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Moving SciPy to the Meson build system

Let's start with an announcement: SciPy now builds with Meson on Linux, and the full test suite passes!

This is a pretty exciting milestone, and good news for SciPy maintainers and contributors - they can look forward to much faster builds and a more pleasant development experience. So how fast is it? Currently the build takes about 1min 50s (a ~4x improvement) on my 3 year old 12-core Intel CPU (i9-7920X @ 2.90GHz):

Profiling result of a parallel build of SciPy with Meson

Profiling result of a parallel build (12 jobs) of SciPy with Meson. Visualization created with ninjatracing and Perfetto.

As you can see from the tracing results, building a single C++ file (bsr.cxx, which is one of SciPy's sparse matrix formats) takes over 90 seconds. So the 1min 50 sec build time is close to optimal - the only ways to improve it are major surgery on that C++ code, or buying a faster CPU.

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Pyflyby: Improving Efficiency of Jupyter Interactive Sessions

Few things hinder productivity more than interruption. A notification, random realization, or unrelated error can derail one's train of thought when deep in a complex analysis – a frustrating experience.

In the software development context, forgetting to import a statement in an interactive Jupyter session is such an experience. This can be especially frustrating when using typical abbreviations, like np, pd, plt, where the meaning is obvious to the human reader, but not to the computer. The time-to-first-plot, and ability to quickly cleanup one's notebook afterward are critical to an enjoyable and efficient workflow.

In this blogpost we present pyflyby, a project and an extension to IPython and JupyterLab, that, among many things, automatically inserts imports and tidies Python files and notebooks.

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Distributed Training Made Easy with PyTorch-Ignite

PyTorch-Ignite logo

Authors: François Cokelaer, Priyansi, Sylvain Desroziers, Victor Fomin

Writing agnostic distributed code that supports different platforms, hardware configurations (GPUs, TPUs) and communication frameworks is tedious. In this blog, we will discuss how PyTorch-Ignite solves this problem with minimal code change.

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Working with pytest on PyTorch

Prerequisites

To run the code in this post yourself, make sure you have torch, ipytest>0.9, and the plugin to be introduced pytest-pytorch installed.

pip install torch 'ipytest>0.9' pytest-pytorch

Before we start testing, we need to configure ipytest. We use the ipytest.autoconfig() as base and add some pytest CLI flags in order to get a concise output.

In [1]:
import ipytest

ipytest.autoconfig(defopts=False)

default_flags = ("--quiet", "--disable-warnings")

def _configure_ipytest(*additional_flags, collect_only=False):
    addopts = list(default_flags)
    if collect_only:
        addopts.append("--collect-only")
    addopts.extend(additional_flags)
    
    ipytest.config(addopts=addopts)

def enable_pytest_pytorch(collect_only=False):
    _configure_ipytest(collect_only=collect_only)
    
def disable_pytest_pytorch(collect_only=False):
    _configure_ipytest("--disable-pytest-pytorch", collect_only=collect_only)
    
disable_pytest_pytorch()

If you work on PyTorch and like pytest you may have noticed that you cannot run some tests in the test suite using the default pytest double colon syntax {MODULE}::TestFoo::test_bar.

In [2]:
%%run_pytest[clean] {MODULE}::TestFoo::test_bar

from torch.testing._internal.common_utils import TestCase
from torch.testing._internal.common_device_type import instantiate_device_type_tests


class TestFoo(TestCase):
    def test_bar(self, device):
        assert False, "Don't worry, this is supposed to happen!"

    
instantiate_device_type_tests(TestFoo, globals(), only_for=["cpu"])
1 warning in 0.01s
ERROR: not found: /home/user/tmp35zsok9u.py::TestFoo::test_bar
(no name '/home/user/tmp35zsok9u.py::TestFoo::test_bar' in any of [<Module tmp35zsok9u.py>])

If the absence of this very basic pytest feature has ever been the source of frustration for you, you don't need to worry anymore. By installing the pytest-pytorch plugin with

pip install pytest-pytorch

or

conda install -c conda-forge pytest-pytorch

you get the default pytest experience back even if your workflow involves running tests from within your IDE!

Putting out the fire: Where do we start with accessibility in JupyterLab?

Multiple fires in an alternating pattern

JupyterLab Accessibility Journey Part 2

I want to be honest with you, I started asking accessibility questions in JupyterLab spaces while filled with anxiety. Anxiety that I was shouting into the void and no one else would work on accessibility with me. Anxiety that I didn’t have the skills or energy or knowledge to back up what I wanted to do. Anxiety that I was going to do it wrong and make JupyterLab even more inaccessible. Sometimes I still feel that way.

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Rethinking Jupyter Interactive Documentation

Jupyter Notebook first release was 8 years ago – under the IPython Notebook name at the time. Even if notebooks were not invented by Jupyter; they were definitely democratized by it. Being Web powered allowed development of many changes in the Datascience world. Objects now often expose rich representation; from Pandas dataframes with as html tables, to more recent Scikit-learn model.

Today I want to look into a topic that has not evolved much since, and I believe could use an upgrade. Accessing interactive Documentation when in a Jupyter session, and what it could become. At the end I'll link to my current prototype if you are adventurous.

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Spot the differences: what is new in Spyder 5?

Spyder 5 versus Spyder 4

In case you missed it, Spyder 5 was released at the beginning of April! This blog post is a conversation attempting to document the long and complex process of improving Spyder's UI with this release. Portions lead by Juanita Gomez are marked as Juanita, and those lead by Isabela Presedo-Floyd are marked as Isabela.

What did we do?

[Juanita] Spyder was created more than 10 years ago and it has had the contributions of a great number of developers who have written code, proposed ideas, opened issues and tested PRs in order to build a piece of Spyder on their own. We (the Spyder team) have been lucky to have such a great community of people contributing throughout the years, but this is the first time that we decided to ask for help from an UX/UI expert! Why? You might wonder. Having the contributions of this great amount of people has resulted in inconsistencies around Spyder’s interface which we didn’t stop to analyze until now.

When Isabela joined Quansight, we realized that we had an opportunity of improving Spyder’s interface with her help. We thought her skill set was everything we needed to make Spyder’s UI better. So we started by reviewing the results of a community survey from a few months ago and realized that some of the most common feedback from users is related to its interface (very crowded, not consistent, many colors). This is why we decided to start a joint project with Isabela, (who we consider now part of the Spyder team) called Spyder 5!!!

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A step towards educating with Spyder

As a community manager in the Spyder team, I have been looking for ways of involving more users in the community and making Spyder useful for a larger number of people. With this, a new idea came: Education.

For the past months, we have been wondering with the team whether Spyder could also serve as a teaching-learning platform, especially in this era where remote instruction has become necessary. We submitted a proposal to the Essential Open Source Software for Science (EOSS) program of the Chan Zuckerberg Initiative, during its third cycle, with the idea of providing a simple way inside Spyder to create and share interactive tutorials on topics relevant to scientific research. Unfortunately, we didn’t get this funding, but we didn’t let this great idea die.

We submitted a second proposal to the Python Software Foundation from which we were awarded $4000. For me, this is the perfect opportunity for us to take the first step towards using Spyder for education.

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PyTorch TensorIterator Internals - 2021 Update

For contributors to the PyTorch codebase, one of the most commonly encountered C++ classes is TensorIterator. TensorIterator offers a standardized way to iterate over elements of a tensor, automatically parallelizing operations, while abstracting device and data type details.

In April 2020, Sameer Deshmukh wrote a blog article discussing PyTorch TensorIterator Internals. Recently, however, the interface has changed significantly. This post describes how to use the current interface as of April 2021. Much of the information from the previous article is directly copied here, but with updated API calls and some extra details.

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