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configue-cli-0.2.0
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مشاهده بیشترتوضیحات
A configue extension that adds the ability to dynamically configure your application via the command line.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | configue-cli-0.2.0 |
| نام | configue-cli |
| نسخه کتابخانه | 0.2.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | ['Illuin Technology <contact@illuin.tech>'] |
| نویسنده | - |
| ایمیل نویسنده | Illuin Technology <contact@illuin.tech> |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/configue-cli/ |
| مجوز | - |
# Configue CLI
A [configue](https://github.com/illuin-tech/configue) extension that adds the ability to dynamically configure your application via the command line.
Configue CLI overlaps in functionality with [Hydra](https://hydra.cc/) but without all the unnecessary boilerplate and with the benefit of being compatible with `configue`.
#### Table of contents
- [Installation](#installation)
- [Quick start](#quick-start)
- [Inspection of the configuration state](#inspection-of-the-configuration-state)
- [Configuration from the command line](#configuration-from-the-command-line)
- [Configuration with YAML files](#configuration-with-yaml-files)
- [Exporting the final configuration](#exporting-the-final-configuration)
- [Unstructured configuration](#unstructured-configuration)
- [Configuring the logging](#configuring-the-logging)
- [Integration with Skypilot](#integration-with-skypilot)
## Installation
To install the library, use
```shell
pip install configue-cli
```
To develop locally, clone the repository and use
```shell
pip install -r requirements-dev.txt
```
## Quick start
With `configue-cli`, configurations are defined with structured and arbitrarily nested Python objects (both native dataclasses and `attr` dataclasses are supported and can be nested).
```python
import dataclasses
import attrs
@dataclasses.dataclass
class DatasetConfig:
name: str
n_samples: int = 10_000
@dataclasses.dataclass
class OptimizerConfig:
learning_rate: float = 0.001
weight_decay: float = 1e-2
@attrs.define
class ModelConfig:
name: str
batch_size: int = 12
optimizer: OptimizerConfig = attrs.Factory(
lambda self: OptimizerConfig(learning_rate=0.001 * self.batch_size), takes_self=True
)
@dataclasses.dataclass
class ExperimentConfig:
model: ModelConfig
dataset: DatasetConfig
```
These objects are injected at configuration time in your application entrypoint by the `inject_from_cli` decorator. To use `configue-cli`, simply wrap a [click](https://github.com/pallets/click) entrypoint with the `configue_cli.click.inject_from_cli` decorator and provide a target type to be injected.
```python
import click
from configue_cli.click import inject_from_cli
@click.command()
@inject_from_cli(ExperimentConfig)
def main(config: ExperimentConfig) -> None:
print("Passed configuration: ", config)
if __name__ == "__main__":
main()
```
To display a help message, use the following:
```shell
python main.py --help
```
## Inspection of the configuration state
To visually inspect your application configuration state, use the following command:
```shell
$ python main.py --dry-run
╭─ Configuration helper ────────────────────────────────╮
│ │
│ model │
│ ├── (): __main__.ModelConfig │
│ ├── name: Missing │
│ ├── batch_size: 12 │
│ └── optimizer │
│ ├── (): __main__.OptimizerConfig │
│ ├── learning_rate: 0.012 │
│ └── weight_decay: 0.01 │
│ │
│ dataset │
│ ├── (): __main__.DatasetConfig │
│ ├── name: Missing │
│ └── n_samples: 10000 │
│ │
╰───────────────────────────────────────────────────────╯
```
This is useful to quickly identify which parameters are not yet defined (those marked with a `Missing`) and which values are used in the other parameters without inspecting the code.
## Configuration from the command line
Parameters can be specified from the command line using dotted notation.
```shell
$ python main.py model.name=camembert-base dataset.name=fquad model.batch_size=48
╭─ Configuration ───────────────────────────────────────────────────────────────────────────╮
│ │
│ model │
│ ├── (): __main__.ModelConfig │
│ ├── name: camembert-base │
│ ├── batch_size: 48 │
│ └── optimizer │
│ ├── (): __main__.OptimizerConfig │
│ ├── learning_rate: 0.048 │
│ └── weight_decay: 0.01 │
│ │
│ dataset │
│ ├── (): __main__.DatasetConfig │
│ ├── name: fquad │
│ └── n_samples: 10000 │
│ │
╰───────────────────────────────────────────────────────────────────────────────────────────╯
Passed configuration: ExperimentConfig(model=ModelConfig(name='camembert-base', batch_size=48, optimizer=OptimizerConfig(learning_rate=0.048, weight_decay=0.01)), dataset=DatasetConfig(name='fquad', n_samples=10000))
```
Any missing required parameter at configuration time will result in an exception:
```shell
$ python main.py model.batch_size=3
Traceback (most recent call last):
...
configue_cli.core.exceptions.MissingMandatoryValueError: Missing mandatory value: dataset.name
```
## Configuration with YAML files
Any parameter can be overridden using a `configue` compliant YAML file. Suppose the model is configured in the following `model.yml` file:
```yaml
model:
(): __main__.ModelConfig
name: camembert-large
batch_size: 72
optimizer:
(): __main__.OptimizerConfig
learning_rate: 0.01
weight_decay: 0.0
```
This configuration file can be loaded from the CLI using the `-c` flag:
```shell
$ python main.py -c model.yml --dry-run
╭─ Configuration helper ────────────────────────────────────╮
│ │
│ model │
│ ├── (): __main__.ModelConfig │
│ ├── name: camembert-large │
│ ├── batch_size: 72 │
│ └── optimizer │
│ ├── (): __main__.OptimizerConfig │
│ ├── learning_rate: 0.01 │
│ └── weight_decay: 0.0 │
│ │
│ dataset │
│ ├── (): __main__.DatasetConfig │
│ ├── name: Missing │
│ └── n_samples: 10000 │
│ │
╰───────────────────────────────────────────────────────────╯
```
Multiple configuration files can be used simultaneously, the final configuration is assembled by merging all files in the order they are provided. For instance, let's suppose we have the following `large_batch.yml` file:
```yaml
model:
batch_size: 512
```
This file can be merged into our previous configuration using the following:
```shell
$ python main.py -c model.yml -c large_batch.yml --dry-run
╭─ Configuration helper ────────────────────────────────────╮
│ │
│ model │
│ ├── (): __main__.ModelConfig │
│ ├── name: camembert-large │
│ ├── batch_size: 512 │
│ └── optimizer │
│ ├── (): __main__.OptimizerConfig │
│ ├── learning_rate: 0.01 │
│ └── weight_decay: 0.0 │
│ │
│ dataset │
│ ├── (): __main__.DatasetConfig │
│ ├── name: Missing │
│ └── n_samples: 10000 │
│ │
╰───────────────────────────────────────────────────────────╯
```
Parameters specified with the command line take precedence over the ones specified in YAML files:
```shell
$ python main.py model.batch_size=32 -c model.yml -c large_batch.yml --dry-run
╭─ Configuration helper ────────────────────────────────────╮
│ │
│ model │
│ ├── (): __main__.ModelConfig │
│ ├── name: camembert-large │
│ ├── batch_size: 32 │
│ └── optimizer │
│ ├── (): __main__.OptimizerConfig │
│ ├── learning_rate: 0.01 │
│ └── weight_decay: 0.0 │
│ │
│ dataset │
│ ├── (): __main__.DatasetConfig │
│ ├── name: Missing │
│ └── n_samples: 10000 │
│ │
╰───────────────────────────────────────────────────────────╯
```
This feature encourages a modular configuration pattern where different subparts of the application (the model and the dataset in this example) are configured in separate YAML files and are dynamically assembled at configuration time. Different variations of these subparts can easily be assembled. All arguments can be overridden using the command line without having to edit the config files.
## Exporting the final configuration
To ease reproducibility, the final configuration used for the run can be exported by using the `-o` flag and specifying an output YAML file:
```shell
$ python main.py dataset.name=hello-world -c model.yml -c large_batch.yml -o output.yml
╭─ Configuration ───────────────────────────────────────────╮
│ │
│ model │
│ ├── (): __main__.ModelConfig │
│ ├── name: camembert-large │
│ ├── batch_size: 512 │
│ └── optimizer │
│ ├── (): __main__.OptimizerConfig │
│ ├── learning_rate: 0.01 │
│ └── weight_decay: 0.0 │
│ │
│ dataset │
│ ├── (): __main__.DatasetConfig │
│ ├── name: hello-world │
│ └── n_samples: 10000 │
│ │
╰───────────────────────────────────────────────────────────╯
Passed configuration ExperimentConfig(model=ModelConfig(name='camembert-large', batch_size=512, optimizer=OptimizerConfig(learning_rate=0.01, weight_decay=0.0)), dataset=DatasetConfig(name='hello-world', n_samples=10000))
$ cat output.yml
model:
(): __main__.ModelConfig
name: camembert-large
batch_size: 512
optimizer:
(): __main__.OptimizerConfig
learning_rate: 0.01
weight_decay: 0.0
dataset:
(): __main__.DatasetConfig
name: hello-world
n_samples: 10000
```
## Unstructured configuration
It is possible to use the `inject_from_cli` decorator without specifying a target type:
```python
@click.command()
@inject_from_cli()
def main(config: configue_cli.core.dict_config.DictConfig) -> None:
...
```
In that case, the wrapped entrypoint will be passed a `configue_cli.core.dict_config.DictConfig` object upon injection.
## Configuring the logging
To load a [logging configuration](https://docs.python.org/3/library/logging.config.html) located under the `"logging"` key in your final configuration, use the following:
```python
@click.command()
@inject_from_cli(ExperimentConfig, logging_config_path="logging")
def main(config: ExperimentConfig) -> None:
...
```
## Integration with Skypilot
[SkyPilot](https://github.com/skypilot-org/skypilot) is a framework for easily running jobs on any cloud through a unified interface. Any function decorated with `inject_from_cli` can easily be executed remotely by providing a Skypilot configuration.
The following configuration defines a job to be executed in a SkyPilot cluster named `test-cluster`. The job is defined under the `task` key, we refer to the [SkyPilot YAML specification](https://skypilot.readthedocs.io/en/latest/reference/yaml-spec.html) for more details on this section.
The Python command and all its arguments are captured and interpolated inside the `run` command, respectively in a `{command}` and `{parameters}` placeholder.
```yaml
# skypilot.yml
skypilot:
cluster-name: test-cluster
task:
resources:
cloud: gcp
accelerators: K80:1
workdir: .
setup: |
echo 'Setup the job...'
run: |
set -e
cd ~/sky_workdir
{command} {parameters}
```
To load the SkyPilot configuration in your final configuration, use the following:
```python
@click.command()
@inject_from_cli(ExperimentConfig, skypilot_config_path="skypilot")
def main(config: ExperimentConfig) -> None:
...
```
As with the other arguments, all SkyPilot configuration arguments can be redefined on the fly:
```shell
python main.py -c skypilot.yml skypilot.cluster-name=another-cluster
```
نیازمندی
| مقدار | نام |
|---|---|
| >=22.0.0 | attrs |
| >=8.0.0 | click |
| >=4.0.0 | configue |
| >=5.1.0 | pyyaml |
| >=13.0.0 | rich |
| ==0.2.5 | skypilot |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7 | Python |
نحوه نصب
نصب پکیج whl configue-cli-0.2.0:
pip install configue-cli-0.2.0.whl
نصب پکیج tar.gz configue-cli-0.2.0:
pip install configue-cli-0.2.0.tar.gz