From a58482cb2b726ca51ad382d4c0529f59d1cc2117 Mon Sep 17 00:00:00 2001 From: Mert <101130780+mertalev@users.noreply.github.com> Date: Sun, 25 Jun 2023 14:20:45 -0400 Subject: [PATCH] added locustfile (#2926) --- machine-learning/README.md | 9 + machine-learning/load_test.sh | 24 + machine-learning/locustfile.py | 52 ++ machine-learning/poetry.lock | 1472 +++++++++++++++++++++++++------ machine-learning/pyproject.toml | 2 + 5 files changed, 1301 insertions(+), 258 deletions(-) create mode 100755 machine-learning/load_test.sh create mode 100644 machine-learning/locustfile.py diff --git a/machine-learning/README.md b/machine-learning/README.md index cbfa3a28f0..61ecc23ba9 100644 --- a/machine-learning/README.md +++ b/machine-learning/README.md @@ -11,3 +11,12 @@ Running `poetry install --no-root --with dev` will install everything you need i To add or remove dependencies, you can use the commands `poetry add $PACKAGE_NAME` and `poetry remove $PACKAGE_NAME`, respectively. Be sure to commit the `poetry.lock` and `pyproject.toml` files to reflect any changes in dependencies. + + +# Load Testing + +To measure inference throughput and latency, you can use [Locust](https://locust.io/) using the provided `locustfile.py`. +Locust works by querying the model endpoints and aggregating their statistics, meaning the app must be deployed. +You can run `load_test.sh` to automatically deploy the app locally and start Locust, optionally adjusting its env variables as needed. + +Alternatively, for more custom testing, you may also run `locust` directly: see the [documentation](https://docs.locust.io/en/stable/index.html). Note that in Locust's jargon, concurrency is measured in `users`, and each user runs one task at a time. To achieve a particular per-endpoint concurrency, multiply that number by the number of endpoints to be queried. For example, if there are 3 endpoints and you want each of them to receive 8 requests at a time, you should set the number of users to 24. \ No newline at end of file diff --git a/machine-learning/load_test.sh b/machine-learning/load_test.sh new file mode 100755 index 0000000000..2aaf6c4151 --- /dev/null +++ b/machine-learning/load_test.sh @@ -0,0 +1,24 @@ +export MACHINE_LEARNING_CACHE_FOLDER=/tmp/model_cache +export MACHINE_LEARNING_MIN_FACE_SCORE=0.034 # returns 1 face per request; setting this to 0 blows up the number of faces to the thousands +export MACHINE_LEARNING_MIN_TAG_SCORE=0.0 +export PID_FILE=/tmp/locust_pid +export LOG_FILE=/tmp/gunicorn.log +export HEADLESS=false +export HOST=127.0.0.1:3003 +export CONCURRENCY=4 +export NUM_ENDPOINTS=3 +export PYTHONPATH=app + +gunicorn app.main:app --worker-class uvicorn.workers.UvicornWorker \ + --bind $HOST --daemon --error-logfile $LOG_FILE --pid $PID_FILE +while true ; do + echo "Loading models..." + sleep 5 + if cat $LOG_FILE | grep -q -E "startup complete"; then break; fi +done + +# "users" are assigned only one task, so multiply concurrency by the number of tasks +locust --host http://$HOST --web-host 127.0.0.1 \ + --run-time 120s --users $(($CONCURRENCY * $NUM_ENDPOINTS)) $(if $HEADLESS; then echo "--headless"; fi) + +if [[ -e $PID_FILE ]]; then kill $(cat $PID_FILE); fi \ No newline at end of file diff --git a/machine-learning/locustfile.py b/machine-learning/locustfile.py new file mode 100644 index 0000000000..c9fbae36dc --- /dev/null +++ b/machine-learning/locustfile.py @@ -0,0 +1,52 @@ +from io import BytesIO + +from locust import HttpUser, events, task +from PIL import Image + + +@events.test_start.add_listener +def on_test_start(environment, **kwargs): + global byte_image + image = Image.new("RGB", (1000, 1000)) + byte_image = BytesIO() + image.save(byte_image, format="jpeg") + + +class InferenceLoadTest(HttpUser): + abstract: bool = True + host = "http://127.0.0.1:3003" + data: bytes + headers: dict[str, str] = {"Content-Type": "image/jpg"} + + # re-use the image across all instances in a process + def on_start(self): + global byte_image + self.data = byte_image.getvalue() + + +class ClassificationLoadTest(InferenceLoadTest): + @task + def classify(self): + self.client.post( + "/image-classifier/tag-image", data=self.data, headers=self.headers + ) + + +class CLIPLoadTest(InferenceLoadTest): + @task + def encode_image(self): + self.client.post( + "/sentence-transformer/encode-image", + data=self.data, + headers=self.headers, + ) + + +class RecognitionLoadTest(InferenceLoadTest): + @task + def recognize(self): + self.client.post( + "/facial-recognition/detect-faces", + data=self.data, + headers=self.headers, + ) diff --git a/machine-learning/poetry.lock b/machine-learning/poetry.lock index 0b7eab18d3..c829e02b68 100644 --- a/machine-learning/poetry.lock +++ b/machine-learning/poetry.lock @@ -18,13 +18,13 @@ redis = ["redis (>=4.2.0)"] [[package]] name = "albumentations" -version = "1.3.0" +version = "1.3.1" description = "Fast image augmentation library and easy to use wrapper around other libraries" optional = false -python-versions = ">=3.6" +python-versions = ">=3.7" files = [ - {file = "albumentations-1.3.0-py3-none-any.whl", hash = "sha256:294165d87d03bc8323e484927f0a5c1a3c64b0e7b9c32a979582a6c93c363bdf"}, - {file = "albumentations-1.3.0.tar.gz", hash = "sha256:be1af36832c8893314f2a5550e8ac19801e04770734c1b70fa3c996b41f37bed"}, + {file = "albumentations-1.3.1-py3-none-any.whl", hash = "sha256:6b641d13733181d9ecdc29550e6ad580d1bfa9d25e2213a66940062f25e291bd"}, + {file = "albumentations-1.3.1.tar.gz", hash = "sha256:a6a38388fe546c568071e8c82f414498e86c9ed03c08b58e7a88b31cf7a244c6"}, ] [package.dependencies] @@ -33,7 +33,7 @@ opencv-python-headless = ">=4.1.1" PyYAML = "*" qudida = ">=0.0.4" scikit-image = ">=0.16.1" -scipy = "*" +scipy = ">=1.1.0" [package.extras] develop = ["imgaug (>=0.4.0)", "pytest"] @@ -107,6 +107,108 @@ d = ["aiohttp 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