The enhanced Python fork of JILL -- Julia Installer for Linux (and every other platforms) -- Light
For the first time users of jill
, you will need to install it using pip
: pip install jill --user -U
. Also use this to upgrade JILL version.
Python >= 3.6
is required. For base docker images, you also need to make sure wget
and gnupg
are installed.
When you type jill install
, it does the following things:
julia
, julia-1
, julia-1.6
For common Julia users:
jill install
jill install 1
jill install 1.6
jill install 1.6.2
, jill install 1.7.0-beta3
jill install --unstable
For Julia developers and maintainers:
jill install latest
. This gives you julia-latest
.jill install 1.8.0+cc4be25c
(<major>.<minor>.<patch>+<build>
with at least the first 7 characters of thehash). This gives you julia-dev
.Some flags that can be useful:
jill install --confirm
jill install --upstream Official
jill install --keep_downloads
jill install --reinstall
To start Julia, you can use predefined JILL symlinks such as julia
. jill install
uses the following rule makes surethat you're always using the latest stable release.
Stable releases:
julia
points to the latest Julia release.julia-1
points to the latest 1.y.z Julia release.julia-1.6
points to the latest 1.6.z Julia release.For unstable releases such as 1.7.0-beta3
, installing it via jill install 1.7 --unstable
orjill install 1.7.0-beta3
will only give you julia-1.7
; it won't make symlinks for julia
orjulia-1
.
To dance on edge:
julia-latest
points to the nightly build from jill install latest
julia-dev
points to the julia CI build artifacts from, for example, jill install 1.8.0+cc4be25c
.jill list [version]
gives you every symlinks and their target Julia versions.
For non-windows system, you are free to use ln
command to change the symlink targets. For Windowsit uses an entry .cmd
file for this so you'll need to copy them. In the meantime, jill switch
provides a simple and unified way to do this:
jill switch 1.6
: let julia
points to the latest julia 1.6.z release.jill switch <path/to/my/own/julia/executable>
: let julia
points to custom executables.jill switch 1.6 --target julia-1
: let julia-1
points to the latest julia 1.6.z release.By default, JILL tries to be smart and will download contents from the nearest upstream. You canget the information of all upstreams via jill upstream
. Here's what I get in my laptop, I live inChina so the official upstreams aren't so accessible for me :(
To temporarily disable this feature, you can use flag --upstream <server_name>
. For instance,jill install --upstream Official
will faithfully download from the official julialang s3 bucket.
To permanently disable this feature, you can set environment variable JILL_UPSTREAM
.
Note that flag is of higher priority than environment variable. For example, if JILL_UPSTREAM
isset to mirror server "TUNA"
, you can still download from the official source via jill install --upstream Official
.
Here's the default JILL installation and symlink directories:
system | installation directory | symlink directory |
---|---|---|
macOS | /Applications |
~/.local/bin |
Linux/FreeBSD | ~/packages/julias |
~/.local/bin |
Windows | ~\AppData\Local\julias |
~\AppData\Local\julias\bin |
For example, on Linux jill install 1.6.2
will have a julia folder in ~/packages/julias/julia-1.6
and symlinks julia
/julia-1
/julia-1.6
created in ~/.local/bin
.
Particularly, if you're using jill
as root
user, you will do a system-wide installation:
/opt/julias
for Linux/FreeBSD./usr/local/bin
for Linux/FreeBSD/macOS.To change the default JILL installation and symlink directories, you can set environment variablesJILL_INSTALL_DIR
and JILL_SYMLINK_DIR
.
(Deprecated) jill install
also provides two flag --install_dir <dirpath>
and --symlink_dir <dirpath>
, they have higher priority than the environment variables JILL_INSTALL_DIR
andJILL_SYMLINK_DIR
.
jill
is made as a convenient tool and it can sometimes be annoying passing flags to it. There aresome predefined environment variables that you can use to set the default values:
JILL_UPSTREAM
: --upstream
JILL_SYMLINK_DIR
: --symlink_dir
JILL_INSTALL_DIR
: --install_dir
The flag version has higher priority than the environment variable version.
If you're tired of seeing (xx days old master)
in your nightly build version, then jill
canmake your nightly build always the latest version using cron
:
# /etc/cron.d/jill
PATH=/usr/local/bin:/usr/sbin:/usr/sbin:/usr/bin:/sbin:/bin
# install a fresh nightly build every day
* 0 * * * root jill install latest --confirm --upstream Official
If it's an public mirror and you want to share it worldwide to other users of JILL. You can add anentry to the public registry, make a PR, then I will tag a new releasefor that.
Please check the sources.json
format for more detailed information on theformat.
To add new private upstream, you can create a file ~/.config/jill/sources.json
(fow Windows it is~/AppData/Local/julias/sources.json
) and add your own upstream configuration just like the JILLsources.json
does. Once this is done JILL will recognize this newupstream entry.
Please check the sources.json
format for more detailed information on theformat.
There are two ways to do so:
aws s3 sync
, this should be the easiest way to do so I highly recommend this.jill mirror
command with mirror config example. Ididn't know about the aws s3 sync
stuff when I implemented this.The Julia release mirror does not contain Julia package contents, to mirror all the Julia packagesand artifacts (which requires >1.5Tb storage), you can use StorageMirrorServer.jl.
jill.py
also provides a set of Python API:
from jill.install import install_julia
from jill.download import download_package
# equivalent to `jill install --confirm`
install_julia(confirm=True)
# equivalent to `jill download`
download_package()
You can read its docstring (e.g., ?install_julia
) for more information.
Distro package managers (e.g., apt
, pac
) is likely to provide a broken Julia with incorrectbinary dependencies (e.g., LLVM ) versions. Hence it's recommended to download and extract theJulia binary provided in Julia Downloads. jill.py
doesn't doanything magical, but just makes such operation even stupid.
At first I found myself needing a simple tool to download and install Julia on my macbook andservers in our lab, I made my own shell scripts and I'd like to share it with others. Then I foundthe jill.sh project, Abel knows a lot shell so I decide to contribute my macOS Juliainstaller to jill.sh
.
There are three main reasons for why I decided to start my Python fork:
For some "obvious" reason, Julia People don't like Python and I understand it. (I also don't likePython after being advanced Julia user for more than 3 years) But to be honest, revisiting thisproject, I find using Python is one of the best-made decision during the entire project. Here is thereason: no matter how you enjoy Julia (or C++, Rust), Python is one of the best successfulprogramming language for sever maintenance purpose. Users can easily found tons of "how-to"solutions about Python and it's easy to write, deploy, and ship Python codes to the world via PyPI.
And again, I live in China so I want to rely on services that are easily accessible in China, PyPIis, GitHub and AWS S3 bucket aren't. A recent Julia installer project juliaup written in Rustsolves the Python dependency problem very well, but the tradeoff is that juliaup
needs its owndistributing system (currently GitHub and S3 bucket) to make sure it can be reliably downloaded touser machine. And for this it just won't be as good as PyPI in the foreseeable future.
jill.py
?Yes, jill.py
use GPG to check every tarballs after downloading. Also, *.dmg
/*.pkg
for macOSand .exe
for Windows are already signed.
jill.sh
and jill.py
jill.sh
is a shell script that works quite well on Linux x86/x64 machines. jill.py
isan enhanced python package that focus on Julia installation and version management, and brings aunified user experience on all platforms.
julia
fails to startThe symlink julia
are stored in JILL predefined symlinksdir thus you have to make sure this folder is inPATH
. Search "how to add folder to PATH on xxx system" you will get a lot of solutions.
1.6.1
and 1.6.2
)Generally, you should not care about patch version differences so jill.py
make it explicitly thatonly one of 1.6.x can exist. If you insist to have multiple patch versions, you could use jill install --install_dir <some_other_folder>
to install Julia in other folder, and then manually makea symlink back. As I just said, in most cases, common users should not care about this patch versiondifference and should just use the latest patch release.
Use jill download [version] [--sys <system>] [--arch <arch>]
. Check jill download --help
formore details.
For Julia (>= 1.5.0) in Linux with musl libc, you can just do jill install
and it gives you theright Julia binary. To download the musl libc binary using jill download
, you will need to pass--sys musl
flag.
Yes it's supported. But I don't have access to M1 machine so I can't test it.
【问题描述】Jill要进行一次旅行,沿途中要经过若干个城市。对于每两个相邻城市之间的路程,他都可以选择骑自选车或是坐公车汽车。如果沿途风景怡人,则他更喜欢骑自选车来完成这段路程。 Jill对每段路程都有评出了一个满意度,这是一个非零整数,所有他喜欢的路程标以正数,不喜欢的路程标以负数,数的绝对值大小代表他喜欢/不喜欢的程度。 如果在本次旅行中只允许在一段连续路程中骑自行车,而剩下的路程都坐公共汽车
一、 作为一个模板语言,它的主要优势是可以省去很多重复的前端代码,用类似于后台循环代码等方式来产生html,首先,需要读入一个带有模板语言的html 模板,类似于如下: <html> <head> <meta charset="UTF-8"> </head> <body> <p style='font-size:15px; font-
一:渲染模版 要渲染一个模板,通过render_template方法即可。 @app.route('/about/') def about(): return render_template('about.html',**{'user':'username'}) 渲染模版时有两种传递参数的方式:用 var='value' 传递一个参数;使用字典组织多个参数,并且加两个*号转换成关键字参数传入。 p
Jupyter Lab 安装 史上最全Jupyter Lab 配置 JupyterLab:程序员的笔记本神器 Jupyter Lab 用法 JupyterLab基本使用 利器|JupyterLab 数据分析必备IDE完全指南 Jupyter Notebook插入图片的4种方法 Jupyter Lab 启动 $ conda activate tf_2c $ jupyter lab
在android 开发中,经常会出现稀奇古怪的问题,今天发现一个奇怪的问题, Cannot load Jill from build tools. 在Android studio 2.3.3上面,以前的项目从来没有出现这个问题,在android studio 3.2的时候,在用android studio 2.3.3 就出现问题,为什么呢? 经过多次尝试得到一个结论,在添加Lambda 时,
DISCLAIMER: This is not an official google project, this is just something I wrote while at Google. Pyringe What this is Pyringe is a python debugger capable of attaching to running processes, inspect
GIL:Global Interpreter Lock, 全局解释器锁定,是指python虚拟机在执行多线程程序时,任一时刻只有一个线程在执行,这使得多线程程序无法充分利用CPU。对于一般的多线程跟IO绑定的程序,GIL影响并不明显,因为多线程跟IO绑定大多时间在等待,CPU并不是瓶颈,只有计算量大的应用才是瓶颈,GIL会限制性能的发挥。 为什么会存在GIL,因为python为了模拟多线程
输出整体内容,不带省略号 import pandas as pd pd.set_option('display.max_columns', None) pd.set_option('display.max_rows', None) pd.set_option('max_colwidth',200)