Metadata-Version: 2.1
Name: mlops-tempo
Version: 0.5.2
Summary: Machine Learning Operations Toolkit
Home-page: https://github.com/SeldonIO/tempo
Author: Seldon Technologies Ltd.
Author-email: hello@seldon.io
License: Apache 2.0
Description: 
        [![Version](https://badge.fury.io/py/mlops-tempo.svg)](https://badge.fury.io/py/mlops-tempo)
        ![Python version](https://img.shields.io/badge/Python-3.5—3.8-blue.svg)
        ![License](https://img.shields.io/badge/License-Apache-black.svg)
        [![Documentation Status](https://readthedocs.org/projects/tempo/badge/?version=latest)](https://tempo.readthedocs.io/en/latest/)
        
        # Tempo: The MLOps Software Development Kit
        
        [Documentation](https://tempo.readthedocs.io/en/latest/)
        
        An open source framework to enable data scientists to productionise, test and deploy models with simple workflows that abstract the underlying complexity of scalable MLOps platforms.
        
        ![](https://raw.githubusercontent.com/SeldonIO/tempo/master/docs/assets/tempo-logo.jpg)
        
        ## Highlights
        
        Tempo provides a unified interface to multiple MLOps projects that enable data scientists to deploy and productionise machine learning systems.
        
        
         * Package your trained model artifacts to optimized server runtimes (Tensorflow, PyTorch, Sklearn, XGBoost etc)
         * Package custom business logic to production servers.
         * Build an inference pipeline of models and orchestration steps.
         * Include any custom python components as needed. Examples:
             * Outlier detectors with Alibi-Detect.
             * Explainers with Alibi-Explain.
         * Test Locally - Deploy to Production
             * Run with local unit tests.
             * Deploy locally to Docker to test with Docker runtimes.
             * Deploy to production on Kubernetes
             * Extract declarative Kubernetes yaml to follow GitOps workflows.
         * Supporting Seldon production runtimes
             * Seldon Core open source
             * Seldon Deploy enterprise
         * Create stateful services. Examples:
            * Multi-Armed Bandits.
        
        
        
        ## Workflow
        
        1. Develop locally.
        2. Test locally on Docker with production artifacts.
        3. Push artifacts to remote bucket store and launch remotely (on Kubernetes).
        
        ![overview](https://raw.githubusercontent.com/SeldonIO/tempo/master/docs/assets/tempo-overview.png)
        
        ## Motivating Synopsis
        
        Data scientists can easily test their models and orchestrate them with pipelines.
        
        Below we see two `Model`s (sklearn and xgboost) with a function decorated `pipeline` to call both.
        
        
        ```python
        def get_tempo_artifacts(artifacts_folder: str) -> Tuple[Pipeline, Model, Model]:
        
            sklearn_model = Model(
                name="test-iris-sklearn",
                platform=ModelFramework.SKLearn,
                local_folder=f"{artifacts_folder}/{SKLearnFolder}",
                uri="s3://tempo/basic/sklearn",
            )
        
            xgboost_model = Model(
                name="test-iris-xgboost",
                platform=ModelFramework.XGBoost,
                local_folder=f"{artifacts_folder}/{XGBoostFolder}",
                uri="s3://tempo/basic/xgboost",
            )
        
            @pipeline(
                name="classifier",
                uri="s3://tempo/basic/pipeline",
                local_folder=f"{artifacts_folder}/{PipelineFolder}",
                models=PipelineModels(sklearn=sklearn_model, xgboost=xgboost_model),
            )
            def classifier(payload: np.ndarray) -> Tuple[np.ndarray, str]:
                res1 = classifier.models.sklearn(input=payload)
        
                if res1[0] == 1:
                    return res1, SKLearnTag
                else:
                    return classifier.models.xgboost(input=payload), XGBoostTag
        
            return classifier, sklearn_model, xgboost_model
        
        ```
        
        Save the pipeline code.
        
        ```python
        from tempo.serve.loader import save
        save(classifier)
        ```
        
        Deploy locally to docker.
        
        ```python
        from tempo import deploy_local
        remote_model = deploy_local(classifier)
        ```
        
        Make predictions on containerized servers that would be used in production.
        
        ```python
        remote_model.predict(np.array([[1, 2, 3, 4]]))
        ```
        
        Deploy to Kubernetes for production.
        
        ```python
        from tempo.serve.metadata import SeldonCoreOptions
        from tempo import deploy_remote
        
        runtime_options = SeldonCoreOptions(**{
            "remote_options": {
                "namespace": "production",
                "authSecretName": "minio-secret"
            }
        })	
        remote_model = deploy_remote(classifier, options=runtime_options)
        ```
        
        This is an extract from the [multi-model introduction](https://tempo.readthedocs.io/en/latest/examples/multi-model/README.html) demo.
        
Platform: UNKNOWN
Requires-Python: >=3.7
Description-Content-Type: text/markdown
