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README.md

Torchserve custom metrics with prometheus support

In this example, we show how to use a pre-trained custom MNIST model and export custom metrics using prometheus.

We use the following pytorch example of MNIST model for digit recognition : https://github.com/pytorch/examples/tree/master/mnist

Run the commands given in following steps from the root directory of the repository. For example, if you cloned the repository into /home/my_path/serve, run the steps from /home/my_path/serve

Steps

  • Step 1: In this example we add the following custom metrics and access them in prometheus format via the metrics API endpoint:

    • InferenceRequestCount
    • InitializeCallCount
    • PreprocessCallCount
    • PostprocessCallCount
    • RequestBatchSize
    • SizeOfImage
    • HandlerMethodTime
    • ExamplePercentMetric

    The custom metrics configuration file metrics.yaml in this example builds on top of the default metrics configuration file to include the custom metrics listed above. Note that, HandlerMethodTime and ExamplePercentMetric are not defined in the metrics configuration file to demonstrate auto-detection of backend metrics. The config.properties file in this example configures torchserve to use the custom metrics configuration file, sets the metrics_mode to prometheus and enables model_metrics_auto_detect. The custom handler mnist_handler.py updates the metrics listed above.

    Refer: Custom Metrics
    Refer: Custom Handler

  • Step 2: Create a torch model archive using the torch-model-archiver utility.

    torch-model-archiver --model-name mnist --version 1.0 --model-file examples/image_classifier/mnist/mnist.py --serialized-file examples/image_classifier/mnist/mnist_cnn.pt --handler examples/custom_metrics/mnist_handler.py

  • Step 3: Register the model to torchserve using the above model archive file.

    mkdir model_store
mv mnist.mar model_store/
torchserve --ncs --start --model-store model_store --ts-config examples/custom_metrics/config.properties --models mnist=mnist.mar

  • Step 4: Make Inference request

    curl http://127.0.0.1:8080/predictions/mnist -T examples/image_classifier/mnist/test_data/0.png

  • Step 5: Install prometheus using the instructions here.

  • Step 6: Create a minimal prometheus.yaml config file as below and run ./prometheus --config.file=prometheus.yaml.

global:
  scrape_interval:     15s
  evaluation_interval: 15s

scrape_configs:
  - job_name: 'prometheus'
    static_configs:
    - targets: ['localhost:9090']
  - job_name: 'torchserve'
    static_configs:
    - targets: ['localhost:8082'] #TorchServe metrics endpoint
  • Step 7: Test metrics API endpoint
curl http://127.0.0.1:8082/metrics

# HELP Requests2XX Torchserve prometheus counter metric with unit: Count
# TYPE Requests2XX counter
Requests2XX{Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 1.0
# HELP PredictionTime Torchserve prometheus gauge metric with unit: ms
# TYPE PredictionTime gauge
PredictionTime{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",} 17.63
# HELP DiskUsage Torchserve prometheus gauge metric with unit: Gigabytes
# TYPE DiskUsage gauge
DiskUsage{Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 8.538421630859375
# HELP WorkerLoadTime Torchserve prometheus gauge metric with unit: Milliseconds
# TYPE WorkerLoadTime gauge
WorkerLoadTime{WorkerName="W-9000-mnist_1.0",Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 17688.0
# HELP Requests5XX Torchserve prometheus counter metric with unit: Count
# TYPE Requests5XX counter
# HELP CPUUtilization Torchserve prometheus gauge metric with unit: Percent
# TYPE CPUUtilization gauge
CPUUtilization{Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 33.3
# HELP WorkerThreadTime Torchserve prometheus gauge metric with unit: Milliseconds
# TYPE WorkerThreadTime gauge
WorkerThreadTime{Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 2.0
# HELP DiskAvailable Torchserve prometheus gauge metric with unit: Gigabytes
# TYPE DiskAvailable gauge
DiskAvailable{Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 294.73255157470703
# HELP ts_inference_requests_total Torchserve prometheus counter metric with unit: Count
# TYPE ts_inference_requests_total counter
ts_inference_requests_total{model_name="mnist",model_version="default",hostname="88665a372f4b.ant.amazon.com",} 1.0
# HELP GPUMemoryUtilization Torchserve prometheus gauge metric with unit: Percent
# TYPE GPUMemoryUtilization gauge
# HELP HandlerTime Torchserve prometheus gauge metric with unit: ms
# TYPE HandlerTime gauge
HandlerTime{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",} 17.54
# HELP ts_inference_latency_microseconds Torchserve prometheus counter metric with unit: Microseconds
# TYPE ts_inference_latency_microseconds counter
ts_inference_latency_microseconds{model_name="mnist",model_version="default",hostname="88665a372f4b.ant.amazon.com",} 4315280.097
# HELP MemoryUtilization Torchserve prometheus gauge metric with unit: Percent
# TYPE MemoryUtilization gauge
MemoryUtilization{Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 67.1
# HELP MemoryAvailable Torchserve prometheus gauge metric with unit: Megabytes
# TYPE MemoryAvailable gauge
MemoryAvailable{Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 5383.73828125
# HELP PostprocessCallCount Torchserve prometheus counter metric with unit: CallCount
# TYPE PostprocessCallCount counter
PostprocessCallCount{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",} 1.0
# HELP ExamplePercentMetric Torchserve prometheus histogram metric with unit: percent
# TYPE ExamplePercentMetric histogram
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="0.005",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="0.01",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="0.025",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="0.05",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="0.075",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="0.1",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="0.25",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="0.5",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="0.75",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="1.0",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="2.5",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="5.0",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="7.5",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="10.0",} 0.0
ExamplePercentMetric_bucket{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",le="+Inf",} 1.0
ExamplePercentMetric_count{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",} 1.0
ExamplePercentMetric_sum{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",} 50.0
# HELP GPUUtilization Torchserve prometheus gauge metric with unit: Percent
# TYPE GPUUtilization gauge
# HELP MemoryUsed Torchserve prometheus gauge metric with unit: Megabytes
# TYPE MemoryUsed gauge
MemoryUsed{Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 8286.08984375
# HELP QueueTime Torchserve prometheus gauge metric with unit: Milliseconds
# TYPE QueueTime gauge
QueueTime{Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 4295.0
# HELP ts_queue_latency_microseconds Torchserve prometheus counter metric with unit: Microseconds
# TYPE ts_queue_latency_microseconds counter
ts_queue_latency_microseconds{model_name="mnist",model_version="default",hostname="88665a372f4b.ant.amazon.com",} 4295688.02
# HELP PreprocessCallCount Torchserve prometheus counter metric with unit: count
# TYPE PreprocessCallCount counter
PreprocessCallCount{ModelName="mnist",Hostname="88665a372f4b.ant.amazon.com",} 1.0
# HELP RequestBatchSize Torchserve prometheus gauge metric with unit: count
# TYPE RequestBatchSize gauge
RequestBatchSize{ModelName="mnist",Hostname="88665a372f4b.ant.amazon.com",} 1.0
# HELP SizeOfImage Torchserve prometheus gauge metric with unit: kB
# TYPE SizeOfImage gauge
SizeOfImage{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",} 0.265625
# HELP Requests4XX Torchserve prometheus counter metric with unit: Count
# TYPE Requests4XX counter
# HELP HandlerMethodTime Torchserve prometheus gauge metric with unit: ms
# TYPE HandlerMethodTime gauge
HandlerMethodTime{MethodName="preprocess",ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",} 9.392976760864258
# HELP InitializeCallCount Torchserve prometheus counter metric with unit: count
# TYPE InitializeCallCount counter
InitializeCallCount{ModelName="mnist",Level="Model",Hostname="88665a372f4b.ant.amazon.com",} 1.0
# HELP DiskUtilization Torchserve prometheus gauge metric with unit: Percent
# TYPE DiskUtilization gauge
DiskUtilization{Level="Host",Hostname="88665a372f4b.ant.amazon.com",} 2.8
# HELP GPUMemoryUsed Torchserve prometheus gauge metric with unit: Megabytes
# TYPE GPUMemoryUsed gauge
# HELP InferenceRequestCount Torchserve prometheus counter metric with unit: count
# TYPE InferenceRequestCount counter
InferenceRequestCount{Hostname="88665a372f4b.ant.amazon.com",} 1.0
  • Step 8: Navigate to http://localhost:9090/ on a browser to execute queries and create graphs
Screenshot 2023-08-03 at 6 46 47 PM