108. How to Solve Lambda Python Cold Starts
AWS Bites
In this episode, we discuss how you can use Python for data science workloads on AWS Lambda. We cover the pros and cons of using Lambda for these workloads compared to other AWS services. We benchmark cold start times and performance for different Lambda deployment options like zip packages, layers, and container images. The results show container images can provide faster cold starts than zip packages once the caches are warmed up. We summarize the optimizations AWS has made to enable performant container image deployments. Overall, Lambda can be a good fit for certain data science workloads, especially those that are bursty and need high concurrency.
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🔖 Chapters:
00:00 Intro 01:10 Why consider Lambda when doing Data Science or Machine Learning with Python? 03:58 The Lambda 250 MB package size limits 05:27 Packaging Lambda code using container images 07:25 Performance implications of using container images 09:44 Our benchmarks comparing zip vs container images and Lambda layers 12:41 Deep dive on our benchmark process and why container images can be faster than zip 16:16 Our recommendation on zip packages vs container images for Lambda 18:52 Additional resources on optimizing Python on Lambda 20:29 Closing notes
In this episode, we mentioned the following resources.
- Our blog post detailing our research on how to optimise Python Data Science in AWS Lambda: https://fourtheorem.com/optimise-python-data-science-aws-lambda/
- The repository with our benchmarks and related visualizations: https://github.com/fourTheorem/lambda-datasci-perf
- On-demand Container Loading on AWS Lambda (AWS Paper): https://arxiv.org/abs/2305.13162
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#aws #lambda #python #coldstart ... https://www.youtube.com/watch?v=XnC0XbXawuk
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