pydantic-cli 2.3.0

Turn Pydantic defined Data Models into CLI Tools

Pydantic Commandline Tool Interface

Turn Pydantic defined Data Models into CLI Tools and enable loading values from JSON files

Requires Pydantic >=1.5.1.

Installation

pip install pydantic-cli

Features and Requirements

1. Thin Schema driven interfaces constructed from Pydantic defined data models
2. Validation is performed in a single location as defined by Pydantic's validation model and defined types
3. CLI parsing is only structurally validating that the args or optional arguments are provided
4. Enable loading config defined in JSON to override or set specific values
5. Clear interface between the CLI and your application code
6. Leverage the static analyzing tool mypy to catch type errors in your commandline tool
7. Easy to test (due to reasons defined above)

Quick Start

To create a commandline tool that takes an input file and max number of records to process as arguments:

my-tool --input_file /path/to/file.txt --max_records 1234

This requires two components.

• Create Pydantic Data Model of type T
• write a function that takes an instance of T and returns the exit code (e.g., 0 for success, non-zero for failure).
• pass the T into to the to_runner function, or the run_and_exit

Explicit example show below.

import sys

from pydantic import BaseModel
from pydantic_cli import run_and_exit, to_runner

class MinOptions(BaseModel):
    input_file: str
    max_records: int


def example_runner(opts: MinOptions) -> int:
    print(f"Mock example running with options {opts}")
    return 0

if __name__ == '__main__':
    # to_runner will return a function that takes the args list to run and 
    # will return an integer exit code
    sys.exit(to_runner(MinOptions, example_runner, version='0.1.0')(sys.argv[1:]))

Or to implicitly use sys.argv[1:], call can leverage run_and_exit (to_runner is also useful for testing).

if __name__ == '__main__':
    run_and_exit(MinOptions, example_runner, description="My Tool Description", version='0.1.0')

WARNING: Boolean values must be communicated explicitly (e.g., --run_training True). This explicitness is chosen to avoid confusion with auto-generated option flags (--is-run_training or --no-run_training) that do not directly map to the core Pydantic data model.

Loading Configuration using JSON

Tools can also load entire models or partially defined Pydantic data models from JSON files.

For example, given the following Pydantic data model:

from pydantic import BaseModel
from pydantic_cli import run_and_exit, DefaultConfig

class Opts(BaseModel):
    class Config(DefaultConfig):
        CLI_JSON_ENABLE = True

    hdf_file: str
    max_records: int = 10
    min_filter_score: float
    alpha: float
    beta: float

def runner(opts: Opts):
    print(f"Running with opts:{opts}")
    return 0

if __name__ == '__main__':
    run_and_exit(Opts, runner, description="My Tool Description", version='0.1.0')

Can be run with a JSON file that defines all the (required) values.

{"hdf_file": "/path/to/file.hdf5", "max_records": 5, "min_filter_score": 1.5, "alpha": 1.0, "beta": 1.0}

The tool can be executed as shown below. Note, options required at the commandline as defined in the Opts model (e.g., 'hdf_file', 'min_filter_score', 'alpha' and 'beta') are NO longer required values supplied to the commandline tool.

my-tool --json-config /path/to/file.json

To override values in the JSON config file, or provide the missing required values, simply provide the values at the commandline.

These values will override values defined in the JSON config file. The provides a general mechanism of using configuration "preset" files.

my-tool --json-config /path/to/file.json --alpha -1.8 --max_records 100 

Similarly, a partially described data model can be used combined with explict values provided at the commandline.

In this example, hdf_file and min_filter_score are still required values that need to be provided to the commandline tool.

{"max_records":10, "alpha":1.234, "beta":9.876}

my-tool --json-config /path/to/file.json --hdf_file /path/to/file.hdf5 --min_filter_score -12.34

Catching Type Errors with mypy

If you've used argparse, you've probably been bitten by an AttributeError exception raised on the Namespace instance returned from parsing the raw args.

For example,

import sys
from argparse import ArgumentParser


def to_parser() -> ArgumentParser:
    p = ArgumentParser(description="Example")
    f = p.add_argument

    f('hdf5_file', type=str, help="Path to HDF5 records")
    f("--num_records", required=True, type=int, help="Number of records to filter over")
    f('-f', '-filter-score', required=True, type=float, default=1.234, help="Min filter score")
    f('-g', '--enable-gamma-filter', action="store_true", help="Enable gamma filtering")
    return p


def my_library_code(path: str, num_records: float, min_filter_score, enable_gamma=True) -> int:
    print("Mock running of code")
    return 0


def main(argv) -> int:
    p = to_parser()
    pargs = p.parse_args(argv)
    return my_library_code(pargs.hdf5_file, pargs.num_record, pargs.min_filter_score, pargs.enable_gamma_filter)


if __name__ == '__main__':
    sys.exit(main(sys.argv[1:]))

The first error found at runtime is show below.

Traceback (most recent call last):
  File "junk.py", line 35, in <module>
    sys.exit(main(sys.argv[1:]))
  File "junk.py", line 31, in main
    return my_library_code(pargs.hdf5_file, pargs.num_record, pargs.min_filter_score, pargs.enable_gamma_filter)
AttributeError: 'Namespace' object has no attribute 'num_record'

The errors in pargs.num_records and pargs.filter_score are inconsistent with what is defined in to_parser method. Each error will have to be manually hunted down.

With pydantic-cli, it's possible to catch these errors by running mypy. This also enables you to refactor your code with more confidence.

For example,

from pydantic import BaseModel

from pydantic_cli import run_and_exit


class Options(BaseModel):
    input_file: str
    max_records: int


def bad_func(n: int) -> int:
    return 2 * n


def example_runner(opts: Options) -> int:
    print(f"Mock example running with {opts}")
    return 0


if __name__ == "__main__":
    run_and_exit(Options, bad_func, version="0.1.0")

With mypy, it's possible to proactively catch this types of errors.

 mypy pydantic_cli/examples/simple.py                                                                                                                                                                  ✘ 1 
pydantic_cli/examples/simple.py:36: error: Argument 2 to "run_and_exit" has incompatible type "Callable[[int], int]"; expected "Callable[[Options], int]"
Found 1 error in 1 file (checked 1 source file)

Customization and Hooks

If the description is not defined and the Pydantic data model fields are tersely named (e.g., 'total', or 'n'), this can yield a call to --help that is quite minimal (due to the lack of metadata). However, verbosely named arguments can often be good enough to communicate the intent of the commandline interface.

For customization of the CLI args, such as max number of records is -m 1234 in the above example, there are two approaches.

• The first is the quick method that is a minor change to the core Config of the Pydantic Data model.
• The second method is use Pydantic's "Field" metadata model is to define richer set of metadata. See Field model in Pydantic more details.

Customization using Quick Model

We're going to change the usage from my-tool --input_file /path/to/file.txt --max_records 1234 to my-tool -i /path/to/file.txt -m 1234 using the "quick" method by customizing the Pydantic data model "Config".

This only requires adding CLI_EXTRA_OPTIONS to the Pydantic Config.

from pydantic import BaseModel

class MinOptions(BaseModel):

    class Config:
        CLI_EXTRA_OPTIONS = {'input_file': ('-i,), 'max_records': ('-m', ) }

    input_file: str
    max_records: int = 10

You can also override the "long" argument. However, note this is starting to add a new layer of indirection on top of the fields defined in the Pydantic model. For example, 'max_records' maps to '--max-records' at the commandline interface and perhaps might create annoying inconsistencies.

from pydantic import BaseModel

class MinOptions(BaseModel):

    class Config:
        CLI_EXTRA_OPTIONS = {'input_file': ('-i, '), 'max_records': ('-m', '--max-records')}

    input_file: str
    max_records: int = 10

Customization using Quick Model using Schema Driven Approach using Pydantic Field

from pydantic import BaseModel, Field


class Options(BaseModel):

    class Config:
        validate_all = True
        validate_assignment = True

    input_file: str = Field(
        ..., # this implicitly means required=True
        title="Input File",
        description="Path to the input file",
        required=True,
        extras={"cli": ('-f', '--input-file')}
    )

    max_records: int = Field(
        123,
        title="Max Records",
        description="Max number of records to process",
        gt=0,
        extras={'cli': ('-m', '--max-records')}
    )

This will metadata (e.g., title, description) will be communicated in the --help of the commandline tool.

Hooks into the CLI Execution

There are three core hooks into the customization of CLI execution.

• exception handler (log or write to stderr and map specific exception classes to integer exit codes)
• prologue handler (pre-execution hook)
• epilogue handler (post-execution hook)

Both of these cases can be customized to by passing in a function to the running/execution method.

The exception handler should handle any logging or writing to stderr as well as mapping the specific exception to non-zero integer exit code.

For example:

import sys

from pydantic import BaseModel
from pydantic_cli import run_and_exit


class MinOptions(BaseModel):

    class Config:
        CLI_EXTRA_OPTIONS = {'input_file': ('-i, '), 'max_records': ('-m', '--max-records')}

    input_file: str
    max_records: int = 10


def example_runner(opts: MinOptions) -> int:
    return 0


def custom_exception_handler(ex) -> int:
    exception_map = dict(ValueError=3, IOError=7)
    sys.stderr.write(str(ex))
    exit_code = exception_map.get(ex.__class__, 1)
    return exit_code


if __name__ == '__main__':
    run_and_exit(MinOptions, example_runner, exception_handler=custom_exception_handler)

A general pre-execution hook can be called using the prologue_handler. This function is Callable[[T], None], where T is an instance of your Pydantic data model.

This setup hook will be called before the execution of your main function (e.g., example_runner).

import sys
import logging

def custom_prologue_handler(opts) -> None:
    logging.basicConfig(level="DEBUG", stream=sys.stdout)

if __name__ == '__main__':
    run_and_exit(MinOptions, example_runner, prolgue_handler=custom_prologue_handler)

Similarly, the post execution hook can be called. This function is Callable[[int, float], None] that is the exit code and program runtime in sec as input.

from pydantic_cli import run_and_exit


def custom_epilogue_handler(exit_code: int, run_time_sec:float):
    m = "Success" if exit_code else "Failed"
    msg = f"Completed running ({m}) in {run_time_sec:.2f} sec"
    print(msg)


if __name__ == '__main__':
    run_and_exit(MinOptions, example_runner, epilogue_handler=custom_epilogue_handler)

SubParsers

Defining a subparser to your commandline tool is enabled by creating a container SubParser dict and calling run_sp_and_exit

import typing as T
from pydantic import BaseModel, AnyUrl



from pydantic_cli.examples import ExampleConfigDefaults
from pydantic_cli import run_sp_and_exit, SubParser


class AlphaOptions(BaseModel):

    class Config(ExampleConfigDefaults):
        CLI_EXTRA_OPTIONS = {'max_records': ('-m', '--max-records')}

    input_file: str
    max_records: int = 10


class BetaOptions(BaseModel):

    class Config(ExampleConfigDefaults):
        CLI_EXTRA_OPTIONS = {'url': ('-u', '--url'),
                             'num_retries': ('-n', '--num-retries')}

    url: AnyUrl
    num_retries: int = 3


def printer_runner(opts: T.Any):
    print(f"Mock example running with {opts}")
    return 0


def to_runner(sx):
    def example_runner(opts) -> int:
        print(f"Mock {sx} example running with {opts}")
        return 0
    return example_runner


def to_subparser_example():

    return {
        'alpha': SubParser(AlphaOptions, to_runner("Alpha"), "Alpha SP Description"),
        'beta': SubParser(BetaOptions, to_runner("Beta"), "Beta SP Description")}


if __name__ == "__main__":
    run_sp_and_exit(to_subparser_example(), description=__doc__, version='0.1.0')

More Examples

More examples are provided here

Limitations

• Currently only support flat "simple" types (e.g., floats, ints, strings, boolean). There's no current support for List[T] or nested dicts.
• Leverages argparse underneath the hood and argparse is a bit thorny of an API to build on top of.

To Improve

• Better type descriptions in --help
• Better support for boolean values to avoid having --run_training True and have more natural CLI arg style, such as --run_training and --no_run_training.
• Better error messages