The pypy project is currently adding support for numpy.
My impression is that sklearn library is mainly based on numpy.
Would I be able to use most of this library or there are other requirements that are not supported yet?
Officially, none of it. If you want to do a port, go ahead (and please report results on the mailing list), but PyPy is simply not supported because scikit-learn uses many, many parts of NumPy and SciPy as well as having a lot of C, C++ and Cython extension code.
The official website of sklearn (https://scikit-learn.org/stable/faq.html), see here:
Do you support PyPy?
In case you didn’t know, PyPy is an alternative Python implementation with a built-in just-in-time compiler. Experimental support for PyPy3-v5.10+ has been added, which requires Numpy 1.14.0+, and scipy 1.1.0+.
Also see what pypy has to say (https://www.pypy.org/)
Compatibility: PyPy is highly compatible with existing python code. It supports cffi, cppyy, and can run popular python libraries like twisted, and django. It can also run NumPy, Scikit-learn and more via a c-extension compatibility layer.
Related
I heard from various sources (mostly from the official documents) that Tensorflow Lite (for
ARM) uses these three libraries - Ruy, Eigen, XNNPACK - for its operation.
I understand they somehow accelerate the computation (mainly convolution) in TF Lite, but I'm not exactly sure what purpose each library serves. I know Eigen is a BLAS library, but I'm not sure what others are and how they are related to each other in TF Lite.
Would someone care to explain what different purposes they serve and how they are used in conjunction in TF Lite? (Call Stacks maybe?)
I've been looking around the official documentations of each libraries but I was unable to find much details for Ruy and XNNPACK. Ruy says that it provides efficient matrix multiplication, but isn't that what BLAS libraries do?
Older version of TensorFlow Lite used Eigen and Gemmlowp library to accelerate the computation. However on Arm platforms the performance was worst compared to e.g. Arm Compute Library.
TensorFlow Lite replaced the Eigen and Gemmlowp around version 2.3 and with Ruy matrix multiplication library. They serves similar purpose, but Ruy performs better. The Ruy is default on Arm platform, but you can still compile the TensorFlow Lite without use of Ruy.
XNNPACK outperforms Ruy even more, but it focus solely on operation on float.
Regarding Ruy performance benchmarks check this thread https://github.com/google/ruy/issues/195, and the benchmarks on Pixel4 https://docs.google.com/spreadsheets/d/1CB4gsI7pujNRAf5Iz5vuD783QQqO2zOu8up9IpTKdlU/edit#gid=510573209.
julia> print(tf.VERSION)
1.4.2
I've found methods to upgrade in python but can't find any solution for Julia.
There's no Tensorflow-2.0 official API/package available for Julia as of now. Note that even implementation of Tensorflow for Julia which is TensorFlow.jl was not official one and it was community based with the approach recommended by the TensorFlow maintainers and also it was just Julia wrapper for Tensorflow.
As stated here in official documentation, for Tensorflow-2.0 API Stability is promised for Python only.
If you're looking for Deep Learning frameworks/packages in Julia then use Flux.jl or Knet.jl as they're active and pure Julia based solutions. If you want to write gpu code directly in Julia, there are JuliaGPU packages available, additionally you can refer generic gpu kernels.
If your intent is to use Tensorflow-2.0, then suggest you to use Python package.
I am putting a model into production and I am required to scan all dependencies (Pytorch and Numpy) beforehand via VeraCode Scan.
I noticed that the majority of the flaws are coming from test scripts and caffe2 modules in Pytorch and numpy.
Is there any way to build/install only part of these packages that I use in my application? (e.g. I won't use testing and caffe2 in the application so there's no need to have them in my PyTorch / Numpy source code)
1. PyInstaller
You could package your application using pyinstaller. This tool packages your app with Python and dependencies and use only the parts you need (simplifying, in reality it's hard to trace your package exactly so some other stuff would be bundled as well).
Also you might be in for some quirks and workarounds to make it work with pytorch and numpy as those dependencies are quite heavy (especially pytorch).
2. Use only PyTorch
numpy and pytorch are pretty similar feature-wise (as PyTorch tries to be compatible with it) hence maybe you could only use only of them which would simplify the whole thing further
3. Use C++
Depending on other parts of your app you may write it (at least neural network) in C++ using PyTorch's C++ frontend which is stable since 1.5.0 release.
Going this route would allow you to compile PyTorch's .cpp source code statically (so all dependencies are linked) which allows you for relatively small binary size (30Mb when compared to PyTorch's 1GB+), but requires a lot of work.
I have been working in TensorFlow for about a year now, and I am transitioning from TF 1.x to TF 2.0, and I am looking for some guidance on how to use the tf.keras.backend library in TF 2.0. I understand that the transition to TF 2.0 is supposed to remove a lot of redundancies in modeling and building graphs, since there were many ways to create equivalent layers in earlier TensorFlow versions (and I'm insanely grateful for that change!), but I'm getting stuck on understanding when to use tf.keras.backend, because the operations appear redundant with other TensorFlow libraries.
I see that some of the functions in tf.keras.backend are redundant with other TensorFlow libraries. For instance, tf.keras.backend.abs and tf.math.abs are not aliases (or at least, they're not listed as aliases in the documentation), but both take the absolute value of a tensor. After examining the source code, it looks like tf.keras.backend.abs calls the tf.math.abs function, and so I really do not understand why they are not aliases. Other tf.keras.backend operations don't appear to be duplicated in TensorFlow libraries, but it looks like there are TensorFlow functions that can do equivalent things. For instance, tf.keras.backend.cast_to_floatx can be substituted with tf.dtypes.cast as long as you explicitly specify the dtype. I am wondering two things:
when is it best to use the tf.keras.backend library instead of the equivalent TensorFlow functions?
is there a difference in these functions (and other equivalent tf.keras.backend functions) that I am missing?
Short answer: Prefer tensorflow's native API such as tf.math.* to thetf.keras.backend.* API wherever possible.
Longer answer:
The tf.keras.backend.* API can be mostly viewed as a remnant of the keras.backend.* API. The latter is a design that serves the "exchangeable backend" design of the original (non-TF-specific) keras. This relates to the historical aspect of keras, which supports multiple backend libraries, among which tensorflow used to be just one of them. Back in 2015 and 2016, other backends, such as Theano and MXNet were quite popular too. But going into 2017 and 2018, tensorflow became the dominant backend of keras users. Eventually keras became a part of the tensorflow API (in 2.x and later minor versions of 1.x). In the old multi-backend world, the backend.* API provides a backend-independent abstraction over the myriad of supported backend. But in the tf.keras world, the value of the backend API is much more limited.
The various functions in tf.keras.backend.* can be divided into a few categories:
Thin wrappers around the equivalent or mostly-equivalent tensorflow native API. Examples: tf.keras.backend.less, tf.keras.backend.sin
Slightly thicker wrappers around tensorflow native APIs, with more features included. Examples: tf.keras.backend.batch_normalization, tf.keras.backend.conv2d(https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/keras/backend.py#L4869). They often perform proprocessing and implement other logics, which make your life easier than using native tensorflow API.
Unique functions that don't have equivalent in the native tensorflow API. Examples: tf.keras.backend.rnn, tf.keras.backend.set_learning_phase
For category 1, use native tensorflow APIs. For categories 2 and 3, you may want to use the tf.keras.backend.* API, as long as you can find it in the documentation page: https://www.tensorflow.org/api_docs/python/, because the documented ones have backward compatibility guarantees, so that you don't need to worry about a future version of tensorflow removing it or changing its behavior.
Numpy can be "linked/compiled" against different BLAS implementations (MKL, ACML, ATLAS, GotoBlas, etc). That's not always straightforward to configure but it is possible.
Is it also possible to "link/compile" numpy against NVIDIA's CUBLAS implementation?
I couldn't find any resources in the web and before I spend too much time trying it I wanted to make sure that it possible at all.
In a word: no, you can't do that.
There is a rather good scikit which provides access to CUBLAS from scipy called scikits.cuda which is built on top of PyCUDA. PyCUDA provides a numpy.ndarray like class which seamlessly allows manipulation of numpy arrays in GPU memory with CUDA. So you can use CUBLAS and CUDA with numpy, but you can't just link against CUBLAS and expect it to work.
There is also a commercial library that provides numpy and cublas like functionality and which has a Python interface or bindings, but I will leave it to one of their shills to fill you in on that.
here is another possibility :
http://www.cs.toronto.edu/~tijmen/gnumpy.html
this is basically a gnumpy + cudamat environment which can be used to harness a GPU. also the same code can be run without the gpu using npmat. refer to the link above to download all these files.