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ABP Detection Example Using Morpheus

Supported Environments

Environment Supported Notes
Conda ✔
Morpheus Docker Container ✔ Requires launching Triton on the host
Morpheus Release Container ✔ Requires launching Triton on the host
Dev Container ✔ Requires using the dev-triton-start script. If using the run.py script this requires adding the --server_url=triton:8000 flag. If using the CLI example this requires replacing --server_url=localhost:8000 with --server_url=triton:8000

Setup

To run this example, an instance of Triton Inference Server and a sample dataset is required. The following steps will outline how to build and run Triton with the provided FIL model.

Triton Inference Server

docker pull nvcr.io/nvidia/morpheus/morpheus-tritonserver-models:25.02
Deploy Triton Inference Server

Run the following to launch Triton and load the abp-pcap-xgb model:

docker run --rm --gpus=all -p 8000:8000 -p 8001:8001 -p 8002:8002 --name tritonserver nvcr.io/nvidia/morpheus/morpheus-tritonserver-models:25.02 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model abp-pcap-xgb
Verify Model Deployment

Once Triton server finishes starting up, it will display the status of all loaded models. Successful deployment of the model will show the following:

+-----------------------------+---------+--------+
| Model                       | Version | Status |
+-----------------------------+---------+--------+
| abp-pcap-xgb                | 1       | READY  |
+-----------------------------+---------+--------+

ABP Detection Pipeline

Use Morpheus to run the Anomalous Behavior Profiling Detection Pipeline with the PCAP data. A pipeline has been configured in run.py with several command line options:

From the root of the Morpheus repo, run:

python examples/abp_pcap_detection/run.py --help

Output:

Usage: run.py [OPTIONS]

Options:
  --num_threads INTEGER RANGE     Number of internal pipeline threads to use.
                                  [x>=1]
  --pipeline_batch_size INTEGER RANGE
                                  Internal batch size for the pipeline. Can be
                                  much larger than the model batch size. Also
                                  used for Kafka consumers.  [x>=1]
  --model_max_batch_size INTEGER RANGE
                                  Max batch size to use for the model.  [x>=1]
  --input_file PATH               Input filepath.  [required]
  --output_file TEXT              The path to the file where the inference
                                  output will be saved.
  --model_fea_length INTEGER RANGE
                                  Features length to use for the model.
                                  [x>=1]
  --model_name TEXT               The name of the model that is deployed on
                                  Tritonserver.
  --iterative                     Iterative mode will emit DataFrames one at a
                                  time. Otherwise a list of DataFrames is
                                  emitted. Iterative mode is good for
                                  interleaving source stages.
  --server_url TEXT               Tritonserver url.  [required]
  --file_type [auto|csv|json]     Indicates what type of file to read.
                                  Specifying 'auto' will determine the file
                                  type from the extension.
  --help                          Show this message and exit.

To launch the configured Morpheus pipeline with the sample data that is provided in examples/data, run the following:

python examples/abp_pcap_detection/run.py

Note: Both Morpheus and Triton Inference Server containers must have access to the same GPUs in order for this example to work.

The pipeline will process the input abp_pcap_dump.jsonlines sample data and write it to .tmp/output/pcap_out.jsonlines.

CLI Example

The above example is illustrative of using the Python API to build a custom Morpheus Pipeline. Alternately, the Morpheus command line could have been used to accomplish the same goal by registering the abp_pcap_preprocessing.py module as a plugin.

From the root of the Morpheus repo, run:

morpheus --log_level INFO --plugin "examples/abp_pcap_detection/abp_pcap_preprocessing.py" \
    run --pipeline_batch_size 100000 --model_max_batch_size 100000 \
    pipeline-fil --model_fea_length 13 --label=probs \
    from-file --filename examples/data/abp_pcap_dump.jsonlines --filter_null False \
    deserialize \
    pcap-preprocess \
    monitor --description "Preprocessing rate" \
    inf-triton --model_name "abp-pcap-xgb" --server_url "localhost:8000" \
    monitor --description "Inference rate" --unit inf \
    add-class --label=probs \
    monitor --description "Add classification rate" --unit "add-class" \
    serialize \
    monitor --description "Serialize rate" --unit ser \
    to-file --filename ".tmp/output/pcap_out_cli.jsonlines" --overwrite \
    monitor --description "Write to file rate" --unit "to-file"