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Endpoint model name revision (#31)

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Change the correct model name for serving in tensorflow and keras example readme.md
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William Zhang 2022-07-11 18:49:20 +07:00 committed by GitHub
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2 changed files with 4 additions and 4 deletions
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4
examples/keras/readme.md
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@ -17,12 +17,12 @@ Prerequisites, Keras/Tensorflow models require Triton engine support, please use
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
`clearml-serving --id <service_id> model add --engine triton --endpoint "test_model_keras" --preprocess "examples/keras/preprocess.py" --name "train keras model - serving_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
`clearml-serving --id <service_id> model auto-update --engine triton --endpoint "test_model_auto" --preprocess "examples/keras/preprocess.py" --name "train keras model - serving_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`

4
examples/sklearn/readme.md
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@ -14,11 +14,11 @@ 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 sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --project "serving examples"`
`clearml-serving --id <service_id> model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model - sklearn-model" --project "serving examples"`
Or auto update
`clearml-serving --id <service_id> model auto-update --engine sklearn --endpoint "test_model_sklearn_auto" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --project "serving examples" --max-versions 2`
`clearml-serving --id <service_id> model auto-update --engine sklearn --endpoint "test_model_sklearn_auto" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model - sklearn-model" --project "serving examples" --max-versions 2`
Or add Canary endpoint