I am working on a project that requires inversion of multiple large dense matrix. I would like to know if ojAlgo supports multi-threading.
I do notice the github page for ojAlgo is tagged with multi-threading, however I am unable to find any documentation about it.
Thanks
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I have been clinging to a problem on multi class classification with a large number of classes (500) and a one vs rest classifier seems to be a good approach to handle it. Is there any support for the same in pytorch itself?
All that I could find is this post which doesn't have answers either. I understand that scikit-learn does offer this feature but I wish to stick to pytorch because of the numerous familiar customisations that can be tweaked with in this framework. I've tried exploring skorch but even it's overly simplified and doesn't have a good documentation to allow modifications which my use case needs
I am doing a meta learning research and am using the MAML optimization provided by learn2learn. However as one of the baseline, I would like to test a non-meta-learning approach, i.e. the traditional training + testing.
Due to the lightning's internal usage of optimizer it seems that it is difficult to make the MAML work with learn2learn in lightning, so I couldn't use lightning in my meta-learning setup, however for my baseline, I really like to use lightning in that it provides many handy functionalities like deepspeed or ddp out of the box.
Here is my question, other than setting up two separate folders/repos, how could I mix the vanilia pytorch (learn2learn) with pytorch lightning (baseline)? What is the best practice?
Thanks!
Decided to answer my question. So I ended up using the torch lightning's manual optimization so that I can customize the optimization step. This would make both approaches using the same framework, and I think is better than maintaining 2 separate repos.
I wanted to see how the conv1d module is implemented
https://pytorch.org/docs/stable/_modules/torch/nn/modules/conv.html#Conv1d. So I looked at functional.py but still couldn’t find the looping and cross-correlation computation.
Then I searched Github by keyword ‘conv1d’, checked conv.cpp https://github.com/pytorch/pytorch/blob/eb5d28ecefb9d78d4fff5fac099e70e5eb3fbe2e/torch/csrc/api/src/nn/modules/conv.cpp 1 but still couldn’t locate where the computation is happening.
My question is two-fold.
Where is the source code that "conv1d” is implemented?
In general, if I want to check how the modules are implemented, where is the best place to find? Any pointer to the documentation will be appreciated. Thank you.
It depends on the backend (GPU, CPU, distributed etc) but in the most interesting case of GPU it's pulled from cuDNN which is released in binary format and thus you can't inspect its source code. It's a similar story for CPU MKLDNN. I am not aware of any place where PyTorch would "handroll" it's own convolution kernels, but I may be wrong. EDIT: indeed, I was wrong as pointed out in an answer below.
It's difficult without knowing how PyTorch is structured. A lot of code is actually being autogenerated based on various markup files, as explained here. Figuring this out requires a lot of jumping around. For instance, the conv.cpp file you're linking uses torch::conv1d, which is defined here and uses at::convolution which in turn uses at::_convolution, which dispatches to multiple variants, for instance at::cudnn_convolution. at::cudnn_convolution is, I believe, created here via a markup file and just plugs in directly to cuDNN implementation (though I cannot pinpoint the exact point in code when that happens).
Below is an answer that I got from pytorch discussion board:
I believe the “handroll”-ed convolution is defined here: https://github.com/pytorch/pytorch/blob/master/aten/src/THNN/generic/SpatialConvolutionMM.c 3
The NN module implementations are here: https://github.com/pytorch/pytorch/tree/master/aten/src
The GPU version is in THCUNN and the CPU version in THNN
I want to use Dynamic Topic Modeling by Blei et al. (http://www.cs.columbia.edu/~blei/papers/BleiLafferty2006a.pdf) for a large corpus of nearly 3800 patent documents.
Does anybody has experience in using the DTM in the gensim package?
I identified two models:
models.ldaseqmodel – Dynamic Topic Modeling in Python Link
models.wrappers.dtmmodel – Dynamic Topic Models (DTM) Link
Which one did you use, of if you used both, which one is "better"? In better words, which one did/do you prefer?
Both packages work fine, and are pretty much functionally identical. Which one you might want to use depends on your use case. There are small differences in the functions each model comes with, and small differences in the naming, which might be a little confusing, but for most DTM use cases, it does not matter very much which you pick.
Are the model outputs identical?
Not exactly. They are however very, very close to being identical (98%+) - I believe most of the differences come from slightly different handling of the probabilities in the generative process. So far, I've not yet come across a case where a difference in the sixth or seventh digit after the decimal point has any significant meaning. Interpreting the topics your models finds matters much more than one version finding a higher topic loading for some word by 0.00002
The big difference between the two models: dtmmodel is a python wrapper for the original C++ implementation from blei-lab, which means python will run the binaries, while ldaseqmodel is fully written in python.
Why use dtmmodel?
the C++ code is faster than the python implementation
supports the Document Influence Model from Gerrish/Blei 2010 (potentially interesting for your research, see this paper for an implementation.
Why use ldaseqmodel?
easier to install (simple import statement vs downloading binaries)
can use sstats from a pretrained LDA model - useful with LdaMulticore
easier to understand the workings of the code
I mostly use ldaseqmodel but thats for convenience. Native DIM support would be great to have, though.
What should you do?
Try each of them out, say, on a small sample set and see what the models return. 3800 documents isn't a huge corpus (assuming the patents aren't hundreds of pages each), and I assume that after preprocessing (removing stopwords, images and metadata) your dictionary won't be too large either (lots of standard phrases and legalese in patents, I'd assume). Pick the one that works best for you or has the capabilities you need.
Full analysis might take hours anyway, if you let your code run overnight there is little practical difference, after all, do you care if it finishes at 3am or 5am? If runtime is critical, I would assume the dtmmodel will be more useful.
For implementation examples, you might want to take a look at these notebooks: ldaseqmodel and dtmmodel
I have had good experiences with Apple's vDSP primitives under OS X and iOS.
http://developer.apple.com/library/mac/#documentation/Accelerate/Reference/vDSPRef/Reference/reference.html
Now I am trying to port some code that relies on vDSP to Linux and I wonder if any equivalents are available built into one of the standard libraries.
While there is not presently any library that matches vDSP, there are several alternatives you might explore. A couple off the top of my head:
OpenCV is an impressive collection of image processing and computer vision
routines with a vibrant user and research community.
Eigen is a C++11 template library for linear algebra:
matrices, vectors, numerical solvers, and related algorithms.
My personal recommendation would be Eigen.