What is the conda version of this?
pip install -r requirements.txt --target ./lib
I've found these commands:
while read requirement; do conda install --yes $requirement; done < requirements.txt
But it doesn't tell how to specify --target ./lib
Alternatively, you can use conda to install all the packages in a requirements. txt file. You can save a requirements. txt file from an existing environment, or manually create a new requirements.
Built into Anaconda, conda is a powerful package manager and environment manager that you use with command-line in the Anaconda Prompt for Windows, or in a terminal window for macOS or Linux. pip is the standard package manager for python, meaning you can use it both inside and outside of Anaconda.
You can run conda install --file requirements.txt
instead of the loop, but there is no target directory in conda install. conda install
installs a list of packages into a specified conda environment.
To create an environment named py37
with python 3.7, using the channel conda-forge and a list of packages:
conda create -y --name py37 python=3.7 conda install --force-reinstall -y -q --name py37 -c conda-forge --file requirements.txt conda activate py37 ... conda deactivate
Flags explained:
-y
: Yes, do not ask for confirmation.--force-reinstall
: Install the package even if it already exists.-q
: Quiet, do not display progress bar.-c
: Channels, additional channels to search for packages. These are URLs searched in the orderAlternatively you can create an environment.yml file instead of requirements.txt:
name: py37 channels: - conda-forge dependencies: - python=3.7 - numpy=1.9.* - pandas
Use these commands to create and activate the conda environment based on the specifications in the Yaml file:
conda env create --file environment.yml conda activate py37
Use this command to list the environments you have:
conda info --envs
Use this command to remove the environment:
conda env remove --name py37
New! The ansible-role dockpack.base_conda can manage conda environments on Linux, Mac and Windows, and can be used to create a docker image with custom conda environments.
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