How to Troubleshoot Common Notebook Deployment Issues

Are you tired of hitting roadblocks when deploying your notebooks? Does the process of getting your models to the cloud seem like an endless maze of errors and frustrations? Fear not, dear reader! We've all been there: the confusion, the doubts, the endless stack traces... But worry not! We are here to help!

In this article, we'll go through the most common notebook deployment issues and provide solutions that will make your life easier. We'll assume you're familiar with cloud computing and have some experience with Jupyter notebooks. If you're not that experienced, don't worry; we'll keep it simple and straightforward.

So, let's dive in and solve those notebook deployment issues!

Issue #1: Package Dependencies

One of the most common problems when deploying your models to the cloud is package dependency. Many times, you may encounter a situation where your notebook runs smoothly on your local machine, but fails to run when you try deploying it to the cloud.

The root of this problem is most likely that you have not installed all the necessary packages or libraries in your cloud environment. To ensure that your cloud environment has all the packages and libraries required to run your notebook, you need to create a requirements.txt file. This file lists all the packages and libraries your notebook depends on to run without errors.

A requirements.txt file can be created using the following command in your local terminal:

pip freeze > requirements.txt

This command exports a list of all the installed packages and dependencies you may have in your local environment. You can then include this file in your project folder and upload it to your cloud environment.

Once you have uploaded the requirements.txt file to your cloud environment, you can install all the necessary packages and libraries using the pip command:

pip install -r requirements.txt

And voilà! Your dependencies are taken care of. You will need to update your requirements.txt file every time you install a new package or library.

Issue #2: File Paths

File paths can be one of the most frustrating issues when deploying notebooks to the cloud. When you deploy a notebook to the cloud, the file paths referenced in your notebook may no longer work as expected.

This is because the path structure of your cloud environment is different from your local machine. To fix this issue, you need to ensure that all file paths are relative to the root of your project.

For example, instead of using an absolute path like this:

df = pd.read_csv("C:/Users/username/Documents/data.csv")

Use a relative path like this:

df = pd.read_csv("./data.csv")

This way, no matter what your cloud environment path structure looks like, your notebook will always know where to find the file.

Issue #3: Memory Constraints

Another common issue with deploying notebooks to the cloud is memory constraints. Many times, your notebook may work well on your local machine because it has enough memory, but may fail when deployed to the cloud because of low memory availability.

To solve this issue, you can either increase the memory capacity of your cloud environment, or reduce the memory usage of your notebook.

Reducing memory usage can be achieved by performing memory optimization of your code. This can be done by minimizing copies of data, using generators instead of lists, and using more efficient data types where possible.

To increase memory capacity, you can resize your cloud environment to have more memory. This can be done using the cloud console or the command line.

Issue #4: Limited Resources

Limited resources can also be a big issue when deploying notebooks to the cloud. You may find that some of the resources you need are not available in your cloud environment. For example, you may not have the permissions to access a certain database or API.

To solve this issue, you need to ensure that you have the necessary permissions and resources in your cloud environment. You can do this by setting up the necessary access privileges and installing any additional packages or resources.

Additionally, if your notebook relies on an external API or a database, you may need to authenticate your credentials or whitelist your IP address to access the resource. Make sure to check the documentation of the resource you are using and follow the steps for authentication.

Issue #5: Limited Execution Time

Limited execution time is a final issue that you may encounter when deploying notebooks to the cloud. Many cloud providers limit the execution time of a notebook to ensure fair usage of their services. This may cause your notebook to be terminated abruptly if it exceeds its allotted time.

To solve this issue, you need to optimize your notebook execution time. This can be achieved by optimizing your code, reducing the number of iterations, and reducing the size of your dataset.

If your notebook still exceeds the allotted execution time, you may need to upgrade your cloud environment to a higher tier that allows for longer execution times.


Deploying notebooks to the cloud can be a cumbersome process, but it doesn't have to be. By following the tips we've provided in this article, you should be able to troubleshoot and solve the most common notebook deployment issues.

Remember to check and update your dependencies, use relative file paths, optimize your memory usage, ensure necessary resources are available, and optimize your code execution time.

So go ahead and deploy your notebooks to the cloud with confidence, and watch as your models come to life on a bigger stage!

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Written by AI researcher, Haskell Ruska, PhD ( Scientific Journal of AI 2023, Peer Reviewed