Korean, Edit

Setting up TensorFlow Environment and Using Jupyter in Python

Recommended article: 【Python】 Python Table of Contents

1. Overview

2. Installing Anaconda

3. Anaconda Prompt (anaconda3)

4. Installing Jupyter

5. Sharing Anaconda Environment


1. Overview

⑴ TensorFlow, Keras, and others are created for implementing artificial neural networks in Python.

⑵ A version compatible with R has been available since 2017.

⑶ MacBooks come with Terminal, so these are naturally installed.

⑷ Anaconda has been offering its services for a fee to users in businesses or government organizations with more than 200 employees since September 2020.



2. Installing Anaconda

⑴ Windows

① Step 1: Installation website: www.anaconda.com/products/individual

② Step 2: Download and installation guide: www.guru99.com/download-install-r-rstudio.html

③ Step 3: Finally, the following is installed:

○ Jupyter Notebook (anaconda3)

○ Anaconda Navigator (anaconda3)

○ Spyder (anaconda3)

○ Anaconda PowerShell Prompt (anaconda3)

○ Anaconda Prompt (anaconda3)

⑵ For Linux

⑵ Linux

① Step 1. Miniconda : Install Miniconda3 Linux 64-bit from here

② Step 1 - Method 1

○ Step 1. Download the file from the link

○ Step 2. Move the .sh file to the desired directory in Linux: You can use commands like cp, rsync, scp

○ cp [OPTION] [SOURCE] [DESTINATION]: Copy and paste the file

○ -r: Copy entire subdirectories and files

○ -p: Copy preserving owner, group, permissions, and timestamp information

○ scp: Low chance of file corruption

○ scp -P **${port}** **${srcFile}** **${id}** @ **${destIP}** : **${destPath}**

○ **${port}**: Port number of the destination

○ **${srcFile}**: Path of the file to be moved from source. Example: “~/Downloads/file.txt”

○ **${id}**: ID of a user at the destination

○ **${destIP}**: IP address of the destination. Example: #.#.#.# (IPv4)

○ **${destPath}**: Directory address to store the file at the destination. Example: ~/DATA1/1.txt

○ Code generator

○ rsync (remote sync): Faster speed

○ rsync --rsh=’ssh -p **${port}** ’ -avzhP **${srcFile}** **${id}**@**${destIP}** : **${destPath}**

○ **${port}**: Port number of the destination

○ **${srcFile}**: Path of the file to be moved from source. Example: “~/Downloads/file.txt”

○ **${id}**: ID of a user at the destination

○ **${destIP}**: IP address of the destination. Example: #.#.#.# (IPv4)

○ **${destPath}**: Directory address to store the file at the destination. Example: ~/DATA1/1.txt

○ Code generator

③ Step 1 - Method 2

○ Step 1. Copy the download link for Miniconda3 Linux 64-bit from the link above

○ Step 2. Use the curl -o command

curl -o my.sh "https://repo.anaconda.com/miniconda/Miniconda3-py38_23.3.1-0-Linux-x86_64.sh"

④ Step 2. Execute .sh file, Processing .sh file

○ File execution: To execute a .sh file, you can do ./[My_File].sh, sh [My_File].sh, or bash [My_File].sh.

○ File modification: There are two kinds of shells, zsh and bash.

○ $ vi script.sh

○ $ nano backup

○ $ sudo apt-get install zsh

○ Granting permissions

○ Example: $ chmod +x run-md5sum.sh

⑤ Step 3. Run the Miniconda .sh file and read the terms and conditions to proceed with the installation. Press Enter to continue.

⑥ Step 4. Respond to the license agreement:

○ The response must be “yes” at the first prompt.

⑦ Step 5. Respond to the second “yes”.

○ Answer whether to perform the install init.

○ The default is “no,” but choosing “no” will prevent /bin/bash from functioning properly.

○ It is recommended to explicitly enter “yes.”

⑧ Step 6. Reboot your Linux system or run /bin/bash.

○ If /bin/bash is not working, execute source ~/.bashrc.



3. Anaconda Prompt (anaconda3)

⑴ For Windows

① Run anaconda3: It is recommended to run it with administrator privileges.

② Install packages and set up virtual environments: Virtual environments are bundles of packages for managing package versions.


Here's the translation of the provided text from Korean to English:

# 0. Move to the directory to install
cd C:/Users/sun/

# 1. Examples of package installation
conda install r-essentials
conda uninstall r-essentials
conda install -c conda-forge r-essentials=4.0

conda install tensorflow
conda install -c conda-forge keras
python -m pip install --upgrade pip

# 2. Update base
conda update -n base conda
conda update --all

# 3. Create virtual environments tensorflow, tensorflow2
conda create --name tensorflow python=3.7
conda create --name tensorflow2 python=3.7

# 4. Check the list of virtual environments (base is default)
conda info --envs

# 5. Check Python version
python --version

# 6. Update virtual environment tensorflow
pip install --ignore-installed --upgrade tensorflow

# 7. Delete virtual environment tensorflow2
conda remove --name tensorflow2 --all

# 8. Activate virtual environment tensorflow
activate tensorflow

# 9. Clean up unused packages
conda clean --all


⑵ For Linux

① In Linux, once conda is installed, you can easily see if it’s installed with (base) in front of the name.

② Base is the name of the default environment, and it changes to a different environment name (see below) when a new environment is installed.

③ Note that environments are stored in ./miniconda3/envs/.

⑶ Troubleshooting

① Avoid using Korean characters in usernames or file paths.

② (Possible) Method 1: Create a new user account with an English username and file path: Settings → Family & other users → Add someone else to this PC.

③ (Not recommended) Method 2: Changing the username: www.tabmode.com/windows10/win10-user-name-change.html#gsc.tab=0

○ Using this method may lead to the “Can’t sign in to your Windows account” issue.

④ (Possible) Method 3: AccountProfilerFixer.exe: m.blog.naver.com/dupen/222081638329



4. Installing Jupyter

⑴ Step 1. Run anaconda3 and enter the following code (reference link: wikidocs.net/25280)


activate tensorflow
pip install jupyter
jupyter notebook


⑵ Step 2. jupyter lab (optional)


pip install jupyterlab
jupyter lab


① jupyter lab is convenient for directory management.

② jupyter lab makes it easy to upload, delete, and rename files.

⑶ Step 3. Installing packages (e.g., tensorflow)


# install tensorflow package
### tensorflow package version should be matched to python version 
pip install tensorflow

# You can specify the exact version of the package for the environment
pip install keras==2.2.4
# You can even remove previous packages
pip uninstall keras
pip install keras

# execute jupyter
jupyter notebook


① The author prefers installing as mentioned above.

② However, installing tensorflow.keras instead of keras may be beneficial for version management.

⑷ Step 4. Installing jupyter ipykernel


pip install ipykernel
python -m ipykernel install --user --name JEONGBIN_ENV --display-name JEONGBIN_ENV


① Replace JEONGBIN_ENV with your environment name.

② You can easily change the environment of a launched Jupyter notebook through this.

⑸ Step 5. Running Jupyter Notebook

① Step 5-1. Click New in the upper right corner of the browser → Python 3.

○ If R is installed in Jupyter, R will also be displayed under Python 3 in Step 1.

② Step 5-2. Enter code → Run.

③ Jupyter Notebook Key Shortcuts

○ a: Add a new cell above the current cell

○ b: Add a new cell below the current cell

○ z: Undo the action

○ Shift + Up/Down Arrow: Select multiple consecutive cells

○ Command (⌘) + Enter: Execute the current cell

○ Command (⌘) + f: Find a string

○ Command (⌘) + s: Save



5. Sharing Anaconda Environment

⑴ Environment File (.yml): The simplest method

① step 1. Extracting the environment file: Extract a .yml file from the environment named JEONGBIN_ENV.


conda activate JEONGBIN_ENV
conda env export > environment.yml
# Now you can see environment.yml at the mother directory.


② step 2. Creating an environment using the environment file


conda env create -f environment.yml


③ Troubleshooting 1. Solving environment: failed. ResolvedPackageNotFound: (ref)

○ It seems that using the above conda command in Windows essentially leads to an error.

○ Method 1. Execute after removing the build info.

○ Method 2. Execute after removing the platform-specific version tag.

⑵ Docker: The most recommended method

⑶ Binder

⑷ requirements.txt: You can list packages using commands like pip list and pip freeze. These packages can be saved to a requirements.txt file, which allows for the bulk downloading of packages later using the following command.

pip install -r requirements.txt

⑸ Sharing an environment through a GitHub repository.


# Example 

conda create -n CellDART python=3.8  
conda activate CellDART  
pip install git+https://github.com/mexchy1000/CellDART.git  
python -m ipykernel install --user --name CellDART --display-name CellDART


⑹ README.md: Simply indicate specifications as plain text.

⑺ To run a .sh file, you can do so by using ./[My_File].sh, sh [My_File].sh, or bash [My_File].sh.

bash ./MyEnvironment.sh

⑻ renv: Dependency management system for R

⑼ Mamba: Manager for Anaconda. Can use bioconda channel. Available for Python, R.

⑽ SnakeMake: Python-based pipeline development, and workflow management system.

⑾ Singularity: A container like Docker

⑿ Podman: A container like Docker


Input: 2021.02.28 17:36

Modified: 2023.05.22 15:04

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