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Supervisor

Supervisor is where the main loop of Enchanted Surrogates is ran. Supervisor orchestrates use of samplers, executors and runners. See the chart below for overall structure of the code.

Workflow chart

Configuration of supervisor

Supervisor needs base_run_dir defined in the configuration file. Example as follows:

supervisor:
  base_run_dir: "path/to/folder"
  run_order:
    - executor: ...
      sampler: ...
      runner: ...

Nested execution

Nested execution allows for running nested sampling schemes, which is useful when one code is used to generate input for another code.

Nested chart

This type of workflow could be configured by

executors:
  A: ...
  B: ...

samplers:
  SomeSampler: ...

runners:
  RunnerA: ...
  RunnerB: ...

supervisor:
  base_run_dir: ...
  run_order:
    - executor: A
      sampler: SomeSampler
      runner: RunnerA
    - executor: B
      sampler: SomeSampler
      runner: RunnerB

In this example configuration, the output of RunnerA can be used as input to RunnerB. Executors, samplers and runners can be used multiple times in different stages of the workflow, note that in this example, both runners use SomeSampler.

See example_nested.yaml.

Multi-runner sequential execution

Alongside nested executing, the supervisor also supports sequential sampling. In sequential sampling, the sampler's batches are called once but the samples go through multiple runners which pass information to each other. This is useful for active learning use cases. Sequential sampling and nested sampling can be used together in the same configuration.

To utilize sequential sampling in configurations, multiple runners need to be defined in the config file as a list. The same applies to executors. The amount of executors and runners defined in run_order must be equal. If this is not met, an exception is thrown. Examples of sequential sampling are provided within the configs directory under example_sequential.yaml.

Example of how a run_order can be defined to perform sequential sampling:

supervisor:
  base_run_dir: "data_dir/sequential_local"
  run_order:
    - sampler: code1_sampler
      executor:
        - code1_executor
        - code2_executor
        - code3_executor
      runner:
        - code1_runner
        - code2_runner
        - code3_runner

Resuming/extending previous runs

The supervisor supports seamlessly resuming a previous run, in case of crashes or timeouts. The previous run can also be extended with more sample points if desired. This is configured with the run_mode config option.

supervisor:
  base_run_dir: ...
  run_mode: "fresh" # "resume" / "extend"
  run_order:
    - executor: ...
      sampler: ...
      runner: ...

"fresh"

Normal run and the default

"resume"

  • Resume an interrupted previous run:
  • Set run_mode: resume under Supervisor in the config file, no other changes are needed.

  • Supervisor keeps track of the run state (how many batches sampled, which nesting depth, and how many samples have been submitted) and using this data restores the run from where it left off and continues from there.

  • Resume a previous run that was completed but increase the budget:

  • Set run mode to resume and also increase the budget of the sampler. From Supervisor point of view, this is same as if the budget always was that much and the run was just interrupted.
  • Eg. increasing budget from 50 to 60 and re-running generates 10 new samples.

"extend"

  • Extend a previous run:
  • Set run_mode: extend under Supervisor in the config file
  • Now setting sampler budget to 10 means that 10 new samples are created.

HPC cluster local storage

Some partitions on some HPC clusters have access to local memory on the run node. Setting the local_storage environment variable appropriately could potentially improve I/O operation performance.

supervisor:
  local_storage: TMPDIR

CSC users, see for example https://docs.csc.fi/computing/disk/#temporary-local-disk-areas

Optional attributes

Also, it is possible to define that enchanted_dataset summary file combining all run results is parquet instead of csv. CSV is default and does not require any configuration.

supervisor:
  summary_datatype: "parquet" # csv by default

Configuring output files

Enchanted surrogates creates lots of intermediate files and by default, all are retained after execution. Keeping or automatically deleting these files can be configured by save_files and save_files_list supervisor config options:

supervisor:
  base_run_dir: ...
  save_files: "all" # or "custom" or "none"
  # if using custom, only the specified files are saved
  save_files_list:
    - enchanted_dataset.csv
    - example_local.csv
    - ...

Hdf5 storage file is not saved if type for it is None. It is created in every other case.

storage:
  type: "hdf5" # or "None"

It is possible to delete unnecessary files from base_run_dir and keep only wanted files. By default all is saved. Option custom saves only described files. None does not save any files.

Note: enchanted_dataset.csv, runs.h5 and logs are always saved.

supervisor:
  save_files: "all" # or "custom" or "none"
  # if using custom, only described files are saved
  save_files_list:
    - file.txt
    - file2.txt

See config folder for example configurations.

Module supervisor.supervisor

Supervisor module.

Provides the Supervisor class, which coordinates configuration, execution, sampling, and result aggregation for simulation runs.

Supervisor 

Supervisor(args, config_path=None)

Creates supervisor which handles configuration, running and file output of the program.

Attributes:

Name Type Description
args Namespace

Namespace containing the configuration parameters

Initializes supervisor and sets class attributes.

Parameters:

Name Type Description Default
args Namespace

Namespace containing the configuration parameters.

 required
config_path str or None

Optional path for configuration file where configuration is fetched from.

 None
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def __init__(self, args, config_path=None):
    """
    Initializes supervisor and sets class attributes.

    Arguments:
        args (argparse.Namespace): Namespace containing the configuration parameters.
        config_path (str or None): Optional path for configuration file where
            configuration is fetched from.
    """
    self.args = args

    self.nested_groups: list[RunGroup] = parse_all_run_groups(args)
    self.base_run_dir = args.supervisor.get("base_run_dir")
    self.run_mode = args.supervisor.get("run_mode", "fresh")
    self.save_files_arg = args.supervisor.get("save_files", "all")
    self.log_failures = args.supervisor.get("log_failures", False)
    if self.base_run_dir is None:
        if sys.stdout.isatty():
            self.base_run_dir = "base_run_dir"
            log.warning(
                "No config for base_run_dir was found, "
                "created base_run_dir folder to working directory"
            )
        else:
            raise ValueError(
                "base_run_dir is not set in the provided configuration"
            )

    self.local_storage = args.supervisor.get("local_storage")

    if self.local_storage and not os.path.exists(self.local_storage):
        env = self.local_storage
        self.local_storage = os.environ.get(env)

        if not self.local_storage:
            log.warning(
                f"Local storage environment variable {env} not set, ignoring..."
            )

    self.data_dir = os.path.join(self.base_run_dir, "data")
    self.current_progress_info_file = os.path.join(self.base_run_dir, LOG_DIR, 'current_progress.txt')
    self.all_progress_info_file = os.path.join(self.base_run_dir, LOG_DIR, 'all_progress.txt')
    self.previous_run_file = os.path.join(self.base_run_dir, "enchanted_run.yaml")
    self.previous_run_data = None

    if self.run_mode in ("resume", "extend"):
        self.previous_run_data = RunData.load(self.previous_run_file)
        if self.previous_run_data:
            if len(self.nested_groups) > self.previous_run_data.depth:
                # Extending should generate budget worth of new samples so add
                # already submitted amount to the current budget
                if self.run_mode == "extend":
                    self.nested_groups[
                        self.previous_run_data.depth
                    ].sampler.budget += self.previous_run_data.submitted_samples

                self.nested_groups[self.previous_run_data.depth].sampler.skip(
                    self.previous_run_data.batch_number + 1
                )
        else:
            raise RuntimeError(
                "Tried to continue from previous sampling but no "
                " enchanted_run.yaml was found"
            )

        self.continue_with_base_run_dir(config_path)
    else:
        self.create_base_run_dir(self.base_run_dir, config_path)

all_processes_done 

all_processes_done(name_filter=None)

Monitors simulation processes and returns boolean describing state. Helper function for wait_all_processes.

Parameters:

Name Type Description Default
filter str or None

Optional filter used to limit checking to run directories containing this text. If None (default), all run directories are checked.

 required

Returns: True when all simulations are done. Helper function for wait_all_processes. Checks inside base_run_dir if folders inside it contain "enchanted_datapoint.csv" files. False If any runner has not yet created the csv file

Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def all_processes_done(self, name_filter=None):
    """
    Monitors simulation processes and returns boolean describing state.
    Helper function for wait_all_processes.

    Args:
        filter (str or None): Optional filter used to limit checking to run directories
            containing this text. If None (default), all run directories are checked.
    Returns:
        True when all simulations are done. Helper function for
            wait_all_processes. Checks inside base_run_dir if folders inside it
            contain "enchanted_datapoint.csv" files.
        False If any runner has not yet created the csv file
    """

    for name in os.listdir(self.data_dir):
        if not name_filter or str(name_filter) in str(name):
            folder_path = os.path.join(self.data_dir, name)
            if os.path.isdir(folder_path):
                datapoint_file = os.path.join(
                    folder_path, "enchanted_datapoint.csv"
                )
                if not os.path.isfile(datapoint_file):
                    return False

    return True

batch_dirs_done 

batch_dirs_done(run_dirs)

Checks if enchanted_datapoint.csv files exist in the directories list given

Attributes:

Name Type Description
run_dirs list[str]

List of running directories within the batch
Return

False if any of the datapoint files in the run_dirs is missing True if all datapoint files are found

Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def batch_dirs_done(self, run_dirs: list[str]) -> bool:
    """
    Checks if enchanted_datapoint.csv files exist in the directories list given

    Attributes:
        run_dirs (list[str]): List of running directories within the batch

    Return:
        False if any of the datapoint files in the run_dirs is missing
        True if all datapoint files are found
    """
    for d in run_dirs:
        if not os.path.isfile(os.path.join(d, "enchanted_datapoint.csv")):
            return False
    return True

continue_with_base_run_dir 

continue_with_base_run_dir(config_path)

Deletes old unfinished bathes prompting the user if they want to keep them Creates a base_run_dir if one does not exist

Attributes:

Name Type Description
config_path str or None

Optional path for configuration file where configuration is fetched from
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def continue_with_base_run_dir(self, config_path):
    """
    Deletes old unfinished bathes prompting the user if they want to keep them
    Creates a base_run_dir if one does not exist

    Attributes:
        config_path (str or None): Optional path for configuration file where
            configuration is fetched from
    """

    if not os.path.exists(self.base_run_dir):
        self.create_base_run_dir(self.base_run_dir, config_path)
        return

    if not os.path.exists(self.data_dir):
        os.makedirs(self.data_dir, exist_ok=True)

    dirs = glob.glob(
        f"{self.data_dir}/"
        + f"d{self.previous_run_data.depth}_b{self.previous_run_data.batch_number + 1}*"
    )

    if not dirs:
        return

    for path in dirs:
        shutil.rmtree(path)

create_base_run_dir 

create_base_run_dir(base_run_dir, config_path)

Creates base directory for simulation run results. Checks if base_run_dir is empty. Prompts user option to delete existing data in base_run_dir. Execution is stopped if user chooses to not delete files. Copies config_file to base_run_dir if config_file was provided.

Attributes:

Name Type Description
base_run_dir str

Path where runner saves result files
config_path str or None

Optional path for configuration file where configuration is fetched from.
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def create_base_run_dir(self, base_run_dir, config_path):
    """
    Creates base directory for simulation run results. Checks if base_run_dir
    is empty. Prompts user option to delete existing data in base_run_dir.
    Execution is stopped if user chooses to not delete files. Copies config_file
    to base_run_dir if config_file was provided.


    Attributes:
        base_run_dir (str): Path where runner saves result files
        config_path (str or None): Optional path for configuration file where
            configuration is fetched from.
    """

    # Make sure that there is nothing in base_run_dir
    if os.path.exists(base_run_dir):
        if next(os.scandir(base_run_dir), None):
            if sys.stdout.isatty():
                value = input(
                    str(os.path.abspath(base_run_dir))
                    + "\nFolders have content. "
                    + "Do you want to delete data in existing folders? y/N "
                )
            else:
                print(
                    str(os.path.abspath(base_run_dir))
                    + "\nFolders have content. If you wish to continue, go delete them"
                )
                value = "n"

            if value.lower() in ("y", "yes"):
                shutil.rmtree(base_run_dir)
                print("base_run_dir was deleted")
            else:
                print("No content was deleted. Enchanted surrogates is exited.")
                sys.exit(1)

    # Create base run dir and data dir inside it
    os.makedirs(base_run_dir, exist_ok=True)
    os.makedirs(self.data_dir, exist_ok=True)

    # Move config path to base_run_dir if config path is given
    if config_path is not None:
        os.makedirs(os.path.join(base_run_dir, "config"), exist_ok=True)
        config_dir = os.path.join(base_run_dir, "config")

        new_config_path = os.path.join(config_dir, os.path.basename(config_path))
        print(f"Moving config file... from {config_path} to {new_config_path}")
        try:
            shutil.copy(config_path, new_config_path)
        except OSError as exe:
            warnings.warn(
                "Failed to copy configuration file to base run directory.\n"
                "Try using the full path to the config file. \n"
                f"Source: '{config_path}'\n"
                f"Target: '{new_config_path}'\n"
                f"Error type: {type(exe).__name__}\n"
                f"Error message: {exe}"
            )

create_dataset 

create_dataset()

Creates pandas DataFrame that includes all the "enchanted_datapoints.csv" files of running directories inside base_run_dir.

Return

pandas.DataFrame containing all the enchanted_datapoint.csv files created by runners.

Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def create_dataset(self):
    """
    Creates pandas DataFrame that includes all the "enchanted_datapoints.csv"
    files of running directories inside base_run_dir.

    Return:
        pandas.DataFrame containing all the enchanted_datapoint.csv files
        created by runners.

    """
    enchanted_dataset = pd.DataFrame()

    for name in os.listdir(self.data_dir):
        folder_path = os.path.join(self.data_dir, name)
        if os.path.isdir(folder_path):
            datapoint_file = os.path.join(folder_path, "enchanted_datapoint.csv")
            if os.path.isfile(datapoint_file):
                enchanted_datapoint = pd.read_csv(datapoint_file)
                enchanted_dataset = pd.concat(
                    [enchanted_dataset, enchanted_datapoint]
                )
    return enchanted_dataset

delete_unwanted_files 

delete_unwanted_files(argument, base_dir=None)

Deletes files according to command given.

Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def delete_unwanted_files(self, argument: str, base_dir: str | None = None):
    """
    Deletes files according to command given.
    """
    default_list = ["enchanted_dataset.csv", "runs.h5"]
    if argument == "all":
        return

    if argument == "custom":
        saved_list = import_saved_files_list(self.args)
        allowed_files = set(default_list) | set(saved_list)
    elif argument == "none":
        allowed_files = set(default_list)
    else:
        return

    if not base_dir:
        base_dir = self.base_run_dir

    for root, dirs, files in os.walk(base_dir, topdown=False):
        # Remove files
        for file in files:
            # Do nothing for log files
            parent = os.path.basename(os.path.dirname(os.path.join(root, file)))
            if parent == LOG_DIR:
                continue

            if file not in allowed_files:
                file_path = os.path.join(root, file)
                os.remove(file_path)
        # Remove dirs
        for dir_ in dirs:
            # Do nothing for log dir
            if dir_ == LOG_DIR:
                continue
            dir_path = os.path.join(root, dir_)
            if not os.listdir(dir_path):
                os.rmdir(dir_path)

fetch_from_local_storage 

fetch_from_local_storage()

Moves all files from local_storage to base_run_dir, if local_storage is defined.

Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def fetch_from_local_storage(self):
    """
    Moves all files from local_storage to base_run_dir, if local_storage is defined.
    """
    if self.local_storage:
        for item in os.listdir(self.local_storage):
            src = os.path.join(self.local_storage, item)
            dst = os.path.join(self.base_run_dir, item)
            shutil.move(src, dst)

finalize_summary 

finalize_summary(filename='enchanted_dataset')

Finalizes summary after all the batches have been processed. Currently creates parquet summary file if configured in the configuration file.

Attributes:

Name Type Description
filename str

base filename without extension for summarized file
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def finalize_summary(self, filename: str = "enchanted_dataset"):
    """
    Finalizes summary after all the batches have been processed. Currently
    creates parquet summary file if configured in the configuration file.

    Attributes:
        filename (str): base filename without extension for summarized file
    """

    if (
        self.args.supervisor
        and self.args.supervisor.get("summary_datatype") == "parquet"
    ):
        csv_path = os.path.join(self.base_run_dir, f"{filename}.csv")
        if os.path.exists(csv_path):
            df = pd.read_csv(csv_path)
            df.to_parquet(
                os.path.join(self.base_run_dir, f"{filename}.parquet"),
                engine="pyarrow",
                index=False,
            )

get_cartesian_product 

get_cartesian_product(samples, last_dataset)

Creates cartesian product of the new samples and the previous dataset. Used for nested sampling.

Parameters:

Name Type Description Default
samples list[dict]

Sample batch from get_next_samples

 required
last_dataset DataFrame

The complete dataset (summary file) from previous nesting level.

 required

Returns:

Name Type Description
out list[dict]

Cartesian product samples x last_dataset. If last_dataset is empty, only the unaltered samples are returned.
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def get_cartesian_product(self, samples: list[dict], last_dataset: pd.DataFrame) -> list[dict]:
    """
    Creates cartesian product of the new samples and the previous dataset.
    Used for nested sampling.

    Arguments:
        samples (list[dict]): Sample batch from get_next_samples
        last_dataset (pd.DataFrame): The complete dataset (summary file) from
            previous nesting level.

    Returns:
        out (list[dict]): Cartesian product samples x last_dataset. If last_dataset is empty,
            only the unaltered samples are returned.
    """
    if last_dataset.empty:
        return samples

    expanded = []
    for parent in last_dataset.to_dict(orient="records"):
        for sample in samples:
            expanded.append({**parent, **sample})
    return expanded

hdf5_append_datapoints 

hdf5_append_datapoints(new_dirs)

Appends new datapoints to the hdf5 storage file. This allows removing the intermediate files and directories after each batch run.

Parameters:

Name Type Description Default
new_dirs list[str]

List of new datapoint directories created during a single batch

 required
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def hdf5_append_datapoints(self, new_dirs: list[str]):
    """
    Appends new datapoints to the hdf5 storage file. This allows removing
    the intermediate files and directories after each batch run.

    Args:
        new_dirs (list[str]): List of new datapoint directories created
            during a single batch

    """
    h5_path = os.path.join(self.base_run_dir, "runs.h5")

    with h5py.File(h5_path, "a") as f:
        runs_group = f.require_group("data/runs")

        for dir in new_dirs:
            csv_path = os.path.join(dir, "enchanted_datapoint.csv")

            if not os.path.isfile(csv_path):
                continue

            df = pd.read_csv(csv_path)

            # Get just the final path component of dir 
            dir_name = os.path.basename(dir)

            run_group = runs_group.require_group(dir_name)

            # Select only numeric values
            numeric_df = df.select_dtypes(include=[np.number])

            run_group.create_dataset("values", data=numeric_df.to_numpy())

            run_group.create_dataset(
                "columns",
                data=np.array(numeric_df.columns, dtype=h5py.string_dtype("utf-8")),
            )

hdf5_write_aggregate_dataset_and_metadata 

hdf5_write_aggregate_dataset_and_metadata(
    enchanted_dataset,
)

Writes the completed dataset and run metadata to the hdf5 storage file. Dataset has only numeric values, column headers are saved separately in the same location. Metadata includes types for sampler, executor and runner.

Parameters:

Name Type Description Default
enchanted_dataset DataFrame

Dataframe containing all run results

 required
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def hdf5_write_aggregate_dataset_and_metadata(self, enchanted_dataset: pd.DataFrame):
    """
    Writes the completed dataset and run metadata to the hdf5 storage file.
    Dataset has only numeric values, column headers are saved separately 
    in the same location. Metadata includes types for sampler, executor and
    runner.

    Args:
        enchanted_dataset (pd.DataFrame): Dataframe containing all run
            results

    """
    h5_path = os.path.join(self.base_run_dir, "runs.h5")

    with h5py.File(h5_path, "a") as f:
        # Aggregated dataset
        # Remove old dataset if continuing a previous run
        if f.get("data/aggregated"):
            del f["data/aggregated"]

        agg_group = f.require_group("data/aggregated")

        agg_group.create_dataset(
            "values",
            data=enchanted_dataset.select_dtypes(include=[np.number]).to_numpy(),
        )

        agg_group.create_dataset(
            "columns",
            data=np.array(
                enchanted_dataset.select_dtypes(include=[np.number]).columns,
                dtype=h5py.string_dtype(encoding="utf-8"),
            ),
        )

        # Metadata
        # Remove old metadata if continuing a previous run
        if f.get("metadata"):
            del f["metadata"]

        meta_group = f.create_group("metadata")
        run_groups = meta_group.create_group("run_groups")
        for i, run_group in enumerate(self.nested_groups):
            meta_run_group = run_groups.create_group(str(i))

            meta_run_group.attrs["sampler"] = str(
                run_group.sampler.__class__.__name__
            )

            meta_run_group.attrs["executors"] = []
            meta_run_group.attrs["runners"] = []
            for j in range(len(run_group.executors)):
                np.append(
                    meta_run_group.attrs["executors"],
                    str(run_group.executors[j].__class__.__name__),
                )
                np.append(
                    meta_run_group.attrs["runners"],
                    str(run_group.runners[j].get("type")),
                )

load_batch_to_df 

load_batch_to_df(run_dirs)

Creates pd.DataFrame combining enchanted_datapoint.csv files in given path list folders

Attributes:

Name Type Description
run_dirs list[str]

List of running directories within the batch

Returns:

Type Description
DataFrame

pd.DataFrame containing batch datapoints combined
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def load_batch_to_df(self, run_dirs: list[str]) -> pd.DataFrame:
    """
    Creates pd.DataFrame combining enchanted_datapoint.csv files in given path list folders

    Attributes:
        run_dirs (list[str]): List of running directories within the batch

    Returns:
        pd.DataFrame containing batch datapoints combined
    """
    dfs = []
    for d in run_dirs:
        file = os.path.join(d, "enchanted_datapoint.csv")
        dfs.append(pd.read_csv(file))
    return pd.concat(dfs)

monitor_runs 

monitor_runs(run_dirs, depth, batch_number)

Keeps checking all the run_dirs for failures and logs the failures it finds

Attributes:

Name Type Description
run_dirs list[str]

List of running directories to monitor
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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    def monitor_runs(self, run_dirs: list[str], depth, batch_number):
        """
        Keeps checking all the run_dirs for failures and logs the failures it finds

        Attributes:
            run_dirs (list[str]): List of running directories to monitor
        """

        run_dirs = set(run_dirs)   # if it isn't already a set
        total = len(run_dirs)
        completed = 0
        num_successes = 0
        while run_dirs:
            for run_dir in list(run_dirs):   # iterate over a snapshot
                result = self.load_run_result(run_dir)
                if result is not None:
                    # remove so it is not rechecked and we are closer to while loop stopping
                    run_dirs.remove(run_dir)
                    completed += 1
                    if result['success']:
                        num_successes += 1
                    else:
                        if self.log_failures:
                            details = "\n".join(f"  {k}: {v}" for k, v in result.items())
                            log_message = f"""\n\n

=== FAILURE =========================================
Run directory: {run_dir}

Result:
{details}
======================================================

                            \n""".strip()

                            log.error(log_message)                            
                    latest_progress_string = self.write_current_progress_string(depth, batch_number, total, completed, num_successes)
                sleep(0.1)
        os.makedirs(os.path.dirname(self.all_progress_info_file), exist_ok=True)
        with open(self.all_progress_info_file, 'a') as file:
            file.write(latest_progress_string)

read_summary 

read_summary(filename='enchanted_dataset')

Reads the summary written by write_summary.

Attributes:

Name Type Description
filename str

base filename without extension for the file to be read

Returns:

Type Description
DataFrame

pd.Dataframe: dataset from the disk or an empty DataFrame if not found
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def read_summary(self, filename: str = "enchanted_dataset") -> pd.DataFrame:
    """
    Reads the summary written by write_summary.

    Attributes:
        filename (str): base filename without extension for the file to be read

    Returns:
        pd.Dataframe: dataset from the disk or an empty DataFrame if not found
    """

    csv_path = os.path.join(self.base_run_dir, f"{filename}.csv")
    if os.path.exists(csv_path):
        return pd.read_csv(csv_path)

    return pd.DataFrame()

start 

start()

Main function of the supervisor. Starts the simulation process. Currently is the only function, that is accessed outside of supervisor.py. Gathers samples and paths, and gives them to executor. After all processes are finished, creates summary file.

Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def start(self):
    """
    Main function of the supervisor. Starts the simulation process. Currently
    is the only function, that is accessed outside of supervisor.py.
    Gathers samples and paths, and gives them to executor. After all processes
    are finished, creates summary file.
    """

    log.info("Starting runs...")
    if self.local_storage:
        real_run_dir = self.local_storage
    else:
        real_run_dir = self.base_run_dir

    last_complete_dataset = pd.DataFrame()

    for depth, group in enumerate(self.nested_groups):
        batch_number = 0
        batch_dataset = pd.DataFrame()

        # Restore run state from previous data, if needed and in correct position of the loops
        if self.previous_run_data:
            if depth < self.previous_run_data.depth:
                continue

            if depth == self.previous_run_data.depth:
                batch_number = self.previous_run_data.batch_number + 1

                batch_dataset = self.read_summary()
                last_complete_dataset = self.read_summary(
                    "last_complete_enchanted_dataset"
                )
                group.sampler.register_future(last_complete_dataset)

        while group.sampler.has_budget:
            samples = group.sampler.get_next_samples()

            # Merge parameter names for nesting. On first depth run, expanded=samples
            expanded = self.get_cartesian_product(samples, last_complete_dataset)

            # Run for each sequential runner/executor combination
            df_batch = pd.DataFrame()
            for i, (executor, runner) in enumerate(
                zip(group.executors, group.runners)
            ):
                run_dirs = [
                    os.path.join(
                        real_run_dir, "data", f"d{depth}_b{batch_number}_s{j}_r{i}"
                    )
                    for j in range(len(expanded))
                ]
                executor.execute(list(zip(run_dirs, expanded)), runner)

                # monitor runs for failures and update progress file
                self.write_current_progress_string(depth, batch_number, len(run_dirs), 0, 0)
                self.monitor_runs(run_dirs, depth = depth, batch_number = batch_number)

                # Wait processes of current batch to complete
                self.wait_batch_dirs(run_dirs)

                # Load runner output of this batch, used as input for next sequential run
                df_batch = self.load_batch_to_df(run_dirs)
                expanded = df_batch.to_dict(orient="records")

            # Save batch results into summary files
            batch_dataset = pd.concat([batch_dataset, df_batch])
            if batch_number == 0:
                self.write_summary(df_batch, write_mode="w")
            else:
                self.write_summary(df_batch, write_mode="a")
            group.sampler.register_future(batch_dataset)

            run_data = RunData(
                batch_number=batch_number,
                depth=depth,
                submitted_samples=group.sampler.submitted,
            )
            run_data.save(self.previous_run_file)

            # Appends hdf5 file with new datapoints
            # The final dataset is written later
            if not hasattr(self.args, "storage") or self.args.storage.get("type") != "None":
                self.hdf5_append_datapoints(run_dirs)

            self.fetch_from_local_storage()

            # Clean unwanted files
            self.delete_unwanted_files(self.save_files_arg, real_run_dir)

            batch_number += 1

        # Update data rows for next nesting level
        last_complete_dataset = batch_dataset.copy()

        # Create a summary file with last_complete_dataset for nesting
        if depth < len(self.nested_groups) - 1:
            self.write_summary(
                dataset=last_complete_dataset,
                filename="last_complete_enchanted_dataset",
                write_mode="w",
            )

        self.fetch_from_local_storage()

    # Convert summary now after batches if configured
    self.finalize_summary()

    # Create HDF5 file by default
    if not hasattr(self.args, "storage") or self.args.storage.get("type") != "None":
        self.hdf5_write_aggregate_dataset_and_metadata(last_complete_dataset)

    # Clean unwanted files
    self.delete_unwanted_files(self.save_files_arg, real_run_dir)

    # Clean run_dirs
    print("Shutting down scheduler and workers...")
    for group in self.nested_groups:
        for executor in group.executors:
            executor.clean()

wait_all_processes 

wait_all_processes(name_filter=None)

Waits in while loop until all simulations are done. Loop is broken when all_processes_done returns true. Checks condition once in second.

Parameters:

Name Type Description Default
filter str or None

Optional filter used to limit waiting to run directories containing this text. If None (default), all run directories are waited.

 required
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def wait_all_processes(self, name_filter=None):
    """
    Waits in while loop until all simulations are done. Loop is broken
    when all_processes_done returns true. Checks condition once in
    second.

    Args:
        filter (str or None): Optional filter used to limit waiting to run directories
            containing this text. If None (default), all run directories are waited.
    """

    while True:
        if self.all_processes_done(name_filter):
            break
        sleep(1)

wait_batch_dirs 

wait_batch_dirs(run_dirs)

Waits for batch_dirs_done function to return True

Attributes:

Name Type Description
run_dirs list[str]

List of running directories within the batch
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def wait_batch_dirs(self, run_dirs: list[str]):
    """
    Waits for batch_dirs_done function to return True

    Attributes:
        run_dirs (list[str]): List of running directories within the batch
    """
    while not self.batch_dirs_done(run_dirs):
        sleep(1)

write_summary 

write_summary(
    dataset, filename="enchanted_dataset", write_mode="a"
)

Writes a summary of dataset to base_run_dir/filename This functionality is used within the start function to enable seamless sampling. It appends each dataset on top of the previous dataset by default.

Attributes:

Name Type Description
dataset DataFrame

batch to be written
filename str

base filename without extension for the written file
write_mode str

style of writing summary. appending ("a") is default, write ("w") is used for overwriting summary
Source code in src/enchanted_surrogates/supervisor/supervisor.py 
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def write_summary(
    self,
    dataset: pd.DataFrame,
    filename: str = "enchanted_dataset",
    write_mode: str = "a",
):
    """
    Writes a summary of dataset to base_run_dir/filename
    This functionality is used within the start function to
    enable seamless sampling. It appends each dataset on top of the
    previous dataset by default.

    Attributes:
        dataset (pd.DataFrame): batch to be written
        filename (str): base filename without extension for the written file
        write_mode (str): style of writing summary. appending ("a") is default, write ("w")
            is used for overwriting summary
    """

    csv_path = os.path.join(self.base_run_dir, f"{filename}.csv")
    write_header = write_mode != "a"
    dataset.to_csv(csv_path, mode=write_mode, header=write_header, index=False)