clearml-serving/examples/keras

Train and Deploy Keras model with Nvidia Triton Engine

training mock model

Run the mock python training code

python3 train_keras_mnist.py

The output will be a model created on the project "serving examples", by the name "train keras model"

setting up the serving service

1. Create serving Service: clearml-serving create --name "serving example" (write down the service ID)

2. Create model endpoint: clearml-serving --id <service_id> model add --engine triton --endpoint "test_model_keras" --preprocess "preprocess.py" --name "train keras model" --project "serving examples" --input-size 1 784 --input-name "dense_input" --input-type float32 --output-size -1 10 --output-name "activation_2" --output-type float32 Or auto update clearml-serving --id <service_id> model auto-update --engine triton --endpoint "test_model_auto" --preprocess "preprocess.py" --name "train keras model" --project "serving examples" --max-versions 2 --input-size 1 784 --input-name "dense_input" --input-type float32 --output-size -1 10 --output-name "activation_2" --output-type float32 Or add Canary endpoint clearml-serving --id <service_id> model canary --endpoint "test_model_auto" --weights 0.1 0.9 --input-endpoint-prefix test_model_auto

3. Run the Triton Engine docker run -v ~/clearml.conf:/root/clearml.conf -p 8001:8001 -e CLEARML_SERVING_TASK_ID=<service_id> clearml-serving-triton:latest

4. Configure the Triton Engine IP on the Serving Service (if running on k8s, the gRPC ingest of the triton container) clearml-serving --id <service_id> config --triton-grpc-server <local_ip_here>:8001

5. Run the clearml-serving container docker run -v ~/clearml.conf:/root/clearml.conf -p 8001:8001 -e CLEARML_SERVING_TASK_ID=<service_id> clearml-serving:latest

6. Test new endpoint: curl -X POST "http://127.0.0.1:8080/serve/test_model_keras" -H "accept: application/json" -H "Content-Type: application/json" -d '{"url": "https://camo.githubusercontent.com/8385ca52c9cba1f6e629eb938ab725ec8c9449f12db81f9a34e18208cd328ce9/687474703a2f2f706574722d6d6172656b2e636f6d2f77702d636f6e74656e742f75706c6f6164732f323031372f30372f6465636f6d707265737365642e6a7067"}'

Notice: You can also change the serving service while it is already running! This includes adding/removing endpoints, adding canary model routing etc.

Running / debugging the serving service manually

Once you have setup the Serving Service Task

$ pip3 install -r clearml_serving/serving/requirements.txt
$ CLEARML_SERVING_TASK_ID=<service_id> PYHTONPATH=$(pwd) python3 -m gunicorn \
    --preload clearml_serving.serving.main:app \ 
    --workers 4 \
    --worker-class uvicorn.workers.UvicornWorker \
    --bind 0.0.0.0:8080