PyTorch 1.X.X and Pipenv and Specific versions of CUDA

Published on February 2, 2020 by James Ravenscroft

I recently ran into an issue where the newest version of Torch (as of writing 1.4.0) requires a newer version of CUDA/Nvidia Drivers than I have installed.

Last time I tried to upgrade my CUDA version it took me several hours/days so I didn’t really want to have to spend lots of time on that.

As it happens PyTorch has an archive of compiled python whl objects for different combinations of Python version (3.5, 3.6, 3.7, 3.8 – heck even 2.X which is no longer officially supported), CUDA Version (9.2, 10.0, 10.1) and Torch version (from 0.1 to 1.4). You can specify which you want to install if you know the right incantation. The full index is available here

How can AI practitioners reduce our carbon footprint?

Published on June 20, 2019 by James Ravenscroft

#AI #climate catastrophe #machine learning #nlp #work

In recent weeks and months the impending global climate catastrophe has been at the forefront of many peoples’ minds. Thanks to movements like Extinction Rebellion and high profile environmentalists like Greta Thunberg and David Attenborough as well as damning reports from the IPCC, it finally feels like momentum is building behind significant reduction of carbon emissions. That said, knowing how we can help on an individual level beyond driving and flying less still feels very overwhelming.

Why I’m excited about Kubernetes + Google Anthos: the Future of Enterprise AI deployment

Published on April 24, 2019 by James Ravenscroft

#devops #docker #google #kubernetes #work

Filament build and deploy enterprise AI applications on behalf of incumbent institutions in finance, biotech, facilities management and other sectors. James Ravenscroft, CTO at Filament, writes about the challenges of enterprise software deployment and the opportunities presented by Kubernetes and Google’s Anthos offering.

It is a big myth that bringing a software package to market starts and ends with developers and testers. One of the most important, complex and time consuming parts of enterprise software projects is around packaging up the code and making it run across lots of different systems: commonly and affectionately termed “DevOps” in many organisations.

Spacy Link or “How not to keep downloading the same files over and over”

Published on January 15, 2019 by James Ravenscroft

#nlp #python #work #phd

If you’re a frequent user of spacy and virtualenv you might well be all too familiar with the following:

python -m spacy download en_core_web_lg
Collecting en_core_web_lg==2.0.0 from https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.0.0/en_core_web_lg-2.0.0.tar.gz#egg=en_core_web_lg==2.0.0
Downloading https://github.com/explosion/spacy-models/releases/download/en_core_web_lg-2.0.0/en_core_web_lg-2.0.0.tar.gz (852.3MB)
5% |█▉ | 49.8MB 11.5MB/s eta 0:01:10

If you’re lucky and you have a decent internet connection then great, if not it’s time to make a cup of tea.

Applied AI in 2019

Published on January 6, 2019 by James Ravenscroft

#AI #Work

Looking back at some of the biggest AI and ML developments from 2018 and how they might influence applied AI in the coming year.

2018 was a pretty exciting year for AI developments. It’s true to say there is still a lot of hype in the space but it feels like people are beginning to really understand where AI can and can’t help them solve practical problems.

🤐🤐Can Bots Keep Secrets? The Future of Chatbot Security and Conversational “Hacks”

Published on December 9, 2018 by James Ravenscroft

#machine-learning #chatbots #nlp #security #Work

As adoption of chatbots and conversational interfaces continues to grow, how will businesses keep their brand safe and their customer’s data safer?

From deliberate infiltration of systems to bugs that cause accidental data leakage, these days, the exposure or loss of personal data is a large part of what occupies almost every self-respecting CIO’s mind. Especially since the EU has just slapped its first defendant with a GDPR fine.

Uploading HUGE files to Gitea

Published on October 20, 2018 by James Ravenscroft

#devops #docker #git #lfs #PhD #Work

I recently stumbled upon and fell in love with Gitea – a lightweight self-hosted Github and Gitlab alternative written in the Go programming language. One of my favourite things about it – other than the speed and efficiency that mean you can even run it on a raspberry pi – is the built in LFS support. For the unfamiliar, LFS is a protocol initially introduced by GitHub that allows users to version control large binary files – something that Git is traditionally pretty poor at.

Don’t forget your life jacket: the ‘dangers’ of diving in deep at the deep end with deep learning

Published on October 18, 2018 by James Ravenscroft

#machine learning #PhD #Work

Deep Learning is a powerful technology but you might want to try some “shallow” approaches before you dive in.

Neural networks are made up of neurones and synapses

GPUs are not just for images any more…

Published on May 13, 2018 by James Ravenscroft

#gpu #machine learning #work

As a machine learning professional specialising in computational linguistics (helping machines to extract meaning from human text), I have confused people on multiple occasions by suggesting that their document processing problem could be solved by neural networks trained using a Graphics Processing Unit (GPU). You’d be well within your rights to be confused. To the uninitiated what I just said was “Let’s solve this problem involving reading lots of text by building a system that runs on specialised computer chips designed specifically to render images at high speed”.

Re-using machine learning models and the “no free lunch” theorem

Published on March 21, 2018 by James Ravenscroft

#machine-learning #ai #work

Why re-use machine learning models?

Model re-use can be a huge cost saver when developing AI systems. But how well will your models perform in their new environment?

Content tagged with "Work"

Deep Learning is a powerful technology but you might want to try some “shallow” approaches before you dive in.

Why re-use machine learning models?