Name: IJulia.jl
Owner: The Julia Language
Description: Julia kernel for Jupyter
Created: 2013-03-05 18:20:21.0
Updated: 2018-01-10 22:42:48.0
Pushed: 2018-01-13 13:25:28.0
Size: 885
Language: Julia
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IJulia is a Julia-language backend combined with the Jupyter interactive environment (also used by IPython). This combination allows you to interact with the Julia language using Jupyter/IPython's powerful graphical notebook, which combines code, formatted text, math, and multimedia in a single document.
(IJulia notebooks can also be re-used in other Julia code via the NBInclude package.)
First, download Julia version 0.4
or later and run the installer. Then run the Julia application
(double-click on it); a window with a julia> prompt will appear. At
the prompt, type:
add("IJulia")
to install IJulia.
By default on Mac and Windows, the Pkg.add process will use the Conda.jl
package to install a minimal Python+Jupyter distribution (via
Miniconda) that is
private to Julia (not in your PATH). (You can use using IJulia followed by
IJulia.jupyter to find the location jupyter where was installed.)
On Linux, it defaults to looking for jupyter in your PATH first,
and only installs the Conda Jupyter if that fails; you can force
it to use Conda on Linux by setting ENV["JUPYTER"]="" first (see below).
Alternatively, you can install Jupyter (or
IPython 3 or later) yourself before adding the IJulia package.
To tell IJulia to use your own jupyter installation, you need
to set ENV["JUPYTER"] to the path of the jupyter program
before running Pkg.add("IJulia"). Alternatively, you can change
which jupyter program IJulia is configured with by setting
ENV["JUPYTER"] and then running Pkg.build("IJulia").
The simplest way to install Jupyter yourself on Mac and Windows, other than using Julia's Conda distro, is to download the Anaconda package and run its installer. (We recommend that you not use Enthought Canopy/EPD, since that can cause problems with the PyCall package.)
On subsequent builds (e.g. when IJulia is updated via Pkg.update),
it will use the same jupyter program by default, unless you
override it by setting the JUPYTER environment variable, or
delete the file joinpath(Pkg.dir("IJulia"), "deps", "JUPYTER").
You can go back to using the Conda jupyter by setting
ENV["JUPYTER"]="" and re-running Pkg.build("IJulia").
In Julia, at the julia> prompt, you can type
g IJulia
book()
to launch the IJulia notebook in your browser. You can
use notebook(detached=true) to launch a notebook server
in the background that will persist even when you quit Julia.
This is also useful if you want to keep using the current Julia
session instead of opening a new one.
a> using IJulia; notebook(detached=true)
ess(`'C:\Users\JuliaUser\.julia\v0.4\Conda\deps\usr\Scripts\jupyter' notebook`, ProcessRunning)
a>
By default, the notebook “dashboard” opens in your
home directory (homedir()), but you can open the dashboard
in a different directory with notebook(dir="/some/path").
Alternatively, you can run
ter notebook
from the command line (the
Terminal program
in MacOS or the Command Prompt in Windows).
Note that if you installed jupyter via automated Miniconda installer
in Pkg.add, above, then jupyter may not be in your PATH; type
import Conda; Conda.SCRIPTDIR in Julia to find out where Conda
installed jupyter.
A “dashboard” window like this should open in your web browser. Click on the New button and choose the Julia option to start a new “notebook”. A notebook will combine code, computed results, formatted text, and images, just as in IPython. You can enter multiline input cells and execute them with shift-ENTER, and the menu items are mostly self-explanatory. Refer to the the IPython documentation for more information, and see also the “Help” menu in the notebook itself.
Given an IJulia notebook file, you can execute its code within any other Julia file (including another notebook) via the NBInclude package.
Julia is improving rapidly, so it won't be long before you want to update to a more recent version. To update the packages only, keeping Julia itself the same, just run:
update()
at the Julia prompt (or in IJulia).
If you download and install a new version of Julia from the Julia web
site, you will also probably want to update the packages with
Pkg.update() (in case newer versions of the packages are required
for the most recent Julia). In any case, if you install a new Julia
binary (or do anything that changes the location of Julia on your
computer), you must update the IJulia installation (to tell Jupyter
where to find the new Julia) by running
build("IJulia")
at the Julia command line (important: not in IJulia).
You can also install additional Julia kernels, for example to
pass alternative command-line arguments to the julia executable,
by using the IJulia.installkernel function. See the help for this
function (? IJulia.installkernel in Julia) for complete details.
For example, if you want to run Julia with all deprecation warnings disabled, you can do:
g IJulia
ia.installkernel("Julia nodeps", "--depwarn=no")
and a kernel called Julia nodeps 0.6 (if you are using Julia 0.6)
will be installed (will show up in your main Jupyter kernel menu) that
lets you open notebooks with this flag.
Pkg.build() to try to rerun the install scripts.Pkg.update() and try again:
this will fetch the latest versions of the Julia packages in case
the problem you saw was fixed. Run Pkg.build("IJulia") if your Julia version may have changed. If this doesn't work, you could try just deleting the whole .julia directory in your home directory (on Windows, it is called Users\USERNAME\.julia in your home directory) via rm(Pkg.dir(),recursive=true) in Julia and re-adding the packages.In[*] indefinitely), try creating a new Python notebook (not Julia) from the New button in the Jupyter dashboard, to see if 1+1 works in Python. If it is the same problem, then probably you have a firewall running on your machine (this is common on Windows) and you need to disable the firewall or at least to allow the IP address 127.0.0.1. (For the Sophos endpoint security software, go to “Configure Anti-Virus and HIPS”, select “Authorization” and then “Websites”, and add 127.0.0.1 to “Authorized websites”; finally, restart your computer.)jupyter --version and make sure that it prints 3.0.0 or larger; earlier versions of IPython are no longer supported by IJulia.ENV["JUPYTER"]=""; Pkg.build("IJulia") to force IJulia to go back to its own Conda-based Jupyter version (if you previously tried a different jupyter).There are various features of IJulia that allow you to interact with a running IJulia kernel.
If your code needs to detect whether it is running in an IJulia notebook
(or other Jupyter client), it can check isdefined(Main, :IJulia) && Main.IJulia.inited.
One difference from IPython is that the IJulia kernel does
not use “magics”, which are special commands prefixed with % or
%% to execute code in a different language. Instead, other
syntaxes to accomplish the same goals are more natural in Julia,
work in environments outside of IJulia code cells, and are often
more powerful.
However, if you enter an IPython magic command
in an IJulia code cell, it will print help explaining how to
achieve a similar effect in Julia if possible.
For example, the analogue of IPython's %load filename in IJulia
is IJulia.load("filename").
When you are running in a notebook, ordinary I/O functions on STDIN do
not function. However, you can prompt for the user to enter a string
in one of two ways:
readline() and readline(STDIN) both open a STDIN> prompt widget where the user can enter a string, which is returned by readline.
IJulia.readprompt(prompt) displays the prompt string prompt and
returns a string entered by the user. IJulia.readprompt(prompt, password=true) does the same thing, but hides the text the user types.
Analogous to the IPython.display.clear_output() function in IPython, IJulia provides a function:
ia.clear_output(wait=false)
to clear the output from the current input cell. If the optional
wait argument is true, then the front-end waits to clear the
output until new output is available to replace it (to minimize
flickering). This is useful to make simple animations, via repeated
calls to IJulia.clear_output(true) followed by calls to
display(...) to display a new animation frame.
When Julia displays a large data structure such as a matrix, by default
it truncates the display to a given number of lines and columns. In IJulia,
this truncation is to 30 lines and 80 columns by default. You can change
this default by the LINES and COLUMNS environment variables, respectively,
which can also be changed within IJulia via ENV (e.g. ENV["LINES"] = 60).
(Like in the REPL, you can also display non-truncated data structures via print(x).)
The new default behavior of IJulia is to truncate stdout (via show or println)
after 512kb. This to prevent browsers from getting bogged down when displaying the
results. This limit can be increased to a custom value, like 1MB, as follows
ia.set_max_stdio(1 << 20)
While we strongly recommend using IPython version 3 or later (note that this
has nothing to do with whether you use Python version 2 or 3), we recognize
that in the short term some users may need to continue using IPython 2.x. You
can do this by checkout out the ipython2 branch of the IJulia package:
checkout("IJulia", "ipython2")
build("IJulia")
First, you will need to install a few prerequisites:
You need version 3.0 or later of IPython, or version 4 or later of Jupyter. Note that IPython 3.0 was released in February 2015, so if you have an older operating system you may have to install IPython manually. On Mac and Windows systems, it is currently easiest to use the Anaconda Python installer.
To use the IPython notebook interface, which runs in your web
browser and provides a rich multimedia environment, you will need
to install the jsonschema, Jinja2, Tornado,
and pyzmq (requires apt-get install libzmq-dev and possibly pip install --upgrade --force-reinstall pyzmq on Ubuntu if you are using pip) Python packages.
(Given the pip installer, pip install jsonschema jinja2 tornado pyzmq
should normally be sufficient.) These should have been automatically installed if you installed IPython itself
via easy_install or pip.
To use the IPython qtconsole interface, you will need to install PyQt4 or PySide.
You need Julia version 0.4 or later.
Once IPython 3.0+ and Julia 0.4+ are installed, you can install IJulia from a Julia console by typing:
add("IJulia")
This will download IJulia and a few other prerequisites, and will set up a Julia kernel for IPython.
If the command above returns an error, you may need to run Pkg.update(), then
retry it, or possibly run Pkg.build("IJulia") to force a rebuild.
Most people will use the notebook (browser-based) interface, but you
can also use the IPython
qtconsole
or IPython terminal interfaces by running ipython qtconsole --kernel
julia-0.4 or ipython console --kernel julia-0.4, respectively.
(Replace 0.4 with whatever major Julia version you are using.)
If IJulia is crashing (e.g. it gives you a “kernel appears to have
died” message), you can modify it to print more descriptive error
messages to the terminal: edit your IJulia/src/IJulia.jl file (in
your .julia package directory) to change the line verbose = false
at the top to verbose = true and const capture_stderr = true to
const capture_stderr = false. Then restart the kernel or open a new
notebook and look for the error message when IJulia dies.