aws / aws/aws-step-functions-data-science-sdk-python
Execution inputs as container arguments for processing jobs
- Lenguaje dominante
- Python
- Estrellas
- 299
- Forks
- 84
- Métricas de merge de PR
- Sin PR fusionados en 30 d
Descripción
I'm trying to use execution inputs as container arguements for my processing job:
```
execution_input = ExecutionInput(
schema={
"IngestaJobName": str,
"PreprocessingJobName": str,
"InferenceJobName": str,
"Fecha": str,
}
)
```
```
#Call step
ingesta_step = ProcessingStep(
inference_config["ingesta_step_name"],
processor=ingesta_processor,
job_name=execution_input['IngestaJobName'],
inputs=inputs_ingesta,
outputs=outputs_ingesta,
container_arguments=["--fecha", "$$.Execution.Input['Fecha']"],
container_entrypoint=["python3", "/opt/ml/processing/input/code/"+inference_config["ingesta_function"]],
)
```
I've also tried to replace container_arguments for `["--fecha", execution_input["Fecha"]] `
But in both cases it doesn't work.
### Use Case
When I lunch a new execution of my state machine, it would be useful to get some execution inputs as a container argument in order to define some parameters of intereset that will be define the behaviour of the step directly by the execution input without updating the state machine definition
---
This is a :rocket: Feature Request
Guía de contribución
Línea de trabajo
Start by tracing the ProcessingStep container_arguments entry point and how ExecutionInput values are represented in the SDK. Reproduce the supplied processing-job example, then inspect the related implementation and tests if available. Done means an execution input can reach the processing container as an argument without changing the state machine definition.
Escrito por el modelo de indexación a partir del texto del issue.
Evaluación
- Stack tecnológico
- aws, python
- Área
- cloud, machine-learning
- Tipo de issue
- Nueva funcionalidad
- Dificultad
- 4/5
- Tiempo estimado
- 3-5 días
- Estado de actividad
- Estancado
- Claridad
- Bastante claro
- Aptitud para principiantes
- 35/100