Author: Deep Learner

Tracking

A Python application for tracking, reporting on timing and complexity in Python code

A command-line application for tracking, reporting on complexity of Python tests and applications. wily [a]: quick to think of things, having a very good understanding of situations and possibilities, and often willing to use tricks to achieve an aim. Wily uses git to go through each revision (commit) in a branch and run complexity and code-analysis metrics over the code. You can use this to limit your code or report on trends for complexity, length etc. Installation Wily can be […]

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Tool

Cohesion: A tool for measuring Python class cohesion

Cohesion is a tool for measuring Python class cohesion. In computer programming, cohesion refers to the degree to which the elements of a module belong together. Thus, cohesion measures the strength of relationship between pieces of functionality within a given module. For example, in highly cohesive systems functionality is strongly related. When cohesion is high, it means that the methods and variables of the class are co-dependent and hang together as a logical whole. Some of the advantages of high […]

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Command-line Tools

Pretty-print tabular data in Python, a library and a command-line utility

Pretty-print tabular data in Python, a library and a command-line utility. The main use cases of the library are: printing small tables without hassle: just one function call, formatting is guided by the data itself authoring tabular data for lightweight plain-text markup: multiple output formats suitable for further editing or transformation readable presentation of mixed textual and numeric data: smart column alignment, configurable number formatting, alignment by a decimal point Installation To install the Python library and the command line […]

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Logging

Structured Logging for Python

structlog makes logging in Python faster, less painful, and more powerful by adding structure to your log entries. It’s up to you whether you want structlog to take care about the output of your log entries or whether you prefer to forward them to an existing logging system like the standard library’s logging module. Once you feel inspired to try it out, check out our friendly Getting Started tutorial that also contains detailed installation instructions! If you prefer videos over […]

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Progress

A progress bar for Python 2 and Python 3 – pip install progressbar2

Travis status: Coverage: Install The package can be installed through pip (this is the recommended method): pip install progressbar2 Or if pip is not available, easy_install should work as well: easy_install progressbar2 Or download the latest release from Pypi (https://pypi.python.org/pypi/progressbar2) or Github. Note that the releases on Pypi are signed with my GPG key (https://pgp.mit.edu/pks/lookup?op=vindex&search=0xE81444E9CE1F695D) and can be checked using GPG: gpg –verify progressbar2-.tar.gz.asc progressbar2-.tar.gz Introduction A text progress bar is typically used to display the progress of a long […]

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Json

Json Formatter for the standard python logger

This library is provided to allow standard python logging to output log data as json objects. With JSON we can make our logs more readable by machines and we can stop writing custom parsers for syslog type records. Hi, I see this package is quiet alive and I am sorry for ignoring it so long. I will be stepping up my maintenance of this package so please allow me a week to get things back in order (and most likely […]

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Natural Language Processing, NLP, Python

The NLP Cypher | 11.21.21

Hey … so have you ever deployed a state-of-the-art production level inference server? Don’t know how to do it? Well… last week, Michael Benesty dropped a bomb when he published one of the first ever detailed blogs on how to not only deploy a production level inference API but benchmarking some of the most widely used frameworks such as FastAPI and Triton servers and runtime engines such as ONNX runtime (ORT) and TensorRT (TRT). Eventually, Michael recreated Hugging Face’s ability […]

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Python

Python Community Interview With Eric Wastl

This week, we’re joined by Eric Wastl, a Senior Architect at TCGPlayer and the founder of Advent of Code (AoC). Join us as we discuss the inception of AoC, how to avoid common pitfalls when solving code challenges in AoC, and what Eric gets up to in the “3.8 seconds” of free time he has per year. Ricky: Thanks for joining me, Eric. You’re perhaps most well known for being the founder of Advent of Code, which we’ll talk about […]

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Tutel: An efficient mixture-of-experts implementation for large DNN model training

Mixture of experts (MoE) is a deep learning model architecture in which computational cost is sublinear to the number of parameters, making scaling easier. Nowadays, MoE is the only approach demonstrated to scale deep learning models to trillion-plus parameters, paving the way for models capable of learning even more information and powering computer vision, speech recognition,  

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