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examples/pytorch/readme.md
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@ -15,16 +15,20 @@ 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_pytorch" --preprocess "examples/pytorch/preprocess.py" --name "train pytorch model" --project "serving examples"
--input-size 28 28 1 --input-name "INPUT__0" --input-type float32
--output-size -1 10 --output-name "OUTPUT__0" --output-type float32
`
Or auto update
`clearml-serving --id <service_id> model auto-update --engine triton --endpoint "test_model_pytorch_auto" --preprocess "examples/pytorch/preprocess.py" --name "train pytorch model" --project "serving examples" --max-versions 2
--input-size 28 28 1 --input-name "INPUT__0" --input-type float32
--output-size -1 10 --output-name "OUTPUT__0" --output-type float32
`
--output-size -1 10 --output-name "OUTPUT__0" --output-type float32`
Or add Canary endpoint
`clearml-serving --id <service_id> model canary --endpoint "test_model_pytorch_auto" --weights 0.1 0.9 --input-endpoint-prefix test_model_pytorch_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`