Introduction
Google Colab, short for Google Colaboratory, is a powerful cloud-based platform that has become immensely popular among developers, data scientists, and researchers. It offers a great blend of convenience and accessibility for coding, documenting, and running code, primarily in Python. However, what if you want to run code in languages other than Python, such as C++ or Java, is it possible and is this a convenient platform?
In this article, we will explore how to leverage Google Colab’s underlying Linux system to run C++ and Java code. We’ll also discuss some of the limitations and look into potential solutions for an even more enhanced experience.
The Power of Google Colab
Google Colab has gained immense popularity for several reasons:
- Accessibility from Anywhere: It offers a cloud-based environment, which means you can access your work and run code from anywhere with an internet connection. No need to worry about configuring development environments on different machines.
- Integrated Code and Documentation: Colab notebooks seamlessly combine code cells (for execution) and text cells (for documentation) in a single document. This makes it an excellent tool for research and sharing knowledge.
- Free GPU and TPU: Google Colab provides free (although limited) access to Graphics Processing Units (GPUs) and Tensor Processing Units (TPUs), which can significantly speed up machine learning tasks and helps in experimenting and research for algorithms that need the hardware acceleration.
- Linux under the hood: While Colab’s primary focus is on Python, the underlying infrastructure is a fully capable Linux system. This Linux environment opens up the possibility of running code written in other languages, including C++ and Java.
Running C++ Code in Google Colab
Running C++ code in Colab can be achieved using magic commands. The magic command %%writefile is used to create and save a C++ code file in Colab, while the %%script magic command is used to compile and run the code. Here's a step-by-step guide:
- Create a Code Cell: Start by creating a new code cell in your Colab notebook.
- Write C++ Code: Use the %%writefile magic command followed by the filename (with a .cpp extension) to create a C++ file. Then, write your C++ code within the cell. Run the code cell — this will write the .cpp file to an underlying Linux folder.
- Compile and Run: Use the %%script magic command to compile and execute the C++ code. Specify the compiler (g++) and the filename as arguments. Run the code cell — this will execute the script and show the C++ output. If you’d like to compile for a specific C++ standard, add the -std flag followed by the standard version to the compilation command. For example, to compile for C++11, add -std=c++11.
C++ in Colab code cell
Running Java Code in Google Colab
The procedure to create, build, and run a Java program is very similar, follow these steps:
- Write the Java Program in a code cell: Use the %%writefile magic command followed by the filename (with a .java extension) to create a java file. Then, write your Java code within the cell.
- Run the code cell — this will write the .java file to the underlying Linux folder.
- Compile and Run: Use the %%script magic command to compile and execute the Java code. Run the code cell — this will execute the script and show the Java code output.
Limitations and Future Enhancements
While it’s possible to run C++ and Java code (and possibly other languages as well) in Google Colab, there are some limitations:
- Syntax Highlighting: Colab’s code cells provide syntax highlighting for Python but not for C++ or Java. This can make coding less convenient, especially for complex programs.
- Integration: Running non-Python code in Colab currently requires extra steps with magic commands. The experience is not at the same level as writing and running Python code.
As we saw in above examples, Google Colab, with its powerful Linux-based underlying infrastructure, offers the potential to run code in languages in addition to Python. While there are some limitations and extra steps required, it’s a valuable resource for those looking to experiment with, learn, document code in a collaborative and accessible environment.
