Python -m ipykernel install -user -name=my_env Conclusion – the conda-pack benefit Then it is simply a case of activating the environment with:Īnd if you need to access the environment from a Jupyter notebook kernel, run: Once you have installed anaconda, you can extract your packaged environment into the anaconda3/envs/ folder. Step 3 – MoveĪssuming limited internet access on the target machine or client-side, you will need to get the client to drop the packaged environment into the server that you’ll be working on, along with a copy of Anaconda installer. If you have created the environment on a remote machine and it is too big to upload to GitHub, try sharing via OneDrive or SharePoint. Or you can specify the output name and file extension: tar.gz file of the environment with the same name, my_. Use conda-pack to package your environment by running: I recommend you do this from your root conda environment, as it will then be available in all your sub-environments as well: Remember to include the installation of Jupyter if you use Jupyter notebooks for your projects, and ipykernel in order to make the environment available in your Jupyter kernel list. Step 1 – Set UpĬreate a conda environment as you usually would, along with the package requirements, e.g. It may be helpful to spin up a remote machine on AWS to achieve this. It is a great tool for reproducing a conda environment with limited or no internet access.Ĭonda-pack is specific to both the platform and OS, so ensure you set up your environment on the same platform and OS as you plan to use it on.
#Conda install package environment archive
You might end up in this situation due to poor internet connection, or because of data security reasons and a client wanting to keep your working environment siloed.Ĭonda-pack is a command line tool allowing you to archive a conda environment, along with all the binaries of the packages installed on the environment. But if you ever need to move a Python environment without internet access, conda-pack could be your answer. Sharing a Python project environment can be done with a spec list (for computers on the same OS) or an environment.yaml (if you are sharing across OS or platforms). A CKM Developers’ Blog Why would you use conda-pack?
0 kommentar(er)
