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examples/keras/readme.md
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@ -14,14 +14,18 @@ The output will be a model created on the project "serving examples", by the nam
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 "examples/keras/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 "examples/keras/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
`
--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`