The goal of the Data Science Infrastructure Project (DSI) is to provide a flexible, AI-ready metadata query capability which returns data subject to strict, POSIX-enforced file security. The data lifecycle for AI/ML requires seamless transitions from data-intensive/AI/ML research activity to long-term archiving and shared data repositories. DSI enables flexible, data-intensive scientific workflows that meet researcher needs.
DSI is implemented in three parts:
- Plugins (Readers and Writers)
- Backends
- Core middleware
Plugins curate metadata for query and data return. Plugins can have read or write funcitonality acting as Readers and Writers for DSI. Plugins acting as readers harvest data from files and streams. Plugins acting as writers execute containerized or baremetal applications to supplement queriable metadata and data. Plugins may be user contributed and a default set of plugins is available with usage examples in our Core documentation.
Backends are interfaces for the Core middleware. Backends consist mostly of back-end/storage functionalities and are the interface between the Core Middleware and a data store. Backends may also have some front-end functionality interfacing between a DSI user and the Core middleware. Backends may be user contributed and a default set of backends are available with usage examples in our Core documentation.
DSI Core middleware provides the user/machine interface. The Core middleware defines a Terminal object. An instantiated Core Terminal can load zero or more plugins and backends. A Terminal object can be used in scripting workflows and program loops.
- python3 (3.11 tested)
- Linux OS (RHEL- and Debian-based distributions tested)
- Git
- Plugins and Backends introduce further requirements
DSI does not yet have a versioned release and should be considered pre-alpha. Project contributors are encouraged to prototype solutions which do not contain sensitive data at this time. Consequently a PyPA release is planned but incomplete. It is possible to install DSI locally instead.
We recommend Miniconda3 for managing virtual environments for DSI:
. ~/miniconda3/bin/activate
conda create -n dsi python=3.11
conda activate dsiPython virtual environments can also be used for DSI:
python3 -m venv dsienv
source dsienv/bin/activate
pip install --upgrade pipAfter activating your environment:
git clone https://github.com/lanl/dsi.git
cd dsi/
python3 -m pip install .This program is open source under the BSD-3 License.
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