Comments (4)
I'm thinking pipenv could be suitable solution to this. Although you could specify a version manually, pipenv gives us the ability to easily install and manage packages as well as specifying their minimum version requirements
from mann-eller-kvinne.
I think the virtual environment can be arbitrary to whoever uses the repo, whether it's pipenv or conda. Were you thinking of using pipenv in deployment? I think in addition to specifying the lib versions, we should have a reference python version for development (e.g. the version you currently use).
I'd prefer to use virtual environments for all my projects, but specifying versions should be the only thing necessary.
from mann-eller-kvinne.
You can simply use pip freeze to get all your current versions and go from there
from mann-eller-kvinne.
I think the virtual environment can be arbitrary to whoever uses the repo, whether it's pipenv or conda. Were you thinking of using pipenv in deployment? I think in addition to specifying the lib versions, we should have a reference python version for development (e.g. the version you currently use). I'd prefer to use virtual environments for all my projects, but specifying versions should be the only thing necessary.
I was thinking of using it in deployment as well. I don't know how it impacts performance but it's great in development IMO. I don't need to setup a virtual environment myself nor remember to activate it everytime.
As you mentioned we should have a reference python version, which pipenv takes care of for us.
However I'm open to just use pip freeze if that's what you think is the best option. If we're going to go ahead with it, we need to remove pipenv from the current rewrite branch
from mann-eller-kvinne.
Related Issues (20)
- Auto-publish prebuilt docker images to dockerhub/github
- Add frontend switch to swap between classifiers
- Fix text margins. About page
- Validate classifiers
- Make About page logo image responsive
- Add automatic "awaiting approval" labeling using some bot
- Add automated frontend code style linting HOT 1
- Translate README into english
- API won't run due to Tensorflow error - NotImplementedEror: Cannot convert a symbolic Tensor to a numpy array. HOT 4
- Replace all NLTK with scikit-learn HOT 1
- Make rnn (or other) model to beat naïve bayes baseline HOT 1
- Make config setup for hyperparameter tuning of deep model HOT 2
- Add option for environment variables in docker compose HOT 1
- Move front end/backend into separate repos? HOT 7
- Add Black formatting git hook / github action HOT 1
- Use more semantic variable names and field names
- Fix CORS errors HOT 1
- Implement a logistic regression classifier HOT 2
- consider using git submodules for corpus
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