This is prompted by a couple of problems I've come across with kernelspecs:
a) In nbval, when you want to check notebooks against multiple Python versions, the obvious approach is to create an environment (e.g. a Travis job) for each, and run the tests inside it. But the notebook always runs with the kernel in its metadata (e.g. if it's saved with Python 3, testing on Python 2 will still run a Python 3 kernel). We worked around this by adding a --current-env flag. b) Anaconda installs a notebook server extension which exposes conda environments as kernelspecs. But this doesn't affect other code using Jupyter, causing problems in e.g. nbconvert ( https://github.com/jupyter/nbconvert/issues/515 ). More generally, identifying kernels with an environment name only makes sense within one computer. I've been turning this over in my head for a while. I think there are three kinds of information relevant to starting a kernel for a notebook: 1. In what programming language does the code make sense? This is mostly captured by our language_info metadata, and the notebook application's fallback behaviour when it can't find a named kernel. But there's still a bit of ambiguity with different versions of a language (e.g. do we treat Python 3 and Python 2 as one language?). 2. How do we set up an environment with the dependencies for the notebook? There's some excellent work going on for this at https://github.com/jupyter/nbformat/pull/60 , but it's not what I want to discuss here. 3. Which available kernel for this notebook's language should we start to run it? At present, we use the name of the kernel when the notebook was saved - this is convenient for some use cases, but leads to problems (a) and (b) described above. I propose that we change how we pick a kernel, by depreacting the kernelspec metadata in notebooks and adding a pluggable KernelPicker class. The default KernelPicker would follow these rules: i) If the calling code explicitly specifies a kernel, start that one. ii) If there is only one kernel available for the notebook's language, start that one. iii) If the notebook is in Python and ipykernel is installed in the current environment, start ipykernel in this environment. This is a bit specific, but it's often what you want for tools like nbval and nbconvert. iv) There are either no kernels or multiple kernels installed for the language in question. Error out, indicating to the user that they should specify a kernel to be used (see (i)). For the notebook application, we may plug in a different KernelPicker which records which kernels have been used for which notebooks, similar to the present behaviour. Even if we don't, Continuum or other people may implement something like this. But we wouldn't use this in tools like nbconvert and nbval. Once there is a way to store environment descriptions in notebook metadata, and to create an environment for a notebook, another KernelPicker class may be involved in associating notebooks with the environment created for them. This proposal is still rough, but I think that we need to move away from storing local kernel names in notebook metadata, now that we're getting more insight into how kernelspecs are used. Thomas -- You received this message because you are subscribed to the Google Groups "Project Jupyter" group. To unsubscribe from this group and stop receiving emails from it, send an email to [email protected]. To post to this group, send email to [email protected]. To view this discussion on the web visit https://groups.google.com/d/msgid/jupyter/CAOvn4qhcgx-HG8UDQcpBMmoChvy%3D71%3Dyjqw5VK4B3uFz6BHKfA%40mail.gmail.com. For more options, visit https://groups.google.com/d/optout.
