Skip to content

Copyedit README.md #20

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Open
wants to merge 1 commit into
base: main
Choose a base branch
from
Open

Copyedit README.md #20

Changes from all commits
Commits
Show all changes
1 commit
Select commit
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
18 changes: 9 additions & 9 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -16,7 +16,7 @@ Simple full example available: https://github.com/GuillaumeLeclerc/fastargs/blob

### Declare the arguments

As demonstrated here you can declare your parameters in multiple files over your project. To make your code more readable it is recommended to declare the parameters as close as from where they are used.
As demonstrated here you can declare your parameters in multiple files over your project. To make your code more readable it is recommended to declare the parameters as close as possible to their use.

In `train.py`:
```python
Expand Down Expand Up @@ -103,7 +103,7 @@ config.collect_argparse_args(parser)
# It will:
# - Add a CLI argument for each fastargs argument
# - Generate a user friendly --help message
# - Allow the user to pass config files through CLI arguments and collect them aumatically for you
# - Allow the user to pass config files through CLI arguments and collect them automatically for you
# - collect env variables
#
# Priority for duplicated parameters is: env variable, cli argument, config files from last to first
Expand Down Expand Up @@ -143,7 +143,7 @@ config.summary()

### Accessing arguments

#### Option 1: Explicitely
#### Option 1: Explicitly

```python
# One can read individual arguments
Expand All @@ -157,7 +157,7 @@ print(arguments.training.optimizer.learning_rate)

#### Option 2: Through decorators

It is possible to automatically feed arguments to functions without having to explicitely use the API of `fastargs`.
It is possible to automatically feed arguments to functions without having to explicitly use the API of `fastargs`.


```python
Expand All @@ -179,10 +179,10 @@ def train_my_model(model, learning_rate, momentum):
# Note that if one does:
train_my_model(model, learning_rate=10)
# the learning from the config will be ignored
# (but momentum will be since it wasn't explicitely overriden)
# (but momentum will be since it wasn't explicitly overriden)
```

By default the parameter will be passed to the argument with name the last component of the path (eg. `a.b.c` -> `c`). However, it is possible that multiple parameter section share some parameter names. It is therefore possible to explicitely chose the name of the argument:
By default the parameter will be passed to the argument with name the last component of the path (eg. `a.b.c` -> `c`). However, it is possible that multiple parameter sections share some parameter names. It is therefore possible to explicitely chose the name of the argument:

```python
@param('a.b.c', 'param1')
Expand Down Expand Up @@ -233,11 +233,11 @@ loaded_module.testme()
print(cfg['imported_section.blah.p1']) # => 42.5
```

If we need to get a variable/function/class, we can use the import type `ImportedObject`. In the the case of the previous example, the user would have to pass `test_module.with_params.testme`, and the value in the configuration object would be the function itself and not the whole module.
If we need to get a variable/function/class, we can use the import type `ImportedObject`. In the case of the previous example, the user would have to pass `test_module.with_params.testme`, and the value in the configuration object would be the function itself and not the whole module.

#### Conditional sections

It is pretty common to have parameters that only makes sense if another parameter is defined and/or has a specific value. For example, in the context of optimization, stochastic gradient descent only has one parameter `learning_rate`. But if we use `Adam` we have extra parameters. In this situation one can do the following:
It is pretty common to have parameters which only make sense if another parameter is defined and/or has a specific value. For example, in the context of optimization, stochastic gradient descent only has one parameter `learning_rate`. But if we use `Adam` we have extra parameters. In this situation one can do the following:

```python
Section('optim').params(
Expand All @@ -250,7 +250,7 @@ Section('optim.alpha').enable_if(lambda cfg: cfg['optim.algorithm'] == 'Adam').p
'beta': Param(float, default=1.0)
)
```
This way users won't see the option `momentum` until they define `optim.algorithm=Adam` and the momentum will not trigger validation error if not filled if another optimizer is chosen.
This way users won't see the option `momentum` until they define `optim.algorithm=Adam` and failing to fill it will not trigger a validation error if another optimizer is chosen.

## Tests

Expand Down