This page covers how to explore NVIDIA Data Center GPU Manager (DCGM) metrics associated with your Vertex AI Inference endpoints.
What is DCGM
NVIDIA Data Center GPU Manager (DCGM) is a set of tools from NVIDIA that let you manage and monitor NVIDIA GPUs. Vertex AI Inference automatically exports Vertex AI DCGM metrics to Cloud Monitoring if your endpoints utilize supported GPUs . Those metrics provide a comprehensive view of GPU utilization, performance, and health.
Prerequisites
Before you start, make sure your project has enabled Cloud Monitoring. See Enable the Monitoring API for more information.
Use DCGM metrics
To view DCGM metrics on Metrics Explorer, do the following:
-
Go to the Metrics Explorerpage in the Google Cloud console.
-
Under Select a metric, select Prometheus Target.
-
Under Active metric categories, select Vertex.
-
Under Active metrics, select the desired metric.
-
Click Apply.
You can also query metrics using Grafana , or Prometheus API or UI .
Quota
DCGM metrics consume the Time series ingestion requests per minutequota of the Cloud Monitoring API. Before enabling the metrics packages, check your recent peak usage of that quota. If you are already approaching that quota limit, you can request a quota-limit increase .
Vertex AI DCGM metrics
The Cloud Monitoring metric names in this table must be prefixed with prometheus.googleapis.com/
. That prefix has been omitted from the
entries in the table.
Along with labels on the prometheus_target
monitored resource, all collected
DCGM metrics on Vertex AI have the following labels attached to
them:
GPU labels:
-
gpu_model
: the GPU device model, such asNVIDIA L4
. -
gpu_uuid
: the GPU device UUID. -
gpu_i_id
: the NVIDIA Multi-Instance GPU (MIG) instance ID.
Vertex AI labels:
-
deployed_model_id
: the ID of a deployed model which serves inference requests. -
model_display_name
: the display name of a deployed model. -
replica_id
: the unique ID corresponding to the deployed model replica (pod name). -
endpoint_id
: the ID of a model endpoint. -
endpoint_display_name
: the display name of a model endpoint. -
product
: the name of the feature under Vertex AI. This is alwaysOnline Inference
.
Cloud Monitoring metric name
Monitored resources
vertex_dcgm_fi_dev_fb_free
vertex_dcgm_fi_dev_fb_free/gauge
vertex_dcgm_fi_dev_fb_total
vertex_dcgm_fi_dev_fb_total/gauge
vertex_dcgm_fi_dev_fb_used
vertex_dcgm_fi_dev_fb_used/gauge
vertex_dcgm_fi_dev_gpu_temp
vertex_dcgm_fi_dev_gpu_temp/gauge
vertex_dcgm_fi_dev_gpu_util
vertex_dcgm_fi_dev_gpu_util/gauge
vertex_dcgm_fi_dev_mem_copy_util
vertex_dcgm_fi_dev_mem_copy_util/gauge
vertex_dcgm_fi_dev_memory_temp
vertex_dcgm_fi_dev_memory_temp/gauge
vertex_dcgm_fi_dev_power_usage
vertex_dcgm_fi_dev_power_usage/gauge
vertex_dcgm_fi_dev_sm_clock
vertex_dcgm_fi_dev_sm_clock/gauge
vertex_dcgm_fi_dev_total_energy_consumption
vertex_dcgm_fi_dev_total_energy_consumption/counter
vertex_dcgm_fi_prof_dram_active
vertex_dcgm_fi_prof_dram_active/gauge
vertex_dcgm_fi_prof_gr_engine_active
vertex_dcgm_fi_prof_gr_engine_active/gauge
vertex_dcgm_fi_prof_nvlink_rx_bytes
vertex_dcgm_fi_prof_nvlink_rx_bytes/gauge
vertex_dcgm_fi_prof_nvlink_tx_bytes
vertex_dcgm_fi_prof_nvlink_tx_bytes/gauge
vertex_dcgm_fi_prof_pcie_rx_bytes
vertex_dcgm_fi_prof_pcie_rx_bytes/gauge
vertex_dcgm_fi_prof_pcie_tx_bytes
vertex_dcgm_fi_prof_pcie_tx_bytes/gauge
vertex_dcgm_fi_prof_pipe_fp16_active
vertex_dcgm_fi_prof_pipe_fp16_active/gauge
vertex_dcgm_fi_prof_pipe_fp32_active
vertex_dcgm_fi_prof_pipe_fp32_active/gauge
vertex_dcgm_fi_prof_pipe_fp64_active
vertex_dcgm_fi_prof_pipe_fp64_active/gauge
vertex_dcgm_fi_prof_pipe_tensor_active
vertex_dcgm_fi_prof_pipe_tensor_active/gauge
vertex_dcgm_fi_prof_sm_active
vertex_dcgm_fi_prof_sm_active/gauge
Supported GPUs
All NVIDIA GPUs are supported, except the following, due to resource constraints:
What's next
- Learn more about the Metrics Explorer .