![]() Popular machine learning libraries such as PyTorch, TensorFlow, and Keras greatly simplify the development of AI systems. APIsĪnother common problem for notebook users is misuse of machine-learning APIs. Based on this model, we are able to leverage our static-analysis engine for Python and design new static-analysis rules to catch issues in notebooks. Our tool collects dynamic information during the execution of notebooks, then converts notebook files with Python code cells into a novel Python representation that models the execution order as well as the code cells as such. To catch problems resulting from out-of-order execution in Jupyter notebooks, we developed a hybrid approach that combines dynamic information capture and static analysis. New tool can spot problems - such as overfitting and vanishing gradients - that prevent machine learning models from learning. We on the Amazon CodeGuru team, which has developed a portfolio of code analysis tools for Amazon Web Services customers, saw a great opportunity to adapt our existing tools for notebooks and build solutions that best fit this new problem area. Traditional software development environments commonly use static-analysis tools to identify and prevent bugs and enforce coding standards, but Jupyter notebooks currently lack such tools. To learn more about how to install and use this extension, check out this user guide. The extension seamlessly integrates with JupyterLab and SageMaker Studio, and with a single button click, it can provide users feedback and suggestions for improving their code quality and security. We are excited to share our recent launch of the Amazon CodeGuru extension for JupyterLab and SageMaker Studio. Another 15% found reproduction of notebooks to be hard, and 6% had difficulty detecting and remediating security vulnerabilities within notebooks. Similarly, 23% found silent bugs hard to detect, and 18% agreed that global variables are inconsistently used. We recently surveyed 2,669 machine learning (ML) practitioners, and 33% of them mentioned that notebooks get easily cluttered due to the mix of code, documentation, and visualization. ![]() However, using Jupyter Notebook poses several challenges related to code maintenance and machine learning best practices. In a pilot study, an automated code checker found about 100 possible errors, 80% of which turned out to require correction.
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