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Merge pull request #11 from mhkarsten/master
PicoLog CM3 + Self Measurement Implementation
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‎.gitignore‎

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.idea
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.venv
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.vagrant
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*/__pycache__/*
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examples/linux-ps-profiling/primer
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examples/linux-powerjoular-profiling/primer
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_trial_temp/
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pycharm-interpreter.sh
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python
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Session.vim
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Vagrantfile

‎examples/measure-self-profiling/README.md‎

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# Self Measurement with `EnergiBridge`
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A simple Linux example, of how to enable the self-measurement feature of experiment-runner.
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We use the [EnergiBridge](https://github.com/tdurieux/EnergiBridge) program to measure power consuption
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of the entire system while performing any experiment.
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As an example program, we simply sleep for a number of seconds while
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## Requirements
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[EnergiBridge](https://github.com/tdurieux/EnergiBridge) is assumed to be already installed.
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To install EnergiBridge, please follow the instructions on their GitHub repo.
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By default we assume that (on Linux) your EnergiBridge binary executable is located at /usr/local/bin/energibridge.
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If you have installed it in a different location you can specify this in RunnerConfig.
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## Running
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Set RunnerConfig to True in your RunnerConfig.
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Optionally set self_measure_bin to the path of your executable.
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From the root directory of the repo, run the following command:
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```bash
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python experiment-runner/ examples/measure-self-profiling/RunnerConfig.py
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```
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## Results
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The results are generated in the `examples/measure-self-profiling/experiments` folder, and are added to your run table model.
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A log file can be specified to additionally save the full energibridge logs to a separate file.
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**!!! WARNING !!!**: COLUMNS IN THE `energibridge.log` FILES CAN BE DIFFERENT ACROSS MACHINES.
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ADJUST YOUR ANALYSIS OF THE RESULTS ACCORDINGLY.
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‎examples/measure-self-profiling/RunnerConfig.py‎

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from EventManager.Models.RunnerEvents import RunnerEvents
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from EventManager.EventSubscriptionController import EventSubscriptionController
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from ConfigValidator.Config.Models.RunTableModel import RunTableModel
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from ConfigValidator.Config.Models.RunnerContext import RunnerContext
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from ConfigValidator.Config.Models.OperationType import OperationType
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from ProgressManager.Output.OutputProcedure import OutputProcedure as output
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from typing import Optional, Dict, Any
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from pathlib import Path
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from os.path import dirname, realpath
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import time
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class RunnerConfig:
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ROOT_DIR = Path(dirname(realpath(__file__)))
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# ================================ USER SPECIFIC CONFIG ================================
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"""The name of the experiment."""
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name: str = "new_runner_experiment"
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"""The path in which Experiment Runner will create a folder with the name `self.name`, in order to store the
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results from this experiment. (Path does not need to exist - it will be created if necessary.)
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Output path defaults to the config file's path, inside the folder 'experiments'"""
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results_output_path: Path = ROOT_DIR / 'experiments'
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"""Experiment operation type. Unless you manually want to initiate each run, use `OperationType.AUTO`."""
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operation_type: OperationType = OperationType.AUTO
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"""The time Experiment Runner will wait after a run completes.
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This can be essential to accommodate for cooldown periods on some systems."""
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time_between_runs_in_ms: int = 1000
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"""
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Whether EnergiBridge should be used to measure the energy consumption of the entire system during
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the experiment.
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This parameter is optional and defaults to False
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"""
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self_measure: bool = True
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"""
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Where the EnergiBridge executable is located. As its not a package on linux distros, and must be
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installed manually, the install location can vary.
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This parameter is optional and defaults to /usr/local/bin/energibridge
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"""
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self_measure_bin: Path = "/usr/local/bin/energibridge"
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"""
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Where to save the full log files for energibridge. If specified, log files are saved to context.run_dir/<self_measure_logfile>.
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If self_measure_logfile is None, then no log file is generated.
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This parameter is optional and defaults to None, where only summary results will be saved in the run table.
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"""
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self_measure_logfile: Path = "energibridge.log"
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# Dynamic configurations can be one-time satisfied here before the program takes the config as-is
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# e.g. Setting some variable based on some criteria
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def __init__(self):
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"""Executes immediately after program start, on config load"""
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EventSubscriptionController.subscribe_to_multiple_events([
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(RunnerEvents.BEFORE_EXPERIMENT, self.before_experiment),
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(RunnerEvents.BEFORE_RUN , self.before_run ),
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(RunnerEvents.START_RUN , self.start_run ),
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(RunnerEvents.START_MEASUREMENT, self.start_measurement),
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(RunnerEvents.INTERACT , self.interact ),
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(RunnerEvents.STOP_MEASUREMENT , self.stop_measurement ),
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(RunnerEvents.STOP_RUN , self.stop_run ),
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(RunnerEvents.POPULATE_RUN_DATA, self.populate_run_data),
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(RunnerEvents.AFTER_EXPERIMENT , self.after_experiment )
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])
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self.run_table_model = None # Initialized later
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output.console_log("Custom config loaded")
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def create_run_table_model(self) -> RunTableModel:
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"""Create and return the run_table model here. A run_table is a List (rows) of tuples (columns),
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representing each run performed"""
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self.run_table_model = RunTableModel(
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factors = [],
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data_columns=['example_data']
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)
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return self.run_table_model
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def before_experiment(self) -> None:
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"""Perform any activity required before starting the experiment here
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Invoked only once during the lifetime of the program."""
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pass
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def before_run(self) -> None:
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"""Perform any activity required before starting a run.
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No context is available here as the run is not yet active (BEFORE RUN)"""
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pass
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def start_run(self, context: RunnerContext) -> None:
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"""Perform any activity required for starting the run here.
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For example, starting the target system to measure.
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Activities after starting the run should also be performed here."""
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pass
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def start_measurement(self, context: RunnerContext) -> None:
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"""Perform any activity required for starting measurements."""
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pass
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def interact(self, context: RunnerContext) -> None:
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"""Perform any interaction with the running target system here, or block here until the target finishes."""
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# No interaction. We just run it for XX seconds while EnergiBridge measures.
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output.console_log("Sleeping 5 seconds to measure system power consumption")
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time.sleep(5)
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def stop_measurement(self, context: RunnerContext) -> None:
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"""Perform any activity here required for stopping measurements."""
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pass
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def stop_run(self, context: RunnerContext) -> None:
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"""Perform any activity here required for stopping the run.
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Activities after stopping the run should also be performed here."""
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pass
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def populate_run_data(self, context: RunnerContext) -> Optional[Dict[str, Any]]:
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"""Parse and process any measurement data here.
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You can also store the raw measurement data under `context.run_dir`
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Returns a dictionary with keys `self.run_table_model.data_columns` and their values populated"""
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return {"example_data": "test value"}
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def after_experiment(self) -> None:
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"""Perform any activity required after stopping the experiment here
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Invoked only once during the lifetime of the program."""
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pass
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# ================================ DO NOT ALTER BELOW THIS LINE ================================
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experiment_path: Path = None

‎examples/picocm3-profiling/README.md‎

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# `PicoLog CM3` profiler
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A simple Linux example that uses stress-ng on a storage device and measures power consumption
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using a [PicoLog CM3](https://www.picotech.com/download/manuals/PicoLogCM3CurrentDataLoggerUsersGuide.pdf).
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As an example program, a simple program is used that repeatedly checks
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if random numbers are prime or not.
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## Requirements
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A [PicoLog CM3](https://www.picotech.com/download/manuals/PicoLogCM3CurrentDataLoggerUsersGuide.pdf) device is required with a clamp of
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the correct variety for your task.
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The [PicoLog CM3 Driver](https://www.picotech.com/downloads/linux) is assumed to be already installed.
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Instructions to install the drivers for your operating system can be found [here](https://www.picotech.com/downloads/linux).
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For this example program, [stress-ng](https://github.com/ColinIanKing/stress-ng) is additionally required as an example of something to measure, but this can be replaced with any other program, or simply a call to sleep if the device is not controlled through software.
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Ensure you have the paremeters for stress-ng properly configured, such that an appropriate storage device is selected, and the PicoLog clamp has been correctly attached to the wire.
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## Running
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From the root directory of the repo, run the following commands:
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```bash
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# REQUIRED: Ensure this path matches where your PicoLog CM3 drivers are stored
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export LD_LIBRARY_PATH="/opt/picoscope/lib"
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python experiment-runner/ examples/picocm3-profiling/RunnerConfig.py
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```
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## Results
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The results are generated in the `examples/picocm3-profiling/experiments` folder.
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There should be a unique log file for each variation in the experiment, as well as a run_table.csv file summarizing these log files.

‎examples/picocm3-profiling/RunnerConfig.py‎

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from EventManager.Models.RunnerEvents import RunnerEvents
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from EventManager.EventSubscriptionController import EventSubscriptionController
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from ConfigValidator.Config.Models.RunTableModel import RunTableModel
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from ConfigValidator.Config.Models.FactorModel import FactorModel
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from ConfigValidator.Config.Models.RunnerContext import RunnerContext
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from ConfigValidator.Config.Models.OperationType import OperationType
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from ProgressManager.Output.OutputProcedure import OutputProcedure as output
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from typing import Dict, Any, Optional
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from pathlib import Path
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from os.path import dirname, realpath
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import subprocess
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import shlex
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from statistics import mean
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from Plugins.Profilers.PicoCM3 import PicoCM3, CM3DataTypes, CM3Channels
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class RunnerConfig:
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ROOT_DIR = Path(dirname(realpath(__file__)))
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# ================================ USER SPECIFIC CONFIG ================================
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"""The name of the experiment."""
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name: str = "new_runner_experiment"
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"""The path in which Experiment Runner will create a folder with the name `self.name`, in order to store the
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results from this experiment. (Path does not need to exist - it will be created if necessary.)
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Output path defaults to the config file's path, inside the folder 'experiments'"""
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results_output_path: Path = ROOT_DIR / 'experiments'
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"""Experiment operation type. Unless you manually want to initiate each run, use `OperationType.AUTO`."""
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operation_type: OperationType = OperationType.AUTO
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"""The time Experiment Runner will wait after a run completes.
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This can be essential to accommodate for cooldown periods on some systems."""
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time_between_runs_in_ms: int = 1000
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# Dynamic configurations can be one-time satisfied here before the program takes the config as-is
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# e.g. Setting some variable based on some criteria
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def __init__(self):
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"""Executes immediately after program start, on config load"""
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EventSubscriptionController.subscribe_to_multiple_events([
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(RunnerEvents.BEFORE_EXPERIMENT, self.before_experiment),
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(RunnerEvents.BEFORE_RUN , self.before_run ),
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(RunnerEvents.START_RUN , self.start_run ),
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(RunnerEvents.START_MEASUREMENT, self.start_measurement),
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(RunnerEvents.INTERACT , self.interact ),
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(RunnerEvents.STOP_MEASUREMENT , self.stop_measurement ),
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(RunnerEvents.STOP_RUN , self.stop_run ),
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(RunnerEvents.POPULATE_RUN_DATA, self.populate_run_data),
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(RunnerEvents.AFTER_EXPERIMENT , self.after_experiment )
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])
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self.latest_log = None
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self.run_table_model = None # Initialized later
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output.console_log("Custom config loaded")
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def create_run_table_model(self) -> RunTableModel:
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"""Create and return the run_table model here. A run_table is a List (rows) of tuples (columns),
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representing each run performed"""
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workers_factor = FactorModel("num_workers", [2, 4])
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write_factor = FactorModel("write_size", [1024, 4096])
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self.run_table_model = RunTableModel(
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factors = [workers_factor, write_factor],
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data_columns=['timestamp', 'channel_1(avg)', 'channel_2(off)', 'channel_3(off)']) # Channel 1 is in Amps
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return self.run_table_model
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def before_experiment(self) -> None:
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"""Perform any activity required before starting the experiment here
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Invoked only once during the lifetime of the program."""
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# Setup the picolog cm3 here (the parameters passed are also the default)
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self.meter = PicoCM3(sample_frequency = 1000, # Sample the CM3 every second
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mains_setting = 0, # Account for 50hz mains frequency
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channel_settings = { # Which channels are enabled in what mode
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CM3Channels.PLCM3_CHANNEL_1.value: CM3DataTypes.PLCM3_1_MILLIVOLT.value,
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CM3Channels.PLCM3_CHANNEL_2.value: CM3DataTypes.PLCM3_OFF.value,
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CM3Channels.PLCM3_CHANNEL_3.value: CM3DataTypes.PLCM3_OFF.value})
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# Open the device
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self.meter.open_device()
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def before_run(self) -> None:
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"""Perform any activity required before starting a run.
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No context is available here as the run is not yet active (BEFORE RUN)"""
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pass
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def start_run(self, context: RunnerContext) -> None:
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"""Perform any activity required for starting the run here.
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For example, starting the target system to measure.
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Activities after starting the run should also be performed here."""
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num_workers = context.run_variation['num_workers']
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write_size = context.run_variation['write_size']
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# Start stress-ng
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stress_cmd = f"sudo stress-ng \
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--hdd {num_workers} \
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--hdd-write-size {write_size} \
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--hdd-ops 1000000 \
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--hdd-dev /dev/sda1 \
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--timeout 60s \
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--metrics-brief"
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stress_log = open(f'{context.run_dir}/stress-ng.log', 'w')
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self.stress_ng = subprocess.Popen(shlex.split(stress_cmd), stdout=stress_log)
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def start_measurement(self, context: RunnerContext) -> None:
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"""Perform any activity required for starting measurements."""
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# Start the picologs measurements here, create a unique log file for each (or pass the values through a variable)
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self.latest_log = str(context.run_dir.resolve() / 'picocm3.log')
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self.meter.log(finished_fn=lambda: self.stress_ng.poll() == None, logfile=self.latest_log)
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def interact(self, context: RunnerContext) -> None:
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"""Perform any interaction with the running target system here, or block here until the target finishes."""
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# Wait for stress-ng to finish or time.sleep(60)
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self.stress_ng.wait()
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def stop_measurement(self, context: RunnerContext) -> None:
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"""Perform any activity here required for stopping measurements."""
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# Wait for stress-ng to finish
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self.stress_ng.wait()
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def stop_run(self, context: RunnerContext) -> None:
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"""Perform any activity here required for stopping the run.
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Activities after stopping the run should also be performed here."""
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pass
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def populate_run_data(self, context: RunnerContext) -> Optional[Dict[str, Any]]:
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"""Parse and process any measurement data here.
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You can also store the raw measurement data under `context.run_dir`
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Returns a dictionary with keys `self.run_table_model.data_columns` and their values populated"""
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if self.latest_log == None:
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return {}
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# Read data from the relavent CM3 log
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log_data = self.meter.parse_log(self.latest_log)
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return {'timestamp': log_data['timestamp'][0] + " - " + log_data['timestamp'][-1],
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'channel_1(avg)': mean(log_data['channel_1']),
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'channel_2(off)': mean(log_data['channel_2']),
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'channel_3(off)': mean(log_data['channel_3'])}
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def after_experiment(self) -> None:
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"""Perform any activity required after stopping the experiment here
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Invoked only once during the lifetime of the program."""
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# This must always be run
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self.meter.close_device()
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# ================================ DO NOT ALTER BELOW THIS LINE ================================
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experiment_path: Path = None

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