I run the following code to install a package using pip (in this case from GitHub) on a server and on my local machine using Conda to handle my environments:
conda activate base
conda env remove --name test-phonetic
conda create --name test-phonetic python=3.8 -y &&
conda activate test-phonetic &&
python -m pip install --upgrade pip &&
# remember to set your GIT_TOKEN
python -m pip install -e git+https://${GIT_TOKEN}@github.com/username/phonetic-transcription.git@feature/transcriptor-class#egg=phonetic-transcription # from branch "feature/transcriptor-class"
I receive the following output from pip when running on the server:
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
jupyter-client 7.3.1 requires entrypoints, which is not installed.
jupyter-client 7.3.1 requires jupyter-core>=4.9.2, which is not installed.
jupyter-client 7.3.1 requires pyzmq>=22.3, which is not installed.
jupyter-client 7.3.1 requires tornado>=6.0, which is not installed.
jupyter-client 7.3.1 requires traitlets, which is not installed.
I receive no error message when installing on my local machine.
Another user receives the following output when installing on the server via the same commands (also using conda for environment management):
ERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.
bokeh 2.4.2 requires Jinja2>=2.9, which is not installed.
bokeh 2.4.2 requires numpy>=1.11.3, which is not installed.
bokeh 2.4.2 requires tornado>=5.1, which is not installed.
Why do we receive different "error" messages in each case given that Conda environments are theoretically distinct and do not see each other?
At first I thought Conda was using versions of packages from other environments (and thought to use the --copy flag to install all packages using copies instead of hard- or soft-linking which Conda presumably does across environments to save space) but indeed this is an option available with conda install, not pip install.
Why are the errors inconsistent when running the same code on different machines and how can I resolve these "errors"?
pip ERROR: pip's dependency resolver does not currently take into account all the packages that are installed has no answers and comments suggest using a clean environment to resolve the issue, but I have done this. Similar story for cannot resolve urllib3 version issue
