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dvclive-2.8.1
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تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشترتوضیحات
Metric logger for ML projects.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | dvclive-2.8.1 |
| نام | dvclive |
| نسخه کتابخانه | 2.8.1 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | ['support@dvc.org'] |
| نویسنده | - |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/iterative/dvclive |
| آدرس اینترنتی | https://pypi.org/project/dvclive/ |
| مجوز | Apache-2.0 |
# DVCLive
[](https://pypi.org/project/dvclive/)
[](https://pypi.org/project/dvclive/)
[](https://pypi.org/project/dvclive)
[](https://opensource.org/licenses/Apache-2.0)
[](https://github.com/iterative/dvclive/actions?workflow=Tests)
[](https://app.codecov.io/gh/iterative/dvclive)
[](https://github.com/pre-commit/pre-commit)
[](https://github.com/psf/black)
DVCLive is a Python library for logging machine learning metrics and other
metadata in simple file formats, which is fully compatible with DVC.
# [Documentation](https://dvc.org/doc/dvclive)
- [Get Started](https://dvc.org/doc/start/experiments)
- [How it Works](https://dvc.org/doc/dvclive/how-it-works)
- [API Reference](https://dvc.org/doc/dvclive/live)
- [Integrations](https://dvc.org/doc/dvclive/ml-frameworks)
______________________________________________________________________
# Quickstart
<p align='center'>
<a href="https://colab.research.google.com/github/iterative/dvclive/blob/main/examples/DVCLive-Quickstart.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" /></a>
</p>
## Install *dvclive*
```console
$ pip install dvclive
```
## Initialize DVC Repository
```console
$ git init
$ dvc init
$ git commit -m "DVC init"
```
## Example code
Copy the snippet below as a basic example of the API usage:
```python
# train.py
import random
import sys
from dvclive import Live
with Live(save_dvc_exp=True) as live:
epochs = int(sys.argv[1])
live.log_param("epochs", epochs)
for epoch in range(epochs):
live.log_metric("train/accuracy", epoch + random.random())
live.log_metric("train/loss", epochs - epoch - random.random())
live.log_metric("val/accuracy",epoch + random.random() )
live.log_metric("val/loss", epochs - epoch - random.random())
live.next_step()
```
See [Integrations](https://dvc.org/doc/dvclive/ml-frameworks) for examples using
DVCLive alongside different ML Frameworks.
## Running
Run couple of times passing different values:
```console
$ python train.py 5
$ python train.py 5
$ python train.py 7
```
## Comparing
DVCLive outputs can be rendered in different ways:
### DVC CLI
You can use [dvc exp show](https://dvc.org/doc/command-reference/exp/show) and
[dvc plots](https://dvc.org/doc/command-reference/plots) to compare and
visualize metrics, parameters and plots across experiments:
```console
$ dvc exp show
```
```
─────────────────────────────────────────────────────────────────────────────────────────────────────────────
Experiment Created train.accuracy train.loss val.accuracy val.loss step epochs
─────────────────────────────────────────────────────────────────────────────────────────────────────────────
workspace - 6.0109 0.23311 6.062 0.24321 6 7
master 08:50 PM - - - - - -
├── 4475845 [aulic-chiv] 08:56 PM 6.0109 0.23311 6.062 0.24321 6 7
├── 7d4cef7 [yarer-tods] 08:56 PM 4.8551 0.82012 4.5555 0.033533 4 5
└── d503f8e [curst-chad] 08:56 PM 4.9768 0.070585 4.0773 0.46639 4 5
─────────────────────────────────────────────────────────────────────────────────────────────────────────────
```
```console
$ dvc plots diff $(dvc exp list --names-only) --open
```
### DVC Extension for VS Code
Inside the
[DVC Extension for VS Code](https://marketplace.visualstudio.com/items?itemName=Iterative.dvc),
you can compare and visualize results using the
[Experiments](https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/experiments-table.md)
and
[Plots](https://github.com/iterative/vscode-dvc/blob/main/extension/resources/walkthrough/plots.md)
views:
While experiments are running, live updates will be displayed in both views.
### DVC Studio
If you push the results to [DVC Studio](https://dvc.org/doc/studio), you can
compare experiments against the entire repo history:
You can enable
[Studio Live Experiments](https://dvc.org/doc/studio/user-guide/projects-and-experiments/live-metrics-and-plots)
to see live updates while experiments are running.
______________________________________________________________________
# Comparison to related technologies
**DVCLive** is an *ML Logger*, similar to:
- [MLFlow](https://mlflow.org/)
- [Weights & Biases](https://wandb.ai/site)
- [Neptune](https://neptune.ai/)
The main difference with those *ML Loggers* is that **DVCLive** does not
**require** any additional services or servers to run.
Logged metrics, parameters, and plots are stored as plain text files that can be
versioned by tools like Git or tracked as pointers to files in DVC storage.
You can then use different [options](#comparing) to visualize the metrics,
parameters, and plots across experiments.
______________________________________________________________________
# Contributing
Contributions are very welcome. To learn more, see the
[Contributor Guide](CONTRIBUTING.rst).
# License
Distributed under the terms of the
[Apache 2.0 license](https://opensource.org/licenses/Apache-2.0), *dvclive* is
free and open source software.
نیازمندی
| مقدار | نام |
|---|---|
| >2.45.1 | dvc |
| <1,>=0.7.0 | dvc-studio-client |
| - | funcy |
| - | ruamel.yaml |
| - | scmrepo |
| - | numpy |
| - | pillow |
| - | mmcv |
| - | tensorflow |
| - | xgboost |
| - | lightgbm |
| - | transformers |
| - | datasets |
| >22 | catalyst |
| - | fastai |
| >=1.9 | pytorch-lightning |
| <2.1 | torch |
| - | optuna |
| - | scikit-learn |
| - | matplotlib |
| >22 | catalyst |
| <8.0,>=7.2.0 | pytest |
| <1.0,>=0.9.6 | pytest-sugar |
| <4.0,>=3.0.0 | pytest-cov |
| <4.0,>=3.8.2 | pytest-mock |
| - | numpy |
| - | pillow |
| - | scikit-learn |
| - | matplotlib |
| - | ipython |
| - | mmcv |
| - | tensorflow |
| - | xgboost |
| - | lightgbm |
| - | transformers |
| - | datasets |
| >22 | catalyst |
| - | fastai |
| >=1.9 | pytorch-lightning |
| <2.1 | torch |
| - | optuna |
| >=1.1.1 | mypy |
| - | fastai |
| - | transformers |
| - | datasets |
| - | numpy |
| - | pillow |
| - | lightgbm |
| - | matplotlib |
| - | mmcv |
| - | optuna |
| - | scikit-learn |
| >=1.9 | pytorch-lightning |
| <2.1 | torch |
| - | scikit-learn |
| <8.0,>=7.2.0 | pytest |
| <1.0,>=0.9.6 | pytest-sugar |
| <4.0,>=3.0.0 | pytest-cov |
| <4.0,>=3.8.2 | pytest-mock |
| - | numpy |
| - | pillow |
| - | scikit-learn |
| - | matplotlib |
| - | ipython |
| - | tensorflow |
| - | xgboost |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl dvclive-2.8.1:
pip install dvclive-2.8.1.whl
نصب پکیج tar.gz dvclive-2.8.1:
pip install dvclive-2.8.1.tar.gz