Bringing code analysis tools to Jupyter notebooks

Based on a survey of thousands of machine learning practitioners, a new CodeGuru extension addresses common problems, such as code cell execution order, incorrect API calls, and security.

The computational notebook is an interactive, web-based programming interface based on the concept of a lab notebook. Users can describe the computations they’re performing — including diagrams — and embed code in the notebook, and the notebook backend will execute the code, integrating the results into the notebook layout.

Jupyter Notebook is the most popular implementation of computational notebooks, and it has become the tool of choice for data scientists. By September 2018, there were more than 2.5 million public Jupyter notebooks on GitHub, and this number has been growing rapidly.

Related content
In a pilot study, an automated code checker found about 100 possible errors, 80% of which turned out to require correction.

However, using Jupyter Notebook poses several challenges related to code maintenance and machine learning best practices. 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. Similarly, 23% found silent bugs hard to detect, and 18% agreed that global variables are inconsistently used. Another 15% found reproduction of notebooks to be hard, and 6% had difficulty detecting and remediating security vulnerabilities within notebooks.

We are excited to share our recent launch of the Amazon CodeGuru extension for JupyterLab and SageMaker Studio. 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. To learn more about how to install and use this extension, check out this user guide.

Static analysis

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. 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.

Notebook-Interface.jpg
An example of how the notebook environment can integrate discussion, code, and visualizations.

We presented our initial efforts in a paper published at the 25th International Symposium on Formal Methods in March 2023. The paper reports insights from our survey and from interviews with ML practitioners to understand what specific issues need to be addressed in the notebook context. In the following, we give two examples of how our new technologies can help machine learning experts to be more productive.

Execution order

Code is embedded in computational notebooks in code cells, which can be executed in an arbitrary order and edited on the fly; that is, cells can be added, deleted, or changed after other cells have been executed.

While this flexibility is great for exploring data, it raises problems concerning reproducibility, as cells with shared variables can produce different results when running in different orders.

Jupyter code examples.png
Left: code cells executed in nonlinear order; right: code cells executed in linear order.

Once a code cell is executed, it is assigned an integer number in the square bracket on its left side. This number is called the execution count, and it indicates the cell’s position in the execution order. In the example above, when code cells are executed in nonlinear order, the variable z ends up with the value 6. However, execution count 2 is missing in the notebook file, which can happen for multiple reasons: perhaps the cell was executed and deleted afterwards, or perhaps one of the cells was executed twice. In any case, it would be hard for a second person to reproduce the same result.

Related content
New tool can spot problems — such as overfitting and vanishing gradients — that prevent machine learning models from learning.

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. 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. 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.

APIs

Another common problem for notebook users is misuse of machine-learning APIs. Popular machine learning libraries such as PyTorch, TensorFlow, and Keras greatly simplify the development of AI systems. However, due to the complexity of the field, the libraries’ high level of abstraction, and the sometimes obscure conventions governing library functions, library users often misuse these APIs and inject faults into their notebooks without even knowing it.

Related content
ICSE paper presents techniques piloted by Amazon Web Services’ Automated Reasoning team.

The code below shows such a misuse. Some layers of a neural network, such as dropout layers, may behave differently during the training and evaluation of the network. PyTorch mandates explicit calls to train() and eval() to denote the start of training and evaluation, respectively. The code example is intended to load a trained model from disk and evaluate it on some test data.

However, it misses the call to eval(), as by default, every model is in the training phase. In this case, some layers will indirectly change the architecture of the network, which would make all prediction unstable; i.e., for the same input, the predictions would be different at different times.

# noncompliant case
model.load_state_dict(torch.load("model.pth"))
predicted = model.evaluate_on(test_data)

# compliant case
model.load_state_dict(torch.load("model.pth"))
model.eval()
predicted = model.evaluate_on(test_data)

Instabilities caused by this bug can have a serious impact. Even when the bug is found (currently, through manual code review) and fixed, the model needs to be retrained. Depending on how large the model is and how late in the development process the bug is found, this could mean a waste of thousands of hours.

The best case would be to detect the bug directly after the developer writes the code. Static analysis can help with this. In our paper, we implemented a set of static-analysis rules that automatically analyze machine learning code in Jupyter notebooks and could detect such bugs with high precision.

In experiments involving a large set of notebook files, our rules found an average of one bug per seven notebooks. This result motivates us to dive deep into bug detection in Jupyter notebooks.

Our survey identified the following issues that notebook users care about:

  • Reproducibility: People often find it difficult to reproduce results when moving notebooks between different environments. Notebook code cells are often executed in nonlinear order, which may be not reproducible. About 14% of the survey participants collaborate on notebooks with others only when models need to be pushed into production; reproducibility is even more crucial for production notebooks.
  • Correctness: People introduce silent correctness bugs without knowing it when using machine learning libraries. Silent bugs affect model outputs but do not cause program crashes, which makes them extremely hard to find. In our survey, 23% of participants confirmed this.
  • Readability: During data exploration, notebooks can easily get messy and hard to read. This hampers maintainability as well as collaboration. In our survey, 32% of participants mentioned that readability is one of the biggest difficulties in using notebooks.
  • Performance: It is time- and memory-consuming to train big models. People want help to make both training and the runtime execution of their code more efficient.
  • Security: In our survey, 34% of participants said that security awareness among ML practitioners is low and that there is a consequent need for security scanning. Because notebooks often rely on external code and data, they can be vulnerable to code injection and data-poisoning attacks (manipulating machine learning models).

These findings pointed us toward the kinds of issues that our new analysis rules should address. During the rule sourcing and specification phase, we asked ML experts for feedback on the usefulness of the rules as well as examples of compliant and noncompliant cases to illustrate the rules. After developing the rules, we invited a group of ML experts to evaluate our tools on real-world notebooks. We used their feedback to improve the accuracy of the rules.

The newly launched Amazon CodeGuru extension for JupyterLab and SageMaker Studio enables the enforcement of code quality and security in computational notebooks to “shift left”, or move earlier in the development process. Users can now detect security vulnerabilities — such as injection flaws, data leaks, weak cryptography, and missing encryption — within notebook cells, along with other common issues that affect the readability, reproducibility, and correctness of the computations performed by notebooks.

Acknowledgements: Martin Schäf, Omer Tripp

Research areas

Related content

US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multi-modal systems. You will support projects that work on technologies including multi-modal model alignment, moderation systems and evaluation. Key job responsibilities As an Applied Scientist with the AGI team, you will support the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). You are also expected to publish in top tier conferences. About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems. Specifically, we focus on model alignment with an aim to maintain safety while not denting utility, in order to provide the best-possible experience for our customers.
IN, HR, Gurugram
Our customers have immense faith in our ability to deliver packages timely and as expected. A well planned network seamlessly scales to handle millions of package movements a day. It has monitoring mechanisms that detect failures before they even happen (such as predicting network congestion, operations breakdown), and perform proactive corrective actions. When failures do happen, it has inbuilt redundancies to mitigate impact (such as determine other routes or service providers that can handle the extra load), and avoids relying on single points of failure (service provider, node, or arc). Finally, it is cost optimal, so that customers can be passed the benefit from an efficiently set up network. Amazon Shipping is hiring Applied Scientists to help improve our ability to plan and execute package movements. As an Applied Scientist in Amazon Shipping, you will work on multiple challenging machine learning problems spread across a wide spectrum of business problems. You will build ML models to help our transportation cost auditing platforms effectively audit off-manifest (discrepancies between planned and actual shipping cost). You will build models to improve the quality of financial and planning data by accurately predicting ship cost at a package level. Your models will help forecast the packages required to be pick from shipper warehouses to reduce First Mile shipping cost. Using signals from within the transportation network (such as network load, and velocity of movements derived from package scan events) and outside (such as weather signals), you will build models that predict delivery delay for every package. These models will help improve buyer experience by triggering early corrective actions, and generating proactive customer notifications. Your role will require you to demonstrate Think Big and Invent and Simplify, by refining and translating Transportation domain-related business problems into one or more Machine Learning problems. You will use techniques from a wide array of machine learning paradigms, such as supervised, unsupervised, semi-supervised and reinforcement learning. Your model choices will include, but not be limited to, linear/logistic models, tree based models, deep learning models, ensemble models, and Q-learning models. You will use techniques such as LIME and SHAP to make your models interpretable for your customers. You will employ a family of reusable modelling solutions to ensure that your ML solution scales across multiple regions (such as North America, Europe, Asia) and package movement types (such as small parcel movements and truck movements). You will partner with Applied Scientists and Research Scientists from other teams in US and India working on related business domains. Your models are expected to be of production quality, and will be directly used in production services. You will work as part of a diverse data science and engineering team comprising of other Applied Scientists, Software Development Engineers and Business Intelligence Engineers. You will participate in the Amazon ML community by authoring scientific papers and submitting them to Machine Learning conferences. You will mentor Applied Scientists and Software Development Engineers having a strong interest in ML. You will also be called upon to provide ML consultation outside your team for other problem statements. If you are excited by this charter, come join us!
IN, HR, Gurugram
We're on a journey to build something new a green field project! Come join our team and build new discovery and shopping products that connect customers with their vehicle of choice. We're looking for a talented Senior Applied Scientist to join our team of product managers, designers, and engineers to design, and build innovative automotive-shopping experiences for our customers. This is a great opportunity for an experienced engineer to design and implement the technology for a new Amazon business. We are looking for a Applied Scientist to design, implement and deliver end-to-end solutions. We are seeking passionate, hands-on, experienced and seasoned Senior Applied Scientist who will be deep in code and algorithms; who are technically strong in building scalable computer vision machine learning systems across item understanding, pose estimation, class imbalanced classifiers, identification and segmentation.. You will drive ideas to products using paradigms such as deep learning, semi supervised learning and dynamic learning. As a Senior Applied Scientist, you will also help lead and mentor our team of applied scientists and engineers. You will take on complex customer problems, distill customer requirements, and then deliver solutions that either leverage existing academic and industrial research or utilize your own out-of-the-box but pragmatic thinking. In addition to coming up with novel solutions and prototypes, you will directly contribute to implementation while you lead. A successful candidate has excellent technical depth, scientific vision, project management skills, great communication skills, and a drive to achieve results in a unified team environment. You should enjoy the process of solving real-world problems that, quite frankly, haven’t been solved at scale anywhere before. Along the way, we guarantee you’ll get opportunities to be a bold disruptor, prolific innovator, and a reputed problem solver—someone who truly enables AI and robotics to significantly impact the lives of millions of consumers. Key job responsibilities Architect, design, and implement Machine Learning models for vision systems on robotic platforms Optimize, deploy, and support at scale ML models on the edge. Influence the team's strategy and contribute to long-term vision and roadmap. Work with stakeholders across , science, and operations teams to iterate on design and implementation. Maintain high standards by participating in reviews, designing for fault tolerance and operational excellence, and creating mechanisms for continuous improvement. Prototype and test concepts or features, both through simulation and emulators and with live robotic equipment Work directly with customers and partners to test prototypes and incorporate feedback Mentor other engineer team members. A day in the life - 6+ years of building machine learning models for retail application experience - PhD, or Master's degree and 6+ years of applied research experience - Experience programming in Java, C++, Python or related language - Experience with neural deep learning methods and machine learning - Demonstrated expertise in computer vision and machine learning techniques.
US, WA, Seattle
Do you want to re-invent how millions of people consume video content on their TVs, Tablets and Alexa? We are building a free to watch streaming service called Fire TV Channels (https://techcrunch.com/2023/08/21/amazon-launches-fire-tv-channels-app-400-fast-channels/). Our goal is to provide customers with a delightful and personalized experience for consuming content across News, Sports, Cooking, Gaming, Entertainment, Lifestyle and more. You will work closely with engineering and product stakeholders to realize our ambitious product vision. You will get to work with Generative AI and other state of the art technologies to help build personalization and recommendation solutions from the ground up. You will be in the driver's seat to present customers with content they will love. Using Amazon’s large-scale computing resources, you will ask research questions about customer behavior, build state-of-the-art models to generate recommendations and run these models to enhance the customer experience. You will participate in the Amazon ML community and mentor Applied Scientists and Software Engineers with a strong interest in and knowledge of ML. Your work will directly benefit customers and you will measure the impact using scientific tools.
US, MA, Boston
The Artificial General Intelligence (AGI) team is looking for a passionate, talented, and inventive Senior Applied Scientist with a strong deep learning background, to build industry-leading technology with Large Language Models (LLMs) and multimodal systems. Key job responsibilities As a Senior Applied Scientist with the AGI team, you will work with talented peers to lead the development of novel algorithms and modeling techniques, to advance the state of the art with LLMs. Your work will directly impact our customers in the form of products and services that make use of speech and language technology. You will leverage Amazon’s heterogeneous data sources and large-scale computing resources to accelerate advances in generative artificial intelligence (GenAI). About the team The AGI team has a mission to push the envelope in LLMs and multimodal systems, in order to provide the best-possible experience for our customers.
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field or relevant science experience (publications/scientific prototypes) in lieu of Masters - Experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment - Papers published in AI/ML venues of repute
IN, KA, Bengaluru
The Amazon Alexa AI team in India is seeking a talented, self-driven Applied Scientist to work on prototyping, optimizing, and deploying ML algorithms within the realm of Generative AI. Key responsibilities include: - Research, experiment and build Proof Of Concepts advancing the state of the art in AI & ML for GenAI. - Collaborate with cross-functional teams to architect and execute technically rigorous AI projects. - Thrive in dynamic environments, adapting quickly to evolving technical requirements and deadlines. - Engage in effective technical communication (written & spoken) with coordination across teams. - Conduct thorough documentation of algorithms, methodologies, and findings for transparency and reproducibility. - Publish research papers in internal and external venues of repute - Support on-call activities for critical issues Basic Qualifications: - Master’s or PhD in computer science, statistics or a related field - 2-7 years experience in deep learning, machine learning, and data science. - Proficiency in coding and software development, with a strong focus on machine learning frameworks. - Experience in Python, or another language; command line usage; familiarity with Linux and AWS ecosystems. - Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc. - Excellent communication skills (written & spoken) and ability to collaborate effectively in a distributed, cross-functional team setting. - Papers published in AI/ML venues of repute Preferred Qualifications: - Track record of diving into data to discover hidden patterns and conducting error/deviation analysis - Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations - The motivation to achieve results in a fast-paced environment. - Exceptional level of organization and strong attention to detail - Comfortable working in a fast paced, highly collaborative, dynamic work environment
IN, KA, Bengaluru
Amazon is investing heavily in building a world class advertising business and we are responsible for defining and delivering a collection of self-service performance advertising products that drive discovery and sales. Our products are strategically important to our Retail and Marketplace businesses driving long term growth. We deliver billions of ad impressions and millions of clicks daily and are breaking fresh ground to create world-class products. We are highly motivated, collaborative and fun-loving with an entrepreneurial spirit and bias for action. With a broad mandate to experiment and innovate, we are growing at an unprecedented rate with a seemingly endless range of new opportunities. The ATT team, based in Bangalore, is responsible for ensuring that ads are relevant and is of good quality, leading to higher conversion for the sellers and providing a great experience for the customers. We deal with one of the world’s largest product catalog, handle billions of requests a day with plans to grow it by order of magnitude and use automated systems to validate tens of millions of offers submitted by thousands of merchants in multiple countries and languages. In this role, you will build and develop ML models to address content understanding problems in Ads. These models will rely on a variety of visual and textual features requiring expertise in both domains. These models need to scale to multiple languages and countries. You will collaborate with engineers and other scientists to build, train and deploy these models. As part of these activities, you will develop production level code that enables moderation of millions of ads submitted each day.
US, WA, Seattle
The Search Supply & Experiences team, within Sponsored Products, is seeking an Applied Scientist to solve challenging problems in natural language understanding, personalization, and other areas using the latest techniques in machine learning. In our team, you will have the opportunity to create new ads experiences that elevate the shopping experience for our hundreds of millions customers worldwide. As an Applied Scientist, you will partner with other talented scientists and engineers to design, train, test, and deploy machine learning models. You will be responsible for translating business and engineering requirements into deliverables, and performing detailed experiment analysis to determine how shoppers and advertisers are responding to your changes. We are looking for candidates who thrive in an exciting, fast-paced environment and who have a strong personal interest in learning, researching, and creating new technologies with high customer impact. Key job responsibilities As an Applied Scientist on the Search Supply & Experiences team you will: - Perform hands-on analysis and modeling of enormous datasets to develop insights that increase traffic monetization and merchandise sales, without compromising the shopper experience. - Drive end-to-end machine learning projects that have a high degree of ambiguity, scale, and complexity. - Build machine learning models, perform proof-of-concept, experiment, optimize, and deploy your models into production; work closely with software engineers to assist in productionizing your ML models. - Design and run experiments, gather data, and perform statistical analysis. - Establish scalable, efficient, automated processes for large-scale data analysis, machine-learning model development, model validation and serving. - Stay up to date on the latest advances in machine learning. About the team We are a customer-obsessed team of engineers, technologists, product leaders, and scientists. We are focused on continuous exploration of contexts and creatives where advertising delivers value to shoppers and advertisers. We specifically work on new ads experiences globally with the goal of helping shoppers make the most informed purchase decision. We obsess about our customers and we are continuously innovating on their behalf to enrich their shopping experience on Amazon
US, WA, Seattle
Have you ever wondered how Amazon launches and maintains a consistent customer experience across hundreds of countries and languages it serves its customers? Are you passionate about data and mathematics, and hope to impact the experience of millions of customers? Are you obsessed with designing simple algorithmic solutions to very challenging problems? If so, we look forward to hearing from you! At Amazon, we strive to be Earth's most customer-centric company, where both internal and external customers can find and discover anything they want in their own language of preference. Our Translations Services (TS) team plays a pivotal role in expanding the reach of our marketplace worldwide and enables thousands of developers and other stakeholders (Product Managers, Program Managers, Linguists) in developing locale specific solutions. Amazon Translations Services (TS) is seeking an Applied Scientist to be based in our Seattle office. As a key member of the Science and Engineering team of TS, this person will be responsible for designing algorithmic solutions based on data and mathematics for translating billions of words annually across 130+ and expanding set of locales. The successful applicant will ensure that there is minimal human touch involved in any language translation and accurate translated text is available to our worldwide customers in a streamlined and optimized manner. With access to vast amounts of data, cutting-edge technology, and a diverse community of talented individuals, you will have the opportunity to make a meaningful impact on the way customers and stakeholders engage with Amazon and our platform worldwide. Together, we will drive innovation, solve complex problems, and shape the future of e-commerce. Key job responsibilities * Apply your expertise in LLM models to design, develop, and implement scalable machine learning solutions that address complex language translation-related challenges in the eCommerce space. * Collaborate with cross-functional teams, including software engineers, data scientists, and product managers, to define project requirements, establish success metrics, and deliver high-quality solutions. * Conduct thorough data analysis to gain insights, identify patterns, and drive actionable recommendations that enhance seller performance and customer experiences across various international marketplaces. * Continuously explore and evaluate state-of-the-art modeling techniques and methodologies to improve the accuracy and efficiency of language translation-related systems. * Communicate complex technical concepts effectively to both technical and non-technical stakeholders, providing clear explanations and guidance on proposed solutions and their potential impact. About the team We are a start-up mindset team. As the long-term technical strategy is still taking shape, there is a lot of opportunity for this fresh Science team to innovate by leveraging Gen AI technoligies to build scalable solutions from scratch. Our Vision: Language will not stand in the way of anyone on earth using Amazon products and services. Our Mission: We are the enablers and guardians of translation for Amazon's customers. We do this by offering hands-off-the-wheel service to all Amazon teams, optimizing translation quality and speed at the lowest cost possible.