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Posted to commits@beam.apache.org by da...@apache.org on 2022/10/24 17:40:57 UTC
[beam] branch master updated: Benchmarking RunInference Example (#23554)

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The following commit(s) were added to refs/heads/master by this push:
     new de8f4ba446d Benchmarking RunInference Example (#23554)
de8f4ba446d is described below

commit de8f4ba446d7d1b275e3731d71404615bc459665
Author: Shubham Krishna <sh...@gmail.com>
AuthorDate: Mon Oct 24 19:40:48 2022 +0200

    Benchmarking RunInference Example (#23554)
    
    * Add code for runinference pipeline demonstrating different metrics
    
    * Update Documentation
    
    * Complete documentation and improve docstrings
    
    * Add RunInference Metrics snapshot
    
    * Fix formatting and pylinting issue
    
    * Fix pylinting and code bug
    
    * Remove raise statement and fix formatting
    
    * Fix importing package order
    
    * Add newlines to fix linting and formatting errors
    
    * Improve documentation and docstrings
    
    * Add newline and fix packages import order
    
    * Change argument name in transformations.py
    
    * Remove print statement and unset PROJECT_ID
    
    * Add image to repo
    
    * Update image
    
    * Fix merging typo
    
    * Fix Image rendering in documentation
    
    Co-authored-by: Shubham Krishna <“shubham.krishna@ml6.eu”>
---
 .../inference/runinference_metrics/__init__.py     |   16 +
 .../inference/runinference_metrics/config.py       |   30 +
 .../inference/runinference_metrics/main.py         |  127 +
 .../runinference_metrics/pipeline/__init__.py      |   16 +
 .../runinference_metrics/pipeline/options.py       |   74 +
 .../pipeline/transformations.py                    |   94 +
 .../inference/runinference_metrics/setup.py        |   43 +
 .../en/documentation/ml/runinference-metrics.md    |  102 +
 .../partials/section-menu/en/documentation.html    |    1 +
 .../images/runinference_metrics_snapshot.svg       | 4751 ++++++++++++++++++++
 10 files changed, 5254 insertions(+)

diff --git a/sdks/python/apache_beam/examples/inference/runinference_metrics/__init__.py b/sdks/python/apache_beam/examples/inference/runinference_metrics/__init__.py
new file mode 100644
index 00000000000..cce3acad34a
--- /dev/null
+++ b/sdks/python/apache_beam/examples/inference/runinference_metrics/__init__.py
@@ -0,0 +1,16 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
diff --git a/sdks/python/apache_beam/examples/inference/runinference_metrics/config.py b/sdks/python/apache_beam/examples/inference/runinference_metrics/config.py
new file mode 100644
index 00000000000..61b9a21bea4
--- /dev/null
+++ b/sdks/python/apache_beam/examples/inference/runinference_metrics/config.py
@@ -0,0 +1,30 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""The file defines global variables."""
+
+PROJECT_ID = ""
+REGION = "us-central1"
+JOB_NAME = "benchmarking-runinference"
+NUM_WORKERS = 1
+TOKENIZER_NAME = "distilbert-base-uncased-finetuned-sst-2-english"
+MODEL_STATE_DICT_PATH = (
+    f"gs://{PROJECT_ID}-ml-examples/{TOKENIZER_NAME}/pytorch_model.bin")
+MODEL_CONFIG_PATH = TOKENIZER_NAME
+IMG_NAME = "kfp-components-preprocessing/pytorch-gpu"
+TAG = "latest"
+DOCKER_IMG = f"{REGION}-docker.pkg.dev/{PROJECT_ID}/{IMG_NAME}:{TAG}"
diff --git a/sdks/python/apache_beam/examples/inference/runinference_metrics/main.py b/sdks/python/apache_beam/examples/inference/runinference_metrics/main.py
new file mode 100644
index 00000000000..7feeda4ea8e
--- /dev/null
+++ b/sdks/python/apache_beam/examples/inference/runinference_metrics/main.py
@@ -0,0 +1,127 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""This file contains the pipeline for loading a ML model, and exploring
+the different RunInference metrics."""
+import argparse
+import logging
+import sys
+
+import apache_beam as beam
+import config as cfg
+from apache_beam.ml.inference import RunInference
+from apache_beam.ml.inference.base import KeyedModelHandler
+from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerKeyedTensor
+from pipeline.options import get_pipeline_options
+from pipeline.transformations import CustomPytorchModelHandlerKeyedTensor
+from pipeline.transformations import HuggingFaceStripBatchingWrapper
+from pipeline.transformations import PostProcessor
+from pipeline.transformations import Tokenize
+from transformers import DistilBertConfig
+
+
+def parse_arguments(argv):
+  """
+    Parses the arguments passed to the command line and
+    returns them as an object
+    Args:
+      argv: The arguments passed to the command line.
+    Returns:
+      The arguments that are being passed in.
+    """
+  parser = argparse.ArgumentParser(description="benchmark-runinference")
+
+  parser.add_argument(
+      "-m",
+      "--mode",
+      help="Mode to run pipeline in.",
+      choices=["local", "cloud"],
+      default="local",
+  )
+  parser.add_argument(
+      "-p",
+      "--project",
+      help="GCP project to run pipeline on.",
+      default=cfg.PROJECT_ID,
+  )
+  parser.add_argument(
+      "-d",
+      "--device",
+      help="Device to run the dataflow job on",
+      choices=["CPU", "GPU"],
+      default="CPU",
+  )
+
+  args, _ = parser.parse_known_args(args=argv)
+  return args
+
+
+def run():
+  """
+    Runs the pipeline that loads a transformer based text classification model
+    and does inference on a list of sentences.
+    At the end of pipeline, different metrics like latency,
+    throughput and others are printed.
+    """
+  args = parse_arguments(sys.argv)
+
+  inputs = [
+      "This is the worst food I have ever eaten",
+      "In my soul and in my heart, I’m convinced I’m wrong!",
+      "Be with me always—take any form—drive me mad!"\
+      "only do not leave me in this abyss, where I cannot find you!",
+      "Do I want to live? Would you like to live with your soul in the grave?",
+      "Honest people don’t hide their deeds.",
+      "Nelly, I am Heathcliff!  He’s always,"\
+      "always in my mind: not as a pleasure,"\
+      "any more than I am always a pleasure to myself, but as my own being.",
+  ] * 1000
+
+  pipeline_options = get_pipeline_options(
+      job_name=cfg.JOB_NAME,
+      num_workers=cfg.NUM_WORKERS,
+      project=args.project,
+      mode=args.mode,
+      device=args.device,
+  )
+  model_handler_class = (
+      PytorchModelHandlerKeyedTensor
+      if args.device == "GPU" else CustomPytorchModelHandlerKeyedTensor)
+  device = "cuda:0" if args.device == "GPU" else args.device
+  model_handler = model_handler_class(
+      state_dict_path=cfg.MODEL_STATE_DICT_PATH,
+      model_class=HuggingFaceStripBatchingWrapper,
+      model_params={
+          "config": DistilBertConfig.from_pretrained(cfg.MODEL_CONFIG_PATH)
+      },
+      device=device,
+  )
+
+  with beam.Pipeline(options=pipeline_options) as pipeline:
+    _ = (
+        pipeline
+        | "Create inputs" >> beam.Create(inputs)
+        | "Tokenize" >> beam.ParDo(Tokenize(cfg.TOKENIZER_NAME))
+        | "Inference" >>
+        RunInference(model_handler=KeyedModelHandler(model_handler))
+        | "Decode Predictions" >> beam.ParDo(PostProcessor()))
+  metrics = pipeline.result.metrics().query(beam.metrics.MetricsFilter())
+  logging.info(metrics)
+
+
+if __name__ == "__main__":
+  run()
diff --git a/sdks/python/apache_beam/examples/inference/runinference_metrics/pipeline/__init__.py b/sdks/python/apache_beam/examples/inference/runinference_metrics/pipeline/__init__.py
new file mode 100644
index 00000000000..cce3acad34a
--- /dev/null
+++ b/sdks/python/apache_beam/examples/inference/runinference_metrics/pipeline/__init__.py
@@ -0,0 +1,16 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
diff --git a/sdks/python/apache_beam/examples/inference/runinference_metrics/pipeline/options.py b/sdks/python/apache_beam/examples/inference/runinference_metrics/pipeline/options.py
new file mode 100644
index 00000000000..b32200ed733
--- /dev/null
+++ b/sdks/python/apache_beam/examples/inference/runinference_metrics/pipeline/options.py
@@ -0,0 +1,74 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""This file contains the pipeline options to configure
+the Dataflow pipeline."""
+
+from datetime import datetime
+from typing import Any
+
+import config as cfg
+from apache_beam.options.pipeline_options import PipelineOptions
+
+
+def get_pipeline_options(
+    project: str,
+    job_name: str,
+    mode: str,
+    device: str,
+    num_workers: int = cfg.NUM_WORKERS,
+    **kwargs: Any,
+) -> PipelineOptions:
+  """Function to retrieve the pipeline options.
+    Args:
+        project: GCP project to run on
+        mode: Indicator to run local, cloud or template
+        num_workers: Number of Workers for running the job parallely
+    Returns:
+        Dataflow pipeline options
+    """
+  job_name = f'{job_name}-{datetime.now().strftime("%Y%m%d%H%M%S")}'
+
+  staging_bucket = f"gs://{cfg.PROJECT_ID}-ml-examples"
+
+  # For a list of available options, check:
+  # https://cloud.google.com/dataflow/docs/guides/specifying-exec-params#setting-other-cloud-dataflow-pipeline-options
+  dataflow_options = {
+      "runner": "DirectRunner" if mode == "local" else "DataflowRunner",
+      "job_name": job_name,
+      "project": project,
+      "region": cfg.REGION,
+      "staging_location": f"{staging_bucket}/dflow-staging",
+      "temp_location": f"{staging_bucket}/dflow-temp",
+      "setup_file": "./setup.py",
+  }
+  flags = []
+  if device == "GPU":
+    flags = [
+        "--experiment=worker_accelerator=type:nvidia-tesla-p4;count:1;"\
+          "install-nvidia-driver",
+        "--experiment=use_runner_v2",
+    ]
+    dataflow_options.update({
+        "sdk_container_image": cfg.DOCKER_IMG,
+        "machine_type": "n1-standard-4",
+    })
+
+  # Optional parameters
+  if num_workers:
+    dataflow_options.update({"num_workers": num_workers})
+  return PipelineOptions(flags=flags, **dataflow_options)
diff --git a/sdks/python/apache_beam/examples/inference/runinference_metrics/pipeline/transformations.py b/sdks/python/apache_beam/examples/inference/runinference_metrics/pipeline/transformations.py
new file mode 100644
index 00000000000..e7f6f9d4468
--- /dev/null
+++ b/sdks/python/apache_beam/examples/inference/runinference_metrics/pipeline/transformations.py
@@ -0,0 +1,94 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""This file contains the transformations and utility functions for
+the pipeline."""
+import apache_beam as beam
+import torch
+from apache_beam.io.filesystems import FileSystems
+from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerKeyedTensor
+from transformers import DistilBertForSequenceClassification
+from transformers import DistilBertTokenizer
+
+
+class CustomPytorchModelHandlerKeyedTensor(PytorchModelHandlerKeyedTensor):
+  """Wrapper around PytorchModelHandlerKeyedTensor to load a model on CPU."""
+  def load_model(self) -> torch.nn.Module:
+    """Loads and initializes a Pytorch model for processing."""
+    model = self._model_class(**self._model_params)
+    model.to(self._device)
+    file = FileSystems.open(self._state_dict_path, "rb")
+    model.load_state_dict(torch.load(file, map_location=self._device))
+    model.eval()
+    return model
+
+
+# Can be removed once https://github.com/apache/beam/issues/21863 is fixed
+class HuggingFaceStripBatchingWrapper(DistilBertForSequenceClassification):
+  """Wrapper around HuggingFace model because RunInference requires a batch
+    as a list of dicts instead of a dict of lists. Another workaround
+    can be found here where they disable batching instead.
+    https://github.com/apache/beam/blob/master/sdks/python/apache_beam/examples/inference/pytorch_language_modeling.py"""
+  def forward(self, **kwargs):
+    output = super().forward(**kwargs)
+    return [dict(zip(output, v)) for v in zip(*output.values())]
+
+
+class Tokenize(beam.DoFn):
+  """A DoFn for tokenizing texts"""
+  def __init__(self, model_name: str):
+    """Initialises a tokenizer based on the model_name"""
+    self._model_name = model_name
+
+  def setup(self):
+    """Loads the tokenizer"""
+    self._tokenizer = DistilBertTokenizer.from_pretrained(self._model_name)
+
+  def process(self, text_input: str):
+    """Prepocesses the text using the tokenizer"""
+    # We need to pad the tokens tensors to max length to make sure
+    # that all the tensors are of the same length and hence
+    # stack-able by the RunInference API, normally you would batch first
+    # and tokenize the batch after and pad each tensor
+    # the the max length in the batch.
+    tokens = self._tokenizer(
+        text_input, return_tensors="pt", padding="max_length", max_length=512)
+    # squeeze because tokenization add an extra dimension, which is empty
+    # in this case because we're tokenizing one element at a time.
+    tokens = {key: torch.squeeze(val) for key, val in tokens.items()}
+    return [(text_input, tokens)]
+
+
+class PostProcessor(beam.DoFn):
+  """Postprocess the RunInference output"""
+  def process(self, element):
+    """
+        Takes the input text and the prediction result, and returns a dictionary
+        with the input text and the softmax probabilities
+
+        Args:
+          element: The tuple of input text and the prediction result
+
+        Returns:
+          A list of dictionaries, each containing the input text
+          and the softmax output.
+        """
+    text_input, prediction_result = element
+    softmax = (
+        torch.nn.Softmax(dim=-1)(
+            prediction_result.inference["logits"]).detach().numpy())
+    return [{"input": text_input, "softmax": softmax}]
diff --git a/sdks/python/apache_beam/examples/inference/runinference_metrics/setup.py b/sdks/python/apache_beam/examples/inference/runinference_metrics/setup.py
new file mode 100644
index 00000000000..d6fb9742ac4
--- /dev/null
+++ b/sdks/python/apache_beam/examples/inference/runinference_metrics/setup.py
@@ -0,0 +1,43 @@
+#
+# Licensed to the Apache Software Foundation (ASF) under one or more
+# contributor license agreements.  See the NOTICE file distributed with
+# this work for additional information regarding copyright ownership.
+# The ASF licenses this file to You under the Apache License, Version 2.0
+# (the "License"); you may not use this file except in compliance with
+# the License.  You may obtain a copy of the License at
+#
+#    http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+#
+
+"""Setup.py module for the workflow's worker utilities.
+
+All the workflow related code is gathered in a package that will be built as a
+source distribution, staged in the staging area for the workflow being run and
+then installed in the workers when they start running.
+
+This behavior is triggered by specifying the --setup_file command line option
+when running the workflow for remote execution.
+"""
+
+import setuptools
+from setuptools import find_packages
+
+REQUIREMENTS = [
+    "apache-beam[gcp]==2.41.0", "transformers==4.21.0", "torch==1.12.0"
+]
+
+setuptools.setup(
+    name="write-to-pubsub-pipeline",
+    version="1.1.1",
+    install_requires=REQUIREMENTS,
+    packages=find_packages(),
+    author="Apache Software Foundation",
+    author_email="dev@beam.apache.org",
+    py_modules=["config"],
+)
diff --git a/website/www/site/content/en/documentation/ml/runinference-metrics.md b/website/www/site/content/en/documentation/ml/runinference-metrics.md
new file mode 100644
index 00000000000..e58cf20c0d4
--- /dev/null
+++ b/website/www/site/content/en/documentation/ml/runinference-metrics.md
@@ -0,0 +1,102 @@
+---
+title: "RunInference Metrics"
+---
+<!--
+Licensed under the Apache License, Version 2.0 (the "License");
+you may not use this file except in compliance with the License.
+You may obtain a copy of the License at
+
+http://www.apache.org/licenses/LICENSE-2.0
+
+Unless required by applicable law or agreed to in writing, software
+distributed under the License is distributed on an "AS IS" BASIS,
+WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+See the License for the specific language governing permissions and
+limitations under the License.
+-->
+
+# RunInference Metrics Example
+
+The main purpose of the example is to demonstrate and explain different metrics that are available when using the [RunInference](https://beam.apache.org/documentation/transforms/python/elementwise/runinference/) transform to perform inference using a machine learning model. We use a pipeline that reads a list of sentences, tokeinzes the text, and uses a Transformer based model `distilbert-base-uncased-finetuned-sst-2-english` for classifying the texts into two different classes using `Ru [...]
+
+We showcase different RunInference metrics when the pipeline is executed using the Dataflow Runner on CPU and GPU. The full example code can be found [here](https://github.com/apache/beam/tree/master/sdks/python/apache_beam/examples/inference/runinference_metrics/).
+
+
+The file structure for entire pipeline is:
+
+    runinference_metrics/
+    ├── pipeline/
+    │   ├── __init__.py
+    │   ├── options.py
+    │   └── transformations.py
+    ├── __init__.py
+    ├── config.py
+    ├── main.py
+    └── setup.py
+
+`pipeline/transformations.py` contains the code for `beam.DoFn` and additional functions that are used for pipeline
+
+`pipeline/options.py` contains the pipeline options to configure the Dataflow pipeline
+
+`config.py` defines some variables like GCP `PROJECT_ID`, `NUM_WORKERS` that are used multiple times
+
+`setup.py` defines the packages/requirements for the pipeline to run
+
+`main.py` contains the pipeline code and some additional functions used for running the pipeline
+
+
+### How to Run the Pipeline
+First, make sure you have installed the required packages. One should have access to a Google Cloud Project and then correctly configure the GCP variables like `PROJECT_ID`, `REGION`, and others in `config.py`. To use GPUs, follow the setup instructions [here](https://github.com/GoogleCloudPlatform/python-docs-samples/tree/main/dataflow/gpu-examples/pytorch-minimal).
+
+
+1. Dataflow with CPU: `python main.py --mode cloud --device CPU`
+2. Dataflow with GPU: `python main.py --mode cloud --device GPU`
+
+The pipeline can be broken down into few simple steps:
+1. Create a list of texts to use as an input using `beam.Create`
+2. Tokenize the text
+3. Use RunInference to do inference
+4. Postprocess the output of RunInference
+
+{{< highlight >}}
+  with beam.Pipeline(options=pipeline_options) as pipeline:
+    _ = (
+        pipeline
+        | "Create inputs" >> beam.Create(inputs)
+        | "Tokenize" >> beam.ParDo(Tokenize(cfg.TOKENIZER_NAME))
+        | "Inference" >>
+        RunInference(model_handler=KeyedModelHandler(model_handler))
+        | "Decode Predictions" >> beam.ParDo(PostProcessor()))
+{{< /highlight >}}
+
+
+## RunInference Metrics
+
+As mentioned above, we benchmarked the performance of RunInference using Dataflow on both CPU and GPU. These metrics can be seen in the GCP UI and can also be printed using
+
+{{< highlight >}}
+metrics = pipeline.result.metrics().query(beam.metrics.MetricsFilter())
+{{< /highlight >}}
+
+
+A snapshot of different metrics from GCP UI when using Dataflow on GPU:
+
+  ![RunInference GPU metrics rendered on Dataflow](/images/runinference_metrics_snapshot.svg)
+
+Some metrics commonly used for benchmarking are:
+
+* `num_inferences`: represents the total number of elements passed to `run_inference()`.
+
+* `inference_batch_latency_micro_secs_MEAN`: represents the average time taken to perform the inference across all batches of examples, measured in microseconds.
+
+* `inference_request_batch_size_COUNT`: represents the total number of samples across all batches of examples (created from `beam.BatchElements`) to be passed to run_inference()
+
+* `inference_request_batch_byte_size_MEAN`: represents the average size of all elements for all samples in all batches of examples (created from `beam.BatchElements`) to be passed to run_inference(). This is measured in bytes.
+
+* `model_byte_size_MEAN`: represents the average memory consumed to load and initialize the model. This is measured in bytes.
+
+* `load_model_latency_milli_secs_MEAN`: represents the average time taken to load and initialize the model. This is measured in milliseconds.
+
+One can derive other relevant metrics like
+* `Total time taken for inference` = `num_inferences x inference_batch_latency_micro_secs_MEAN`
+
diff --git a/website/www/site/layouts/partials/section-menu/en/documentation.html b/website/www/site/layouts/partials/section-menu/en/documentation.html
index 576b21e38cb..345ab8bc819 100644
--- a/website/www/site/layouts/partials/section-menu/en/documentation.html
+++ b/website/www/site/layouts/partials/section-menu/en/documentation.html
@@ -217,6 +217,7 @@
     <li><a href="/documentation/ml/data-processing/">Data processing</a></li>
     <li><a href="/documentation/ml/multi-model-pipelines/">Multi-model pipelines</a></li>
     <li><a href="/documentation/ml/online-clustering/">Online Clustering</a></li>
+    <li><a href="/documentation/ml/runinference-metrics/">RunInference Metrics</a></li>
     <li><a href="/documentation/ml/anomaly-detection/">Anomaly Detection</a></li>
   </ul>
 </li>
diff --git a/website/www/site/static/images/runinference_metrics_snapshot.svg b/website/www/site/static/images/runinference_metrics_snapshot.svg
new file mode 100644
index 00000000000..a1b41b2c208
--- /dev/null
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+ regarding copyright ownership.  The ASF licenses this file
+ to you under the Apache License, Version 2.0 (the
+ "License"); you may not use this file except in compliance
+ with the License.  You may obtain a copy of the License at
+
+   http://www.apache.org/licenses/LICENSE-2.0
+
+ Unless required by applicable law or agreed to in writing,
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