Working with Alternative Kernels
Working with alternative or multiple kernels, basically in the context of Jupyter Notebook, is the ability to use multiple programming languages within the same single jupyter notebook. It encompasses flexibility, portability and ability to handle specific use cases without reinstallation of another operating system multiple times. One system can have multiple kernels for its various uses according to its requirements and needs. Through this process, you will be installing and removing kernels from your operating system, also, with the help of this, you will be able to see the lists of all types of kernels which your system is carrying with it.
Similarly, if you want to add or work with multiple alternative kernels such as Python, R, Julia, etc. in Jupyter Notebook, you can do it very easily. Since, here R programming language is used, following steps can be taken for adding an alternative Julia kernel in Jupyter Notebook.
Step 1: Install R
First of all, you need to install R in your system. For this, you can use the link https://cran.rstudio.com/ in your web browser and, then after install it as per your system type.
Step 2: Install the IRKERNEL package in R
Now, open R terminal in your system and type the following commands to install the ‘IRkernel‘ package in R.
install.packages('IRkernel')
IRkernel is an open-source software package that provides an interface for running R programming language in Jupyter Notebook
After downloading the ‘IRkernel’ Kernel, run this command to add the kernel:
IRkernel::installspec(user = FALSE)
The user = FALSE argument installs the kernel system-wide, making it available for all users on the system. If you want to install it only for your user, set user = TRUE.
This will add R kernel to your Jupyter Notebook.
Step 4: Select a Kernel to your notebook
When you create a new notebook or open an existing one, you can select the kernel you want to use from the “Kernel” menu. The available kernels will be listed, and you can choose the one you installed earlier.
Step 5: Run code in Different languages
Once you’ve selected a kernel, you can write and execute code in the chosen language within your Jupyter Notebook cells. Each cell in the notebook can have a different kernel, allowing you to mix and match languages as needed.
Note: If you want to work with more languages or environments, you can install additional kernels following the same process as described previously.
How to Fix Kernel Error in Jupyter Notebook
Jupyter Notebook is an open-source framework that comes under Anaconda software. It is a free cloud-based interactive platform used for computing in different types of languages. It is used for data analysis, visualizations, numerical calculations and simulations, equations, executing codes, machine learning, and sharing data easily. So, while performing any of such tasks, you may have encountered some errors at some point in time. One such error is the kernel error in Jupyter Notebook. This issue can be frustrating, especially when you are working on an important project and have to deal with some critical datasets.
In this article, we will be reading about the basic concepts of kernels and their various causes for errors. Also, we will see some of the specific scenarios that arise due to kernel errors, along with their diagnostic ways and troubleshooting techniques or steps that can be taken to identify and remove such errors. Moreover, we will also deal with working with alternative kernels.