Tesseract is an intelligent resource prediction tool for scientific applications and workflows. It provides a generic and user-friendly way to predict resource usage from historical performance data. Tesseract is used primarily to predict resource usage of scientific workflows for the purpose of resource provisioning, but it can also be used to collect and analyze performance data for whatever purpose, such as identifying execution anomalies or predicting failures.
This repository contains the code for Tesseract as well as some additional scripts that were used to generate performance data for a number of scientific workflows:
For more details about this project, please refer to the associated workshop paper and dissertation.
Note: Not to be confused with the Tesseract OCR Engine.
To use Tesseract, you will need to create an Anaconda environment to provide the necessary Python dependencies:
conda env create -f environment.ymlTesseract is intended to work with Nextflow pipelines. Additionally, Tesseract is itself a Nextflow "meta-pipeline" which provides a more user-friendly way to use the various Python scripts, so Nextflow is required to run your own Nextflow pipelines as well as the meta-pipeline.
Tesseract can be applied to any application or Nextflow workflow. Individual applications can be easily wrapped into a Nextflow pipeline; refer to the HemeLB pipeline in this repository. Workflows based on a different workflow manager must be converted into a Nextflow pipeline, which should also not be difficult as Nextflow is highly generic and easy to use. Tesseract relies on Nextflow's trace feature, which automatically collects a performance trace of every task that is executed. Again, refer to the HemeLB example in this repository.
Once you have your application in a Nextflow pipeline, you must annotate each process statement by adding "trace" directives to the process script. These directives will specify the input features of the execution, which will be parsed by Tesseract and supplied as the input features of your performance dataset. You can specify whatever features you like, and each process can have different features, as Tesseract will create separate prediction models for each process.
Here are some example trace directives that were taken from the KINC pipeline:
echo "#TRACE dataset=${dataset}"
echo "#TRACE n_rows=`tail -n +1 ${emx_txt_file} | wc -l`"
echo "#TRACE n_cols=`head -n +1 ${emx_txt_file} | wc -w`"
echo "#TRACE hardware_type=${params.similarity_hardware_type}"
echo "#TRACE chunks=${params.similarity_chunks}"In this example, dataset, hardware_type, and chunks are defined directly from existing Nextflow variables. On the other hand, n_rows and n_cols are the dimensions of a text file which must be computed during task execution. All of these directives will be printed to the execution log. After the workflow completes, Tesseract will extract these input features from the execution log for each executed task.
The variables in this example have been determined to be the most relevant input features for KINC. You will have to make a similar selection for each process in your pipeline. It is better to be inclusive rather than exclusive at this stage; you can include as many features as you want and you can always remove them from your dataset later, but to add a new feature after the fact you will have to redo all of your application runs.
Keep in mind that trace directives should be fast and easy to compute, otherwise they may affect the execution trace.
Once you have an annotated Nextflow pipeline, you just need to run it many times to generate plenty of performance data. There are multiple ways to do this; you can do a parameter sweep like the examples in this repo, or you can simply use it in your normal work and allow the performance data to accumulate. In any case, each workflow run will create a performance trace called trace.txt. You must collect these trace files into one location and aggregate them into a performance dataset:
bin/aggregate.py <trace-files>This script will concatenate the trace files, obtain the input features for each individual run, and output a performance dataset for each process in your pipeline. These datasets can then be used for normal data science tasks such as visualization and training models.
Note: Tesseract parses the #TRACE directives from the execution logs, which are located in Nextflow's work directory tree. If you delete this work directory, or if it gets automatically deleted by some data retention policy, the input features for your application runs will be lost. So make sure to aggregate your performance data in a timely fashion!
The visualize.py script provides a simple but powerful interface for creating many kinds of visualizations from a dataframe. You can visualize one or two variables, and you can disaggregate across up to three dimensions. The script supports both discrete and continuous variables, and it will by default select the most appropriate type of plot for the variables that you specify. Refer to the example pipelines for example uses of this script.
Use train.py to train prediction models on your performance dataset. You can use any columns as inputs or outputs, categorical features are automatically one-hot encoded, and you can pick from a variety of machine learning models. The multi-layer perceptron (MLP) with default settings is a good start. This script will save your trained model to a file so that you can use it later for inference.
Use predict.py to predict resource usage for a particular workflow process.
Here is an end-to-end example usage of Tesseract to predict resource usage for the KINC pipeline.
# provide input data for kinc
mkdir workflows/kinc/input
# ...
# generate conditions file
workflows/kinc/bin/make-conditions.sh
# perform several runs of kinc
nextflow run main.nf \
-params-file workflows/kinc/params.json \
--run
# aggregate trace files into datasets
nextflow run main.nf \
-params-file workflows/kinc/params.json \
--aggregate
# train models for each kinc process
nextflow run main.nf \
-params-file workflows/kinc/params.json \
--train
# predict resource usage of kinc/similarity_mpi
nextflow run main.nf \
-params-file workflows/kinc/params.json \
--predict \
--predict_process similarity_mpi \
--predict_inputs 'np=1 n_rows=7050 n_cols=188 hardware_type=cpu'Tesseract is integrated into Nextflow-API, a workflow-as-a-service that allows you to run Nextflow pipelines through a browser interface atop any execution environment. Nextflow-API automatically collects execution traces, including any custom trace directives, and provides interfaces for visualizing performance data, training models, and downloading the raw data for manual analysis. Refer to the Nextflow-API repo for more information.
Trace directives allow you to collect features such as input parameters and input data characteristics, which will allow you to predict resource usage for different inputs. However, if you need to predict resource usage across different computing platforms, particularly runtime, you will also need system metrics such as FLOP rate, memory bandwidth, and disk bandwidth.
We have developed a tool called minibench which allows you to collect metrics for a computing platform and incorporate those metrics into your prediction models as input features. Refer to the minibench repo for more information.