rra-tools 1.0.11

Common utilities for IHME Rapid Response team pipelines.

RRA Tools

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Documentation: https://collijk.github.io/rra-tools

Source Code: https://github.com/collijk/rra-tools

PyPI: https://pypi.org/project/rra-tools/

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Common utilities for IHME Rapid Response team pipelines.

CLI Tools

The provided cli tools break down into three categories.

Exception handling

The cli_tools subpackage provides a handle_exceptions wrapper that can be used to wrap functions that may raise exceptions and drop into a pdb shell when an exception is raised. This is useful for debugging functions that are failing in a pipeline.

from rra_tools.cli_tools import handle_exceptions
from loguru import logger

def my_task():
    ...


if __name__ == "__main__":
    runner = handle_exceptions(my_task, logger, with_debugger=True)
    runner()

Dynamic module import

The cli_tools subpackage provides a load_module_from_info function that can be used to dynamically import a module. This is a useful pattern for creating a CLI that can dynamically add new commands and subcommands based on subpackage structure.

TODO: Usage example

Click options

The cli_tools subpackage provides several click options that can be used to create CLI commands with common options.

• with_verbose - Add a --verbose, -v option to the command to increase the log verbosity
• with_debugger - Add a --pdb option to the command to drop into a pdb shell on exception
• with_input_directory - Add a parameterizeable --{dir-name}-dir option to the command to specify an input directory.
• with_output_directory - Add a --output-dir, -o option to the command to specify an output directory.
• with_num_cores - Add a --num-cores, -c option to the command to specify the number of cores to use for parallel processing.
• with_queue - Add a --queue, -q option to the command to specify a slurm queue to run jobs on.
• with_progress_bar - Add a --progress-bar, --pb option to the command to display a progress bar for long-running tasks.
• with_dry_run - Add a --dry-run, -n option to the command to run the command without actually executing the task.

These options do not provide implementations for the options, but rather provide a standard interface for adding these options to a command to ensure consistency across commands. Several of these options are meant to be used with other tools provided in this package like exception handling, logging, and parallel processing.

import click
from loguru import logger

from rra_tools.cli_tools import (
    with_verbose,
    with_debugger,
    with_output_directory,
    handle_exceptions,
)
from rra_tools.logging import configure_logging_to_terminal

def my_task_main(output_dir: str):
    ...


@click.command()
@with_output_directory("/path/to/default/output")
@with_verbose()
@with_debugger()
def my_task(output_dir: str, verbose: int, debugger: bool):
    configure_logging_to_terminal(verbose)
    runner = handle_exceptions(my_task_main, logger, with_debugger=debugger)
    runner(output_dir)

Logging

The logging subpackage provides a number of utilities for configuring loggers and for performance logging. This package is built on top of the loguru package, which provides a more flexible and powerful logging interface than the standard library logging package.

Configuration

There are three main functions for configuring loggers:

• configure_logging_to_terminal - Configure a logger to log to the terminal
• configure_logging_to_file - Configure a logger to log to a file
• add_logging_sink - A generic function to add a sink to a logger

The first two options are convenience functions that set up a logger with a standard configuration. The third option is more flexible and can be used to add additional sinks to a logger.

Performance Logging

The logging subpackage provides a task_performance_logger that is a drop-in replacement for the loguru logger that logs the time taken to run a task.

from rra_tools.logging import task_performance_logger as logger

def my_task():
    logger.debug("Loading training data", context="load_data")
    # Load the data

    logger.debug("Training model", context="train_model")
    # Train the model

    # Using the same context for logging will accumulate time
    # spent in that context across both usages.
    logger.debub("Loading inference_data", context="load_data")
    # Load the inference data

    logger.debug("Evaluating model", context="evaluate_model")
    # Evaluate the model

    logger.debug("Saving results", context="save_results")
    # Save the results

    logger.report()  # Prints out the time spent in each logging context

Additionally, rra_tools provides a command line tool parse_logs that can be used to summarize the performance logs generated by the task_performance_logger. This is useful when trying to understand the runtime characteristics of a pipeline that may run hundreds or thousands of tasks.

parse_logs path/to/output/log/directory/

Shell Tools

The shell_tools module provides a few functions to run common shell commands.

1. wget - Download a file from a URL

    from rra_tools.shell_tools import wget
   
    wget("https://example.com/file.txt", "path/to/output.txt")
   

2. unzip_and_delete_archive - Unzip a file and delete the archive

    from rra_tools.shell_tools import unzip_and_delete_archive
   
    unzip_and_delete_archive("path/to/archive.zip", "path/to/output")
   

   Note: you may need to install unzip on your system to use this function. You can do so with conda install -c conda-forge unzip.

3. mkdir - Create a directory with correct permissions. The default operation of mkdir via the os module or pathlib translates uses the umask of the user running the script along with the permissions set. This often results in unexpected permissions on the created directory. This function allows you to specify the permissions of the directory without relying on the umask.

   from rra_tools.shell_tools import mkdir
   
   mkdir("path/to/directory", mode=0o755)
   # Can also make parents
   mkdir("path/to/other/directory", parents=True, mode=0o775)
   # Can also do a no-op if the directory already exists
   mkdir("path/to/other/directory", mode=0o775)
   

4. touch - Create a file with the correct permissions. Like mkdir, touch allows you to specify the permissions of the file without relying on the umask.

   from rra_tools.shell_tools import touch
   
   touch("path/to/file.txt", mode=0o664)
   

Parallel Processing with Multiprocessing

The parallel module provides a utility to run a function of a single argument in parallel across a list of inputs using multiprocessing.

from rra_tools.parallel import run_parallel

# Trivial example
def my_runner(x):
    return x ** 2

inputs = list(range(1000))

results = run_parallel(
    my_runner,
    inputs,
    num_cores=3,  # By default, num_cores is set to 1 and will run sequentially
)

In practice, the function we want to parallelize will be significantly more complex than the trivial example above. Generally, you want to set things up so that:

1. The function you want to parallelize is self-contained and does not rely on any global state.
2. The function you want to parallelize is relatively expensive to run. If the function is cheap to run, the overhead of parallelization can outweigh the benefits of parallelization.
3. The input argument to the function is relatively small in memory. Multiprocessing needs to copy the input data to each worker process, so if the input data is large, the overhead of copying the data can outweigh the benefits of parallelization. A common way to overcome this limitation is to pass the path to the input data instead of the data itself and then have the function read the data from the path.
4. The function is not too complex. If your runner function is complicated, you may end up with resource contention between the worker processes that is hard to understand (e.g. you may run out of memory because each worker process is trying is loading a big dataset at the same time). There's no hard and fast rule here, but once functions get to be more than a few dozen lines long, you should start thinking about whether process-based parallelization is the right choice, and maybe opt for a different parallelization strategy (like jobmon described below).

Jobmon Integration

The jobmon module provides a set of utilities to run more complicated parallel jobs by interfacing with a job scheduler like slurm. See Jobmon documentation for more information.

Installation

Jobmon is not installed by default with rra-tools and is only available to download and install on the IHME cluster. To install jobmon, you must have a conf file in your home directory at ~/.pip/pip.conf with the following contents:

[global]
extra-index-url = https://artifactory.ihme.washington.edu/artifactory/api/pypi/pypi-shared/simple
trusted-host = artifactory.ihme.washington.edu/artifactory/api/pypi/pypi-shared

Then you can install jobmon with:

pip install jobmon[ihme]

Usage

TBD

Translation

The translate module provides functions to translate text files from one language to another.

from rra_tools.translate import translate_text_file

translate_text_file("path/to/input.txt", "path/to/output.txt")

By default, it will attempt to autodetect the language in the input file and produce outputs in English, but you can specify the source and target languages:

from rra_tools.translate import translate_text_file
# Translate from German to Spanish
translate_text_file(
    "path/to/input.txt",
    "path/to/output.txt",
    source_language="de",
    target_language="es",
)

The translate subpackage can also translate dataframe columns

import pandas as pd
from rra_tools.translate import translate_dataframe

df = pd.DataFrame({"text": ["hola", "mundo"]})
translated_df = translate_dataframe(df, columns=["text"])

---

Installation

pip install rra-tools

Development

Instructions using conda:

1. Clone this repository.

   Over ssh:

   git clone [e-mail]:ihmeuw/climate-downscale.git
   

   Over https:

   git clone https://github.com/ihmeuw/climate-downscale.git
   

2. Create a new conda environment.

   conda create -n climate-downscale python=3.10
   conda activate climate-downscale
   

3. Install poetry and the project dependencies.

   conda install poetry
   poetry install
   

Documentation

The documentation is automatically generated from the content of the docs directory and from the docstrings of the public signatures of the source code. The documentation is updated and published as a Github project page automatically as part each release.

Releasing

Trigger the Draft release workflow (press Run workflow). This will update the changelog & version and create a GitHub release which is in Draft state.

Find the draft release from the GitHub releases and publish it. When a release is published, it'll trigger release workflow which creates PyPI release and deploys updated documentation.

Pre-commit

Pre-commit hooks run all the auto-formatting (ruff format), linters (e.g. ruff and mypy), and other quality checks to make sure the changeset is in good shape before a commit/push happens.

You can install the hooks with (runs for each commit):

pre-commit install

Or if you want them to run only for each push:

pre-commit install -t pre-push

Or if you want e.g. want to run all checks manually for all files:

poetry run pre-commit run --all-files