sematic-ee 0.0.1

No project description provided

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.

Features

• 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

Integrations

• 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:

• Sematic landing page

• Documentation

• Discord channel

• YouTube channel

• Our Blog

Contribute!

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