Chris Lattner on Julia programming language | Lex Fridman Podcast Clips 

With Chris Rackauckas would be awesome

I agree about JuMP. Been using it for a long time .

Hey there,Where can I learn about JuMP and optimization?

@@ajaybhaskarreddy3381 JuMP documentation. Also, the book: Math Programming with Julia

See that is the problem. You are an optimization scientist, who doesn't seem to understand programmers and engineers very well. Python has huge ecosystem because its syntax is loved by engineers with how intuitive it is. Mojo is superior to Julia in almost every way. It can go lower level, become faster than Julia while simulteniously enjoying benefits of dynamisms and clean, friendly syntax of Python.

There is no point discussing the syntax. Anyone who tried Julia will immediately notice how easy the syntax is. It is just like Python and matlab. If syntax is the reason to all you said, that falls apart really quickly. I suggest you give Julia a try before praising Python syntax😂

Race him 🤪

Coming from astronomy, Python was a game changer. We were stuck with old programs with awful scripting, Matlab, IDL (another matlab like proprietary blob). Astronomy was very conservative with codes. A lot of Fortran77 (the old, awful one) is still on use. Python with Numpy+Scipy+Matplotlib was so good in comparison with old stuff that I never look back. It allowed you to access an real ecosystem in modern numerical computing. Everything now in Astronomy is Python based. I believe Julia will have a place for small to medium simulations (several hours to days of runtime), but it will take a long time to ditch C and Fortran for large simulations and Python for data reduction. Remember, most of the stuff we do are images and they are natural objects in array languages. Numpy is very good at that. And with polars, dask and xarray you can do stuff that don't fit on memory. So Julia needs to do more than Python and better in all aspects if Julia supporters want Julia to replace Python (I am looking at you JIT latency and lack of static binaries).

He has to justify his efforts somehow. This seems to be the coping story he’s telling himself…

julia i think is a clean version of c and c++ but robust version of python.. i use those 3 languages in my programming career learning julia is just an automatic understanding of the evolution of programming in general it's like the bugatti version of all the cars invented..

The only reason I still code in Python is native Excel support and thus many macros i have to write in Python, even if I would prefer to just export an Excel doc to a csv then edit it with a Julia script

The very thing Mojo is trying to be will hamstring it. An easier rust would be better than a harder python.

@@offensivearch Mojo is not “Easier Rust”. It’s a language that is a superset of Python, somehow adopting ownership and borrowing. The truth is: Python doesn’t really need this “Memory-safe” things, and Python is not used because of its speed. If performance was really that important people would have already switched to Julia. Python is nothing but a script glue for existing c-cpp-Fortran code, it does not need all this new bullshit that Mojo is trying to whack into it. Python is used because it is easy, and because there is a lot of packages built for it. And also with the amount of info on the internet every possible Python problem is spoonfed to you once you google it.

your comment won't age well

@@rakaboy1619 I didn't say mojo was an easier rust, I said the opposite. I said an easier rust would be more useful than a harder python (mojo).

@@rakaboy1619 Python is used because Google revived it from the dead a while ago. Yes, it is easy but so are many other languages. Many other languages would also be far better glue languages for this application. The main reason python is popular for data work is because of Google and their influence. I doubt this status quo will last.

Pluto is awesome. Another cool thing is that a pluto notebook is just a julia script, all the "pluto stuff" (cell tags, cell order, etc) are just comments. So you don't get massive dumps of stuff you don't want if you open a pluto notebook in a text editor. Also makes it very easy to develop a package or script iteratively and use it as a module later.

I used Pluto and I really disliked it. If you have some big computation in your notebook then making some change forces a recompute resulting in a poor user experience. Like it has some nice properties, but in the end, the juice is not worth the squeeze with Pluto.

​@@yevgeniygrechka6431 There is a feature in Pluto now where you can disable particular cells so they don't run. I used to think the same as you, so I used Neptune. It's a pluto fork that is not interactive. Later I went back to pluto and realized that 90% of the time the responsiveness is useful for me. Now that we have the ability to switch off cells, it's even better. The one thing I wish Pluto allowed now is the ability to disable auto running the cells on startup. I often want to edit the notebook from my last edit before rerunning everything.

Pluto.CONFIG["PLUTO_RUN_NOTEBOOK_ON_LOAD"] = "false" , just set before starting Pluto. @@yevgeniygrechka6431

Newer, Pluto.run(Pluto.Configuration.from_flat_kwargs(;run_notebook_on_load=false))

Or just use a library written in C hahaha.

@@Michallote well, if you're into toy projects, I guess that would suffice...

This is true

Julia is one of the laguages id like to learn, can you give me cool stuff(like rust traits..) it has?

@@birdbeakbeardneck3617 Multiple dispatch probably. Maybe not unique but multithreading is extremely simple and it just works, also great GPU (CUDA) support.

@@birdbeakbeardneck3617 It has traits, but not rust like traits (there is research being funded in this area though). Biggest draw is the massive code reuse and interoperability. Metaprogamming and DSLs are very powerful. You can write cuda kernels in julia (without knowing cuda) that can be compiled and run on your gpu. Julia's hierarchical type system + multiple dispatch is also pretty cool. You can write very generic code.

are you sure it's not a dream?

A few years later I made the first finite element program in my country.

Python lambda can't have statements or multilines and is long... so Python devs had to invent iterators and with statement and you need complex stuff to use decorators with the with statement. Python could have used monads lol to embed statements within lambdas. Why have the pass keyword when you can write None. You have to rewrite lots of code to use objects as they use different syntax from functions vs multimethods in Lisp/Julia/Haskell. Python doesn't allow you to rerun code after you get a trackback. Python has no notion of "programmable/first-class semantics" like LISP/Haskell, there is no easy way of writing a compiler for python.

But if it’s versatile and offers extensive tools and integrations, why the hate? It’s powerful

Mojo will eventually be a superset of Python. It’ll be like JavaScript and TypeScript. This means Python codebases can be ported to Mojo. You don’t have to rewrite any of your old code, you just have to add in new Mojo optimizations. Switching to Julia means rewriting your code.

I like turtles. :-)

For ML prototyping work zig is a non-starter. Great for embedded systems though

@@offensivearch Pretty much what I meant by “I like turtles.” ;-). I didn’t want to sound like a douche bag with a PhD. I try not to be one ALL the time.

but zig is not really competing with any of those languages? zig is competing with c, maybe c++ and rust. If people keep going in the direction of making super optimized data structures as they seem to be doing, zig is going to become the standard for programs that want to mill through a lot of data without databases. I can also see zig being used to write data processing libraries for python / julia.

Not as mad as I get when I use Julia…

@@neuropuritan_zealot8455 why

@@PimentelES one recent example is taking nanmean of a 4d array on axes 1,2,3 to produce 1d timeseries is broken in julia. Try it yourself. Numpy works to do that easily. Insane amount of broken stuff in Julia, including external libraries.

How about ``` f(A) = eachslice(A; dims=4) .|> filter(!isnan) .|> mean ```

@@neuropuritan_zealot8455 that's not broken, the NaN handling in maths was deliberately moved into Julia's core "NaNMath" library, for both reasons of speed and explicitness. The code is as simple as `mapslices(NaNMath.mean, A, dims = 1:3)`, where A is your array. The point being that using Julia can be annoying if you don't know the language, which is the argument of familiarity, but that does not make it broken.

Oh julia killed itself

Well, first all the ecosystem needs to be rebuilt for Mojo, and apart from the big libraries or project actively developed and maintained by a huge comunity, that is going to take a long time. Meanwhile Mojo will be a fast python without his ecosystem. On the opposite, julia ecosystem is increasing at crazy speed and the modern design makes it a clear winner for starting new projects (the multiple dispatch is a banger!). Then, we already have cython example: a superset of python that compiles in "fast" C code, except it is way more difficult to use it effectively than simply learning julia and integrate it with your python scripts and packages. Lastly, julia design makes the ecosystem to interact in ways that python syntax would never allow. This makes it way more powerful even if more limited. Look at how complex it is to use autodiff in python, especially if you depend on a lot of packages. Python proceeds through frameworks which define their own subset of ecosystem mostly incompatibile with others ( jax, pytorch, tensorflow, ....). This ecosystem gets doprecated so quickly that it makes python not that much ahead of julia (actually on all scientific problems julia ecosystem is already way ahead of python)

@@lorenzomonacelli Mojo is a superset of Python, so this entire comment is wrong and I won't waste my time going over each point. Suffice to say, when Mojo 1.0 is ready, the entire Numpy+Pydata ecosystem will work, as-is, day one. Modules can be mojo-fied as time goes and as resources for doing so are available, I'm sure of the MILLIONS of Python programmers, there will be plenty who step up. Julia will probably still exist for school projects, but 99% of the real world is not school problems. Python excels here, Julia excels there. If you feel confident, I'd be willing to wager $10,000 that there will be more Mojo jobs than Julia jobs in 2028 (5 years from now).

@@HaydenDoingThings There is a big graveyard to projects like Mojo, fast python, python but fast like C or whatever.

There is no point of Julia when Mojo is more mature. It will die off and be forgotten for good except for some fanatics that will insistently use it in their hobby projects.

@@lorenzomonacelli Your comment makes no sense whatsoever. Mojo directly adopts entire Python ecosystem. Transition to Mojo will be much-much faster than Julia, because you don't need to translate entire dependency chain to make something work on Mojo. Jax, PyTorch and Tensorflow ecosystems are completely compatible with each other. PyTorch 2.0 is literally built on top of Jax. Claiming julia is ahead of Python in scientific problems is a complete and utter bullshit.

What a weird take... Blaming an entire language and ecosystem because "someone stole my code".

I'm a Julia programmer and this is correct, there are many unethical people in the community

Can you share the paper

Your paper is listed as the reference on that Julia libraries Github. Have you not seen any of the papers that have been published in the last decade that have been exactly this format: something was written in cpp/fortran/Matlab and now it is translated into R, wam bam there's a paper.

It's your code. Registering it in the public domain to claim your ownership is your responsibility. Don't blame an entire programming language for your problem.