User Guide for Neuron Monitor (BETA)

WARNING: This tool is currently in “Beta”. Metric organization and naming might change during the Beta period.

neuron-monitor collects metrics and stats from the Neuron Runtimes running on the system and streams the collected data to stdout in JSON format.

These metrics and stats are organized into metric groups which can be configured by providing a configuration file as described in Using neuron-monitor

When running, neuron-monitor will:

  • Collect the data for the metric groups which, based on the elapsed time since their last update, need to be updated

  • Take the newly collected data and consolidate it into a large report

  • Serialize that report to JSON and stream it to stdout from where it can be consumed by other tools - such as the sample neuron-monitor-cloudwatch.py and neuron-monitor-prometheus.py scripts.

  • Wait until at least one metric group needs to be collected and repeat this flow

Using neuron-monitor

neuron-monitor takes the following 2 optional arguments:

--verbose= Verbosity level (default: 0)
 -c, --config-file= Path to configuration file

  • --verbose Verbosity level, where Verbosity level can be 0 to 4, controls the amount of debugging and verbose information sent to stderr; 0: no output, 4: maximum verbosity, default is 0

  • -c, --config-file path, where path is a valid path to a neuron-monitor JSON configuration file

Example:

neuron-monitor -c monitor.conf

Not specifying any option will enable collecting all the metric groups with a period of 5 seconds.

Example of a configuration file which enables all available metric groups for a single Neuron Runtime with a global update period of 1 second and sets an update period of 2 seconds for the "hw_counters" metric group:

{
  "period": "1s",
  "neuron_runtimes": [
    {
      "address": "unix:/run/neuron.sock",
      "metrics": [
        {
          "type": "neuroncore_counters"
        },
        {
          "type": "memory_used"
        },
        {
          "type": "neuron_runtime_vcpu_usage"
        },
        {
          "type": "inference_stats"
        },
        {
          "period": "2s",
          "type": "hw_counters"
        }
      ]
    }
  ],
  "system_metrics": [
    {
      "type": "vcpu_usage"
    },
    {
      "type": "memory_info"
    }
  ]
}

JSON objects and fields in the settings file

  • "neuron_runtimes" - array of objects specifying which Neuron Runtimes to monitor and what metric groups are enabled for each runtime

    • "address" - address of this Neuron Runtime

    • "metrics" - array of objects specifying which metric groups to capture for this Neuron Runtime

      • "type" - type of metric group

  • "period" - this field applies to metric group objects and sets the amount of time between two updates for that metric group

    • if can be specified as part of the root and/or neuron_runtime objects where it applies to all their children, and/or as part of a metric group object

    • if there’s no period specified, a default value of 5 seconds will be used

  • "system_metrics" - array of objects specifying which system level metric groups are enabled

Neuron Runtime-level metric groups

System-wide metric groups

Execution model

image

neuron-monitor waits for one or more metric groups to be up for update, then collects the corresponding data, consolidates it into a report which is streamed to stdout as a JSON and goes back to waiting.

The JSON output format

Whenever the report gets updated, a complete JSON is written to stdout. This is its structure:

{
  "neuron_runtime_data": [
    {
      "neuron_runtime_index": 0,
      "error": "",
      "report": {
        "neuroncore_counters": {
            [...]
        },
        "inference_stats": {
            [...]
        },
        "memory_used": {
            [...]
        },
        "hw_counters": {
            [...]
        },
        "neuron_runtime_vcpu_usage": {
            [...]
        }
      }
    }
  ],
  "system_data": {
    "vcpu_usage": {
            [...]
    },
    "memory_info": {
            [...]
    }
  },
  "instance_data": {
            [...]
  }
}

  • "neuron_runtime_data" is an array containing one entry per each Neuron Runtime specified in the settings file

    • "neuron_runtime_index" is the zero-based index of this Neuron Runtime in the configuration file

    • "error" specifies any error that occurred when collecting data from this Neuron Runtime

    • "report" will contain the results for the Neuron Runtime-level metric groups; their formats are described below

  • "system_data" is similar to "neuron_runtime_data"‘s "report" but only contains system-level metric groups (not associated to any Neuron Runtime)

There is also instance information added to the root object regardless of the configuration:

"instance_data": {
  "instance_name": "My_Instance",
  "instance_id": "i-0011223344556677a",
  "instance_type": "inf1.xlarge",
  "instance_availability_zone": "us-west-2b",
  "instance_availability_zone_id": "usw2-az2",
  "instance_region": "us-west-2",
  "ami_id": "ami-0011223344556677b",
  "subnet_id": "subnet-112233ee",
  "error": ""
}

Depending on when the instance was launched, the following fields might not be available:

  • instance_availability_zone_id : available only for instances launched in 2020-08-24 and later

  • instance_region : available only for instances launched on 2020-08-24 and later

  • instance_name : available only if instance_region is set and aws-cli tools are installed The error will contain an error string if getting one of the fields, except those mentioned above, resulted in error.

Each metric group requested in the settings file will get an entry in the resulting output. The general format for such an entry is:

"metric_group": {
  "period": 1.015, // Actual captured period, in seconds
  "error": "",     // Error, if any occurred, otherwise an empty string
  [...]            // Metric group specific data
}

Neuron Runtime level metric groups

“neuroncore_counters”

"neuroncore_counters": {
  "period": 1.000113182,
  "neuroncores_in_use": {
    "0": {
      "neuroncore_utilization": 42.01,
      "loaded_models": [
        "my_model:my_subgraph1"
      ]
    },
    "1": {
      "neuroncore_utilization": 42.02,
      "loaded_models": [
        "my_model:my_subgraph2"
      ]
    },
    "2": {
      "neuroncore_utilization": 42.03,
      "loaded_models": [
        "my_model:my_subgraph3"
      ]
    },
    "3": {
      "neuroncore_utilization": 42.04,
      "loaded_models": [
        "my_model:my_subgraph4"
      ]
    }
  },
  "error": ""
}

  • "neuroncores_in_use" is an object containing data for all the NeuronCores that were active when the data was captured, indexed by NeuronCore index: "neuroncore_index": { neuroncore_data }

    • "neuroncore_utilization" - NeuronCore utilization, in percent, during the captured period

    • "loaded_models" - array containing strings formatted as "model_name:subgraph_name" which represent what models and subgraphs are loaded and associated with this NeuronCore

  • "error" - string containing any error that occurred when collecting the data

inference_stats

"inference_stats": {
  "period": 1.030613214,
  "error_summary": {
    "generic": 0,
    "numerical": 0,
    "transient": 0,
    "runtime": 0,
    "hardware": 0
  },
  "inference_summary": {
    "completed": 123,
    "completed_with_err": 0,
    "completed_with_num_err": 0,
    "timed_out": 0,
    "incorrect_input": 0,
    "failed_to_queue": 0
  },
  "latency_stats": {
    "total_latency": {
      "p0": 0.01100001,
      "p1": 0.01100002,
      "p25": 0.01100004,
      "p50": 0.01100008,
      "p75": 0.01100010,
      "p99": 0.01100012,
      "p100": 0.01100013
    },
    "device_latency": {
      "p0": 0.01000001,
      "p1": 0.01000002,
      "p25": 0.01000004,
      "p50": 0.01000008,
      "p75": 0.01000010,
      "p99": 0.01000012,
      "p100": 0.01000013
    }
  },
  "error": ""
},

  • "error_summary" is an object containing the error counts for the captured period indexed by their type

    • "generic" - generic inference errors

    • "numeric" - NAN inference errors

    • "transient" - recoverable errors, such as ECC corrections

    • "runtime" - Neuron Runtime errors

    • "hardware" - hardware errors such as uncorrectable ECC issues

  • "inference_summary" is an object containing all inference outcome counts for the captured period indexed by their type

    • "completed" - inferences completed successfully

    • "completed_with_err" - inferences that ended in an error other than numeric

    • "completed_with_num_err" - inferences that ended in a numeric error

    • "timed_out" - inferences that took longer than the Neuron Runtime configured timeout value

    • "incorrect_input" - inferences that failed to start due to incorrect input being provided

    • "failed_to_queue" - inference requests that were rejected due to Neuron Runtime not being able to queue them

  • "latency_stats" contains two objects containing latency percentiles, in seconds, for the data captured for inferences executed during the captured period. If there are no inferences being executed during this time, the two objects will be null (i.e. "total_latency": null)

    • "total_latency" - percentiles, in seconds, representing latency for an inference as measured by the Neuron Runtime

    • "device_latency" - percentiles, in seconds, representing time spent by an inference exclusively on the Neuron device

  • "error" - string containing any error that occurred when collecting the data

memory_used

"memory_used": {
  "period": 1.030366715,
  "neuron_runtime_used_bytes": {
    "host": 1000000,
    "neuron_device": 2000000
  },
  "loaded_models": [
    {
      "name": "my_model",
      "uuid": "aaaaaaaaaaabbbbbbbbbbb0000000000099999999999",
      "model_id": 10234,
      "is_running": true,
      "memory_used_bytes": {
        "host": 250000,
        "neuron_device": 500000
      },
      "subgraphs": {
        "sg00": {
          "memory_used_bytes": {
            "host": 250000,
            "neuron_device": 500000
          },
          "neuroncore_index": 2,
          "neuron_device_index": 0
        }
      }
    },
    [...]
  ],
  "error": ""
},

  • "runtime_memory" summarizes the amount of memory used by the Neuron Runtime at the time of capture

    • "neuron_runtime_used_bytes" - current amount of memory used by the Neuron Runtime

    • all memory usage objects contain these two fields:

      • "host" - host DRAM usage in bytes

      • "neuron_device" - Neuron device DRAM usage in bytes

  • "loaded_models" - array containing objects representing loaded models

    • "name" - name of the model

    • "uuid" - unique id for the model

    • "model_id" - Neuron Runtime-assigned ID for this model

    • "is_running" - true if this model is currently started, false otherwise

    • "memory_used_bytes" - total memory usage for the model

    • “subgraphs" - object containing all the subgraph for the model indexed by their name: "subgraph_name": { subgraph_data }

      • "memory_used_bytes" - memory usage for this subgraph

      • "neuroncore_index" - NeuronCore index with which the subgraph is associated

      • "neuron_device_index" - Neuron device index on which the subgraph is loaded

  • "error" - string containing any error that occurred when collecting the data

hw_counters

"hw_counters": {
  "period": 1.030359284,
  "neuron_devices": [
    {
      "neuron_device_index": 0,
      "mem_ecc_corrected": 0,
      "mem_ecc_uncorrected": 0,
      "sram_ecc_uncorrected": 0
    }
  ],
  "error": ""
},

  • "neuron_devices" - array containing ECC data for all Neuron devices controlled by this Neuron Runtime for the captured period

    • "neuron_device_index" - Neuron device index

    • "mem_ecc_corrected" - number of corrected ECC events in the Neuron device’s DRAM

    • "mem_ecc_uncorrected" - number of uncorrected ECC events in the Neuron device’s DRAM

    • "sram_ecc_uncorrected" - number of uncorrected ECC events in the Neuron device’s SRAM

  • "error" - string containing any error that occurred when collecting the data

“neuron_runtime_vcpu_usage”

"neuron_runtime_vcpu_usage": {
  "period": 1.030604818,
  "vcpu_usage": {
    "user": 42.01,
    "system": 12.34
  },
  "error": ""
}

  • "vcpu_usage" - object showing vCPU usage in percentages for the Neuron Runtime during the captured period

    • "user" - percentage of time spent in user code by this Neuron Runtime

    • "system" - percentage of time spent in kernel code by this Neuron Runtime

  • "error" - string containing any error that occurred when collecting the data

System level metric groups

“vcpu_usage”

"vcpu_usage": {
  "period": 0.999974868,
  "average_usage": {
    "user": 32.77,
    "nice": 0,
    "system": 22.87,
    "idle": 39.36,
    "io_wait": 0,
    "irq": 0,
    "soft_irq": 0
  },
  "usage_data": {
    "0": {
      "user": 34.41,
      "nice": 0,
      "system": 27.96,
      "idle": 37.63,
      "io_wait": 0,
      "irq": 0,
      "soft_irq": 0
    },
    "1": {
      "user": 56.84,
      "nice": 0,
      "system": 28.42,
      "idle": 14.74,
      "io_wait": 0,
      "irq": 0,
      "soft_irq": 0
    },
    [...]
  },
  "context_switch_count": 123456,
  "error": ""
}

  • each vCPU usage object contains the following fields:

    • "user" - percentage of time spent in user code

    • "nice" - percentage of time spent executing niced user code

    • "system" - percentage of time spent executing kernel code

    • "idle" - percentage of time spent idle

    • "io_wait" - percentage of time spent waiting for IO operations

    • "irq" - percentage of time spent servicing hardware interrupts

    • "soft_irq" - percentage of time spent servicing software interrupts

  • "average_usage" - contains the average usage across all vCPUs during the captured period

  • "usage_data" - contains per vCPU usage during the captured period

  • "context_switch_count" - contains the number of vCPU context switches during the captured period

  • "error" - string containing any error that occurred when collecting the data

memory_info

"memory_info": {
  "period": 5.346411129,
  "memory_total_bytes": 49345835008,
  "memory_used_bytes": 16042344448,
  "swap_total_bytes": 0,
  "swap_used_bytes": 0,
  "error": ""
}

  • "memory_total_bytes" - total size of the host memory, in bytes

  • "memory_used_bytes" - amount of host memory in use, in bytes

  • "swap_total_bytes" - total size of the host swap file, in bytes

  • "swap_used_bytes" - amount of swap memory in use, in bytes

Companion scripts

neuron-monitor is installed with two example Python companion script: neuron-monitor-cloudwatch.py and neuron-monitor-prometheus.py.

neuron-monitor-cloudwatch.py

It requires Python3 and the boto3 Python module. It is installed to: /opt/aws/neuron/bin/neuron-monitor-cloudwatch.py.

Using neuron-monitor-cloudwatch.py

neuron-monitor | neuron-monitor-cloudwatch.py --namespace <namespace> --region <region>

For example:

neuron-monitor | neuron-monitor-cloudwatch.py --namespace neuron_monitor_test --region us-west-2

neuron-monitor-prometheus.py

It requires Python3 and the Prometheus Python client module. It is installed to: /opt/aws/neuron/bin/neuron-monitor-prometheus.py.

Using neuron-monitor-prometheus.py

neuron-monitor | neuron-monitor-prometheus.py --port <port>

For example:

neuron-monitor | neuron-monitor-prometheus.py --port 8008

The default value for --port is 8000.

If your data visualization framework is Grafana, we provided a Grafana dashboard which integrates with Prometheus and this script.