ABP Detection Example Using Morpheus

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

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docker pull nvcr.io/nvidia/tritonserver:23.06-py3

Deploy Triton Inference Server

From the root of the Morpheus repo, navigate to the anomalous behavior profiling example directory:

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cd examples/abp_pcap_detection

The following creates the Triton container, mounts the abp-pcap-xgb directory to /models/abp-pcap-xgb in the Triton container, and starts the Triton server:

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docker run --rm --gpus=all -p 8000:8000 -p 8001:8001 -p 8002:8002 -v $PWD/abp-pcap-xgb:/models/abp-pcap-xgb --name tritonserver nvcr.io/nvidia/tritonserver:23.06-py3 tritonserver --model-repository=/models --exit-on-error=false

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:

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

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:

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cd examples/abp_pcap_detection python run.py --help

Output:

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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|json|csv] 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, from the examples/abp_pcap_detection directory run the following:

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python run.py \ --input_file ../data/abp_pcap_dump.jsonlines \ --output_file ./pcap_out.jsonlines \ --model_name 'abp-pcap-xgb' \ --server_url localhost:8001

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 pcap_dump.jsonlines sample data and write it to 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:

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morpheus --log_level INFO --plugin "examples/abp_pcap_detection/abp_pcap_preprocessing.py" \ run --use_cpp False --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:8001" --force_convert_inputs=True \ 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 "pcap_out.jsonlines" --overwrite \ monitor --description "Write to file rate" --unit "to-file"

Note: Triton is still needed to be launched from the examples/abp_pcap_detection directory.

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