Debugger

A pure-Python library for parsing and analyzing ELF files and DWARF debugging information

pyelftools is a pure-Python library for parsing and analyzing ELF files and DWARF debugging information. See the User’s guide for more details. Pre-requisites As a user of pyelftools, one only needs Python to run. It works with Python versions 2.7 and 3.x (x >= 5). For hacking on pyelftools the requirements are a bit more strict, please see the hacking guide. Installing pyelftools can be installed from PyPI (Python package index): > pip install pyelftools Alternatively, you can download the […]

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Debugger

IceCream: Never use print() to debug again

IceCream — Never use print() to debug again Do you ever use print() or log() to debug your code? Of course you do. IceCream, or ic for short, makes print debugging a little sweeter. IceCream is well tested, permissively licensed, and supports Python 2, Python 3, PyPy2, and PyPy3. Inspect Variables Have you ever printed variables or expressions to debug your program? If you’ve ever typed something like or the more thorough print(“foo(‘123’)”, foo(‘123’)) then ic() is here to help. […]

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Deep Learning

Caffe: a fast open framework for deep learning

Caffe Caffe is a deep learning framework made with expression, speed, and modularity in mind. It is developed by Berkeley AI Research (BAIR)/The Berkeley Vision and Learning Center (BVLC) and community contributors. Check out the project site for all the details like and step-by-step examples. Custom distributions Community Please join the caffe-users group or gitter chat to ask questions and talk about methods and models. Framework development discussions and thorough bug reports are collected on Issues. Happy brewing! License and […]

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Deep Learning

Keras: Deep Learning for Python

Keras Under Construction In the near future, this repository will be used once again for developing the Keras codebase. For the time being, the Keras codebase is being developed at tensorflow/tensorflow, and any PR or issue should be directed there. GitHub https://github.com/keras-team/keras    

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tensorflow

Tensors and Dynamic neural networks in Python with strong GPU acceleration

PyTorch is a Python package that provides two high-level features: Tensor computation (like NumPy) with strong GPU acceleration Deep neural networks built on a tape-based autograd system You can reuse your favorite Python packages such as NumPy, SciPy, and Cython to extend PyTorch when needed. See also the ci.pytorch.org HUD. More About PyTorch At a granular level, PyTorch is a library that consists of the following components: Component Description torch a Tensor library like NumPy, with strong GPU support torch.autograd […]

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Automation

A radically simple IT automation platform that makes your applications and systems easier

Ansible Ansible is a radically simple IT automation system. It handles configuration management, application deployment, cloud provisioning, ad-hoc task execution, network automation, and multi-node orchestration. Ansible makes complex changes like zero-downtime rolling updates with load balancers easy. More information on the Ansible website. Design Principles Have a dead-simple setup process with a minimal learning curve. Manage machines very quickly and in parallel. Avoid custom-agents and additional open ports, be agentless by leveraging the existing SSH daemon. Describe infrastructure in a […]

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Pythonic

Simple, Pythonic remote execution and deployment

Welcome to Fabric! Fabric is a high level Python (2.7, 3.4+) library designed to execute shell commands remotely over SSH, yielding useful Python objects in return. It builds on top of Invoke (subprocess command execution and command-line features) and Paramiko (SSH protocol implementation), extending their APIs to complement one another and provide additional functionality. For a high level introduction, including example code, please see our main project website; or for detailed API docs, see the versioned API website. GitHub https://github.com/fabric/fabric […]

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Python

The best Docker base image for your Python application (August 2021)

When you’re building a Docker image for your Python application, you’re building on top of an existing image—and there are many possible choices. There are OS images like Ubuntu, and there are the many different variants of the python base image. Which one should you use? Which one is better? There are many choices, and it may not be obvious which is the best for your situation. So to help you make a choice that fits your needs, in this […]

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tensorflow

TensorFlow implementation of Wav2Vec2

This repository presents an implementation of the Wav2Vec2 model [1] in TensorFlow 2.0 as a part of Google Summer of Code. For a quick demo, please check out this. Final report of the project can be found here. Notebooks The repository comes with shiny Colab Notebooks. Below you can find a list of them. Spin them up and don’t forget to have fun! Checkpoints Below is a summary of checkpoints obtained during the project: To know more about the process […]

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processing

A Python package for easy multiprocessing

MPIRE, short for MultiProcessing Is Really Easy, is a Python package for multiprocessing, but faster and more user-friendly than the default multiprocessing package. It combines the convenient map like functions of multiprocessing.Pool with the benefits of using copy-on-write shared objects of multiprocessing.Process, together with easy-to-use worker state, worker insights, and progress bar functionality. Full documentation is available at https://slimmer-ai.github.io/mpire/. Features Faster execution than other multiprocessing libraries. See benchmarks. Intuitive, Pythonic syntax Multiprocessing with map/map_unordered/imap/imap_unordered functions Easy use of copy-on-write shared […]

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