joegiralt
With a wide range of libraries focused on the machine learning market, such as TensorFlow, NumPy, Pandas, Keras, and others, Python has made a name for itself as one of the main programming languages. In February 2021, José Valim and Sean Moriarity published the first version of the Numerical Elixir (Nx) library, a library for tensor operations written in Elixir. Nx aims to allow the language be a good choice for GPU-intensive operations. This work aims to compare the results of Python and Elixir on training convolutional neural networks (CNN) using MNIST and CIFAR-10 datasets, concluding that Python achieved overall better results, and that Elixir is already a viable alternative.
Why would Python achieve “overall better results?” What does that mean? Is the elixir code they used even idiomatic or current?
Where Next?
Trending in Discussions
Other Trending Topics
Latest on Elixir Forum
Sponsor Spotlight
Deploy Phoenix to your own VPS with PaaS ease: push-to-deploy, zero downtime, auto SSL, managed Postgres. 14-day free trial at potions.io
Categories:
Sub Categories:
Forums
Popular Tags
- #ecto
- #liveview
- #troubleshooting
- #learning-elixir
- #library
- #deployment
- #erlang
- #testing
- #genserver
- #mix
- #absinthe
- #remote-other
- #otp
- #plug
- #how-to-question
- #macros
- #postgres
- #elixirconf
- #channels
- #exunit
- #discussion
- #code-sync
- #podcasts
- #javascript
- #onsite
- #dialyzer
- #docker
- #authentication
- #ai
- #umbrella
- #full-time-contract
- #podcasts-by-brainlid
- #ecto-query
- #blog-post
- #elixirconf-us
- #elixir-ls
- #phoenix_html
- #iex
- #graphql
- #genstage
- #websockets
- #supervisor
- #advent-of-code
- #distillery
- #processes
- #elixirconf-eu
- #api
- #forms
- #security
- #metaprogramming
Our Sponsors
Supporting innovation across the BEAM ecosystem.
We build reliable cloud platforms for business-critical systems.
Catch errors, track performance, monitor hosts and more.
Error tracking, logs, uptime, and Just Enough APM. Built for developers.
Elixir, Erlang & BEAM conferences from Code Sync - join us in 2026!
Practical resources that improve the lives of professional developers.
Master modern tools, write cleaner code, and tackle real-world challenges.
Bringing learning, work and compliance together since 1987.
The team behind Membrane, Popcorn, LiveDebugger. Available for hire.
Courses that'll move you from confusion to "Aha, now I get it!"
Showing Posts 1 to 8- Show Best Posts
- Show All (oldest first)
- Show All (newest first)
ChrisYammine
I’m skimming the paper and their citations for the model code used are from the example directory in axon
Their python models are cited to be from here
[10] Available at: GitHub - sallaumen/python_neural_network_labs: Python implementation of MNIST and CIFER10 neural networks · GitHub
I encourage you to read the paper to answer the question of why and what
it’s very short
joegiralt
I read the paper, they said that the python app had better CPU assuage but the elixir app had better memory usage. in the paper summary it states that the python app was better over all.
josevalim
It is also worth noticing that the paper uses Nx v0.2 and a lot has changed since then given it is relatively new technology. In particular, the new Axon version has many improvements on training, so I would be eager to see more recent results and see if those improvements are proven on paper!
ChrisYammine
Ok apologies for assuming you didn’t read anything beyond the summary, but the question of what code was used was answered in the citations
ChrisYammine
I didn’t notice that! Agreed I’d be curious to see the updated results
ityonemo
Looking at the graphs it looks like the time difference can be accounted for by 1) late startup of elixir GPU usage and 2) mysterious gaps in the GPU usage. A SWAG (“scientific wild-ass guess”) here guess that the late startup likely scales based on training set size and not training epochs, but those gaps scale in number based on epoch count. So for a more useful machine learning training problem, it’s likely to scale to somewhere between 15-25% slower (the mnist and cifar very nearly represents an upper bound on the pessimization).
Maybe I missed something but it doesn’t appear that the paper tries to explain what is happening in those gaps… My gut feeling guess that there’s some GPU data shuffling back and forth with the cpu that is blocking progress and probably could be run concurrently towards the end of the first chunk. Don’t know if the python libs proactively figure that out and schedule those data transfers in advance concurrently, would be interesting to find out.
josevalim
We fixed those in the next Axon. We did have some code that would take longer the more epochs you had but it is all fixed now.
We would also recompile the network between epochs, but also fixed.
ityonemo
Beautiful!