README.md

Example cyBERT Morpheus Pipeline for Apache Log Parsing

Example Morpheus pipeline using Triton Inference server and Morpheus.

Set up Triton Inference Server

Pull Triton Inference Server Docker Image

Pull Docker image from NGC (https://ngc.nvidia.com/catalog/containers/nvidia:tritonserver) suitable for your environment.

Example:

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

Start Triton Inference Server Container

From the Morpheus repo root directory, run the following to launch Triton and load the log-parsing-onnx model:

docker run --rm -ti --gpus=all -p8000:8000 -p8001:8001 -p8002:8002 -v $PWD/models:/models nvcr.io/nvidia/tritonserver:23.06-py3 tritonserver --model-repository=/models/triton-model-repo --exit-on-error=false --model-control-mode=explicit --load-model log-parsing-onnx

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 |
+------------------+---------+--------+
| log-parsing-onnx | 1       | READY  |
+------------------+---------+--------+

Note: If this is not present in the output, check the Triton log for any error messages related to loading the model.

Run Log Parsing Pipeline

Run the following from the root of the Morpheus repo to start the log parsing pipeline:

python examples/log_parsing/run.py \
    --input_file=./models/datasets/validation-data/log-parsing-validation-data-input.csv \
    --model_vocab_hash_file=data/bert-base-cased-hash.txt \
    --model_vocab_file=./models/training-tuning-scripts/sid-models/resources/bert-base-cased-vocab.txt \
    --model_name log-parsing-onnx \
    --model_config_file=./models/log-parsing-models/log-parsing-config-20220418.json

Use --help to display information about the command line options:

python run.py --help

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_vocab_hash_file FILE    Model vocab hash file to use for pre-
                                  processing  [required]
  --model_vocab_file FILE         Model vocab file to use for post-processing
                                  [required]
  --model_seq_length INTEGER RANGE
                                  Sequence length to use for the model  [x>=1]
  --model_name TEXT               The name of the model that is deployed on
                                  Triton server  [required]
  --model_config_file TEXT        Model config file  [required]
  --server_url TEXT               Tritonserver url  [required]
  --help                          Show this message and exit.

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. To do this we must ensure the examples/log_parsing directory is available in the PYTHONPATH and each of the custom stages are registered as plugins.

From the root of the Morpheus repo, run:

PYTHONPATH="examples/log_parsing" \
morpheus --log_level INFO \
	--plugin "inference" \
	--plugin "postprocessing" \
	run --pipeline_batch_size 1024 --model_max_batch_size 32  \
	pipeline-nlp \
	from-file --filename ./models/datasets/validation-data/log-parsing-validation-data-input.csv  \
	deserialize \
	preprocess --vocab_hash_file data/bert-base-cased-hash.txt --stride 64 --column=raw \
	monitor --description "Preprocessing rate" \
	inf-logparsing --model_name log-parsing-onnx --server_url localhost:8001 --force_convert_inputs=True \
	monitor --description "Inference rate" --unit inf \
	log-postprocess --vocab_path ./models/training-tuning-scripts/sid-models/resources/bert-base-cased-vocab.txt \
		--model_config_path=./models/log-parsing-models/log-parsing-config-20220418.json \
	to-file --filename ./log-parsing-output.jsonlines --overwrite  \
	monitor --description "Postprocessing rate"