Skip to main content

How to properly declare optional dependencies both as extras and in dedicated groups in Poetry?

From Poetry documentation about the difference between groups and extras:

Dependency groups, other than the implicit main group, must only contain dependencies you need in your development process. Installing them is only possible by using Poetry. To declare a set of dependencies, which add additional functionality to the project during runtime, use extras instead. Extras can be installed by the end user using pip.

This perfectly makes sense and works fine most of the time. However, during development one usually wants to install all the extras dependencies in order to test all the functionalities of the package. However, extras are not installed by default contrary to groups. Moreover, the Poetry documentation states that:

The dependencies specified for each extra must already be defined as project dependencies.

Dependencies listed in dependency groups cannot be specified as extras.

Thus, because it is not possible to define extras in a Poetry project that are defined in dependency groups and because extras are not installed by default, this left 2 suboptimal options for the developer to get a nice developer experience:

  • Installing the project with poetry install --all-extras. This has the downside that the developer ha to remember to pass this option during development, even when the dev dependency group is installed.
  • Mirroring the extras dependencies in a corresponding dependency group. This has the downside to introduce a lot of boilerplate and possible errors since dependencies are listed multiple times.

For instance, the second case would look like:

[tool.poetry.dependencies]
python = "^3.8,<3.12"
numpy = "^1.22"
scipy = { version = "^1.8", optional = true }

[tool.poetry.group.dev.dependencies]
scipy = "^1.8"

[tool.poetry.extras]
plot = ["scipy"]

In this example there is an extra that requires the SciPy dependency. This dependency is declared as optional in the main dependency group, as recommended by Poetry. However, this dependency should additionally be declared in the dev dependency group in order to organize the development dependencies and have it installed automatically.

Is there a simpler approach to solve this problem that do not requires specifying multiple times the same dependency?



source https://stackoverflow.com/questions/77512072/how-to-properly-declare-optional-dependencies-both-as-extras-and-in-dedicated-gr

Comments

Popular posts from this blog

ValueError: X has 10 features, but LinearRegression is expecting 1 features as input

So, I am trying to predict the model but its throwing error like it has 10 features but it expacts only 1. So I am confused can anyone help me with it? more importantly its not working for me when my friend runs it. It works perfectly fine dose anyone know the reason about it? cv = KFold(n_splits = 10) all_loss = [] for i in range(9): # 1st for loop over polynomial orders poly_order = i X_train = make_polynomial(x, poly_order) loss_at_order = [] # initiate a set to collect loss for CV for train_index, test_index in cv.split(X_train): print('TRAIN:', train_index, 'TEST:', test_index) X_train_cv, X_test_cv = X_train[train_index], X_test[test_index] t_train_cv, t_test_cv = t[train_index], t[test_index] reg.fit(X_train_cv, t_train_cv) loss_at_order.append(np.mean((t_test_cv - reg.predict(X_test_cv))**2)) # collect loss at fold all_loss.append(np.mean(loss_at_order)) # collect loss at order plt.plot(np.log(al...

Sorting large arrays of big numeric stings

I was solving bigSorting() problem from hackerrank: Consider an array of numeric strings where each string is a positive number with anywhere from to digits. Sort the array's elements in non-decreasing, or ascending order of their integer values and return the sorted array. I know it works as follows: def bigSorting(unsorted): return sorted(unsorted, key=int) But I didnt guess this approach earlier. Initially I tried below: def bigSorting(unsorted): int_unsorted = [int(i) for i in unsorted] int_sorted = sorted(int_unsorted) return [str(i) for i in int_sorted] However, for some of the test cases, it was showing time limit exceeded. Why is it so? PS: I dont know exactly what those test cases were as hacker rank does not reveal all test cases. source https://stackoverflow.com/questions/73007397/sorting-large-arrays-of-big-numeric-stings

How to load Javascript with imported modules?

I am trying to import modules from tensorflowjs, and below is my code. test.html <!DOCTYPE html> <html lang="en"> <head> <meta charset="UTF-8"> <title>Document</title </head> <body> <script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs@2.0.0/dist/tf.min.js"></script> <script type="module" src="./test.js"></script> </body> </html> test.js import * as tf from "./node_modules/@tensorflow/tfjs"; import {loadGraphModel} from "./node_modules/@tensorflow/tfjs-converter"; const MODEL_URL = './model.json'; const model = await loadGraphModel(MODEL_URL); const cat = document.getElementById('cat'); model.execute(tf.browser.fromPixels(cat)); Besides, I run the server using python -m http.server in my command prompt(Windows 10), and this is the error prompt in the console log of my browser: Failed to loa...