# Configure an environment for DMM

> Configure an environment for DMM - Configure an environment to develop custom metrics with the
> DataRobot Model Metrics library.

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Companion generated at `2026-04-24T16:03:56.255190+00:00` (UTC).

## Primary page

- [Configure an environment for DMM](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-environment-setup.html): Full documentation for this topic (HTML).

## Sections on this page

- [Initialize the environment](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-environment-setup.html#initialize-the-environment): In-page section heading.
- [Using thesave_to_csv()utility](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/dmm-environment-setup.html#using-the-save_to_csv-utility): In-page section heading.

## Related documentation

- [Developer documentation](https://docs.datarobot.com/en/docs/api/index.html): Linked from this page.
- [Code-first tools](https://docs.datarobot.com/en/docs/api/code-first-tools/index.html): Linked from this page.
- [Model Metrics](https://docs.datarobot.com/en/docs/api/code-first-tools/dr-model-metrics/index.html): Linked from this page.

## Documentation content

# Configure an environment for DMM

There are two primary ecosystems where you can develop a custom metric with the DataRobot Model Metrics library (DMM):

1. Within the DataRobot application (via a notebook or a scheduled job).
2. In a local development environment.

> [!NOTE] Environment considerations
> Installing Python modules:
> 
> When running DMM from within the DataRobot application, the ecosystem is pre-configured with all required Python modules.
> When running DMM locally, you need to install the
> dmm
> module. This automatically updates the Python environment with all required modules.
> 
> Setting DMM parameters:
> 
> When running DMM from within the DataRobot application, the parameters are set through environment variables.
> When running DMM locally, it is recommended that you pass values using arguments to set these parameters, rather than setting environment variables.

## Initialize the environment

The `CustomMetricArgumentParser` class wraps the standard `argparser.ArgumentParser`. This class provides convenience functions to allow reading values from the environment or normal argument parsing. When `CustomMetricArgumentParser.parse_args()` is called, it checks for missing values.

The `log_manager` provides a set of functions to help with logging. The DMM library and the DataRobot public API client use standard Python `logging` primitives. A complete list of log classes with their current levels is available using `get_log_levels()`. The `initialize_loggers()` function initializes all loggers:

```
2024-08-09 02:19:50 PM - dmm.data_source.datarobot_source - INFO - fetching the next predictions dataframe... 2024-07-15 00:00:00 - 2024-08-09 14:19:46.643722
2024-08-09 02:19:56 PM - urllib3.connectionpool - DEBUG - https://app.datarobot.com:443 "POST /api/v2/deployments/66a90a711zd81645df8c469c/predictionDataExports/ HTTP/1.1" 202 368
```

The following snippet shows how to set up your runtime environment using the previously mentioned classes:

```
import sys
from dmm import CustomMetricArgumentParser
from dmm.log_manager import initialize_loggers

parser = CustomMetricArgumentParser(description="My new custom metric")
parser.add_base_args()  # adds standard arguments
# Add more with standard ArgumentParser primitives, or some convenience functions such as add_environment_arg()

# Parse the program arguments (if any) to an argparse.Namespace.
args = parser.parse_args(sys.argv[1:])

# Initialize the logging based on the 'LOG' environment variable, or the --log option
initialize_loggers(args.log)
```

The standard/base arguments include the following:

| Argument | Description |
| --- | --- |
| BASE_URL | The URL of the public API. |
| API_KEY | The API token used for authentication to the server located at BASE_URL. |
| DEPLOYMENT_ID | The deployment ID from the application. |
| CUSTOM_METRIC_ID | The custom metric ID from the application. |
| DRY_RUN | The flag to indicate whether to report the custom metric result to the deployment. With the DRY_RUN runtime parameter set to 1, the run is a test run and does not report metric data. |
| START_TS | The start of the time range for metric calculation. |
| END_TS | The end of the time for metric calculation. |
| MAX_ROWS | The maximum number of prediction rows to process. |
| LOG | The initialization of logging—defaults to setting all dmm and datarobot modules to WARNING. |

The following is an example of the help provided using `CustomMetricArgumentParser`:

```
(model-runner) $ python3 custom.py --help
usage: custom.py [-h] [--api-key KEY] [--base-url URL] [--deployment-id ID] [--custom-metric-id ID] [--dry-run] [--start-ts TIMESTAMP] [--end-ts TIMESTAMP] [--max-rows ROWS] [--required] [--log [[NAME:]LEVEL ...]]

My new custom metric

optional arguments:
  -h, --help            show this help message and exit
  --api-key KEY         API key used to authenticate to server. Settable via 'API_KEY', required.
  --base-url URL        URL for server. Settable via 'BASE_URL' (default: https://staging.datarobot.com/api/v2), required.
  --deployment-id ID    Deployment ID. Settable via 'DEPLOYMENT_ID' (default: None), required.
  --custom-metric-id ID
                        Custom metric ID. Settable via 'CUSTOM_METRIC_ID' (default: None), required.
  --dry-run             Dry run. Settable via 'DRY_RUN' (default: False).
  --start-ts TIMESTAMP  Start timestamp. Settable with 'START_TS', or 'LAST_SUCCESSFUL_RUN_TS' (when not dry run). Default is 2024-08-08 14:27:55.493027
  --end-ts TIMESTAMP    End timestamp. Settable with 'END_TS' or 'CURRENT_RUN_TS'. Default is 2024-08-09 14:27:55.493044.
  --max-rows ROWS       Maximum number of rows. Settable via 'MAX_ROWS' (default: 100000).
  --required            List the required properties and exit.
  --log [[NAME:]LEVEL ...]
                        Logging level list. Settable via 'LOG' (default: WARNING).
(model-runner) $ 
```

## Using the save_to_csv() utility

During development, it is common to run your code over the same data multiple times to see how changes impact the results. The `save_to_csv()` utility allows you to save your results to a CSV file, so you can compare the results between successive runs on the same data.