danieldeutsch / repro Goto Github PK
View Code? Open in Web Editor NEWRepro is a library for easily running code from published papers via Docker.
License: Apache License 2.0
Repro is a library for easily running code from published papers via Docker.
License: Apache License 2.0
Is it possible to run QAEval on cpu?
As far as I see the device param (int) is used to select which gpu to run on, but does not allow an option for cpu.
Trying to run the demo. When I run
lewis2020 = BART(model="bart.large.cnn")
summary2 = lewis2020.predict(document)
I Get
Not Found ("no such image: danieldeutsch/lewis2020:1.1: No such image: danieldeutsch/lewis2020:1.1")
and for dou
404 Client Error for http+docker://localhost/v1.41/images/danieldeutsch/dou2021:1.0/json: Not Found ("no such image: danieldeutsch/dou2021:1.0: No such image: danieldeutsch/dou2021:1.0")
But this model works
liu2019 = BertSumExtAbs(device=0)
BaryScore: https://arxiv.org/abs/2108.12463
DepthScore: https://arxiv.org/abs/2103.12711
InfoLM: https://arxiv.org/abs/2112.01589
Code repository: https://github.com/PierreColombo/nlg_eval_via_simi_measures/
Hi,
I am using qaeval. I observed a bunch of closed danieldeutsch/deutsch2021:1.0 containers on my system. Are they needed for reuse purposes or could that be avoided?
kind regards
Hi,
thanks for sharing qaeval and your work on making metrics accessible! As I think making things accessible is very important to enhance further research and community dynamics I want to share my impression on setting up qaeval metric in my pipeline.
It seems the information is scattered around different places and it might be useful to be aligned and condensed.
Hi,
has anybody else experienced issues running qaeval on recent gpus?
I am using a NVIDIA GeForce RTX 3080 Ti. As far as I see, it requires cuda >= 11 and the project requires pytorch 1.7 which is only provided with cuda up to 10.2. If I try to run the project I get an error:
RuntimeError: CUDA error: no kernel image is available for execution on the device
Did i miss something? has anybody managed to circumvent such an issue?
If anybody needs further information I can provide a minimal running code example. I also experienced the same issue for another old metric, I described it in more details on stackoverflow.
Pulling image danieldeutsch/lewis2020:1.0
runs into an error:
docker: failed to register layer: Error processing tar file(exit status 1): lchown /app/bart.large.cnn: invalid argument.
When the pre-trained BART models are untarred during building the Docker image, there was a warning about being unable to change the ownership of the files. I don't remember how that issue was resolved, but I think it's related to this current error.
pytest
is currently pinned to exactly 6.2.4, which makes it a little tough to use packages (like qaeval
) that depend on sacrerouge
and therefore repro
in different environments. Could this versioning be relaxed to make it easier to install repro
in different environments? e.g. pytest>=6.2.4
. Another option would be to move it to the test_requirements
of the setup file (or move it under some extra_requirements
named dev
or test
or w/e), assuming it's not needed for actually running any metrics.
A declarative, efficient, and flexible JavaScript library for building user interfaces.
๐ Vue.js is a progressive, incrementally-adoptable JavaScript framework for building UI on the web.
TypeScript is a superset of JavaScript that compiles to clean JavaScript output.
An Open Source Machine Learning Framework for Everyone
The Web framework for perfectionists with deadlines.
A PHP framework for web artisans
Bring data to life with SVG, Canvas and HTML. ๐๐๐
JavaScript (JS) is a lightweight interpreted programming language with first-class functions.
Some thing interesting about web. New door for the world.
A server is a program made to process requests and deliver data to clients.
Machine learning is a way of modeling and interpreting data that allows a piece of software to respond intelligently.
Some thing interesting about visualization, use data art
Some thing interesting about game, make everyone happy.
We are working to build community through open source technology. NB: members must have two-factor auth.
Open source projects and samples from Microsoft.
Google โค๏ธ Open Source for everyone.
Alibaba Open Source for everyone
Data-Driven Documents codes.
China tencent open source team.