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54 lines
2.2 KiB
Markdown
54 lines
2.2 KiB
Markdown
<!--
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---
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title: "Nvidia NVML GPM metric collector"
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description: Collect metrics for Nvidia GPUs using the NVML GPM interface
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categories: [cc-metric-collector]
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tags: ['Admin']
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weight: 2
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hugo_path: docs/reference/cc-metric-collector/collectors/nvidiaGPM.md
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---
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-->
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## `nvidiaGPM` collector
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```json
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"nvidia_gpm": {
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"metrics": [
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"nv_fb_mem_used",
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"nv_fan"
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],
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"exclude_devices": [
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"0","1", "0000000:ff:01.0"
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],
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"process_mig_devices": false,
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"use_pci_info_as_type_id": true,
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"add_pci_info_tag": false,
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"add_uuid_meta": false,
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"add_board_number_meta": false,
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"add_serial_meta": false,
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"use_uuid_for_mig_device": false,
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"use_slice_for_mig_device": false
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}
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```
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The `nvidia_gpm` collector can be configured to leave out specific devices with the `exclude_devices` option. It takes IDs as supplied to the NVML with `nvmlDeviceGetHandleByIndex()` or the PCI address in NVML format (`%08X:%02X:%02X.0`). Commonly only the physical GPUs are monitored. If MIG devices should be analyzed as well, set `process_mig_devices` (adds `stype=mig,stype-id=<mig_index>`). With the options `use_uuid_for_mig_device` and `use_slice_for_mig_device`, the `<mig_index>` can be replaced with the UUID (e.g. `MIG-6a9f7cc8-6d5b-5ce0-92de-750edc4d8849`) or the MIG slice name (e.g. `1g.5gb`).
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The metrics sent by the `nvidia_gpm` collector use `accelerator` as `type` tag. For the `type-id`, it uses the device handle index by default. With the `use_pci_info_as_type_id` option, the PCI ID is used instead. If both values should be added as tags, activate the `add_pci_info_tag` option. It uses the device handle index as `type-id` and adds the PCI ID as separate `pci_identifier` tag.
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Optionally, it is possible to add the UUID, the board part number and the serial to the meta informations. They are not sent to the sinks (if not configured otherwise).
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Available Metrics:
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* `nv_gpm_graphics_util`
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* `nv_gpm_sm_util`
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* `nv_gpm_sm_occupancy`
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* `nv_gpm_integer_util`
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* `nv_gpm_any_tensor_util`
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* `nv_gpm_dfma_tensor_util`
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* `nv_gpm_hmma_tensor_util`
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* `nv_gpm_imma_tensor_util`
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* `nv_gpm_dram_bw_util`
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* `nv_gpm_fp64_util`
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* `nv_gpm_fp32_util`
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* `nv_gpm_fp16_util` |