Comments (13)
@3ripleM @farinamhz
no problem. but also put a quick comment in the requirement.txt. Also, update the environment.yml, if needed. Tnx.
from lady.
Dockerization is done and the readme is updated.
from lady.
Thank you very much @3ripleM and welcome to LADy :)
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@hosseinfani
Hossein, our current method of processing toy datasets consumes a significant amount of time and resources.
I would like to propose a solution to address this issue. We could consider releasing two versions of our model:
- Full Version: This version would require the use of a separate system equipped with a GPU.
- Lite Version
Furthermore, I am of the opinion that we could explore the possibility of setting up a GitHub Actions runner on our servers that have GPUs. This approach could potentially streamline and automate the process. However, further investigation is necessary to determine the feasibility of this solution.
from lady.
- really? it's a toy dataset. it shouldn't take a lot of time. let's discuss it in person.
- we don't have any internal GPU server, nor we should think of having that. We need to provide a unit test on any pcs.
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@3ripleM
Please prepare two guideline as a md file for our library:
1- From producer's perspective to create dockerization
2- From the consumer perspective how to use the docker
from lady.
I also dockerize the setup process so its now easy to reproduce on any machine :) and also we can publish the docker image to a private container registry to make it available for anyone who wants to run the codebase (with all the requirements already installed)
something like this:
docker run fani_lab/lady --volume ./output:/app/output \
python main.py \
-naspects 5 \
-am rnd \
-data ../data/raw/semeval/toy.2016SB5/ABSA16_Restaurants_Train_SB1_v2.xml \
-output ../output/toy.2016SB5/
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@3ripleM
how can we have a container registry? why not a public container registry?
from lady.
@hosseinfani
Yeah, we can have a public one. It was just only a suggestion. but in either case it would be great to have one.
For setting it up I think we can use github registry (packages) for that. we have two options:
- Manually push to the registry (which means that we have to make the image on a local computer)
- Automatic build process (which relies on github action to do image building and pushing for us)
for the second option we can decide to do it automatically for us when we push to the master branch or we can do it manually trigger the pipeline via a button
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@3ripleM
can we go with the second option and automatic build, but also running a quick test on a toy dataset?
if so, would you please handle that?
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@hosseinfani
Yes, I believe we can build on multi stage, means when the build is finished we can test our image by running it on the cloud to test that every thing is working. After that, we can push the image to the registry.
I'll keep you updated
from lady.
Great, thank you very much, @3ripleM!
from lady.
@3ripleM
thank you. pls close the issue then.
from lady.
Related Issues (20)
- batch execution of nllb translation
- Distribution of aspect terms and aspect categories in datasets HOT 6
- Adding HAST as a supervised baseline HOT 10
- Classification baseline for aspect term extraction HOT 18
- pipeline progress flow
- Check the existing readme and codeline HOT 2
- Gif image/video for illustrating the pipeline HOT 2
- a server for the web app
- Setup and Quickstart HOT 5
- Aspect based sentiment analysis + Running Bert and Cat library
- Adding Twitter Reviews Dataset HOT 1
- Needing for update in OCTIS library HOT 2
- Aspect Sentiment Triplet Extraction Baseline HOT 20
- New baseline for Aspect-Based Sentiment Analysis HOT 2
- Literature Review on Aspect and Sentiment Extraction HOT 1
- Adding a new tanslation model to the pipeline HOT 1
- OCTIS.CTM throws a value error during the training phase HOT 3
- Updating stats on quality of translation HOT 4
- Incorporating Underrepresented Languages: A Focus on Low-Resource Languages HOT 1
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