[HN Gopher] JupyterLab 3.0
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JupyterLab 3.0
Author : kylebarron
Score : 129 points
Date : 2021-01-05 16:19 UTC (6 hours ago)
(HTM) web link (blog.jupyter.org)
(TXT) w3m dump (blog.jupyter.org)
| sgillen wrote:
| I was really excited to see the visual debugger there. I tried it
| myself, it was really easy to get it going. Unfortunately, using
| it briefly in the notebook I was using I found it pretty buggy,
| lots of glitches and big slowdowns in the UI. I would also really
| love to see the option to drop into an interpreter while
| debugging.
|
| Still, can't complain too much about an open source project,
| thanks to the team for all their hard work.
| globuous wrote:
| Nice ! Native debugger :)
| linspace wrote:
| Yes, debugging notebooks is not the best experience so I'm glad
| there are improvements. I will have a look.
| pavlov wrote:
| 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.
| bitanarch wrote:
| You should use Anaconda instead of pip to install data science
| related tools in Python.
|
| ```
|
| $ conda create -n {new_environment_name} python
|
| $ conda activate {new environment name}
|
| $ conda install jupyterlab
|
| ```
| davidf18 wrote:
| You can download anaconda in either the command line or
| graphical user interface versions selecting the versions of
| Python you wish to install.
|
| Then according to the blog posting for jupyterlab 3.0 execute
| the following command:
|
| conda install -c conda-forge jupyterlab=3
| modriano wrote:
| You also have to manually add the kernel for that environment
| via:
|
| ({new_environment_name}) ...$ python -m ipykernel install
| --user --name {new_environment_name} --display-name "Python
| ({new environment display name})"
|
| The env won't be accessible in jupyter (lab or notebook)
| until you do this step.
|
| [0] https://stackoverflow.com/questions/39604271/conda-
| environme...
| flaviocopes wrote:
| Not sure about the Jupyter-specific issues but macOS comes with
| Python 2 installed, so you have to install Python separately
| and use the `python3` binary, or alias `python` to the
| `python3` binary.
|
| Then remember to run pip using `python3 -m pip <command>` and
| use virtual environments (venv)
|
| I stumbled on this a couple weeks ago and documented the
| process here https://flaviocopes.com/python-installation-macos/
|
| I found the same problem with Ruby (old version preinstalled on
| macOS)
| [deleted]
| syntaxing wrote:
| Always try using a virtual env. Also try using pip directly
| through 'python3 -m pip install jupyter' . Same to run it to
| bypass path issues 'python3 -m jupyter notebook'. You should be
| up and running in a couple mins!
| pavlov wrote:
| These were among the suggestions that I tried. I think the
| virtual env failed because of Big Sur, but not sure.
| vmchale wrote:
| > I don't think it's Python's fault. Probably all the
| programming toolchains are this hard to a newbie!
|
| Python is a little worse in that in pushes its dirtiness
| downstream to your users too.
| WrtCdEvrydy wrote:
| Yeah, I use portainer and then load a docker image of Jupiter
| and import the notebook there.
|
| Dependency management hell isn't just a Javascript problem
| these days.
| turtlebits wrote:
| 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.
| unixhero wrote:
| Just install this: https://www.anaconda.com/products/individual
| paozac wrote:
| 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.
| fwip wrote:
| Very true - further evidenced by the five or six different
| approaches your sibling comments are recommending.
| zelphirkalt wrote:
| In a better world, the notebook you downloaded should have come
| with a list of dependencies and have reproducible code in it.
| That said, Python environment tools and package managers are
| still in a state of flux. Some tools make it easier, but are
| not helping much with reproducibility (plain venv + pip, but
| only if you got all required system packages installed!), other
| tools enable reproducibility, but have growing pains and are
| more difficult to use or get working (pipenv, poetry).
|
| If you are in luck, your required libraries and appropriate
| versions are available as GNU Guix packages and you can use
| that.
| baldfat wrote:
| Python is AMAZING and Awesome TILL You want to spread what you
| are doing to more then one computer. Though I have to say I
| love domain specific languages and personal enjoy Racket as my
| most fun language to use. It is sad that this has gotten so
| much worse over the years.
| antpls wrote:
| 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)
| yshvrdhn wrote:
| can look into google colab to directly load the notebook. Link
| : https://colab.research.google.com/
| typomatic wrote:
| 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/
| jackson1442 wrote:
| > I have no idea to this day how macs became the premier
| development environment.
|
| I've been editing a tutorial one of my coworkers wrote that
| targets new Python users on Windows. From my findings, the
| grass is not greener.
|
| Granted, geospatial Python is somewhat of a mess, but a lot
| of tools I have to use are somewhat messy forks of Unix tools
| (looking at you, pyenv-win) with tons of incompatible
| extensions. For development, Windows is the exception because
| you can transfer just about anything from Linux to macOS.
|
| On my Mac, I can easily install all of the Python packages I
| need without needing to install Visual Studio, pipwin,
| anaconda, etc. I have bash/zsh as my default system shell.
| Maybe it's easier to native Windows users, but bash/brew is a
| much better combination than anything I've found in Windows.
|
| WSL is a step in the right direction, but it still feels
| secondary. If a first-class terminal experience existed on
| Windows, I have a strong feeling that it could be the premier
| development environment, or at least closer to Mac.
|
| Maybe I'm using it wrong, but it definitely hasn't been made
| clear on how to use it right.
| sangnoir wrote:
| > I've been editing a tutorial one of my coworkers wrote
| that targets new Python users on Windows. From my findings,
| the grass is not greener.
|
| Maybe check the _other_ other side (Linux) - I found the
| grass is greener there - at least for Python (and
| programming tools in general). I 'm very comfortable on the
| command-line, and moving from a pure Linux environment to
| OS X & brew felt like a huge downgrade, followed by random
| annoyances that remind you you are using inferior, non-GNU
| utilities: $ ls my_dir -l ls: -l: No
| such directory
|
| _Really_ - Mac OS? I know its minor, but that 's just user-
| hostile and it happens every few weeks; I can't get over
| it.
| analog31 wrote:
| >>>> I've been editing a tutorial one of my coworkers wrote
| that targets new Python users on Windows. From my findings,
| the grass is not greener.
|
| I've been using WinPython for a few years. It's the closest
| thing to "just works" that I've found, and non-programming
| colleagues have been able to install it successfully.
|
| Because it works almost like an isolated "container," it's
| also possible to test your code on a clean install of
| WinPython to make sure it will run on someone else's
| computer before you share it.
|
| I don't know the technical difference between WinPython and
| a true container, but you can have multiple WinPython
| installs on one machine, and they don't interfere with one
| another, or with a pre-existing mainstream Python
| installation on the same PC. So you can share your stuff
| without worrying about screwing up someone else's stuff.
| [deleted]
| fastball wrote:
| Relevant XKCD.
|
| https://xkcd.com/1987/
| kbelder wrote:
| 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?
| greazy wrote:
| I've seen it crop up here and there. I've used it a few times
| and its really amazing how fast.
|
| Afaik it's a drop in replacement for conda using the same cli
| parse. Even has miniconda (micromamba).
| laingc wrote:
| Did you reply to the correct comment? I can't understand what
| you're referencing here.
| losvedir wrote:
| 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 able to quickly whip up a
| solution in my notebook. I'm a skilled developer in terms of
| larger systems, but have a weakness when it comes to very early
| stage "throwaway" scripts to answer ad-hoc questions.
|
| I spent a few days trying to set up jupyterlab in an "ideal" way,
| so that I could have easy access to python libraries within my
| notebook, and also a reproducible environment since I plan to
| push my working directory to GitHub and would like to sync across
| different machines. I got confused by virtualenv vs venv, and
| tried conda to install libraries, but ran into various problems.
| Oh, and part of it was trying to have nbdev (from the fastai
| folks) as part of this toolkit.
|
| Anyone have a setup like this that they use and want to share?
| Part of me wants to just throw in the towel and learn R and
| RStudio, since I've heard its ggplot is the best plotting library
| anyway.
| liuliu wrote:
| 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.
| zhengyi13 wrote:
| Neat! I don't suppose you've got your BUILD and WORKSPACE up
| somewhere you wouldn't mind sharing?
| abdullahkhalids wrote:
| On linux, I install Anaconda and just launch JupyterLab from
| Anaconda Navigator.
|
| But you could just install conda and just `conda env export -n
| base` to export your environment.
| BadInformatics wrote:
| mamba is (nominally) a drop-in replacement for Conda's CLI and
| dependency resolver. I've been using it for a couple of months
| and it really does run an order of magnitude faster for
| anything that requires dependency resolution. You'll still want
| to keep ana/miniconda around, but it goes to show how some of
| Conda's performance woes are entirely self-inflicted.
| nxpnsv wrote:
| 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)
| vlgnat wrote:
| 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.
| remram wrote:
| 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
| roseway4 wrote:
| > most people who can write fast C extensions have left the
| scene
|
| Care to elaborate?
| klelatti wrote:
| Have you looked at any of the Jupyter Docker Stacks images? [1]
|
| I've built some personal Docker images based on these with
| quite a lot of additional features installed including Ruby,
| OpenCL and lots of additional Python and R packages and
| JupyterLab extensions. It's been a bit hit and miss - with
| extension incompatibilities being a bit of an issue. I sync
| directories on the host machine with the Docker image and so
| have the benefit of editing in a full text editor (for non-
| notebook scripts).
|
| I'm not a Python env expert so I've probably missed a few
| tricks but all seems to be working well now. Happy to help if
| you want to try this route and have any issues.
|
| Btw after using JupyterLab for over a year now I'm a huge fan.
|
| [1] https://jupyter-docker-stacks.readthedocs.io/en/latest/
| iamlucaswolf wrote:
| 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
| claytonjy wrote:
| I do very similarly, except I avoid installing jupyter lab
| for each project, instead installing `ipykernel` as a dev
| dependency. I install jupyter lab system-wide with pipx [1],
| and for each project issue a command like
| pipenv run python -m ipykernel install --user
| --name=this_directory
|
| Then if I open Jupyter Lab I see "this_directory" as a listed
| kernel to create a notebook from.
|
| This allows me to manage Jupyter settings and plugins in one
| place rather than in each env, have multiple project's
| notebooks open in the same Jupyter Lab instance, etc.
|
| [1]https://pipxproject.github.io/pipx/
| klelatti wrote:
| Just to say a big thank you to all those who have worked on
| JupyterLab. It's by far my favourite notebook interface and I
| think gets the balance between a clean interface and powerful
| features just about right.
|
| I just wish that the cloud providers would adopt it as the basis
| for their products.
| colincooke wrote:
| Ok this may be kind of stupid, but my primary reason for not
| switching over to jupyter lab (from notebooks) was the right-
| click menu wouldn't let me copy images (plotting outputs
| typically) from the in-line output. However I found out today
| that it has always been available if you hold shift...so if that
| was anyone's issue this is a great time to give JupyterLab
| another shot!
| hnews2 wrote:
| I've seen a few projects that have used the JupyterLab UI for
| other projects as a simple interface - it looks really neat and
| slick.
|
| I just wondered if anyone has any ideas how you go about this as
| I've been drawing a blank.
| klelatti wrote:
| Could you share any examples of what you've seen?
| bobbylarrybobby wrote:
| I don't understand why Jupyter notebooks are still in use as a
| writable format when there are editors like VSCode that can treat
| ordinary python files as notebooks. The Python extension can
| submit code blocks to the kernel and import and export Jupyter
| notebooks, but VSCode is also a real editor on top of that
| (debugging, git, vim mode, hover info, etc.). For distribution,
| as a read-only format (like PDF), notebooks are great. But why do
| people continue to do their work in one?
| kaba0 wrote:
| Maybe I misunderstand something, but I use jupyter for sage for
| example and the point of it is that graphical results can be
| shown there immediately. Some plots can be even made
| interactive.
| bobbylarrybobby wrote:
| When running a Python kernel, VSCode splits the editor into
| two panes, the input .py file pane and the output ipython
| pane. The output pane supports interactive graphs just like
| Jupyter does.
| alex-treebeard wrote:
| > why do people continue to do their work in one? Most nb users
| don't use vscode. I'd say main reason is you can't open a
| notebook without starting a kernel which takes >1 second. It
| also is harder to setup.
|
| In terms of a flat file formats, there is actually a mark down
| extension which I would say is better than vscode's format
| because it is more standardised:
|
| https://myst-nb.readthedocs.io/en/latest/
| ajford wrote:
| I don't think anyone who writes production code is using
| Jupyter as a development env. It's more like a self-
| documenting, persistent repl, or perhaps an interactive doc.
|
| I've used it as a way to document data exploration for later
| use and as an interactive teaching tool. For that, being able
| to plot inline and embed interactive widgets is great. Also,
| mixing in rendered markdown content.
|
| If you're using Jupyter to write python software and the
| software is the end result and not the data analysis/process, I
| think you might be doing it wrong.
|
| Not that I'm discounting data scientists (or the like) who use
| it as their primary environment. Their end result is not a
| packaged/deployed software and the code itself is just
| ancillary. For those users, the code and environment is a tool,
| and the extra weight of what a full IDE (like VSCode or
| PyCharm) and the features they bring to the table are
| unnecessary and likely counter-productive.
| jjcon wrote:
| Interesting that they include mamba install instructions before
| conda.
|
| Is mamba taking off with the recent (and upcoming) license
| changes that anaconda made? I'm not familiar with mamba though
| I've had my eyes open for an alternative to anaconda (also seen
| poetry mentioned a few times here).
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(page generated 2021-01-05 23:01 UTC)