معرفی شرکت ها
finetuner-client-0.2.2
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مشاهده بیشتر
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مشاهده بیشتر
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشتر
تبلیغات ما
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مشاهده بیشترتوضیحات
Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | finetuner-client-0.2.2 |
| نام | finetuner-client |
| نسخه کتابخانه | 0.2.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Jina AI |
| ایمیل نویسنده | hello@jina.ai |
| آدرس صفحه اصلی | https://github.com/jina-ai/finetuner/ |
| آدرس اینترنتی | https://pypi.org/project/finetuner-client/ |
| مجوز | Apache 2.0 |
<p align="center">
<img src="https://github.com/jina-ai/finetuner/blob/main/docs/_static/finetuner-logo-ani.svg?raw=true" alt="Finetuner logo: Finetuner helps you to create experiments in order to improve embeddings on search tasks. It accompanies you to deliver the last mile of performance-tuning for neural search applications." width="150px">
</p>
<p align="center">
<b>Fine-tuning embeddings on domain specific data for better performance on neural search tasks.</b>
</p>
<p align=center>
<a href="https://pypi.org/project/finetuner/"><img src="https://img.shields.io/badge/Python-3.9%2B-blue alt="Python 3.9" title="Finetuner supports Python 3.9 and above"></a>
<a href="https://slack.jina.ai"><img src="https://img.shields.io/badge/Slack-2.2k%2B-blueviolet?logo=slack&logoColor=white"></a>
</p>
<!-- start elevator-pitch -->
Fine-tuning deep neural networks (DNNs) significantly improves performance on domain specific neural search tasks.
However, fine-tuning for neural search is not trivial, as it requires a combination of expertise in ML and Information Retrieval.
Finetuner makes fine-tuning simple and fast by handling all related complexity and infrastructure in the cloud. With Finetuner, you can easily make models more performant and production ready.
📈**Performance boost**: Finetuner significantly increases the performance of pretrained models on domain specific neural search applications.
🔱 **Simple yet powerful**: Interacting with Finetuner is simple and seamless, and also supports rich features such as selections of different loss functions, e.g.
siamese/triplet loss, metric learning, layer pruning, weights freezing, dimensionality reduction, and much more.
☁ **Fine-tune in the cloud**: Finetuner runs your fine-tuning jobs in the cloud. You never have to worry about provisioning (cloud) resources! Finetuner handles all related complexity and infrastructure.
<!-- end elevator-pitch -->
## What is the purpose of Finetuner?
Finetuner enables performance gains on domain specific neural search tasks by fine-tuning models in the cloud. We have conducted experiments on various neural search tasks in different domains to illustrate these performance improvements.
Finetuner also aims to make fine-tuning simple and fast. When interacting with Finetuner, the API takes care of all your fine-tuning jobs in the cloud. This only requires a few lines of code from you, as demonstrated in [below](#fine-tuning-resnet50-on-totally-looks-like-dataset).
## How does it work?
<img src="https://github.com/jina-ai/finetuner/blob/docs-update-readme/docs/_static/finetuner-client-journey.svg?raw=true" title="Finetuner Client user journey.">
## Install
Requires Python 3.7+ installed on Linux/MacOS.
```bash
pip install -U finetuner-client
```
## Fine-tuning ResNet50 on Totally Looks Like dataset
```python
import finetuner
from finetuner.callback import EvaluationCallback
finetuner.login()
finetuner.create_experiment(name='tll-experiment')
run = finetuner.fit(
model='resnet50',
train_data='resnet-tll-train-data',
callbacks=[EvaluationCallback(query_data='resnet-tll-eval-data')],
)
print(run.status())
print(run.logs())
run.save_model('resnet-tll')
```
This minimal example code starts a fine-tuning run with only the necessary arguments. It has the following steps:
* Login to Finetuner: This is necessary if you'd like to run fine-tuning jobs with Finetuner in the cloud.
* Create experiment: This experiment will contain various runs with different configurations.
* Start fine-tuning run: Select backbone model, training and evaluation data for your evaluation callback.
* Monitor: Check the status and logs of the progress on your fine-tuning run.
* Save model: If your fine-tuning run has successfully completed, save it for further use and integration.
<!-- start support-pitch -->
## Support
- Take a look at the [step by step](https://ft-docs-polish--jina-docs.netlify.app/2_step_by_step/) documentation for an overview of how Finetuner works.
- Get started with our example use-cases in the [Finetuner in action](https://ft-docs-polish--jina-docs.netlify.app/3_finetuner_in_action/) section.
- Use [Discussions](https://github.com/jina-ai/finetuner/discussions) to talk about your use cases, questions, and
support queries.
- Join our [Slack community](https://slack.jina.ai) and chat with other Jina AI community members about ideas.
- Join our [Engineering All Hands](https://youtube.com/playlist?list=PL3UBBWOUVhFYRUa_gpYYKBqEAkO4sxmne) meet-up to discuss your use case and learn Jina AI new features.
- **When?** The second Tuesday of every month
- **Where?**
Zoom ([see our public events calendar](https://calendar.google.com/calendar/embed?src=c_1t5ogfp2d45v8fit981j08mcm4%40group.calendar.google.com&ctz=Europe%2FBerlin)/[.ical](https://calendar.google.com/calendar/ical/c_1t5ogfp2d45v8fit981j08mcm4%40group.calendar.google.com/public/basic.ics))
and [live stream on YouTube](https://youtube.com/c/jina-ai)
- Subscribe to the latest video tutorials on our [YouTube channel](https://youtube.com/c/jina-ai)
## Join Us
Finetuner is backed by [Jina AI](https://jina.ai) and licensed under [Apache-2.0](./LICENSE). [We are actively hiring](https://jobs.jina.ai) AI engineers, solution engineers to build the next neural search ecosystem in opensource.
<!-- end support-pitch -->
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.7.0 | Python |
نحوه نصب
نصب پکیج whl finetuner-client-0.2.2:
pip install finetuner-client-0.2.2.whl
نصب پکیج tar.gz finetuner-client-0.2.2:
pip install finetuner-client-0.2.2.tar.gz