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# Multivitamin  [](https://travis-ci.org/gumgum/multivitamin) [](https://badge.fury.io/py/multivitamin) [](https://opensource.org/licenses/Apache-2.0) **Multivitamin** is python framework built for serving computer vision (CV), natural language processing (NLP), and machine learning (ML) models. It aims to provide the serving infrastructure around a single service and to allow the flexibility to use any python framework for prediction. ## Main Features * Asynchronous APIs sharing a common interface (`CommAPI`) for pulling requests and pushing responses * An interface (via the `Module` class) for processing images, video, text, or any form of data * A data model for storing the output of the modules ## Getting Started To start an asynchronous service, construct a `Server` object, which accepts 3 input parameters: * An input `CommAPI`, which is an abstract base class that defines the `push()` and `pull()` interface * An output `CommAPI` * A `Module` or sequence of `Module`s, which is an abstract base class that defines the interface for `process(Request)`, `process_properties()` or `process_images(...)` ### Defining input and output `CommAPI`s: ``` from multivitamin.apis import SQSAPI, S3API sqs_api = SQSAPI(queue_name='SQS-ObjectDetector') s3_api = S3API(s3_bucket='od-output', s3_key='2019-03-22') ``` Both `SQSAPI` and `S3API` are concrete implementations of `CommAPI`. ### Defining a `Module`: For convenience, we provide several example modules (which are concrete implementations of `Module`) that you can import for your purposes. Let's say we want a object detector built using [TensorFlow's object detection API](https://github.com/tensorflow/models/tree/master/research/object_detection): ``` from multivitamin.applications.images.detectors import TFDetector obj_det_module = TFDetector(name="IG_obj_det", ver="1.0.0", model="models_dir/") ``` ### Constructing a `Server` Which will pull requests from the AWS SQS queue `queue_name=SQS-ObjectDetector` and push the responses to `s3://aws.amazon.com/od-output/2019-03-22/` ``` from multivitamin.server import Server obj_det_server = Server( modules=[obj_det_module], input_comm=sqs_api, output_comms=[s3_api], ) obj_det_server.start() ``` If we wanted to **send our responses to multiple endpoints**, we could add a second output `CommAPI` like so: ``` from multivitamin.apis import HTTPAPI http_api = HTTPAPI() ``` and modifying the above `Server` we created like: ``` obj_det_server = Server( modules=[obj_det_module], input_comm=sqs_api, output_comms=[ s3_api, http_api, ], ) ``` *note: the `HTTPAPI` assumes that the `Request` has a field called `dst_url`. `HTTPAPI` will send a POST request to that destination URL.* ### Chaining `Modules` If we wanted to **run a sequence of `Module`s**, we could add a second `Module`. Say, we had an image classifier written in [pytorch](https://github.com/pytorch/pytorch) that predicted the make and model of a vehicle. A pytorch image classifier is another example application we provide in `multivitamin.applications.images` ``` from multivitamin.applications.images.classifiers.pyt_classifier import PYTClassifier make_model_clf = PYTClassifier(name="make-model", ver="1.0.0", model="models/mm.pth") make_model_clf.set_previous_properties_of_interest([ {"value":"car"}, {"value":"truck"}, ]) ``` The `set_previous_properties_of_interest` is a method to tell this `make_model_clf` module to only run its `predict_images` function for predictions of `car` OR `truck` found in the previous module (the 600 class TensorFlow object detector). And now, creating a `Server`: ``` vehicle_mm_server = Server( modules=[ obj_det_module, make_model_clf, ], input_comm=sqs_api, output_comms=[ s3_api, http_api, ], ) vehicle_mm_server.start() ``` ## Installation Using [conda](https://conda.io/en/latest/): ``` conda install multivitamin ``` Using [pip](https://pip.pypa.io/en/stable/installing/) *Note: this requires opencv be already installed. We highly recommend installing with conda instead* ``` pip install multivitamin ``` Using [nvidia-docker](https://github.com/NVIDIA/nvidia-docker): ``` docker run --runtime=nvidia multivitamin:cuda9-cudnn7 /bin/bash ``` ## Documentation For API documentation and full details, see [https://multivitamin.readthedocs.io](https://multivitamin.readthedocs.io) ### High-level overview **Data flow**: 1. JSON request is "pulled" by a `CommAPI` object 2. JSON request is used to construct a `Request` class 3. `Server` creates a (typically) empty `Response` from the `Request`. If the `Request`contains a previous module's `Response` (for modules run in a sequence), that is pre-populated in the `Response` 4. `process_request()` sends the `Response` through all `Module`s 5. Each `Module` appends/modifies the `Response` 6. `process_request()` returns the `Response` back to the `Server` 7. `Server` sends the `Response` to the output `CommAPI`(s) and calls the `push(Response)` method  **Repository organization:** * data/ * **Request:** data object encapsulating request JSON * response/ * **Response:** data object encapsulating response that reflects the schema. Contains methods for serialization, modifying internal data * **ResponseInternal:** Python dataclasses with typechecking that matches the schema * module/ * **Module:** abstract parent class that defines an interface for processing requests * **ImagesModule:** abstract child class of `Module` that defines an interface for processing requests with images or video, `process_images(...)` and handles retrieval of media. * **PropertiesModule:** abstract child class of `Module` that defines an interface `process_properties()`  * apis/ * **CommAPI:** abstract parent class that defines an interface, i.e. `push()` and `pull()` * **SQSAPI:** pulls requests from an SQS queue, pushes requests to a queue * **HTTPAPI:** pushes Responses by posting to a HTTP endpoint (provided in the request) * **LocalAPI:** pulls requests from a local directory of JSONs, pushes Responses to a local directory * **S3API:** pulls requests from an S3 bucket of JSONs, pushes Responses to an S3 bucket ## Contributing To file a bug or request a feature, please file a GitHub issue. Pull requests are welcome. ## The Team Multivitamin is currently maintained by [Greg Chu](https://github.com/gregchu), [Matthew Greenberg](https://github.com/magreenberg1), and [Javier Molina](https://github.com/javimol), with contributions from [Divyaa Ravichandran](https://github.com/stalagmite7), and Rohit Annigeri, and with collaboration from Cambron Carter, Shankar Chatterjee, Nandakishore Puttashamachar, Nishita Sant, and Iris Fu.