NVIDIA Morpheus is a GPU-accelerated cybersecurity AI framework that makes it easy to build and scale cybersecurity applications that harness adaptive pipelines supporting a wider range of model complexity than previously feasible. Morpheus makes it possible to analyze up to 100 percent of your data in real-time, for more accurate detection and faster remediation of threats as they occur. Morpheus also provides the ability to leverage AI to adjust to threats and compensate on the fly, at line rate. With Morpheus organizations can attack the issue of cybersecurity head on. Rather than continuously chasing the cybersecurity problem, Morpheus provides the ability to propel you ahead of a breach and address the cybersecurity issue. With the world in a "discover and respond" state, where companies are finding breaches much too late, in a way that is way behind the curve, NVIDIA’s Morpheus cybersecurity AI framework enables any organization to warp to the present and begin to defend itself in real time.
Massive Performance and Scale - Morpheus is GPU-accelerated enabling, for the first time, the ability to inspect all network traffic in real-time, flag anomalies, and provide insights on these anomalies so that threats can be addressed quickly. It enables AI inference and real-time monitoring of every server and packet across the entire network.
Rapid Development and Deployment - Morpheus integrates AI frameworks and tools that make it easier for developers to build cybersecurity solutions. Organizations that lack AI expertise can still leverage AI for cybersecurity because Morpheus leverages tools for every stage of the AI workflow, from data preparation to training, inference, and deploying at scale.
Real-time Telemetry - The Morpheus native graph streaming engine can receive rich, real-time network telemetry from every NVIDIA BlueField DPU-accelerated server or NVIDIA AppShield 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.
AI Cybersecurity Capabilities – Deploy your own models using common deep learning frameworks. Or use a Morpheus pre-trained and tested model to get a jump-start in building applications to identify leaked sensitive information, detect malware or fraud, do network mapping, flag user behavior changes, or and identify errors via logs.
MultiMessage
from Morpheus (#1886) @yczhang-nvMultiMessage
from stages (#1803) @yczhang-nvlog_parsing
output (#2031) @dagardner-nvlog_parsing
example pipeline null output issue (#2024) @yczhang-nvDeserializeStage
to ensure output messages correctly contain the correct rows for each batch (#2015) @dagardner-nvSlicedMessageMeta
(#2006) @dagardner-nvColumn.from_column_view
by copying it and adjusting. (#2004) @cwharrisval-run-all.sh
to run cpp pipeline only (#1986) @yczhang-nvonnx-to-trt
utility (#1984) @dagardner-nvLLMEngineStage
(#1975) @dagardner-nv**kwargs
back to NVFoundationLLMClient.generate_batch()
and generate_batch_async()
(#1967) @ashsong-nvSharedProcessPool
& MultiProcessingStage
(#1940) @yczhang-nvSharedProcessPool.terminate()
related tests to avoid stack traces and blocking remote-ci (#1929) @yczhang-nvHttpServerSourceStage
to avoid spinlocking (#1928) @dagardner-nvpytest
is able to run without optional dependencies (#1927) @dagardner-nvLLMEngine
to not show the stoul
error (#1922) @mdemoret-nvWriteToVectorDBStage
to re-raise errors from the underlying database (#1905) @dagardner-nvpypdfium2
(#1902) @dagardner-nvload_labels_file
method (#1901) @dagardner-nvCan't find 'action.yml'
CI error (#1896) @dagardner-nvmrc.Subscription
(#1881) @dagardner-nvUnregistered type : mrc::pymrc::coro::BoostFibersMainPyAwaitable
error (#1869) @dagardner-nv2.13-3.8.0
(#1856) @cwharrisisort
settings file path in fix_all.sh
(#1855) @yczhang-nv_utils
as known first party (#1842) @dagardner-nvwrite_df_to_file
(#1840) @dagardner-nvCMAKE_INSTALL_PREFIX
if needed (#1815) @dagardner-nvvdb_upload
example (#1813) @dagardner-nvci/release/update-version.sh
to include missed files (#1801) @dagardner-nvTODO
statements from documentation (#1879) @dagardner-nvvdb_upload
to use realistic source data with the --file_source
flag (#1800) @dagardner-nvHttpServerSourceStage
when the queue is empty (#1921) @dagardner-nvMultiProcessingStage
(#1878) @yczhang-nvControlMessage
as an output type for HttpServerSourceStage
and HttpClientSourceStage
(#1834) @dagardner-nvlog_parsing
pipeline (#1795) @dagardner-nvVAULT_HOST
with AWS_ROLE_ARN
(#1962) @jjacobellitest_shared_process_pool.py
to avoid slowing down the test (#1935) @yczhang-nvMultiMessage
from Morpheus (#1886) @yczhang-nvlen(os.sched_getaffinity(0))
over os.cpu_count()
(#1866) @cwharrisConfig
's pipeline_batch_size < model_max_batch_size
(#1858) @cwharrisMultiMessage
from stages (#1803) @yczhang-nvMorpheus is distributed as open source software under the Apache Software License 2.0.
NVIDIA AI Enterprise provides global support for NVIDIA AI software, including Morpheus. For more information on NVIDIA AI Enterprise please consult this overview and the NVIDIA AI Enterprise End User License Agreement.