{
  "source_file": "0930",
  "source_platform": "Medium",
  "author": "Ransaka Ravihara",
  "title_listed": "Quick and Easy Way to Debug SageMaker Pipelines",
  "keywords": "SageMaker,调试,ML管道",
  "domain": "数据处理",
  "content_type": "深度技术解析",
  "is_paid_flag": "部分免费/会员墙",
  "url": "https://towardsdatascience.com/quick-and-easy-way-to-debug-sagemaker-pipelines-473b9c56f015/",
  "publish_date": "2022-10",
  "corpus_stage": "预训练/SFT",
  "notes": "",
  "id": "719c39fd434b",
  "collected_at": "2026-10-01T23:37:38",
  "title": "Quick and Easy Way to Debug SageMaker Pipelines | Towards Data Science",
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It also helps in adverstising on the website.\n\n\n  * Cookie\nedgebucket\n  * Duration\nsession\n  * Description\nReddit sets this cookie to save the information about a log-on Reddit user, for the purpose of advertisement recommendations and updating the content.\n\n\n  * Cookie\ndid\n  * Duration\n1 year\n  * Description\nArbor sets this cookie to show targeted ads to site visitors.This cookie expires after 2 months or 1 year.\n\n\nUncategorised\nOther uncategorised cookies are those that are being analysed and have not been classified into a category as yet.\nNo cookies to display.\nReject All Save My Preferences Accept All\nToggle Search[](https://towardsdatascience.com/)Toggle Mobile Navigation\n[](https://towardsdatascience.com/)\nPublish AI, ML & data-science insights to a global community of data professionals.\nSign in[Submit an Article](https://write.insightmediagroup.io)\n[Latest](https://towardsdatascience.com/latest/)[Editor's Picks](https://towardsdatascience.com/tag/editors-pick/)[Newsletter](https://towardsdatascience.com/tag/the-variable/)[ShipAI](https://towardsdatascience.com/shipai/)\n* * *\n[Write For TDS](https://towardsdatascience.com/submissions/)\n[LinkedIn](https://www.linkedin.com/company/towards-data-science/)[X](https://x.com/TDataScience)Toggle Search\n# Quick and Easy Way to Debug SageMaker Pipelines\nAre you working with SageMaker, struggling with numerous errors, and looking for hassle-free ways to debug and test your pipelines?\n[Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\nOctober 19, 20224 min read\n### A brief introduction to AWS SageMaker local mode\n![Photo by EJ Strat on Unsplash](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/10/0Zqbg_sSgLGJtawh3-scaled.jpg)\nPhoto by [EJ Strat](https://unsplash.com/@xoforoct?utm_source=medium&utm_medium=referral) on [Unsplash](https://unsplash.com?utm_source=medium&utm_medium=referral)\n### Introduction\nAs you may have experienced, debugging a pipeline is time-consuming in a cloud environment. Since our programs interact with cloud-hosted services, we have many points to consider compared to the local environment. For example, imagine your pipeline failed due to a version issue in the last step of the pipeline. Maybe it has been running for 2 hours before failing. In that case, your money on a particular cloud resource is wasted. That's where Data Scientists/ML Engineers should be aware of cost-effective ways to debug and test their pipelines. In this article, I will guide you on how to debug AWS SageMaker pipelines using their Local Mode feature.\nTopics covered in this article.\n  * What SageMaker pipelines are\n  * What is SageMaker local mode\n  * Benefits of using SageMaker local mode\n  * How to use SageMaker local mode for testing and debugging purpose.\n\n\nLet's start.\n* * *\n### What is the Sagamaker pipeline?\n![Example pipeline-illustration - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/10/1-v2qbN2yLJHfp5J5tYddCQ.png)\nExample pipeline-illustration - Image by Author\nIn simple words, the SageMaker pipeline is a directed acyclic graph (DAG) which gives us the capability to define the set of instructions based on our requirements. Each bubble in DAG corresponds to instructions specified by the user. Since these bubbles are interconnected, we can map dependencies among them, which helps us to separate complex instructions into single pieces. In addition, it gives more maintainability to these pipelines.\n> [**SageMaker Pipelines Overview**](https://docs.aws.amazon.com/sagemaker/latest/dg/pipelines-sdk.html)\n### What is SageMaker local mode, and why should we care about it\nBefore explaining the local mode, we should know how SageMaker pipelines work.\n> _**What happens when we execute the SageMaker pipeline?**_\nTo explain this, let me take a sample use case with the below steps.\n  * Data ingestion\n  * Preprocessing\n  * Model building\n  * Inferencing\n  * Postprocessing\n\n\nWhen we execute the above pipeline, SageMaker will automatically spin an EC2 instance for each step and run the script defined by the user. In advance, this whole pipeline will use 5 EC2 instances. And remember, All resources in the cloud have a cost. That's about the cost factor. How about the time factor? It's even worse. Except for the script execution time, we must wait a few minutes to spin the EC2 instance. Imagine you are building a pipeline or debugging a pipeline, which means you have to waste unnecessary money and time just on the EC2 instances. What if we can avoid spinning the EC2 instance and run the whole pipeline locally as a pilot run? That's where SageMaker local mode comes in handy.\n> With SageMaker local mode enabled, we can try our pipelines on the local machine. This means no more time and money wastage on EC2 instances while we are debugging or testing pipelines.\nLet's build a simple pipeline with preprocessing and training steps. Here I'll use a custom container. But local mode works with both in-built and custom-defined containers. You can see below my folder structure.\n![My folder structure - Image by Author](https://assets.insightmediagroup.io/media/wp-content/uploads/2022/10/1l1p9wb3DJ32BiPwwGTQFrQ.png)\nMy folder structure - Image by Author\n### Build preprocessing step\nIn this step, I'll use the training dataset inside my data directory and upload them into the preprocessing container. And then execute the python script located in preprocessing directory. Once the processing job is complete, preprocessed files should save to the configured S3 location.\nFirst, we need to set the below session configurations. It is necessary only if you are interacting with the local file system instead of S3.\nNow time to define the processing step.\n### Build training step\nThis step will perform the below actions.\n  * Get preprocessing step output and train the model with them.\n  * Save the model artifacts into the local directory called `model`\n\n\nSo the question is how to set the estimator `output_path` as a local directory. We can set local paths by adding a`file://` prefix to the local path.\n### Build the pipeline\nLet's test our pipeline.\nWhen you successfully execute the pipeline, you'll notice the model.tar.gz file created in the model directory. You can use it for inferencing and model diagnostics etc.\n### Final Words\nIf you are using SageMaker for development, you may encounter different bugs and errors. That's where SageMaker local mode could be your lifesaver. It will enable you to focus more time on your problem. After a local success run, you can ship your model pipeline into production. When converting local mode into AWS managed pipeline, you must replace your local session with a SageMaker session and use the SageMaker IAM role with the necessary permissions.\nThe complete code is available in my [GitHub repo](https://github.com/Ransaka/Sagemaker-Local-Mode-Example).\nHappy learning!!\n> _References:_\n  * AWS Sample repo - <https://github.com/aws-samples/amazon-sagemaker-local-mode>\n  * Sagemaker Documentation - <https://sagemaker.readthedocs.io/en/stable/overview.html?highlight=local%20mode#local-mode>\n\n\n## Register Now to Continue Reading\n### Unlock Towards Data Science with a Free Account\nAll articles on Towards Data Science are available to registered users. Create a free account today to access this article and thousands more.\nRegister for freeSign in to your account\n* * *\n![](https://assets.insightmediagroup.io/media/1787657265628_qm714e.jpeg)\nWritten By\n## [Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\n[See all from Ransaka Ravihara](https://towardsdatascience.com/author/ransakaravihara/)\nShare This Article\n  * [](https://www.facebook.com/sharer/sharer.php?u=https%3A%2F%2Ftowardsdatascience.com%2Fquick-and-easy-way-to-debug-sagemaker-pipelines-473b9c56f015%2F&title=Quick%20and%20Easy%20Way%20to%20Debug%20SageMaker%20Pipelines)\n  * [](https://www.linkedin.com/shareArticle?mini=true&url=https%3A%2F%2Ftowardsdatascience.com%2Fquick-and-easy-way-to-debug-sagemaker-pipelines-473b9c56f015%2F&title=Quick%20and%20Easy%20Way%20to%20Debug%20SageMaker%20Pipelines)\n  * [](https://x.com/share?url=https%3A%2F%2Ftowardsdatascience.com%2Fquick-and-easy-way-to-debug-sagemaker-pipelines-473b9c56f015%2F&text=Quick%20and%20Easy%20Way%20to%20Debug%20SageMaker%20Pipelines)\n\n\nTowards Data Science is a community publication. 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