line_profiler
RCall.jl
line_profiler | RCall.jl | |
---|---|---|
17 | 8 | |
2,481 | 311 | |
1.3% | 0.6% | |
8.2 | 5.5 | |
8 days ago | about 1 month ago | |
Python | Julia | |
GNU General Public License v3.0 or later | GNU General Public License v3.0 or later |
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For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
line_profiler
- Ask HN: C/C++ developer wanting to learn efficient Python
- New version of line_profiler: 4.1.0
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Making Python 100x faster with less than 100 lines of Rust
LineProfiler is the best tool to learn how to write performant Python and code optimization.
https://github.com/pyutils/line_profiler
You can literally see the hot spot of your code, then you can grind different algorithms or change the whole architecture to make it faster.
For example replace short for loops to list comprehensions, vectorize all numpy operations (only vectorize partially do not help the issue), using 'not any()' instead or 'all()' for boolean, etc.
Doing this for like 2 weeks, basically you can automatically recognized most bad code patterns in a glance.
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Why is my Pubmed plant search app so slow?
You may want to try using a package like line_profiler to narrow down where the time is spent.
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How to make nested for loops run faster
When tuning for performance, always measure. Never assume you know where the slow parts are. Run a line profiler and see where all the time is actually going.
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I'm working on a world map generator, but I have one function in particular that is very slow and keeping me from being able to scale my maps to as large as I'd like... is there a way that I can optimize this depth first search function, or another way of grouping contiguous cells based on criteria?
Either way I would highly recommend running a profiler on your code to see where the program is spending most of its time. line_profiler is a very nice one, as it shows you execution time for each line.
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Is it possible to make a function to check how many lines of code have been executed in the program so far (including said function’s lines)?
There are dedicated tools like line_profiler for python - if this doesn't do exactly what you need it can be easily modified.
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Why does sklearn.Pipeline with regex outperform spacy for text preprocessing?
It's surprising to me that an sklearn pipeline and a spacy pipeline both doing simple regexing are vastly different in performance. I would go one layer deeper with measurement with something like line_profiler, which I've used to great effect to get line-by-line perf stats. This should illuminate why.
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Hot profiling for Python
This looks really nice! Does it use line_profiler or is it a different implementation for the profiling? Either way the interface is fantastic!
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Profiling and Analyzing Performance of Python Programs
# https://github.com/pyutils/line_profiler pip install line_profiler kernprof -l -v some-code.py # This might take a while... Wrote profile results to some-code.py.lprof Timer unit: 1e-06 s Total time: 13.0418 s File: some-code.py Function: exp at line 3 Line # Hits Time Per Hit % Time Line Contents ============================================================== 3 @profile 4 def exp(x): 5 1 4.0 4.0 0.0 getcontext().prec += 2 6 1 0.0 0.0 0.0 i, lasts, s, fact, num = 0, 0, 1, 1, 1 7 5818 4017.0 0.7 0.0 while s != lasts: 8 5817 1569.0 0.3 0.0 lasts = s 9 5817 1837.0 0.3 0.0 i += 1 10 5817 6902.0 1.2 0.1 fact *= i 11 5817 2604.0 0.4 0.0 num *= x 12 5817 13024902.0 2239.1 99.9 s += num / fact 13 1 5.0 5.0 0.0 getcontext().prec -= 2 14 1 2.0 2.0 0.0 return +s
RCall.jl
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Makie, a modern and fast plotting library for Julia
I don't use it personally, but RCall.jl[1] is the main R interop package in Julia. You could call libraries that have no equivalent in Julia using that and write your own analyses in Julia instead.
[1] https://github.com/JuliaInterop/RCall.jl
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Making Python 100x faster with less than 100 lines of Rust
You can have your cake and eat it with the likes of
* PythonCall.jl - https://github.com/cjdoris/PythonCall.jl
* NodeCall.jl - https://github.com/sunoru/NodeCall.j
* RCall.jl - https://github.com/JuliaInterop/RCall.jl
I tend to use Julia for most things and then just dip into another language’s ecosystem if I can’t find something to do the job and it’s too complex to build myself
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Interoperability in Julia
To inter-operate Julia with the R language, the RCall package is used. Run the following commands on the Julia REPL
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Convert Random Forest from Julia to R
https://github.com/JuliaInterop/RCall.jl may help
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I'm considering Rust, Go, or Julia for my next language and I'd like to hear your thoughts on these
If you need to bindings to your existing R packages then Julia is the way. Check out RCall.jl
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translate R code to Julia code
I have no experience with R, but maybe this will be of use: https://github.com/JuliaInterop/RCall.jl
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Julia 1.6: what has changed since Julia 1.0?
You can use RCall to use R from Julia: https://github.com/JuliaInterop/RCall.jl
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Julia Update: Adoption Keeps Climbing; Is It a Python Challenger?
I worked with R and Python during the last 3 years but learning and dabbling with Julia since 0.6. Since the availability of [PyCall.jl] and [RCall.jl], the transition to Julia can already be easier for Python/R users.
I agree that most of the time data wrangling is super confortable in R due to the syntax flexibility exploited by the big packages (tidyverse/data.table/etc). At the same time, Julia and R share a bigger heritage from Lisp influence that with Python, because R is also a Lisp-ish language (see [Advanced R, Metaprogramming]). My main grip from the R ecosystem is not that most of the perfomance sensitive packages are written in C/C++/Fortran but are written so deeply interconnect with the R environment that porting them to Julia that provide also an easy and good interface to C/C++/Fortran (and more see [Julia Interop] repo) seems impossible for some of them.
I also think that Julia reach to broader scientific programming public than R, where it overlaps with Python sometimes but provides the Matlab/Octave public with an better alternative. I don't expected to see all the habits from those communities merge into Julia ecosystem. On the other side, I think that Julia bigger reach will avoid to fall into the "base" vs "tidyverse" vs "something else in-between" that R is now.
[PyCall.jl]: https://github.com/JuliaPy/PyCall.jl
[RCall.jl]: https://github.com/JuliaInterop/RCall.jl
[Julia Interop]: https://github.com/JuliaInterop
[Advanced R, Metaprogramming] by Hadley Wickham: https://adv-r.hadley.nz/metaprogramming.html
What are some alternatives?
SnakeViz - An in-browser Python profile viewer
Makie.jl - Interactive data visualizations and plotting in Julia
memory_profiler - Monitor Memory usage of Python code
org-mode - This is a MIRROR only, do not send PR.
reloadium - Hot Reloading and Profiling for Python
Chain.jl - A Julia package for piping a value through a series of transformation expressions using a more convenient syntax than Julia's native piping functionality.
pprofile - Line-granularity, thread-aware deterministic and statistic pure-python profiler
Revise.jl - Automatically update function definitions in a running Julia session
psutil - Cross-platform lib for process and system monitoring in Python
cmssw - CMS Offline Software
prometeo - An experimental Python-to-C transpiler and domain specific language for embedded high-performance computing
PyCall.jl - Package to call Python functions from the Julia language