NVIDIA Morpheus is an open AI application framework that provides cybersecurity developers with a highly optimized AI framework and pre-trained AI capabilities that allow them to instantaneously inspect all IP traffic across their data center fabric. The Morpheus developer framework allows teams to build their own optimized pipelines that address cybersecurity and information security use cases. Bringing a new level of security to data centers, Morpheus provides development capabilities around dynamic protection, real-time telemetry, adaptive policies, and cyber defenses for detecting and remediating cybersecurity threats.
- Built on RAPIDS
- Built on the RAPIDS™ libraries, deep learning frameworks, and NVIDIA Triton™ Inference Server, Morpheus simplifies the analysis of logs and telemetry to help detect and mitigate security threats.
- AI Cybersecurity Capabilities
- Deploy your own models using common deep learning frameworks. Or get a jump-start in building applications to identify leaked sensitive information, detect malware, and identify errors via logs by using one of NVIDIA's pre-trained and tested models.
- Real-Time Telemetry
- Morpheus can receive rich, real-time network telemetry from every NVIDIA® BlueField® DPU-accelerated server in the data center without impacting performance. Integrating the framework into a third-party cybersecurity offering brings the world's best AI computing to communication networks.
- DPU-Connected
- The NVIDIA BlueField Data Processing Unit (DPU) can be used as a telemetry agent for receiving critical data center communications into Morpheus. As an optional addition to Morpheus, BlueField DPU also extends static security logging to a sophisticated dynamic real-time telemetry model that evolves with new policies and threat intelligence.
- :doc:`getting_started` - Using pre-built Docker containers, building Docker containers from source, and fetching models and datasets
- :doc:`basics/overview` - Brief overview of the command line interface
- :doc:`basics/building_a_pipeline` - Introduction to building a pipeline using the command line interface
- :doc:`Morpheus Examples <examples>` - Example pipelines using both the Python API and command line interface
- :doc:`Pretrained Models <models_and_datasets>` - Pretrained models with corresponding training, validation scripts, and datasets
- :doc:`Developer Guides <developer_guide/guides>` - Covers extending Morpheus with custom stages
- :doc:`developer_guide/contributing` - Covers building from source, making changes and contributing to Morpheus
- :doc:`cloud_deployment_guide` - Kubernetes and cloud based deployments
.. toctree:: :caption: Using Morpheus :maxdepth: 20 :hidden: getting_started basics/overview basics/building_a_pipeline models_and_datasets examples/index developer_guide/guides/index
.. toctree:: :caption: Modifying Morpheus :maxdepth: 20 :hidden: developer_guide/architecture developer_guide/contributing
.. toctree:: :caption: Deploying Morpheus :maxdepth: 20 :hidden: cloud_deployment_guide
.. toctree:: :caption: API :maxdepth: 20 :hidden: py_api _lib/index
.. toctree:: :caption: Morpheus Stages :maxdepth: 20 :hidden: stages/morpheus_stages
.. toctree:: :caption: Morpheus Modules :maxdepth: 20 :hidden: modules/index
.. toctree:: :caption: Morpheus Loaders :maxdepth: 20 :hidden: loaders/index
.. toctree:: :maxdepth: 20 :caption: Extra Information :hidden: extra_info/glossary extra_info/performance extra_info/troubleshooting extra_info/known_issues Code of Conduct <https://docs.rapids.ai/resources/conduct/> License <https://github.com/nv-morpheus/Morpheus/blob/main/LICENSE>