معرفی شرکت ها
defaults-0.3.0
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشتر
تبلیغات ما
مشتریان به طور فزاینده ای آنلاین هستند. تبلیغات می تواند به آنها کمک کند تا کسب و کار شما را پیدا کنند.
مشاهده بیشترتوضیحات
-
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | defaults-0.3.0 |
| نام | defaults |
| نسخه کتابخانه | 0.3.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Michael Petrochuk |
| ایمیل نویسنده | petrochukm@gmail.com |
| آدرس صفحه اصلی | - |
| آدرس اینترنتی | https://pypi.org/project/defaults/ |
| مجوز | MIT |
<p align="center"><img width="544px" src="logo.svg" /></p>
<h3 align="center">Extensible and Fault-Tolerant Hyperparameter Management</h3>
HParams is a thoughtful approach to configuration management for machine learning projects. It
enables you to externalize your hyperparameters into a configuration file. In doing so, you can
reproduce experiments, iterate quickly, and reduce errors.
**Features:**
- Approachable and easy-to-use API
- Battle-tested over three years
- Fast with little to no runtime overhead (< 3e-05 seconds) per configured function
- Robust to most use cases with 100% test coverage and 75 tests
- Lightweight with only one dependency
[](https://codecov.io/gh/PetrochukM/HParams)
[](http://pepy.tech/project/hparams)
[](https://travis-ci.org/PetrochukM/HParams)
[](https://opensource.org/licenses/MIT)
[](https://twitter.com/MPetrochuk)
_Logo by [Chloe Yeo](http://www.yeochloe.com/), Corporate Sponsorship by [WellSaid Labs](https://wellsaidlabs.com/)_
## Installation
Make sure you have Python 3. You can then install `hparams` using `pip`:
```bash
pip install hparams
```
Install the latest code via:
```bash
pip install git+https://github.com/PetrochukM/HParams.git
```
## Oops 🐛
With HParams, you will avoid common but needless hyperparameter mistakes. It will throw a warning
or error if:
- A hyperparameter is overwritten.
- A hyperparameter is declared but not set.
- A hyperparameter is set but not declared.
- A hyperparameter type is incorrect.
Finally, HParams is built with developer experience in mind. HParams includes 13 errors and 6 warnings
to help catch and resolve issues quickly.
## Examples
Add HParams to your project by following one of these common use cases:
### Configure Training 🤗
Configure your training run, like so:
```python
# main.py
from hparams import configurable, add_config, HParams, HParam
from typing import Union
@configurable
def train(batch_size: int = HParam()):
pass
class Model():
@configurable
def __init__(self, hidden_size=HParam(int), dropout=HParam(float)):
pass
add_config({ 'main': {
'train': HParams(batch_size=32),
'Model.__init__': HParams(hidden_size=1024, dropout=0.25),
}})
```
HParams supports optional configuration typechecking to help you find bugs! 🐛
### Set Defaults
Configure PyTorch and Tensorflow defaults to match via:
```python
from torch.nn import BatchNorm1d
from hparams import configurable, add_config, HParams
# NOTE: `momentum=0.01` to match Tensorflow defaults
BatchNorm1d.__init__ = configurable(BatchNorm1d.__init__)
add_config({ 'torch.nn.BatchNorm1d.__init__': HParams(momentum=0.01) })
```
Configure your random seed globally, like so:
```python
# config.py
import random
from hparams import configurable, add_config, HParams
random.seed = configurable(random.seed)
add_config({'random.seed': HParams(a=123)})
```
```python
# main.py
import config
import random
random.seed()
```
### CLI
Experiment with hyperparameters through your command line, for example:
```console
foo@bar:~$ file.py --torch.optim.adam.Adam.__init__ 'HParams(lr=0.1,betas=(0.999,0.99))'
```
```python
import sys
from torch.optim import Adam
from hparams import configurable, add_config, parse_hparam_args
Adam.__init__ = configurable(Adam.__init__)
parsed = parse_hparam_args(sys.argv[1:]) # Parse command line arguments
add_config(parsed)
```
### Hyperparameter optimization
Hyperparameter optimization is easy to-do, check this out:
```python
import itertools
from torch.optim import Adam
from hparams import configurable, add_config, HParams
Adam.__init__ = configurable(Adam.__init__)
def train(): # Train the model and return the loss.
pass
for betas in itertools.product([0.999, 0.99, 0.9], [0.999, 0.99, 0.9]):
add_config({Adam.__init__: HParams(betas=betas)}) # Grid search over the `betas`
train()
```
### Track Hyperparameters
Easily track your hyperparameters using tools like [Comet](comet.ml).
```python
from comet_ml import Experiment
from hparams import get_config
experiment = Experiment()
experiment.log_parameters(get_config())
```
### Multiprocessing: Partial Support
Export a Python `functools.partial` to use in another process, like so:
```python
from hparams import configurable, HParam
@configurable
def func(hparam=HParam()):
pass
partial = func.get_configured_partial()
```
With this approach, you don't have to transfer the global state to the new process. To transfer the
global state, you'll want to use `get_config` and `add_config`.
## Docs 📖
The complete documentation for HParams is available [here](./DOCS.md).
Learn more about related projects to HParams [here](./RELATED.md).
## Contributing
We've released HParams because a lack of hyperparameter management solutions. We hope that
other people can benefit from the project. We are thankful for any contributions from the
community.
### Contributing Guide
Read our [contributing guide](https://github.com/PetrochukM/HParams/blob/master/CONTRIBUTING.md) to
learn about our development process, how to propose bugfixes and improvements, and how to build and
test your changes to HParams.
## Authors
- [Michael Petrochuk](https://github.com/PetrochukM/) — Developer
- [Chloe Yeo](http://www.yeochloe.com/) — Logo Design
## Citing
If you find HParams useful for an academic publication, then please use the following BibTeX to
cite it:
```latex
@misc{hparams,
author = {Petrochuk, Michael},
title = {HParams: Hyperparameter management solution},
year = {2019},
publisher = {GitHub},
journal = {GitHub repository},
howpublished = {\url{https://github.com/PetrochukM/HParams}},
}
```
نیازمندی
| مقدار | نام |
|---|---|
| - | typeguard |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.5 | Python |
نحوه نصب
نصب پکیج whl defaults-0.3.0:
pip install defaults-0.3.0.whl
نصب پکیج tar.gz defaults-0.3.0:
pip install defaults-0.3.0.tar.gz