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JupyterLab 3.0

blog.jupyter.org

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Re: JupyterLab 3.0

#11

Post by iamlucaswolf »

losvedir wrote: post #3

Hmm, what is mamba? I thought I was at least sort of up to date with the various python dependency managers, but that one is new to me. Relatedly, I have a grand vision of having as part of my development environment a jupyter notebook always at hand, in which to explore data as necessary, whip up scripts, accumulate little helper functions, etc. Basically, any time I have that "hm, I wonder..." itch, I'd like to be…

I use a setup like this. Essentially, I use pyenv [1] to manage Python versions and Poetry [2] [2](https://python-poetry.org/) for virtualenvs/dependencies.

The workflow for creating a new project looks like this:

1. Create a project directory (e.g. 'myproject') and `cd` into it. 2. `git init` 3. Fixate the Python version for that project with the `pyenv local` command (e.g. `pyenv local 3.8.6`). This creates a `.python-version` file that you can put under source control. Within the `myproject` directory tree, `python` will now be automatically resolved to the specified version. Your system Python (in fact, any other Python versions you might have installed) remain untouched. 4. Create a new poetry project (`poetry init`). This creates a `pyproject.toml` which contains project metadata + dependencies and can also be checked into git. 5. Add dependencies with `poetry add`. Here, you could for instance add Jupyter Lab (`poetry add jupyterlab`).

To access installed dependencies, such as the `jupyter lab` command, you can either execute one command in the virtualenv directly (`poetry run jupyter lab`) or spawn a shell (`poetry shell`). If you open a Jupyter Notebook that way, the packages installed in the virtualenv are directly available from within Jupyter Notebooks, without having to mess around with installing IPython kernels.

I like this approach, because it gives you full flexibility, while being portable and easy to use. It gets you around having to deal with conda (which I found to be frustrating at times). Also, you're not tied to the Jupyter frontends, but could e.g. just install `ipykernel` and open notebooks in VSCode.

[1](https://github.com/pyenv/pyenv/) [2](https://python-poetry.org/)

Edit: Moved the links

Re: JupyterLab 3.0

#12

Post by remram »

vlgnat wrote: post #9
nxpnsv wrote: post #5

Earlier quoted context omitted.

Mamba is a reimplementation of the conda package manager in C++. (quote from README at https://github.com/mamba-org/mamba , linked to in original post)

Funny, Python is too slow for a package manager and most people who can write fast C extensions have left the scene. So C++ is the natural choice.

Not sure if I agree with you but I was very surprised to find that their Python kernel is also implemented in C++: https://github.com/jupyter-xeus/xeus

Re: JupyterLab 3.0

#13

Post by liuliu »

losvedir wrote: post #3

Hmm, what is mamba? I thought I was at least sort of up to date with the various python dependency managers, but that one is new to me. Relatedly, I have a grand vision of having as part of my development environment a jupyter notebook always at hand, in which to explore data as necessary, whip up scripts, accumulate little helper functions, etc. Basically, any time I have that "hm, I wonder..." itch, I'd like to be…

I have an unconventional setup of Jupyterlab, Python dependencies, Swift (through PythonKit package) with Bazel. Surprisingly, the new `rules_python` and `pip_install` support works great at installing packages through pip. This ensures on any machine, I will have a consistent Python runtime (either downloaded or build from the source), as well as the pinned python dependencies when the repo checked out. It also helps because I have a Swift kernel that packaged inside the repo as well.

One thing I haven't figured out is about Jupyterlab's extension system, which previously requires node.js for delivery. It seems 3.0 removed that requirement, so I am hopeful with some tuning I can deliver the plugins in consistent way as well.

Re: JupyterLab 3.0

#14

Post by pavlov »

I don't write Python code for my work. Last weekend I came across an interesting Jupiter notebook and figured I'd give it a try on my work laptop. "It's probably as easy as brew install pip and then use that to load the other dependencies," I assumed.

Over an hour later I had to give up. There was initially some kind of Python version conflict on my Mac. Eventually some version of JupyterLab was installed somewhere, but it couldn't find any dependencies for the notebook. As a complete newbie to the Python ecosystem, I googled for instructions and found various environment managers, which then failed possibly because something is incompatible with Big Sur. More googling revealed instructions with complex CFLAGS environment setups to fix it, which didn't — and at that point it was very far from the supposed convenience of scripting languages anyway.

I don't think it's Python's fault. Probably all the programming toolchains are this hard to a newbie! But it was a humbling experience after 34 years of programming, not being able to load a piece of sample code in a Sunday afternoon.

Re: JupyterLab 3.0

#15

Post by yshvrdhn »

pavlov wrote: post #14

I don't write Python code for my work. Last weekend I came across an interesting Jupiter notebook and figured I'd give it a try on my work laptop. "It's probably as easy as brew install pip and then use that to load the other dependencies," I assumed. Over an hour later I had to give up. There was initially some kind of Python version conflict on my Mac. Eventually some version of JupyterLab was installed somewhere,…

can look into google colab to directly load the notebook. Link : https://colab.research.google.com/

Re: JupyterLab 3.0

#16

Post by turtlebits »

pavlov wrote: post #14

I don't write Python code for my work. Last weekend I came across an interesting Jupiter notebook and figured I'd give it a try on my work laptop. "It's probably as easy as brew install pip and then use that to load the other dependencies," I assumed. Over an hour later I had to give up. There was initially some kind of Python version conflict on my Mac. Eventually some version of JupyterLab was installed somewhere,…

Yeah I've had this issue in the past with Jupyter. Now I generally look for docker images when I want to check out something new.

Re: JupyterLab 3.0

#17

Post by kbelder »

I skimmed the page and didn't see any mention of sharing or collaborative use. That's the biggest obstacle I'm seeing with getting buy-in at work. I need to be able to let some users see the notebook in read-only mode, others should be able to run it but not edit it, others should have full access.

Maybe there's a non-hacky way to do this and I'm missing it?

Re: JupyterLab 3.0

#18

Post by paozac »

pavlov wrote: post #14

I don't write Python code for my work. Last weekend I came across an interesting Jupiter notebook and figured I'd give it a try on my work laptop. "It's probably as easy as brew install pip and then use that to load the other dependencies," I assumed. Over an hour later I had to give up. There was initially some kind of Python version conflict on my Mac. Eventually some version of JupyterLab was installed somewhere,…

True. I like Python, but the ecosystem can be very confusing for a quick dive. It's easy to get lost between pyenv, virtualenv, pipenv, pip, pip3, easy_install and friends.

Re: JupyterLab 3.0

#19

Post by antpls »

pavlov wrote: post #14

I don't write Python code for my work. Last weekend I came across an interesting Jupiter notebook and figured I'd give it a try on my work laptop. "It's probably as easy as brew install pip and then use that to load the other dependencies," I assumed. Over an hour later I had to give up. There was initially some kind of Python version conflict on my Mac. Eventually some version of JupyterLab was installed somewhere,…

Not sure about Mac, but on Linux, you install some docker images and it just works (modulo some docker flags to learn if you don't already know docker)

Re: JupyterLab 3.0

#20

Post by typomatic »

pavlov wrote: post #14

I don't write Python code for my work. Last weekend I came across an interesting Jupiter notebook and figured I'd give it a try on my work laptop. "It's probably as easy as brew install pip and then use that to load the other dependencies," I assumed. Over an hour later I had to give up. There was initially some kind of Python version conflict on my Mac. Eventually some version of JupyterLab was installed somewhere,…

I have used Python on OSX for years and it is and always will be a horrorshow. Using the system Python installation is a nonstarter for many reasons, chief among them is that I don't have any interest in using py2. So then you're using pyenv or homebrew, but your vim install still thinks that it should be using the system python. And whoops, you fixed that and now virtualenv is not finding your interpreter. And etc., etc.

OSX and its tooling are just ridiculous. I have no idea to this day how macs became the premier development environment.

As always xkcd has a comic for it: https://xkcd.com/1987/

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