mlinfra is the swiss army knife for deploying MLOps tooling anywhere. It aims to make MLOps infrastructure deployment easy and accessible to all ML teams by liberating IaC logic for creating MLOps stacks which is usually tied to other frameworks.
Contribute to the project by opening a issue or joining project roadmap and design related discussion on discord. Complete roadmap will be released soon!
mlinfra requires the following to run perfectly:
terraform >= 1.10.2 should be installed on the system.mlinfra can be installed simply by creating a python virtual environment and installing mlinfra pip package
python -m venv .venv
source .venv/bin/activate
pip install mlinfra
Copy a deployment config from the examples folder, change your AWS account in the config file, configure your AWS credentials and deploy the configuration using
mlinfra terraform apply --config-file <path-to-your-config>
For more information, read the mlinfra user guide
mlinfra deploys infrastructure using declarative approach. It requires resources to be defined in a yaml file with the following format
name: aws-mlops-stack
provider:
name: aws
account-id: xxxxxxxxx
region: eu-central-1
deployment:
type: cloud_vm # (this would create ec2 instances and then deploy applications on it)
stack:
data_versioning:
- lakefs # can also be pachyderm or lakefs or neptune and so on
experiment_tracker:
- mlflow # can be weights and biases or determined, or neptune or clearml and so on...
orchestrator:
- zenml # can also be argo, or luigi, or airflow, or dagster, or prefect or flyte or kubeflow or ray and so on...
model_inference:
- bentoml # can also be ray or KF serving or seldoncore or tf serving
monitoring:
- nannyML # can be grafana or alibi or evidently or neptune or prometheus or weaveworks and so on...
alerting:
- mlflow # can be mlflow or neptune or determined or weaveworks or prometheus or grafana and so on...
For examples, check out the documentation.
NOTE: This was minimal spec for aws cloud as infra with custom applications. Other stacks such as feature_store, event streamers, loggers or cost dashboards can be added via community requests. For more information, please check out the docs.
The core purpose is to build for all cloud and deployment platforms out there. Any user should be able to just change the cloud provider or runtime environment (whether it be linux or windows) and have the capability to deploy the same tools.
mlinfra will be supporting the following providers:
When deploying on managed cloud providers, users can deploy their infrastructure on top of either:
Virtual Machines such as EC2 on AWS Cloud, Google Virtual machine instances on GCP Cloud and Azure Virtual Machine on Azure Cloud.mlinfra intends to support as many MLOps tools deployable in a platform in their standalone as well as high availability across different layers of an MLOps stack:
data_ingestiondata_versioningdata_processingvector_databaseexperiment_trackerorchestratormodel_inferencemonitoringalertinguv syncexamples/ folder by running the following command in root directory python src/mlinfra/cli/cli.py terraform <action> --config-file examples/<deployment-type>/<file>.yaml where <action> corresponds to terraform actions such as plan, apply and destroy.For more information, please refer to the Engineering Wiki of the project (https://mlinfra.io/user_guide/) regarding what are the different components of the project and how they work together.
The mlinfra library is distributed under the Apache-2 license.
FAQs
A tool to deploy mlops tooling at the click of a button.
We found that mlinfra demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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