New Jupyter Magic Functions Even if the above changes to the stack are not possible or desirable, we could simplify the user experience somewhat by introducing %pip and %conda magic functions within the Jupyter notebook that detect the current kernel and make certain packages are installed in the correct location. Managing Python in this way keeps things neat and allows you to work with several versions of Python if you require. And even when needing to install a package from source using pip it is a good idea to at least install the package's dependencies via conda beforehand. I don't really understand the no tags found error. You need an account only to access without a or to share your packages with others.
As Jonathan said, we'll work to develop tools to help you act on this information. The reticulate package includes functions for creating Python environments either virtualenvs or conda envs and installing packages within them. Explicit invocation For symmetry with pip, it would be nice if python -m conda install could be expected to work in the same way the pip counterpart does. If the package is unavailable through conda, try installing it with pip. The command conda list shows packages installed this way, with a label showing that they were installed with pip. If you install openssl 1.
To see more information, click the package name. In the wake of several discussions on this topic with colleagues, some online , and some off, I decided to treat this issue in depth here. Test your Installation Run the test suite to assure everything works correctly. In software, it's said that , and this is true for the Jupyter notebook as it is for any other software. This issue is a perrennial source of StackOverflow questions e. It is likely that these environment were inconsistent prior to updating conda, the new release just made this visible. As noted above, we can get around this by explicitly identifying where we want packages to be installed.
Another useful change conda could make would be to add a channel that essentially mirrors the , so that when you do conda install some-package it will automatically draw from packages available to pip as well. The root of the issue is this: the shell environment is determined when the Jupyter notebook is launched, while the Python executable is determined by the kernel, and the two do not necessarily match. For instance, if a package isn't available as a Conda package, then the install fails. Introduction to Conda is a package manager that can simplify and self-document the installation of python software from source. Using Navigator Navigator is automatically installed when you install Anaconda®.
About conda-forge is a github organization containing repositories of conda recipes. To gain the benefits of conda integration, be sure to install pip inside the currently active conda environment and then install packages with that instance of pip. But it was interrupted by a sudden power failure. Click the top right Sign in to Anaconda Cloud button and type your Cloud username and password, then click the Login button. For instance, environments can be created for the branches of CellProfiler, so these branches of CellProfiler will run independently of each other just like how git keeps the code branches separated for writing code. But if they are implemented carefully, I think it would lead to a much nicer overall user experience. I've tried conda install conda conda install --force conda both from within the conda env as well as within the base environment conda install setuptools conda install requests None of these have worked.
For this reason, it is safer to use python -m pip install, which explicitly specifies the desired Python version , after all. If a pip magic and conda magic similar to the above were added to Jupyter's default set of magic commands, I think it could go a long way toward solving the common problems that users have when trying to install Python packages for use with Jupyter notebooks. Even better, if you can automate the test in the conda recipe, it might be worth. I saw the same messages while updating conda on both my work and personal laptops today 8th April. If need be you can also configure reticulate to use a of Python. RemoveError: 'setuptools' is a dependency of conda and cannot be removed from conda's operating environment. This means, it should acually handle the deps.
These environments organize a collection of packages for a particular programming purpose, e. This is usually unwanted and coming from older and way more sloppy Python installations and bound to bring forth very unexpected and hard to debug behavior and errors. I do note that a common ingredient to the above issues is mkl-. Have a question about this project? The is a good place to start. Conda is cross-platform in part through the use of. The Details: Why is Installation from Jupyter so Messy? Push failed remote: Verifying deploy.
Pip packages do not have all the features of conda packages and we recommend first trying to install any package with conda. In short, it's because in Jupyter, the shell environment and the Python executable are disconnected. So it's not a full solution to the problem by any means, but if Python kernels could be designed to do this sort of shell initialization by default, it would be far less confusing to users:! The important thing to realize is that each Python executable has its own site-packages: what this means is that when you install a package, it is associated with particular python executable and by default can only be used with that Python installation! All conda skeleton are actually implemented there confusing, I know. The recipe will then automatically be built and uploaded to the conda-forge channel. I have a few ideas, some of which might even be useful: Potential Changes to Jupyter As I mentioned, the fundamental issue is a mismatch between Jupyter's shell environment and compute kernel.