Skip to main content

How to add your Conda environment to your jupyter notebook in just 4 steps

 In this article I am going to detail the steps, to add the Conda environment to your Jupyter notebook.

Step 1: Create a Conda environment.

conda create --name firstEnv

once you have created the environment you will see,

output after you create your environment.

Step 2: Activate the environment using the command as shown in the console. After you activate it, you can install any package you need in this environment.

For example, I am going to install Tensorflow in this environment. The command to do so,

conda install -c conda-forge tensorflow

Step 3: Now you have successfully installed Tensorflow. Congratulations!!

Now comes the step to set this conda environment on your jupyter notebook, to do so please install ipykernel.

conda install -c anaconda ipykernel

After installing this,

just type,

python -m ipykernel install --user --name=firstEnv

Using the above command, I will now have this conda environment in my Jupyter notebook.

Step 4: Just check your Jupyter Notebook, to see the shining firstEnv.

USAGE:

List kernels

Use jupyter kernelspec list

$ jupyter kernelspec list
Available kernels:
  global-tf-python-3    /home/felipe/.local/share/jupyter/kernels/global-tf-python-3
  local_venv2           /home/felipe/.local/share/jupyter/kernels/local_venv2
  python2               /home/felipe/.local/share/jupyter/kernels/python2
  python36              /home/felipe/.local/share/jupyter/kernels/python36
  scala                 /home/felipe/.local/share/jupyter/kernels/scala

Remove kernel

Use jupyter kernelspec remove <kernel-name>

$ jupyter kernelspec remove old_kernel
Kernel specs to remove:
  old_kernel            /home/felipe/.local/share/jupyter/kernels/old_kernel
Remove 1 kernel specs [y/N]: y
[RemoveKernelSpec] Removed /home/felipe/.local/share/jupyter/kernels/old_kernel

Change Kernel name

  • 1) Use $ jupyter kernelspec list to see the folder the kernel is located in

  • 2) In that folder, open up file kernel.json and edit option "display_name"


Yayy!! Happy coding :)

Comments

Popular posts from this blog

Apache Spark Discretized Streams (DStreams) with Pyspark

Apache Spark Discretized Streams (DStreams) with Pyspark SPARK STREAMING What is Streaming ? Try to imagine this; in every single second , nearly 9,000 tweets are sent , 1000 photos are uploaded on instagram, over 2,000,000 emails are sent and again nearly 80,000 searches are performed according to Internet Live Stats. So many data is generated without stopping from many sources and sent to another sources simultaneously in small packages. Many applications also generate consistently-updated data like sensors used in robotics, vehicles and many other industrial and electronical devices stream data for monitoring the progress and the performance. That’s why great numbers of generated data in every second have to be processed and analyzed rapidly in real time which means “ Streaming ”. DStreams Spark DStream (Discretized Stream) is the basic concept of Spark Streaming. DStream is a continuous stream of data.The data stream receives input from different kind of sources like Kafka, Kinesis...

6 Rules of Thumb for MongoDB Schema Design

“I have lots of experience with SQL and normalized databases, but I’m just a beginner with MongoDB. How do I model a one-to-N relationship?” This is one of the more common questions I get from users attending MongoDB office hours. I don’t have a short answer to this question, because there isn’t just one way, there’s a whole rainbow’s worth of ways. MongoDB has a rich and nuanced vocabulary for expressing what, in SQL, gets flattened into the term “One-to-N.” Let me take you on a tour of your choices in modeling One-to-N relationships. There’s so much to talk about here, In this post, I’ll talk about the three basic ways to model One-to-N relationships. I’ll also cover more sophisticated schema designs, including denormalization and two-way referencing. And I’ll review the entire rainbow of choices, and give you some suggestions for choosing among the thousands (really, thousands) of choices that you may consider when modeling a single One-to-N relationship. Jump the end of the post ...

Khác nhau giữa các chế độ triển khai giữa Local, Standalone và YARN trong Spark

Trong Apache Spark, có ba chế độ triển khai chính: Local, Standalone và YARN. Dưới đây là sự khác biệt giữa chúng: Chế độ triển khai Local: Chế độ triển khai Local là chế độ đơn giản nhất và được sử dụng cho môi trường phát triển và kiểm thử. Khi chạy trong chế độ Local, Spark sẽ chạy trên một máy tính duy nhất bằng cách sử dụng tất cả các luồng CPU có sẵn trên máy đó. Đây là chế độ phù hợp cho các tác vụ nhỏ và không yêu cầu phân tán dữ liệu. Chế độ triển khai Standalone: Chế độ triển khai Standalone cho phép bạn triển khai một cụm Spark độc lập bao gồm nhiều máy tính. Trong chế độ này, một máy tính được chọn làm "Spark Master" và các máy tính khác được kết nối với Spark Master như là "Spark Workers". Spark Master quản lý việc phân phối công việc và quản lý tài nguyên giữa các Spark Workers. Chế độ Standalone phù hợp cho triển khai Spark trên các cụm máy tính riêng lẻ mà không có hệ thống quản lý cụm chuyên dụng. Chế độ triển khai YARN: YARN (Yet Another Resource N...