From 2d3ac1fe63637db1978df2b3f5ea4903ef59788a Mon Sep 17 00:00:00 2001 From: allegroai Date: Thu, 13 Apr 2023 01:02:27 +0300 Subject: [PATCH] fix model name --- examples/keras/readme.md | 4 ++-- examples/lightgbm/readme.md | 4 ++-- examples/sklearn/readme.md | 4 ++-- examples/xgboost/readme.md | 4 ++-- 4 files changed, 8 insertions(+), 8 deletions(-) diff --git a/examples/keras/readme.md b/examples/keras/readme.md index 429207f..151c821 100644 --- a/examples/keras/readme.md +++ b/examples/keras/readme.md @@ -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 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 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 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 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` diff --git a/examples/lightgbm/readme.md b/examples/lightgbm/readme.md index 606add8..a30661d 100644 --- a/examples/lightgbm/readme.md +++ b/examples/lightgbm/readme.md @@ -16,11 +16,11 @@ The output will be a model created on the project "serving examples", by the nam 2. Create model endpoint: -`clearml-serving --id model add --engine lightgbm --endpoint "test_model_lgbm" --preprocess "examples/lightgbm/preprocess.py" --name "train lightgbm model" --project "serving examples"` +`clearml-serving --id model add --engine lightgbm --endpoint "test_model_lgbm" --preprocess "examples/lightgbm/preprocess.py" --name "train lightgbm model - lgbm_model" --project "serving examples"` Or auto-update -`clearml-serving --id model auto-update --engine lightgbm --endpoint "test_model_auto" --preprocess "examples/lightgbm/preprocess.py" --name "train lightgbm model" --project "serving examples" --max-versions 2` +`clearml-serving --id model auto-update --engine lightgbm --endpoint "test_model_auto" --preprocess "examples/lightgbm/preprocess.py" --name "train lightgbm model - lgbm_model" --project "serving examples" --max-versions 2` Or add Canary endpoint diff --git a/examples/sklearn/readme.md b/examples/sklearn/readme.md index 3c6dfeb..ce94735 100644 --- a/examples/sklearn/readme.md +++ b/examples/sklearn/readme.md @@ -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 model add --engine sklearn --endpoint "test_model_sklearn" --preprocess "examples/sklearn/preprocess.py" --name "train sklearn model" --project "serving examples"` +`clearml-serving --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 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 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 diff --git a/examples/xgboost/readme.md b/examples/xgboost/readme.md index 444ad97..8c6c731 100644 --- a/examples/xgboost/readme.md +++ b/examples/xgboost/readme.md @@ -15,11 +15,11 @@ 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: -3. `clearml-serving --id model add --engine xgboost --endpoint "test_model_xgb" --preprocess "examples/xgboost/preprocess.py" --name "train xgboost model" --project "serving examples"` +3. `clearml-serving --id model add --engine xgboost --endpoint "test_model_xgb" --preprocess "examples/xgboost/preprocess.py" --name "train xgboost model - xgb_model" --project "serving examples"` Or auto update -`clearml-serving --id model auto-update --engine xgboost --endpoint "test_model_xgb_auto" --preprocess "examples/xgboost/preprocess.py" --name "train xgboost model" --project "serving examples" --max-versions 2` +`clearml-serving --id model auto-update --engine xgboost --endpoint "test_model_xgb_auto" --preprocess "examples/xgboost/preprocess.py" --name "train xgboost model - xgb_model" --project "serving examples" --max-versions 2` Or add Canary endpoint