Deterministic debugging with Delve And the state of Delve

By Derek Parker

Session summary 

A clear and confident speaker.

What’s new in Delve

What is deterministic debugging?

Capturing the exact state of a system when a program/process is run, so you can replay the scenario of the bug exactly and repeatedly so you can debug.

Their project makes use of Mozilla RR.

Look at single threaded / thread scheduling that is imposed.

Pro points: Can record remotely and replay locally.

Boosting Python with Rust

By Raphaël Gomès

Summary of sessions

Intro slide

A basic mercurial command was benchmarked after being rewritten with Rust which showed exciting improvements.

Raphaël was excited to take this forward. However, initially this was not successful. Problems included data friction when passing data between languages. As well as missing features in Rusty Python. Work is going on to implement features in hg with mercurial with the aim to attaining the potential speed improvements that have been demonstrated.

Slide Python Love

Extending sudo in Python- Best of both worlds

By Peter Czanik

My first session at FOSDEM is brought to you by Peter Czanik.  Videos and live streams are available on the FOSDEM website.  Here is the session summary.

Need to restrict or monitor sudo access on your servers? Well sudo will be soon supporting Python based plugins and reporting.

Did you know it already supports recording sessions?

Session slide
Summary slide
Questions slide

Machine Learning as a Service – Anand Chitipothu

This talk is about creating a simple end interface for running your machine learning code.

Anand is the co-founder of https://rorodata.com/ a Platform-as-a-Service,
designed for data scientists, for running machine learning code.

Machine learning libraries that were mentioned that I had not come across before:

PyTorch – https://pytorch.org/ – a deep learning framework for fast, flexible experimentation.

joblib – https://pypi.org/project/joblib/ – is a set of tools to provide lightweight pipelining in Python.

The Rorodata firefly tool creates a RESTful API for your client defined functions.

The config format is YAML and not unlike defining a Bitbucket pipeline.

The end user only needs to a Python function, define API with firefly, deploy and an endpoint on the paas is created.

You can add further system requirements to the runtimes available in rorodata.

It also supports configuring CORs domains.

You can define the size and scale of platform your code will be run against.

You can also use the power of rorodata on your own servers or cloud infrastructure using https://github.com/rorodata/rorolite NB: size/scale config are not available but otherwise can use the same code.