Mojo Installation On Colab: Troubleshooting Version Conflicts

Jan 19, 2026 by Editorial Team 62 views

Hey everyone! 👋 If you're anything like me, you're probably super excited about Mojo and itching to get it up and running on Google Colab. The official documentation makes it sound super straightforward, but sometimes, things hit a snag. Let's dive into a common issue: version incompatibility during the Mojo installation process in Colab and figure out how to navigate it. I'll break down the problem, what causes it, and how we can make our lives a whole lot easier.

The Colab Conundrum: Understanding Version Conflicts

So, you followed the steps in the official Mojo documentation (https://docs.modular.com/mojo/tools/notebooks), and everything seemed peachy keen until… BAM! You get a wall of text filled with error messages about version conflicts. Don't worry, you're not alone. It's a pretty common hiccup when dealing with Python environments, especially in Colab, where things can sometimes get a bit messy behind the scenes.

In this scenario, the issue stems from existing packages that are already installed in your Colab environment, which may clash with the dependencies that Mojo needs. Think of it like trying to fit puzzle pieces together when some of the pieces are the wrong shape or size. Specifically, these errors frequently involve packages like opentelemetry-api, protobuf, and their related dependencies. The error messages will tell you that the version of a package required by another package is not compatible with what is already installed. For instance, the errors might look like this:

Found existing installation: opentelemetry-exporter-otlp-proto-http 1.37.0
Uninstalling opentelemetry-exporter-otlp-proto-http-1.37.0:
Successfully uninstalled opentelemetry-exporter-otlp-proto-http-1.37.0
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
google-adk 1.21.0 requires opentelemetry-api<=1.37.0,>=1.37.0, but you have opentelemetry-api 1.35.0 which is incompatible.
google-adk 1.21.0 requires opentelemetry-exporter-otlp-proto-http>=1.36.0, but you have opentelemetry-exporter-otlp-proto-http 1.35.0 which is incompatible.
google-adk 1.21.0 requires opentelemetry-sdk<=1.37.0,>=1.37.0, but you have opentelemetry-sdk 1.35.0 which is incompatible.
grpcio-status 1.71.2 requires protobuf<6.0dev,>=5.26.1, but you have protobuf 6.31.1 which is incompatible.
google-ai-generativelanguage 0.6.15 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.2, but you have protobuf 6.31.1 which is incompatible.
tensorflow 2.19.0 requires protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<6.0.0dev,>=3.20.3, but you have protobuf 6.31.1 which is incompatible.
Successfully installed asgiref-3.11.0 exceptiongroup-1.3.1 gguf-0.17.1 llguidance-1.4.0 max-25.7.0 max-core-25.7.0 max-mojo-libs-25.7.0 nd test

These errors indicate that the versions of the packages Colab has installed are not the versions required by Mojo or its dependencies. This can happen because Colab environments can have a variety of pre-installed packages, and these packages may not always be up-to-date or compatible with the latest versions required by the software you are trying to install.

The Virtual Environment Solution

One of the most effective ways to tackle these conflicts is by using a virtual environment. A virtual environment is like creating a contained space for your project, so it doesn't get tangled up with other projects and their package versions. While the errors suggest a conflict, the good news is that Mojo can still compile and run the examples, including GPU detection, which suggests that the core Mojo installation might be okay, and it's mostly the supporting packages that are causing trouble.

Troubleshooting Steps: How to Fix Version Incompatibility in Colab

Alright, let's get down to brass tacks and explore a couple of ways to address these version conflicts. Here's a breakdown:

1. The Virtual Environment Approach (Recommended)

Creating a virtual environment is the gold standard for managing dependencies and avoiding conflicts. Here's how you can do it in Colab:

Import the necessary modules: First, you need to import the venv module.
```
import venv
```
Create the virtual environment: Choose a name for your environment (e.g., mojo_env) and create it.
```
venv.create('mojo_env')
```
Activate the virtual environment: This step is crucial. You need to activate the environment to ensure that all subsequent installations and commands are executed within it.
```
source mojo_env/bin/activate
```
Important: In Colab, the activation command might differ slightly. You might need to use !source mojo_env/bin/activate or ! . mojo_env/bin/activate. The exclamation mark (!) tells Colab to run the command in the shell.
Install Mojo within the environment: Now, with the virtual environment activated, install Mojo as you normally would, following the official documentation instructions.
```
pip install mojo
```
Test if it Works Try the example, if everything goes well, the conflict is resolved. If it fails, continue with the following steps.

2. Force Installation (Use with Caution)

This is a more direct approach and may involve telling pip to ignore dependencies. Be careful with this, as it can sometimes lead to unexpected behavior or other issues down the line. It's generally best as a last resort.

Try forcing the install: This might involve using the --force-reinstall or --ignore-installed flags with pip. For example:
```
pip install --upgrade --force-reinstall <package-name>
```
or
```
pip install --ignore-installed <package-name>
```
Be mindful of the consequences: Forcing installations can sometimes break other things in your environment, so be prepared to troubleshoot if necessary. It is very useful to create a new session if you are in doubt.

3. Updating `pip` and `setuptools` (A Good Practice)

Before you do anything else, make sure your pip and setuptools are up-to-date. Sometimes, simply updating these can resolve dependency issues.

!pip install --upgrade pip setuptools

Step-by-Step Guide for Colab

Let's get specific on how to apply these solutions inside a Google Colab notebook:

Open a new Colab notebook. Start with a fresh notebook to ensure a clean slate.
Install the Necessary Packages: Start by installing Mojo following the instructions on the official website. However, before that, let's install the virtual environment.
Create and Activate a Virtual Environment:
```
!python3 -m venv mojo_env  # Create the environment
!source mojo_env/bin/activate  # Activate it
```
Note: If the source command doesn't work, try ! . mojo_env/bin/activate or restarting the kernel.
Install Mojo and Dependencies: Now, within the activated environment, install Mojo and any other required libraries.
```
!pip install mojo
```
Test Your Installation: Try running a simple Mojo program to verify that everything is working.
```
# Example: Print a hello message
print("Hello, Mojo!"
```
Troubleshooting: If you still encounter issues, examine the error messages carefully. They often provide clues about which specific packages are causing the problem. Try the force install option. Remember to restart the runtime if necessary.

Additional Tips and Tricks

Here are some extra things to keep in mind to make your Mojo experience in Colab smoother:

Restart the Runtime: After making changes to your environment or installing packages, sometimes you need to restart the Colab runtime (Runtime -> Restart runtime) for the changes to take effect.
Check Your Kernel: Ensure you're using a Python 3 kernel. Mojo requires Python 3.
Keep Colab Updated: Google regularly updates Colab. Make sure you're using the latest version to avoid potential compatibility issues.
Read the Error Messages Carefully: The error messages often provide valuable hints about the root cause of the problem. Don't just skim them; read them carefully!
Document Your Steps: Keep a record of the steps you take to set up Mojo. This will help you troubleshoot in the future and share your knowledge with others.

Conclusion: Mojo on Colab – You Got This!

Version conflicts can be a pain, but with the right approach, they're definitely manageable. By using a virtual environment, understanding the error messages, and following the steps above, you should be well on your way to enjoying the power of Mojo in Google Colab. Remember, it might take a little trial and error, but the learning process is worth it. Don't be afraid to experiment, consult the official documentation, and ask for help when you need it. Happy coding, and have fun with Mojo!

If you have any other tips, tricks, or run into any other issues, don't hesitate to share them! Let's build a community around Mojo and help each other out. Cheers! 🚀