Comments (5)
My team would love if this work was completed and merged. We prefer writing amazon states language in Python as opposed to JSON, and this gives us the functionality to do that. An implementation detail prevents us from using the CDK where the step functions are defined, so making this SDK lighter would make it easier to use.
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Makes sense. We received similar request in #116
I agree this would be a breaking change. I'm not sure how many customers are using this SDK without sagemaker. So far, I've only seen this request from 3 individuals.
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I did some testing, and it looks like some of the implicit imports in stepfunctions.steps
would also need to be conditional if this change is to be implemented. Specifically the ones around sagemaker.
If you want me to, I'll can look more into it and send a PR next week!
As a side-note, here is a bit more info about my use-case:
We're currently using step functions as a deployment pipeline inside our accounts.
I'm currently doing a rewrite to make the pipeline more powerful by using this library to dynamically create step functions based on a configuration file for each repo.
The main problem right now is that I have to build the lambda inside of a docker container running terraform. This container doesn't have gcc, g++ or make by default, so I have to install it at runtime to avoid redoing a bunch of container images. Numpy and pandas also take a long time to install 😞
Luckely, I saw the announcement about lambdas being able to use ECR in other account a few days ago, so that should mostly alleviate the problem for me when I get it implemented (we're using an account per team, per environment).
Here is the current work in progress, if you're interested: nsbno/terraform-aws-delivery-pipeline.
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An implementation detail prevents us from using the CDK where the step functions are defined
Can you elaborate on what issue prevents you from using CDK? @DrewThomasCorps
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For the public record, this conversation was held offline. CDK also adds some bloat and in our case it's preferable to write Amazon States Language with a simple library, rather than requiring constructs, IDs, and stacks.
This is not a pressing concern and there are workarounds in place so we do not need this immediately resolved, but will be keeping an eye on it.
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Related Issues (20)
- Is it Possible to use schema from ExecutionInput into container_arguments of ProcessingStep? HOT 6
- Feature Request: Add Workflow.create_or_update() to update workflow if it exists or create it if it doesn't HOT 1
- feature: adding support for AsyncInferenceConfig in endpoint config HOT 1
- Unable set ModelClientConfig in TransformerStep HOT 3
- object of type TrainingInput is not JSON serializable HOT 1
- [aws-stepfunctions] Support for ResultSelector HOT 1
- Allow drop-in states language HOT 1
- Support "Cycles" with Choice states HOT 1
- pip install stepfunctions fails in SageMaker Studio Notebook
- render_graph() has hidden dependencies
- Workflow Tags are not updated HOT 1
- Retry policies with error codes HOT 1
- stepfunctions package side effect of changing application's root logger logging level to ERROR
- Instructions in CONTRIBUTING no longer work with tox>=4
- Project Maintenance HOT 5
- Execution inputs as container arguments for processing jobs HOT 4
- Stepfunctions-Textract-StartDocumentTextDetection does not accept SNSTopicArn in NotificationChannel HOT 1
- Please add Distributed Map HOT 1
- adding tags to a Sagemaker estimator in the training step does not seem to be supported
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