # Number of epochs to run training for during phase2.   # Default: 10   phased_phase2_num_epochs: 10   # Number of samples the model should see before saving a checkpoint during phase2.   # Disabled when set to 0.   # Default: 0   phased_phase2_samples_per_save: 0   # Training pipeline to use. Simple is for systems with limited resources, full is   # for more capable consumer systems (64 GB of RAM), and accelerated is for systems   # with a dedicated GPU.   # Default: full   # Examples:   #   - simple   #   - full   #   - accelerated   pipeline: accelerated   # Number of samples the model should see before saving a checkpoint.   # Default: 250000   save_samples: 0   # Optional path to a yaml file that tracks the progress of multiphase training.   # Default: None   training_journal: # Configuration file structure version. # Default: 1.0.0 version: 1.0.0
 

bash-5.1# cat ilab-system-info 
time="2025-06-11T14:37:44Z" level=warning msg="The input device is not a TTY. The --tty and --interactive flags might not work properly"
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: 5.14.0-427.65.1.el9_4.x86_64
  platform.machine: x86_64
  platform.node: vshaw-rhelai-amd-test-westus
  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: 1820.96 GB
  memory.available: 1726.23 GB
  memory.used: 86.61 GB


InstructLab:
  instructlab.version: 0.26.1
  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: 0.8.2
  instructlab-training.version: 0.10.3


Torch:
  torch.version: 2.6.0
  torch.backends.cpu.capability: AVX512
  torch.version.cuda: None
  torch.version.hip: 6.3.42134-a9a80e791
  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: AMD Radeon Graphics
  torch.cuda.0.free: 24.1 GB
  torch.cuda.0.total: 191.5 GB
  torch.cuda.0.capability: 9.4 (see https://developer.nvidia.com/cuda-gpus#compute)
  torch.cuda.1.name: AMD Radeon Graphics
  torch.cuda.1.free: 24.2 GB
  torch.cuda.1.total: 191.5 GB
  torch.cuda.1.capability: 9.4 (see https://developer.nvidia.com/cuda-gpus#compute)
  torch.cuda.2.name: AMD Radeon Graphics
  torch.cuda.2.free: 24.2 GB
  torch.cuda.2.total: 191.5 GB
  torch.cuda.2.capability: 9.4 (see https://developer.nvidia.com/cuda-gpus#compute)
  torch.cuda.3.name: AMD Radeon Graphics
  torch.cuda.3.free: 24.2 GB
  torch.cuda.3.total: 191.5 GB
  torch.cuda.3.capability: 9.4 (see https://developer.nvidia.com/cuda-gpus#compute)
  torch.cuda.4.name: AMD Radeon Graphics
  torch.cuda.4.free: 24.2 GB
  torch.cuda.4.total: 191.5 GB
  torch.cuda.4.capability: 9.4 (see https://developer.nvidia.com/cuda-gpus#compute)
  torch.cuda.5.name: AMD Radeon Graphics
  torch.cuda.5.free: 24.3 GB
  torch.cuda.5.total: 191.5 GB
  torch.cuda.5.capability: 9.4 (see https://developer.nvidia.com/cuda-gpus#compute)
  torch.cuda.6.name: AMD Radeon Graphics
  torch.cuda.6.free: 24.3 GB
  torch.cuda.6.total: 191.5 GB
  torch.cuda.6.capability: 9.4 (see https://developer.nvidia.com/cuda-gpus#compute)
  torch.cuda.7.name: AMD Radeon Graphics
  torch.cuda.7.free: 24.3 GB
  torch.cuda.7.total: 191.5 GB
  torch.cuda.7.capability: 9.4 (see https://developer.nvidia.com/cuda-gpus#compute)


llama_cpp_python:
  llama_cpp_python.version: 0.3.6
  llama_cpp_python.supports_gpu_offload: False
bash-5.1#  

bash-5.1# podman images
REPOSITORY                                        TAG         IMAGE ID      CREATED     SIZE        R/O
registry.redhat.io/rhelai1/instructlab-amd-rhel9  1.5.1       994e4185df44  5 days ago  34.3 GB     true
bash-5.1#
 

bash-5.1# ilab model list
+------------------------------------+---------------------+---------+-------------------------------------------------------------+
| Model Name                         | Last Modified       | Size    | Absolute path                                               |
+------------------------------------+---------------------+---------+-------------------------------------------------------------+
| models/granite-3.1-8b-starter-v2.1 | 2025-06-11 15:16:57 | 31.2 GB | /root/.cache/instructlab/models/granite-3.1-8b-starter-v2.1 |
| models/granite-3.1-8b-lab-v2.1     | 2025-06-11 15:29:34 | 31.2 GB | /root/.cache/instructlab/models/granite-3.1-8b-lab-v2.1     |
+--------- 

bash-5.1# ilab shell
(app-root) /$ rocm-smi




============================================ ROCm System Management Interface ============================================
====================================================== Concise Info ======================================================
Device  Node  IDs              Temp        Power     Partitions          SCLK     MCLK    Fan  Perf  PwrCap  VRAM%  GPU% 
              (DID,     GUID)  (Junction)  (Socket)  (Mem, Compute, ID)                                                   
==========================================================================================================================
0       2     0x74b5,   65402  44.0°C      178.0W    NPS1, SPX, 0        2102Mhz  900Mhz  0%   auto  750.0W  88%    0%    
1       3     0x74b5,   27175  40.0°C      179.0W    NPS1, SPX, 0        2105Mhz  900Mhz  0%   auto  750.0W  87%    0%    
2       4     0x74b5,   16561  41.0°C      185.0W    NPS1, SPX, 0        2102Mhz  900Mhz  0%   auto  750.0W  87%    0%    
3       5     0x74b5,   54764  42.0°C      186.0W    NPS1, SPX, 0        2098Mhz  900Mhz  0%   auto  750.0W  87%    0%    
4       6     0x74b5,   10760  42.0°C      181.0W    NPS1, SPX, 0        2110Mhz  900Mhz  0%   auto  750.0W  87%    0%    
5       7     0x74b5,   48981  42.0°C      181.0W    NPS1, SPX, 0        2102Mhz  900Mhz  0%   auto  750.0W  87%    0%    
6       8     0x74b5,   32548  41.0°C      188.0W    NPS1, SPX, 0        2103Mhz  900Mhz  0%   auto  750.0W  87%    0%    
7       9     0x74b5,   60025  43.0°C      179.0W    NPS1, SPX, 0        2098Mhz  900Mhz  0%   auto  750.0W  87%    0%    
==========================================================================================================================
================================================== End of ROCm SMI Log ===================================================
(app-root) /$