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added locustfile (#2926)
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@ -11,3 +11,12 @@ Running `poetry install --no-root --with dev` will install everything you need i
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To add or remove dependencies, you can use the commands `poetry add $PACKAGE_NAME` and `poetry remove $PACKAGE_NAME`, respectively.
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Be sure to commit the `poetry.lock` and `pyproject.toml` files to reflect any changes in dependencies.
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# Load Testing
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To measure inference throughput and latency, you can use [Locust](https://locust.io/) using the provided `locustfile.py`.
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Locust works by querying the model endpoints and aggregating their statistics, meaning the app must be deployed.
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You can run `load_test.sh` to automatically deploy the app locally and start Locust, optionally adjusting its env variables as needed.
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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.
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24
machine-learning/load_test.sh
Executable file
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machine-learning/load_test.sh
Executable file
@ -0,0 +1,24 @@
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export MACHINE_LEARNING_CACHE_FOLDER=/tmp/model_cache
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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
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export MACHINE_LEARNING_MIN_TAG_SCORE=0.0
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export PID_FILE=/tmp/locust_pid
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export LOG_FILE=/tmp/gunicorn.log
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export HEADLESS=false
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export HOST=127.0.0.1:3003
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export CONCURRENCY=4
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export NUM_ENDPOINTS=3
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export PYTHONPATH=app
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gunicorn app.main:app --worker-class uvicorn.workers.UvicornWorker \
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--bind $HOST --daemon --error-logfile $LOG_FILE --pid $PID_FILE
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while true ; do
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echo "Loading models..."
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sleep 5
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if cat $LOG_FILE | grep -q -E "startup complete"; then break; fi
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done
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# "users" are assigned only one task, so multiply concurrency by the number of tasks
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locust --host http://$HOST --web-host 127.0.0.1 \
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--run-time 120s --users $(($CONCURRENCY * $NUM_ENDPOINTS)) $(if $HEADLESS; then echo "--headless"; fi)
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if [[ -e $PID_FILE ]]; then kill $(cat $PID_FILE); fi
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52
machine-learning/locustfile.py
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52
machine-learning/locustfile.py
Normal file
@ -0,0 +1,52 @@
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from io import BytesIO
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from locust import HttpUser, events, task
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from PIL import Image
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@events.test_start.add_listener
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def on_test_start(environment, **kwargs):
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global byte_image
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image = Image.new("RGB", (1000, 1000))
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byte_image = BytesIO()
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image.save(byte_image, format="jpeg")
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class InferenceLoadTest(HttpUser):
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abstract: bool = True
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host = "http://127.0.0.1:3003"
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data: bytes
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headers: dict[str, str] = {"Content-Type": "image/jpg"}
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# re-use the image across all instances in a process
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def on_start(self):
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global byte_image
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self.data = byte_image.getvalue()
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class ClassificationLoadTest(InferenceLoadTest):
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@task
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def classify(self):
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self.client.post(
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"/image-classifier/tag-image", data=self.data, headers=self.headers
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)
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class CLIPLoadTest(InferenceLoadTest):
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@task
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def encode_image(self):
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self.client.post(
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"/sentence-transformer/encode-image",
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data=self.data,
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headers=self.headers,
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)
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class RecognitionLoadTest(InferenceLoadTest):
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@task
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def recognize(self):
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self.client.post(
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"/facial-recognition/detect-faces",
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data=self.data,
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headers=self.headers,
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)
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1472
machine-learning/poetry.lock
generated
1472
machine-learning/poetry.lock
generated
File diff suppressed because it is too large
Load Diff
@ -27,6 +27,8 @@ aiocache = "^0.12.1"
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mypy = "^1.3.0"
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black = "^23.3.0"
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pytest = "^7.3.1"
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locust = "^2.15.1"
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gunicorn = "^20.1.0"
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[[tool.poetry.source]]
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name = "pytorch-cpu"
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