aws / aws/aws-step-functions-data-science-sdk-python
Execution inputs as container arguments for processing jobs
- 主要言語
- Python
- スター
- 299
- フォーク
- 84
- PR マージ指標
- 30日以内にマージされた PR はありません
説明
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
コントリビューションガイド
調査の方向性
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.
索引モデルが issue の本文から書いたものです。
評価
- 技術スタック
- aws, python
- 領域
- cloud, machine-learning
- issue の種類
- 機能追加
- 難易度
- 4/5
- 見積もり時間
- 3〜5日
- 活発さ
- 停滞
- 明瞭さ
- おおむね明確
- 初心者へのやさしさ
- 35/100