anaconda-cli-base#

A base CLI entrypoint supporting Anaconda CLI plugins using Typer.

Registering plugins#

To develop a subcommand in a third-party package, first create a typer.Typer() app with one or more commands. See this example. The commands defined in your package will be prefixed with the subcommand you define when you register the plugin.

In your pyproject.toml subcommands can be registered as follows:

# In pyproject.toml

[project.entry-points."anaconda_cli.subcommand"]
auth = "anaconda_cloud_auth.cli:app"

In the example above:

  • "anaconda_cloud_cli.subcommand" is the required string to use for registration. The quotes are important.

  • auth is the name of the new subcommand, i.e. anaconda auth

    • All typer.Typer commands you define in your package are accessible the registered subcommand

    • i.e. anaconda auth <command>.

  • anaconda_cloud_auth.cli:app signifies the object named app in the anaconda_cloud_auth.cli module is the entry point for the subcommand.

Error handling#

By default any exception raised during CLI execution in your registered plugin will be caught and only a minimal message will be displayed to the user.

You can define a custom callback for individual exceptions that may be thrown from your subcommand. You can register handlers for standard library exceptions or custom defined exceptions. It may be best to use custom exceptions to avoid unintended consequences for other plugins.

To register the callback decorate a function that takes an exception as input, and return an integer error code. The error code will be sent back through the CLI and your subcommand will exit with that error code.

from typing import Type
from anaconda_cli_base.exceptions import register_error_handler

@register_error_handler(MyCustomException)
def better_exception_handling(e: Type[Exception]) -> int:
    # do something or print useful information
    return 1

@register_error_handler(AnotherException)
def just_ignore_it(e: Type[Exception])
    # ignore the error and let the CLI exit successfully
    return 0


@register_error_handler(YetAnotherException)
def fix_the_error_and_try_again(e: Type[Exception]) -> int:
    # do something and retry the CLI command
    return -1

In the second example the handler returns -1. This means that the handler has attempted to correct the error and the CLI subcommand should be re-tried. The handler could call another interactive command, like a login action, before attempting the CLI subcommand again.

See the anaconda-cloud-auth plugin for an example custom handler.

Config file#

If your plugin wants to utilize the Anaconda config file, default location ~/.anaconda/config.toml, to read configuration parameters you can derive from anaconda_cli_base.config.AnacondaBaseSettings to add a section in the config file for your plugin. Each subclass of AnacondaBaseSettings defines the section header. The base class is configured so that parameters defined in subclasses can be read in the following priority from lowest to highest.

  1. default value in the subclass of AnacondaBaseSettings

  2. Global config file at ~/.anaconda/config.toml

  3. ANACONDA_<PLUGIN-NAME>_<FIELD> variables defined in the .env file in your working directory

  4. ANACONDA_<PLUGIN-NAME>_<FIELD> env variables set in your shell or on command invocation

  5. value passed as kwarg when using the config subclass directly

Notes:

  • AnacondaBaseSettings is a subclass of BaseSettings from pydantic-settings.

  • Nested pydantic models are also supported.

Here’s an example subclass

from anaconda_cli_base.config import AnacondaBaseSettings

class MyPluginConfig(AnacondaBaseSettings, plugin_name="my_plugin"):
    foo: str = "bar"

To read the config value in your plugin according to the above priority:

config = MyPluginConfig()
assert config.foo == "bar"

Since there is no value of foo in the config file it assumes the default value from the subclass definition.

The value of foo can now be written to the config file under the section my_plugin

# ~/.anaconda/config.toml
[plugin.my_plugin]
foo = "baz"

Now that the config file has been written, the value of foo is read from the config.toml file:

config = MyPluginConfig()
assert config.foo == "baz"

Nested tables#

The AnacondaBaseSettings supports nested Pydantic models.

from anaconda_cli_base.config import AnacondaBaseSettings
from pydantic import BaseModel

class Nested(BaseModel):
    n1: int = 0
    n2: int = 0

class MyPluginConfig(AnacondaBaseSettings, plugin_name="my_plugin"):
    foo: str = "bar"
    nested: Nested = Nested()

In the ~/.anaconda/config.toml you can set values of nested fields as an in-line table

# ~/.anaconda/config.toml
[plugin.my_plugin]
foo = "baz"
nested = { n1 = 1, n2 = 2}

Or as a separate table entry

# ~/.anaconda/config.toml
[plugin.my_plugin]
foo = "baz"

[plugin.my_plugin.nested]
n1 = 1
n2 = 2

To set environment variables use the __ delimiter

ANACONDA_MY_PLUGIN_NESTED__N1=1
ANACONDA_MY_PLUGIN_NESTED__N2=2

Nested plugins#

You can pass a tuple to plugin_name= in subclasses of AnacondaBaseSettings to nest whole plugins, which may be defined in separate packages.

class Nested(BaseModel):
    n1: int = 0
    n2: int = 0
class MyPluginConfig(AnacondaBaseSettings, plugin_name="my_plugin"):
    foo: str = "bar"
    nested: Nested = Nested()

Then in another package you can nest a new config into my_plugin.

class MyPluginExtrasConfig(AnacondaBaseSettings, plugin_name=("my_plugin", "extras")):
    field: str = "default"

The new config table is now nested in the config.toml

# ~/.anaconda/config.toml
[plugin.my_plugin]
foo = "baz"
nested = { n1 = 1, n2 = 2}
[plugin.my_plugin.extras]
field = "value"

And can be set by env variable using the concatenation of plugin_name

ANACONDA_MY_PLUGIN_EXTRAS_FIELD="value"

See the tests for more examples.

Setup for development#

Ensure you have conda installed. Then run:

make setup

Run the unit tests#

make test

Run the unit tests across isolated environments with tox#

make tox
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