aws / aws/aws-step-functions-data-science-sdk-python

Execution inputs as container arguments for processing jobs

Abierto
#197 6 comentarios 0 reacciones 0 asignados Ver en GitHub
Lenguaje dominante
Python
Estrellas
299
Forks
84
Métricas de merge de PR
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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

Abrir la 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

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