Metadata-Version: 2.1
Name: rikai
Version: 0.0.7
Summary: UNKNOWN
Home-page: https://github.com/eto-ai/rikai
Author: Rikai authors
Author-email: rikai-dev@eto.ai
License: Apache License, Version 2.0
Description: ![Apache License](https://img.shields.io/github/license/eto-ai/rikai?style=for-the-badge)
        [![Read The Doc](https://img.shields.io/readthedocs/rikai?style=for-the-badge)](https://rikai.readthedocs.io/)
        [![javadoc](https://javadoc.io/badge2/ai.eto/rikai_2.12/javadoc.svg?style=for-the-badge)](https://javadoc.io/doc/ai.eto/rikai_2.12)
        ![Pypi version](https://img.shields.io/pypi/v/rikai?style=for-the-badge)
        ![Github Action](https://img.shields.io/github/workflow/status/eto-ai/rikai/Python?style=for-the-badge)
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        Join the community:
        [![Join the chat at https://gitter.im/rikaidev/community](https://img.shields.io/badge/chat-on%20gitter-green?style=for-the-badge)](https://gitter.im/rikaidev/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
        
        > :heavy_exclamation_mark: This repository is still experimental. No API-compatibility is guaranteed.
        
        # Rikai
        
        Rikai is a [`parquet`](https://parquet.apache.org/) based ML data format built for working with
        unstructured data at scale. Processing large amounts of data for ML is never trivial, but is 
        especially true for images and videos often at the core of deep learning applications. We are
        building Rikai with two main goals:
        1. Enable ML engineers/researchers to have a seamless workflow from Feature Engineering (Spark) to 
           Training (PyTorch/Tensorflow), from notebook to production.
        2. Enable advanced analytics capabilities to support much faster active learning, model debugging,
           and monitoring in production pipelines.
        
        Current (v0.0.5) main features:
        1. Native support in Jupyter, Spark and PyTorch for images, videos and annotations: reduce ad-hoc 
           type conversions and boilerplate when moving between ETL and training.
        2. Custom functionality for working with images and videos at scale: high-level APIs for 
           processing, filtering, sampling, and more.
        3. Run ML-models via SQL. Forget Smart Homes, build a Smart Data Warehouse.
        
        Roadmap:
        1. TensorFlow integration
        2. Versioning support built into the dataset
        3. Even richer video capabilities (ffmpeg-python integration)
        4. Declarative annotation API (think vega-lite for annotating images/videos)
        
        ## Example
        
        ```python
        from pyspark.sql import Row
        from pyspark.ml.linalg import DenseMetrix
        from rikai.types import Image, Box2d
        from rikai.numpy import wrap
        import numpy as np
        
        df = spark.createDataFrame(
            [
                {
                    "id": 1,
                    "mat": DenseMatrix(2, 2, range(4)),
                    "image": Image("s3://foo/bar/1.png"),
                    "annotations": [
                        Row(
                            label="cat",
                            mask=wrap(np.random.rand(256, 256)),
                            bbox=Box2d(xmin=1.0, ymin=2.0, xmax=3.0, ymax=4.0),
                        )
                    ],
                }
            ]
        )
        
        df.write.format("rikai").save("s3://path/to/features")
        ```
        
        Train dataset in `Pytorch`
        
        ```python
        from rikai.torch.vision import Dataset
        from rikai.torch import DataLoader # Do not need this with Pytorch 1.8+
        from torchvision import transforms as T
        
        transform = T.Compose([
           T.Resize(640),
           T.ToTensor(),
           T.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225))
        ])
        
        dataset = Dataset(
           "s3://path/to/features",
           columns=["image"],
           transform=transform
        )
        loader = DataLoader(
            dataset,
            batch_size=32,
            shuffle=True,
            num_workers=8,
        )
        for batch in data_loader:
            predicts = model(batch.to(cuda))
        ```
        
        Using a ML model in Spark SQL (**experiemental**)
        
        ```sql
        CREATE MODEL yolo5
        OPTIONS (min_confidence=0.3, device="gpu", batch_size=32)
        USING "s3://bucket/to/yolo5_spec.yaml";
        
        SELECT id, ML_PREDICT(yolo5, image) FROM my_dataset
        WHERE split = "train" LIMIT 100;
        ```
        
        Rikai can use Mlflow as its model registry. This allows you to automatically pickup the latest
        model version if you're using the mlflow model registry.
        
        ```sql
        CREATE MODEL yolo5
        OPTIONS (min_confidence=0.3, device="gpu", batch_size=32)
        USING "mlflow://yolo5_model/";
        
        SELECT id, ML_PREDICT(yolo5, image) FROM my_dataset
        WHERE split = "train" LIMIT 100;
        ```
        
        ## Getting Started
        
        Currently Rikai is maintained for <a name="VersionMatrix"></a>Scala 2.12 and Python 3.7 and 3.8.
        
        There are multiple ways to install Rikai:
        
        1. Try it using the included [Dockerfile](#Docker).
        2. OR install it via pip `pip install rikai`, with
           [extras for gcp, pytorch/tf, and others](#Extras).
        3. OR install it from [source](#Source)
        
        Note: if you want to use Rikai with your own pyspark, please consult rikai documentation for tips.
        
        ### <a name="Docker"></a>Docker
        
        The included Dockerfile creates a standalone demo image with
        Jupyter, Pytorch, Spark, and rikai preinstalled with notebooks for you
        to play with the capabilities of the rikai feature store.
        
        To build and run the docker image from the current directory:
        ```bash
        # Clone the repo
        git clone git@github.com:eto-ai/rikai rikai
        # Build the docker image
        docker build --tag rikai --network host .
        # Run the image
        docker run -p 0.0.0.0:8888:8888/tcp rikai:latest jupyter lab -ip 0.0.0.0 --port 8888
        ```
        
        If successful, the console should then print out a clickable link to JupyterLab. You can also
        open a browser tab and go to `localhost:8888`.
        
        ### <a name="Extras"></a>Install from pypi
        
        Base rikai library can be installed with just `pip install rikai`. Dependencies for supporting
        pytorch (pytorch and torchvision), jupyter (matplotlib and jupyterlab) are all part of
        optional extras. Many open-source datasets also use Youtube videos so we've also added pafy and
        youtube-dl as optional extras as well.
        
        For example, if you want to use pytorch in Jupyter to train models on rikai datasets in s3
        containing Youtube videos you would run:
        
        `pip install rikai[pytorch,jupyter,youtube]`
        
        If you're not sure what you need and don't mind installing some extra dependencies, you can
        simply install everything:
        
        `pip install rikai[all]`
        
        ### <a name="Source"></a>Install from source
        
        To build from source you'll need python as well as Scala with sbt installed:
        
        ```bash
        # Clone the repo
        git clone git@github.com:eto-ai/rikai rikai
        # Build the jar
        sbt publishLocal
        # Install python package
        cd python
        pip install -e . # pip install -e .[all] to install all optional extras (see "Install from pypi")
        ```
        
Platform: UNKNOWN
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3
Classifier: Topic :: Software Development :: Libraries
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: dev
Provides-Extra: pytorch
Provides-Extra: jupyter
Provides-Extra: docs
Provides-Extra: youtube
Provides-Extra: all
