mlops-tempo 0.5.2
Machine Learning Operations Toolkit
License: Apache 2.0
Requires Python: >=3.7

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# 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.