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Asynchronous Tasks with FastAPI and Celery

Example of how to handle background processes with FastAPI, Celery, and Docker.

Quick Start

Spin up the containers:

$ docker-compose up -d --build

Then check services are working:

$ docker ps
CONTAINER ID   IMAGE          COMMAND                  CREATED         STATUS         PORTS                                       NAMES
c887d5256561   mlapi_worker   "celery -A worker.ce…"   8 minutes ago   Up 8 minutes                                               mlapi_worker_1
bfda537b908a   mlapi_web      "uvicorn main:app --…"   8 minutes ago   Up 8 minutes   0.0.0.0:8004->8000/tcp, :::8004->8000/tcp   mlapi_web_1
19feb6a993e4   redis:7        "docker-entrypoint.s…"   8 minutes ago   Up 8 minutes   6379/tcp                                    mlapi_redis_1

Then send requests:

curl -X 'POST' 'http://127.0.0.1:8004/predict' -H 'accept: application/json' -H 'Content-Type: application/json' -d '[{"data": "{\"CLIENT_IP\": \"188.138.92.55\", \"CLIENT_USERAGENT\": NaN, \"REQUEST_SIZE\": 166, \"RESPONSE_CODE\": 404, \"MATCHED_VARIABLE_SRC\": \"REQUEST_URI\", \"MATCHED_VARIABLE_NAME\": NaN, \"MATCHED_VARIABLE_VALUE\": \"//tmp/20160925122692indo.php.vob\", \"EVENT_ID\": \"AVdhXFgVq1Ppo9zF5Fxu\"}"}, {"data": "{\"CLIENT_IP\": \"93.158.215.131\", \"CLIENT_USERAGENT\": \"Mozilla/5.0 (Windows NT 6.3; WOW64; rv:45.0) Gecko/20100101 Firefox/45.0\", \"REQUEST_SIZE\": 431, \"RESPONSE_CODE\": 302, \"MATCHED_VARIABLE_SRC\": \"REQUEST_GET_ARGS\", \"MATCHED_VARIABLE_NAME\": \"url\", \"MATCHED_VARIABLE_VALUE\": \"http://www.galitsios.gr/?option=com_k2\", \"EVENT_ID\": \"AVdcJmIIq1Ppo9zF2YIp\"}"}]'

Service will create Celery task and send you unique identifier:

{"task_id":"bd89d4e7-4d26-4348-be0b-0cbc55683532"}

You can get results using this id by sending:

curl http://localhost:8004/tasks/bd89d4e7-4d26-4348-be0b-0cbc55683532

And receive results:

[{"EVENT_ID":"AVdcJmIIq1Ppo9zF2YIp","LABEL_PRED":"0"},{"EVENT_ID":"AVdcJmIIq1Ppo9zF2YIp","LABEL_PRED":"0"}]

Architecture from here:

https://testdriven.io/blog/fastapi-and-celery/ https://github.com/testdrivenio/fastapi-celery

Multiclass classification idea from:

https://scikit-learn.org/stable/modules/multiclass.html https://scikit-learn.org/stable/modules/generated/sklearn.ensemble.RandomForestClassifier.html#sklearn.ensemble.RandomForestClassifier

Project content:

  • ml_api/model.ipynb - data EDA, feature engineering, data markup, sklearn model build and serialize
  • ml_api/project/data/ - data for model
  • ml_api/project/ml_models - serialized models
  • ml_api/project/main.py - FastAPI app initialization
  • ml_api/project/worker.py - celery worker with redis backend
  • ml_api/project/tasks.py - predict task for celery worker
  • ml_api/project/data_prep.py - data from request prepare for inference
  • ml_api/project/main.py

Model details

Current version of model created to map requests to attack categories (not all known attacks):

  • SQL injection
  • Remote Code Execution
  • Path Traversal

Data was marked up based on suspicious content in MATCHED_VARIABLE_VALUE request feature.
For model generalization and adaptation top unknown keywords, only some general features were used in model learning.
On 0.67/0.33 random train/test split model achieved 0.953 accuracy, and believed this metric can be enhanced.

Why?

https://heyiamsasha.notion.site/ML-PT-0bc4ce5012604ed397f040a1bdc29858
https://heyiamsasha.notion.site/ML-Positive-Technologies-a8016572d97c4eabac99fbdce5b81271