tensorboard_launch_script.py

#!/usr/bin/env python3
from tensorboard import default
from tensorboard import program
import re

# ToDo: Low Priority
#  Investigate if there is a way to derive the default port without having to hard code
#  magic numbers like this.
default_port = 6006

class TBoardLauncher:
    """ This is a utility class for being able to launch the tensorboard server for monitoring
    the model's training without having to mess around with command console.
    """

    def __init__(self, dir_path):
        global default_port
        # monthly_logs = glob.glob(os.path.join(dir_path,"**","logs"),recursive=True)
        self.dir_path = dir_path
        self.specific_port = default_port
        default_port +=1
        self.url=""
        self.tb = None
        
                    

    def run(self)->program.TensorBoard:
        """Emulates issuing a command terminal call to launch tensorboard.
        
        A typical call would look like this:
        
        tensorboard --logdir=C:/absolute/path/to/summary_file.tfevents.{tag}.{id} --port=600X
        
        where {tag} and {id} are unique to the specific training session and terminal
        the training is being run on. A key point to observe is that the target file must have
        the `tfevents` extension in it's name. Whether you need to provide the absolute path, as
        opposed to a relative path, for logdir is system dependent I believe. 
        
        This function will send data to the loop-back socket on your system, `127.0.0.1`, for the user
        to view summary results in their browser. They should be able to view the output at the 
        following url:
            `https://localhost:some_port`
        Where `some_port` is an integer in the range [6006,inf), based upon the number of
        TensorBoard instances you spool up in a given instance of the python interpreter
        
        The initial creation of this function was inspired by looking at:
            /tensorboard/main.py
            
        Subsequent modifications were made after reading commends on the following StackOverflow
        post:
        
            https://stackoverflow.com/a/52295534/7412747
        
        :return:
            A reference to the server object that manages interactions with tfevent files for
            producing data to be viewed on the dashboard.
        """
        # ToDo: low priority;
        #  figure out a way to filter graph scalars based upon the presence of NaN values
        #  See this link for initial inspiriation:
        #  https://stackoverflow.com/q/48945404/7412747
        if self.tb:
            return self.tb
        program.setup_environment()
        self.tb = program.TensorBoard(default.get_plugins(),
                                 program.get_default_assets_zip_provider())
        self.tb.configure(argv=[None, '--logdir', self.dir_path, "--port",str(self.specific_port)])
        url = str(self.tb.launch())
        re_result = re.search("(?<=//).+(?=:)",url)
        url = url[:re_result.regs[0][0]]+"localhost"+url[re_result.regs[0][1]:]
        url = 'TensorBoard at {} \n'.format(url)
        self.url=url
        return self.tb