ChanServ changed the topic of #mlpack to: "mlpack: a fast, flexible machine learning library :: We don't always respond instantly, but we will respond; please be patient :: Logs at http://www.mlpack.org/irc/
< SiddhantJain[m]>
Thank you zoq. I had a slightly silly doubt.
< SiddhantJain[m]>
I have read the documentation of mlpack, it has binding for python, julia but mainly is for C++.
< SiddhantJain[m]>
If we need to add in features to some particular binding say python, do we need to make changes in C++?
< rcurtin[m]>
Yes, everything in mlpack is implemented in C++; so, to make any changes you would have to do them in C++ :)
< rcurtin[m]>
all of the bindings to other languages are actually automatically generated from the C++ source
< SiddhantJain[m]>
Thanks!
< SiddhantJain[m]>
So first i need to build mlpack from source in my local system. And there after if i modify the code do i need to rebuild the entire code base again and check whether it works or not. or is it not needed?
< SiddhantJain[m]>
by rebuilding the code base i mean the entire code in mlpack.
< abernauer[m]>
rcurtin Not mlpack related, but made some easy pulled pork in a pressure cooker today good eats
< rcurtin[m]>
Siddhant Jain yeah, that's correct, though when you rebuild CMake is smart enough to avoid recompiling any files whose dependencies did not change
< rcurtin[m]>
abernauer nice, actually I had some barbecue for dinner too but I did not make it myself :) looks good!
< abernauer[m]>
rcurtin: Yeah it's pretty easy in a pressure cooker just a pork shoulder a dry rub I found online. Then 55 minutes of cook time in the pressure cooker.
< SiddhantJain[m]>
ryan Thanks!
< SiddhantJain[m]>
I have gone through the docs, am comfortable with various algos. (ML & DL) have intermediate understanding of the langauge used here(C++) but haven't worked on any algos. in C++ could you suggest where i could start going through & understanding the codebase.
< rcurtin[m]>
sure, maybe take a look at some machine learning algorithm you are familiar with in src/mlpack/methods/? alternately, maybe a good step is to try to use mlpack for some basic machine learning task
< rcurtin[m]>
for instance, take one of the examples in the mlpack/examples repository and try to dig in and see what's actually happening under the hood, by tracing the code flow, etc.
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< SiddhantJain[m]>
Thank you ryan i go through it.
< _slack_mlpack_10>
<rcurtin[m] "Hey there, make sure that the di"> ey ryan can you please explain, how do I do that? Do I have to set that directory to path variable? my mingw is already is added to the path variable. and now I need to add the directory where I installed mlpack to the path variable??
< _slack_mlpack_10>
> <undefined>
< _slack_mlpack_10>
(edited) ey ryan ... => hey ryan ...
< RishabhGarg108Gi>
@Ayush Singh, you have to provide the path of include directory of `cereal` to the cmake options.
< RishabhGarg108Gi>
Make sure you have cereal on your system.
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< SirishGitter[m]>
Hello everyone. I am trying to build mlpack from the source. But the 'make' command is using RAM around 7 GB and as a result I am not able to complete the build.
< SirishGitter[m]>
I followed the make -j3 command to build mlpack. Am I doing anything wrong?
< SiddhantJain[m]>
I ran into something similar yesterday a couple of times. What you could try is using only "make" since a single core -j1 uses around 2-3 gigs of RAM, so using if you are facing RAM issues its normal i suppose, just remove -j3 and use "make" alone
< SirishGitter[m]>
Oh okay thanks. Will try out.
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< _slack_mlpack_10>
> Hey there, make sure that the directory you installed mlpack to is in the list of include directories used by your compiler 👍
< _slack_mlpack_10>
hey ryan can you please explain, how do I do that? Do I have to set that directory to path variable? my mingw is already is added to the path variable. and now I need to add the directory where I installed mlpack to the path variable??
< _slack_mlpack_10>
what language people use for the neural networks of their self-driving cars? do they use c/c++?
< rcurtin[m]>
zoq: I think I have isolated the issue for the Julia segfault; it's actually an Armadillo change
< rcurtin[m]>
between Armadillo versions 9.800.x and 10.1.x, the way that Mat stores memory internally has changed
< rcurtin[m]>
if you wanted, you could test if switching to 9.800.x in the CI configuration seemed to fix the issue
< zoq>
rcurtin[m]: Hm, don't we use the minimal version for the ci?
< rcurtin[m]>
anyway, I have to keep digging here, but I think that I may have an idea of what the problem is and how to fix it...
< zoq>
rcurtin[m]: Alright, let update the ci.
< rcurtin[m]>
looks like we just use brew install armadillo in osx-steps.yaml
< rcurtin[m]>
basically in 10.1.x, Mat holds a member n_alloc, which is the number of allocated elements, and this could be different than n_elem
< zoq>
Ahh, I see.
< rcurtin[m]>
if this is the issue for both Python and Julia, the fix will be basically the same
< rcurtin[m]>
I'm not sure why it only seems to happen on OS X though
< zoq>
Last space X for this year mission starting now.
< rcurtin[m]>
weren't they going to launch the starship prototype to 50k feet this weekend?
< rcurtin[m]>
I read somewhere that that was going to happen, but I haven't heard anything since
< zoq>
I think so.
< zoq>
Still that's not a misson.
< rcurtin[m]>
true
< SiddhantJain[m]>
Hello I don't intend to disturb you in middle of something. But as I went through the open issues, and questions hardly anything is left free.
< SiddhantJain[m]>
I am having a hard time finding out how to contribute :(
< rcurtin[m]>
Siddhant Jain: there are very many open issues, and while many of them have tons of posts of people saying "I'd like to take this up", so much of the time they don't actually follow through
< rcurtin[m]>
you could look at the dates on when people said that for an issue you're interested in, and if it's been many months and no PR was opened, it's likely that they're not actually doing it, and you could give it a shot
< rcurtin[m]>
also, there are some number of "big" issues open where we need to, e.g., add copy and move constructors to every neural network layer, or implement `InputSize()` for every neural network layer, and there are probably lots of layers there that haven't been done yet
< rcurtin[m]>
zoq: this is really cool, the livestream is just leaving the camera on the first stage as it re-enters
< zoq>
Yeah, I can watch this over an over.
< rcurtin[m]>
they make it look so easy...
< rcurtin[m]>
impressive
< zoq>
yep, once again
< SiddhantJain[m]>
Thank you ryan I'll definitely look into it. Hope to contribute soooon ! :D
< AakashkaushikGit>
Hey I have removed all the visitors from FFN but can't seem to get what to do with the layer initialization, you can check out the code (here)[https://github.com/Aakash-kaushik/ann_exp].
< zoq>
AakashkaushikGit: Great, I'll take a look later.
< AakashkaushikGit>
The layers initializer also uses the visitors and gives out an error about not finding the ```MoreTypes```, because i have removed that boost part.
< AakashkaushikGit>
@zoq thank you, If you find out something can you tag me and let me know, that way i can get a notification.
< RishabhGarg108Gi>
Hey @zoq @rcurtin , since the last few days, I have been trying to make some container for storing the parameters for the parser for ANN CLI. So, while trying it, I eventually ended up with something very similar to the implimentation of boost::variant. So, I wanted to know, would it be a good idea to have our own implementation of a minimalistic boost::variant that will have only the requirements that we need.
< RishabhGarg108Gi>
I found the use of boost variant only inside the ann codebase. So, maybe we could implement a simplified variant that would do only the functionality required in this part.
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< AndrewSpottGitte>
Anyone have familiarity with the Julia bindings? I think that I'm getting a memory leak from the returned values from mlpack... I think they can't be garbage collected from Julia.
< AndrewSpottGitte>
(edited) ... from Julia. => ... from Julia.
< AndrewSpottGitte>
I have a lot of KDE models that I'm generating that I think aren't being garbage collected.
< zoq>
AndrewSpottGitte: Hello, Ryan is the Julia expert; pretty sure once he has time he will get back. Maybe it's a good idea to open an issue.
< zoq>
RishabhGarg108Gi: We are looking into replacing boost::variant with inheritance, so not sure it will be that useful; but not sure since I haven't seen it in action.
< zoq>
RishabhGarg108Gi: I guess since you are working the on the ann cli, we will find out soon?
< rcurtin[m]>
Andrew Spott (Gitter) : how do you have the mlpack Julia bindings installed? Did you build them by hand or use Pkg?
< rcurtin[m]>
if, by chance, you compiled by hand, and used Armadillo version 10, you're probably being bitten by this bug I just solved a couple minutes ago:
< rcurtin[m]>
however... I don't think that bug should appear if you used Pkg to do the install...
< AndrewSpottGitte>
@ryan: I used Pkg
< rcurtin[m]>
I see... do you think you could open a bug report in the mlpack repository with instructions to reproduce the issue?
< AndrewSpottGitte>
Does the automatic binding creation create finalizers for the model types to free them when they are no longer needed?
< AndrewSpottGitte>
I don't have a minimal reproducible example at the moment, I'm just trying to understand how the mlpack models get destructed. (this issue: https://discourse.julialang.org/t/solved-debugging-an-apparent-memory-leak/1828/2 makes it seem that simple void* wrapper objects don't get cleaned up regularly by the GC because they don't look large... but I don't know if that is still an issue)
< AndrewSpottGitte>
calling `finalize` on a KDEModel doesn't seem to do anything to it... (it still can be used to compute a result from that distribution...)
< AndrewSpottGitte>
so it seems that a finalizer isn't added for the KDEModel type... which would imply that there is a memory leak there, right?
< rcurtin[m]>
hmmm, I am not sure. You might be right. I'm actually relatively new to Julia, so there are surely things I have overlooked. I'll read about this tomorrow and see what I find out
< AndrewSpottGitte>
Sounds good. I was going to see if I could manually add the destructor to the finalizer, but I'm not sure how to do that with all the autogenerated bindings.
< rcurtin[m]>
yeah, that part is a little hard---we basically have C++ code that prints the bindings
< rcurtin[m]>
but if all that needs to be done is add a finalizer to each model struct like `KDEModel`, that's actually pretty straightforward, I just need to read about finalizers to make sure I understand them :)
< AndrewSpottGitte>
They are basically destructors (I'm pretty new to julia too... but that is my understanding). It is possible that you just need to get the C++ code to call `delete` on the typed pointer under the hood.
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< rcurtin[m]>
yep, I would guess that's all that's needed
< rcurtin[m]>
anyway, I'll read about it tomorrow and figure out what needs to be done 👍
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