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Sematic Logo PyPI CircleCI PyPI - License Python 3.8 Python 3.9 Discord Made By Sematic PyPI - Downloads Sematic Screenshot

Sematic is an open-source ML development platform. It lets ML Engineers and Data Scientists write arbitrarily complex end-to-end pipelines with simple Python and execute them on their local machine, in a cloud VM, or on a Kubernetes cluster to leverage cloud resources.

Sematic is based on learnings gathered at top self-driving car companies. It enables chaining data processing jobs (e.g. Apache Spark) with model training (e.g. PyTorch, Tensorflow), or any other arbitrary Python business logic into type-safe, traceable, reproducible end-to-end pipelines that can be monitored and visualized in a modern web dashboard.

Read our documentation and join our Discord channel.

Why Sematic

  • Easy onboarding – no deployment or infrastructure needed to get started, simply install Sematic locally and start exploring.

  • Local-to-cloud parity – run the same code on your local laptop and on your Kubernetes cluster.

  • End-to-end traceability – all pipeline artifacts are persisted, tracked, and visualizable in a web dashboard.

  • Access heterogeneous compute – customize required resources for each pipeline step to optimize your performance and cloud footprint (CPUs, memory, GPUs, Spark cluster, etc.)

  • Reproducibility – rerun your pipelines from the UI with guaranteed reproducibility of results

Getting Started

To get started locally, simply install Sematic in your Python environment:

$ pip install sematic

Start the local web dashboard:

$ sematic start

Run an example pipeline:

$ sematic run examples/mnist/pytorch

Create a new boilerplate project:

$ sematic new my_new_project

Or from an existing example:

$ sematic new my_new_project --from examples/mnist/pytorch

Then run it with:

$ python3 -m my_new_project

To deploy Sematic to Kubernetes and leverage cloud resources, see our documentation.


  • Lightweight Python SDK – define arbitrarily complex end-to-end pipelines

  • Pipeline nesting – arbitrarily nest pipelines into larger pipelines

  • Dynamic graphs – Python-defined graphs allow for iterations, conditional branching, etc.

  • Lineage tracking – all inputs and outputs of all steps are persisted and tracked

  • Runtime type-checking – fail early with run-time type checking

  • Web dashboard – Monitor, track, and visualize pipelines in a modern web UI

  • Artifact visualization – visualize all inputs and outputs of all steps in the web dashboard

  • Local execution – run pipelines on your local machine without any deployment necessary

  • Cloud orchestration – run pipelines on Kubernetes to access GPUs and other cloud resources

  • Heterogeneous compute resources – run different steps on different machines (e.g. CPUs, memory, GPU, Spark, etc.)

  • Helm chart deployment – install Sematic on your Kubernetes cluster

  • Pipeline reruns – rerun pipelines from the UI from an arbitrary point in the graph

  • Step caching – cache expensive pipeline steps for faster iteration

  • Step retry – recover from transient failures with step retries

  • Metadata and collaboration – Tags, source code visualization, docstrings, notes, etc.

  • Numerous integrations – See below


  • Apache Spark – on-demand in-cluster Spark cluster

  • Ray – on-demand Ray in-cluster Ray resources

  • Snowflake – easily query your data warehouse (other warehouses supported too)

  • Plotly, Matplotlib – visualize plot artifacts in the web dashboard

  • Pandas – visualize dataframe artifacts in the dashboard

  • Grafana – embed Grafana panels in the web dashboard

  • Bazel – integrate with your Bazel build system

  • Helm chart – deploy to Kubernetes with our Helm chart

  • Git – track git information in the web dashboard

Community and resources

Learn more about Sematic and get in touch with the following resources:


To contribute to Sematic, check out open issues tagged “good first issue”, and get in touch with us on Discord.

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