Metadata-Version: 2.1
Name: finetuner-client
Version: 0.2.2
Summary: Finetuner allows one to tune the weights of any deep neural network for better embeddings on search tasks.
Home-page: https://github.com/jina-ai/finetuner/
Author: Jina AI
Author-email: hello@jina.ai
License: Apache 2.0
Download-URL: https://github.com/jina-ai/finetuner/tags
Project-URL: Documentation, https://finetuner.jina.ai
Project-URL: Source, https://github.com/jina-ai/finetuner/
Project-URL: Tracker, https://github.com/jina-ai/finetuner/issues
Description: <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&amp;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 -->
Keywords: jina neural-search neural-network deep-learning pretraining fine-tuning pretrained-models triplet-loss metric-learning siamese-network few-shot-learning
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Education
Classifier: Intended Audience :: Science/Research
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Environment :: Console
Classifier: Operating System :: OS Independent
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
