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.
Full documentation (including a quick start guide, a developer/user guide, and API documentation) is available online at https://docs.nvidia.com/morpheus/.
There are three ways to get started with Morpheus:
- Using pre-built Docker containers
- Building the Morpheus Docker container
- Building Morpheus from source
The pre-built Docker containers are the easiest way to get started with the latest release of Morpheus. Instructions on how to download and run these containers, including the necessary data and models, can be found on NGC here.
More advanced users, or those who are interested in using the latest pre-release features, will need to build the Morpheus container or build from source. Step-by-step instructions for these users can be found in the following section.
The following sections must be followed prior to building the Morpheus container or building Morpheus from source.
- Pascal architecture GPU or better
- NVIDIA driver
450.80.02or higher - Docker
- The NVIDIA container toolkit
- NVIDIA Triton Inference Server
22.06or higher - Git LFS
MORPHEUS_ROOT=$(pwd)/morpheus
git clone https://github.com/NVIDIA/Morpheus.git $MORPHEUS_ROOT
cd $MORPHEUS_ROOTThe large model and data files in this repo are stored using Git Large File Storage (LFS). Only those files which are strictly needed to run Morpheus are downloaded by default when the repository is cloned.
The scripts/fetch_data.py script can be used to fetch the Morpheus pre-trained models, and other files required for running the training/validation scripts and example pipelines.
Usage of the script is as follows:
scripts/fetch_data.py fetch <dataset> [<dataset>...]At time of writing the defined datasets are:
- all - Metaset includes all others
- examples - Data needed by scripts in the
examplessubdir - models - Morpheus models (largest dataset)
- tests - Data used by unittests
- validation - Subset of the models dataset needed by some unittests
To download just the examples and models:
scripts/fetch_data.py fetch examples modelsTo download the data needed for unittests:
scripts/fetch_data.py fetch tests validationIf Git LFS is not installed the before cloning the repository, the scripts/fetch_data.py script will fail. If this is the case follow the instructions for installing Git LFS from here, and then run the following command:
git lfs installTo assist in building the Morpheus container, several scripts have been provided in the ./docker directory. To build the "release" container, run the following:
./docker/build_container_release.shThis will create an image named nvcr.io/nvidia/morpheus/morpheus:${MORPHEUS_VERSION}-runtime where $MORPHEUS_VERSION is replaced by the output of git describe --tags --abbrev=0.
To run the built "release" container, use the following:
./docker/run_container_release.shYou can specify different Docker images and tags by passing the script the DOCKER_IMAGE_TAG, and DOCKER_IMAGE_TAG variables respectively. For example, to run version v22.08.00a use the following:
DOCKER_IMAGE_TAG="v22.08.00a-runtime" ./docker/run_container_release.shIt's possible to build from source outside of a container. However, due to the large number of dependencies, this can be complex and is only necessary for developers. Instructions for developers and contributors can be found in CONTRIBUTING.md.
Many of the validation tests and example workflows require a Triton server to function. Use the following command to launch a Docker container for Triton loading all of the included pre-trained models:
docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 \
-v $PWD/models:/models \
nvcr.io/nvidia/tritonserver:22.06-py3 \
tritonserver --model-repository=/models/triton-model-repo \
--exit-on-error=false \
--log-info=true \
--strict-readiness=falseThis will launch Triton using the default network ports (8000 for HTTP, 8001 for GRPC, and 8002 for metrics).
To run Morpheus, users will need to choose from the Morpheus Command Line Interface (CLI) or Python interface. Which interface to use depends on the user's needs, amount of customization, and operating environment. More information on each interface can be found below.
The Morpheus python interface allows users to configure their pipelines using a python script file. This is ideal for users who are working in a Jupyter notebook, users who need complex initialization logic or users who have customized stages. Documentation on using the Morpheus python interface can be found at docs/source/developer_guide/guides.rst.
For full example pipelines using the python interface, see the ./examples directory.
The CLI allows users to completely configure a Morpheus pipeline directly from a terminal. This is ideal for users who do not need customized stages and for users configuring a pipeline in Kubernetes. The Morpheus CLI can be invoked using the morpheus command and is capable of running linear pipelines as well as additional tools. Instructions for using the CLI can be queried directly in the terminal using morpheus --help:
$ morpheus
Usage: morpheus [OPTIONS] COMMAND [ARGS]...
Options:
--debug / --no-debug [default: no-debug]
--log_level [CRITICAL|FATAL|ERROR|WARN|WARNING|INFO|DEBUG]
Specify the logging level to use. [default:
WARNING]
--log_config_file FILE Config file to use to configure logging. Use
only for advanced situations. Can accept
both JSON and ini style configurations
--version Show the version and exit. [default: False]
--help Show this message and exit. [default:
False]
Commands:
run Run one of the available pipelines
tools Run a utility toolEach command in the CLI has its own help information. Use morpheus [command] [...sub-command] --help to get instructions for each command and sub command. For example:
$ morpheus run pipeline-nlp inf-triton --help
Configuring Pipeline via CLI
Usage: morpheus run pipeline-nlp inf-triton [OPTIONS]
Options:
--model_name TEXT Model name in Triton to send messages to
[required]
--server_url TEXT Triton server URL (IP:Port) [required]
--force_convert_inputs BOOLEAN Instructs this stage to forcibly convert all
input types to match what Triton is
expecting. Even if this is set to `False`,
automatic conversion will be done only if
there would be no data loss (i.e. int32 ->
int64). [default: False]
--use_shared_memory BOOLEAN Whether or not to use CUDA Shared IPC Memory
for transferring data to Triton. Using CUDA
IPC reduces network transfer time but
requires that Morpheus and Triton are
located on the same machine [default:
False]
--help Show this message and exit. [default:
False]Several examples on using the Morpheus CLI can be found at docs/source/basics/examples.rst.
When configuring a pipeline via the CLI, you start with the command morpheus run pipeline and then list the stages in order from start to finish. The order that the commands are placed in will be the order that data flows from start to end. The output of each stage will be linked to the input of the next. For example, to build a simple pipeline that reads from Kafka, deserializes messages, serializes them, and then writes to a file, use the following:
$ morpheus run pipeline-nlp from-kafka --input_topic test_pcap deserialize serialize to-file --filename .tmp/temp_out.jsonYou should see some output similar to:
====Building Pipeline====
Added source: <from-kafka-0; KafkaSourceStage(bootstrap_servers=localhost:9092, input_topic=test_pcap, group_id=custreamz, poll_interval=10millis)>
└─> morpheus.MessageMeta
Added stage: <deserialize-1; DeserializeStage()>
└─ morpheus.MessageMeta -> morpheus.MultiMessage
Added stage: <serialize-2; SerializeStage(include=[], exclude=['^ID$', '^_ts_'], output_type=pandas)>
└─ morpheus.MultiMessage -> pandas.DataFrame
Added stage: <to-file-3; WriteToFileStage(filename=.tmp/temp_out.json, overwrite=False, file_type=auto)>
└─ pandas.DataFrame -> pandas.DataFrame
====Building Pipeline Complete!====
This is important because it shows you the order of the stages and the output type of each one. Since some stages cannot accept all types of inputs, Morpheus will report an error if you have configured your pipeline incorrectly. For example, if we run the same command as above but forget the serialize stage, you will see the following:
$ morpheus run pipeline-nlp from-kafka --input_topic test_pcap deserialize to-file --filename .tmp/temp_out.json --overwrite
====Building Pipeline====
Added source: from-kafka -> <class 'cudf.core.dataframe.DataFrame'>
Added stage: deserialize -> <class 'morpheus.pipeline.messages.MultiMessage'>
Traceback (most recent call last):
File "morpheus/pipeline/pipeline.py", line 228, in build_and_start
current_stream_and_type = await s.build(current_stream_and_type)
File "morpheus/pipeline/pipeline.py", line 108, in build
raise RuntimeError("The {} stage cannot handle input of {}. Accepted input types: {}".format(
RuntimeError: The to-file stage cannot handle input of <class 'morpheus.pipeline.messages.MultiMessage'>. Accepted input types: (typing.List[str],)This indicates that the to-file stage cannot accept the input type of morpheus.pipeline.messages.MultiMessage. This is because the to-file stage has no idea how to write that class to a file; it only knows how to write strings. To ensure you have a valid pipeline, look at the Accepted input types: (typing.List[str],) portion of the message. This indicates you need a stage that converts from the output type of the deserialize stage, morpheus.pipeline.messages.MultiMessage, to typing.List[str], which is exactly what the serialize stage does.
A complete list of the pipeline stages will be added in the future. For now, you can query the available stages for each pipeline type via:
$ morpheus run pipeline-nlp --help
Usage: morpheus run pipeline-nlp [OPTIONS] COMMAND1 [ARGS]... [COMMAND2
[ARGS]...]...
<Help Paragraph Omitted>
Commands:
add-class Add detected classifications to each message
add-scores Add probability scores to each message
buffer (Deprecated) Buffer results
delay (Deprecated) Delay results for a certain duration
deserialize Deserialize source data from JSON.
dropna Drop null data entries from a DataFrame
filter Filter message by a classification threshold
from-file Load messages from a file
from-kafka Load messages from a Kafka cluster
gen-viz (Deprecated) Write out vizualization data frames
inf-identity Perform a no-op inference for testing
inf-pytorch Perform inference with PyTorch
inf-triton Perform inference with Triton
mlflow-drift Report model drift statistics to ML Flow
monitor Display throughput numbers at a specific point in the pipeline
preprocess Convert messages to tokens
serialize Serializes messages into a text format
to-file Write all messages to a file
to-kafka Write all messages to a Kafka cluster
validate Validates pipeline output against an expected outputAnd for the FIL pipeline:
$ morpheus run pipeline-fil --help
Usage: morpheus run pipeline-fil [OPTIONS] COMMAND1 [ARGS]... [COMMAND2
[ARGS]...]...
<Help Paragraph Omitted>
Commands:
add-class Add detected classifications to each message
add-scores Add probability scores to each message
buffer (Deprecated) Buffer results
delay (Deprecated) Delay results for a certain duration
deserialize Deserialize source data from JSON.
dropna Drop null data entries from a DataFrame
filter Filter message by a classification threshold
from-file Load messages from a file
from-kafka Load messages from a Kafka cluster
inf-identity Perform a no-op inference for testing
inf-pytorch Perform inference with PyTorch
inf-triton Perform inference with Triton
mlflow-drift Report model drift statistics to ML Flow
monitor Display throughput numbers at a specific point in the pipeline
preprocess Convert messages to tokens
serialize Serializes messages into a text format
to-file Write all messages to a file
to-kafka Write all messages to a Kafka cluster
validate Validates pipeline output against an expected outputAnd for the AE pipeline:
$ morpheus run pipeline-fil --help
Usage: morpheus run pipeline-fil [OPTIONS] COMMAND1 [ARGS]... [COMMAND2
[ARGS]...]...
<Help Paragraph Omitted>
Commands:
add-class Add detected classifications to each message
add-scores Add probability scores to each message
buffer (Deprecated) Buffer results
delay (Deprecated) Delay results for a certain duration
filter Filter message by a classification threshold
from-cloudtrail Load messages from a Cloudtrail directory
gen-viz (Deprecated) Write out vizualization data frames
inf-pytorch Perform inference with PyTorch
inf-triton Perform inference with Triton
monitor Display throughput numbers at a specific point in the
pipeline
preprocess Convert messages to tokens
serialize Serializes messages into a text format
timeseries Perform time series anomaly detection and add prediction.
to-file Write all messages to a file
to-kafka Write all messages to a Kafka cluster
train-ae Deserialize source data from JSON
validate Validates pipeline output against an expected output
Note: The available commands for different types of pipelines are not the same. This means that the same stage, when used in different pipelines, may have different options. Please check the CLI help for the most up-to-date information during development.
Please see our guide for contributing to Morpheus.