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metrics.md

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Metrics

Triton provides Prometheus metrics indicating GPU and request statistics. By default, these metrics are available at http://localhost:8002/metrics. The metrics are only available by accessing the endpoint, and are not pushed or published to any remote server. The metric format is plain text so you can view them directly, for example:

$ curl localhost:8002/metrics

The tritonserver --allow-metrics=false option can be used to disable all metric reporting, while the --allow-gpu-metrics=false and --allow-cpu-metrics=false can be used to disable just the GPU and CPU metrics respectively.

The --metrics-port option can be used to select a different port. For now, Triton reuses http address for metrics endpoint. The option --http-address can be used to bind http and metrics endpoints to the same specific address when http service is enabled.

To change the interval at whichs metrics are polled/updated, see the --metrics-interval-ms flag. Metrics that are updated "Per Request" are unaffected by this interval setting. This interval only applies to metrics that are designated as "Per Interval" in the tables of each section below:

Inference Request Metrics

For models that do not support batching, Request Count, Inference Count and Execution Count will be equal, indicating that each inference request is executed separately.

For models that support batching, the count metrics can be interpreted to determine average batch size as Inference Count / Execution Count. The count metrics are illustrated by the following examples:

  • Client sends a single batch-1 inference request. Request Count = 1, Inference Count = 1, Execution Count = 1.

  • Client sends a single batch-8 inference request. Request Count = 1, Inference Count = 8, Execution Count = 1.

  • Client sends 2 requests: batch-1 and batch-8. Dynamic batcher is not enabled for the model. Request Count = 2, Inference Count = 9, Execution Count = 2.

  • Client sends 2 requests: batch-1 and batch-1. Dynamic batcher is enabled for the model and the 2 requests are dynamically batched by the server. Request Count = 2, Inference Count = 2, Execution Count = 1.

  • Client sends 2 requests: batch-1 and batch-8. Dynamic batcher is enabled for the model and the 2 requests are dynamically batched by the server. Request Count = 2, Inference Count = 9, Execution Count = 1.

Category Metric Metric Name Description Granularity Frequency
Count Success Count nv_inference_request_success Number of successful inference requests received by Triton (each request is counted as 1, even if the request contains a batch) Per model Per request
Failure Count nv_inference_request_failure Number of failed inference requests received by Triton (each request is counted as 1, even if the request contains a batch) Per model Per request
Inference Count nv_inference_count Number of inferences performed (a batch of "n" is counted as "n" inferences, does not include cached requests) Per model Per request
Execution Count nv_inference_exec_count Number of inference batch executions (see Inference Request Metrics, does not include cached requests) Per model Per request
Latency Request Time nv_inference_request_duration_us Cumulative end-to-end inference request handling time (includes cached requests) Per model Per request
Queue Time nv_inference_queue_duration_us Cumulative time requests spend waiting in the scheduling queue (includes cached requests) Per model Per request
Compute Input Time nv_inference_compute_input_duration_us Cumulative time requests spend processing inference inputs (in the framework backend, does not include cached requests) Per model Per request
Compute Time nv_inference_compute_infer_duration_us Cumulative time requests spend executing the inference model (in the framework backend, does not include cached requests) Per model Per request
Compute Output Time nv_inference_compute_output_duration_us Cumulative time requests spend processing inference outputs (in the framework backend, does not include cached requests) Per model Per request

GPU Metrics

GPU metrics are collected through the use of DCGM. Collection of GPU metrics can be toggled with the --allow-gpu-metrics CLI flag. If building Triton locally, the TRITON_ENABLE_METRICS_GPU CMake build flag can be used to toggle building the relevant code entirely.

Category Metric Metric Name Description Granularity Frequency
GPU Utilization Power Usage nv_gpu_power_usage GPU instantaneous power Per GPU Per interval
Power Limit nv_gpu_power_limit Maximum GPU power limit Per GPU Per interval
Energy Consumption nv_energy_consumption GPU energy consumption in joules since Triton started Per GPU Per interval
GPU Utilization nv_gpu_utilization GPU utilization rate (0.0 - 1.0) Per GPU Per interval
GPU Memory GPU Total Memory nv_gpu_memory_total_bytes Total GPU memory, in bytes Per GPU Per interval
GPU Used Memory nv_gpu_memory_used_bytes Used GPU memory, in bytes Per GPU Per interval

CPU Metrics

Collection of CPU metrics can be toggled with the --allow-cpu-metrics CLI flag. If building Triton locally, the TRITON_ENABLE_METRICS_CPU CMake build flag can be used to toggle building the relevant code entirely.

Note

CPU Metrics are currently only supported on Linux. They collect information from the /proc filesystem such as /proc/stat and /proc/meminfo.

Category Metric Metric Name Description Granularity Frequency
CPU Utilization CPU Utilization nv_cpu_utilization Total CPU utilization rate [0.0 - 1.0] Aggregated across all cores since last interval Per interval
CPU Memory CPU Total Memory nv_cpu_memory_total_bytes Total CPU memory (RAM), in bytes System-wide Per interval
CPU Used Memory nv_cpu_memory_used_bytes Used CPU memory (RAM), in bytes System-wide Per interval

Response Cache Metrics

Cache metrics can be reported in two ways:

  1. A base set of cache metrics will be reported by Triton directly, such as the cache hit/miss counts and durations described below.

  2. As of 23.03, additional cache metrics may be reported depending on the cache implementation being used through Triton's Metrics API.

Triton-reported Response Cache Metrics

Compute latency metrics in the Inference Request Metrics table above are calculated for the time spent in model inference backends. If the response cache is enabled for a given model (see Response Cache docs for more info), total inference times may be affected by response cache lookup times.

On cache hits, "Cache Hit Time" indicates the time spent looking up the response, and "Compute Input Time" / "Compute Time" / "Compute Output Time" are not recorded.

On cache misses, "Cache Miss Time" indicates the time spent looking up the request hash and inserting the computed output tensor data into the cache. Otherwise, "Compute Input Time" / "Compute Time" / "Compute Output Time" will be recorded as usual.

Category Metric Metric Name Description Granularity Frequency
Count Cache Hit Count nv_cache_num_hits_per_model Number of response cache hits per model Per model Per request
Cache Miss Count nv_cache_num_misses_per_model Number of response cache misses per model Per model Per request
Latency Cache Hit Time nv_cache_hit_duration_per_model Cumulative time requests spend retrieving a cached response per model on cache hits (microseconds) Per model Per request
Cache Miss Time nv_cache_miss_duration_per_model Cumulative time requests spend looking up and inserting responses into the cache on a cache miss (microseconds) Per model Per request

Custom Metrics

Triton exposes a C API to allow users and backends to register and collect custom metrics with the existing Triton metrics endpoint. The user takes the ownership of the custom metrics created through the APIs and must manage their lifetime following the API documentation.

The identity_backend demonstrates a practical example of adding a custom metric to a backend.

Further documentation can be found in the TRITONSERVER_MetricFamily* and TRITONSERVER_Metric* API annotations in tritonserver.h.