Skip to main content

DRUM - develop, test and deploy custom models

Project description


The DataRobot Model Runner (DRUM) is a tool that allows you to work locally with Python, R, and Java custom models. It can be used to verify that a custom model can run and make predictions before it is uploaded to DataRobot. However, this testing is only for development purposes. DataRobot recommends that any model you wish to deploy should also be tested in the Custom Model Workshop.

DRUM can also:

  • run performance and memory usage testing for models.
  • perform model validation tests, e.g., checking model functionality on corner cases, like null values imputation.
  • run models in a Docker container.

DataRobot DRUM is only tested to support Linux/macOS operating systems. To run DRUM in Windows 10 please use WSL (Windows Subsystem for Linux).


Custom inference models quickstart guide

View examples here.

Custom tasks

View examples here.



All models:

  • Install the dependencies needed to run your code.
  • If you are using a drop-in environment found in this repo, you must pip install these dependencies.

Python models:

Java models:

  • JRE >= 11.

R models:

  • Python >= 3.6.
  • The R framework must be installed.
  • DRUM uses the rpy2 package (by default the latest version is installed) to run R. You may need to adjust the rpy2 and pandas versions for compatibility.

To install DRUM with Python/Java models support:
pip install datarobot-drum

To install DRUM with R support:
pip install datarobot-drum[R]


DRUM supports autocompletion based on the argcomplete package. Additional configuration is required to use it:

  • run activate-global-python-argcomplete --user; this should create a file: ~/.bash_completion.d/python-argcomplete,
  • source created file source ~/.bash_completion.d/python-argcomplete in your ~/.bashrc or another profile-related file according to your system.

If global completion is not completing your script, bash may have registered a default completion function:

  • run complete | grep drum; if there is an output complete -F _minimal <some_line_containing_drum> do
  • complete -r <some_line_containing_drum>

For more information and troubleshooting visit the argcomplete information page.


**drum** -help


Code Directory --code-dir

The --code-dir (code directory) argument is required in all commands and should point to a folder which contains your model artifacts and any other code needed for DRUM to run your model. For example, if you're running DRUM from testdir with a test input file at the root and your model in a subdirectory called model, you would enter:

drum score --code-dir ./model/ --input ./testfile.csv

Additional model code dependencies

Code dir may contain a requirements.txt file, listing dependency packages which are required by code. Only Python and R models are supported.

Format of requirements.txt file

  • for Python: pip requrements file format
  • for R: a package per line

DRUM will attempt to install dependencies only when running with --docker option.

Model template generation

DRUM can help you to generate a code folder template with the custom file described above.
drum new model --code-dir ~/user_code_dir/ --language r
This command creates a folder with a file and a short description:

Batch scoring mode

Run a binary classification custom model

Make batch predictions with a binary classification model. Optionally, specify an output file. Otherwise, predictions are returned to the command line:
drum score --code-dir ~/user_code_dir/ --input 10k.csv --target-type binary --positive-class-label yes --negative-class-label no --output 10k-results.csv --verbose

Run a regression custom model

Make batch predictions with a regression model:
drum score --code-dir ~/user_code_dir/ --input fast-iron.csv --target-type regression --verbose

Testing model performance

You can test how the model performs and get its latency times and memory usage.
In this mode, the model is started with a prediction server. Different request combinations are submitted to it. After it completes, it returns a report.
drum perf-test --code-dir ~/user_code_dir/ --input 10k.csv --target-type binary --positive-class-label yes --negative-class-label no
Report example:

samples   iters    min     avg     max    used (MB)   total (MB)
Test case         1     100   0.028   0.030   0.054     306.934    31442.840
Test case        10     100   0.030   0.034   0.069     307.375    31442.840
Test case       100      10   0.036   0.038   0.045     307.512    31442.840
Test case      1000      10   0.042   0.047   0.058     308.258    31442.840
Test case    100000       1   0.674   0.674   0.674     330.902    31442.840
50MB file    838861       1   5.206   5.206   5.206     453.121    31442.840

For more feature options, see: drum perf-test --help

Model validation checks

You can validate the model on a set of various checks. It is highly recommended running these checks, as they are performed in DataRobot before the model can be deployed.

List of checks:

  • null values imputation: each feature of the provided dataset is set to missing and fed to the model.

To run: drum validation --code-dir ~/user_code_dir/ --input 10k.csv --target-type binary --positive-class-label yes --negative-class-label no Sample report:

Validation check results
Test case         Status
Null value imputation   PASSED

In case of check failure more information will be provided.

Runtime Parameters

Runtime parameters are created by the user via the custom model's version create routes (.e.g DataRobot WEB UI, DataRobot client, etc.). These runtime parameters can then be loaded into the custom model using the RuntimeParameters class, as follows:

from datarobot_drum import RuntimeParameters

def load_model(code_dir):
    url = RuntimeParameters.get("URL_PARAM_1")
    aws_credential = RuntimeParameters.get("AWS_CREDENTIAL_PARAM_1")

During testing and debugging in a local development environment, the user can write the runtime parameter values into a YAML file and provide it as an input to the drum utility. The YAML file can have any name ending with .yaml and should follow the example layout below:

URL_PARAM_1: http://any-desired-location/
    # See the REST API documentation for details on all supported credential types:
    credentialType: s3
    awsAccessKeyId: ABDEFGHIJK...
    awsSecretAccessKey: asdjDFSDJafslkjsdDLKGDSDlkjlkj...
    awsSessionToken: null

For credential type parameters, the value matches the Credentials REST API payload. For a complete example, see the following model template with runtime parameters.

And here is how you use it when running the drum utility:

drum score --runtime-params-file <filepath> --code-dir ~/user_code_dir/ --target-type <target type> --input dataset.csv

Prediction server mode

DRUM can also run as a prediction server. To do so, provide a server address argument:
drum server --code-dir ~/user_code_dir --target-type regression --address localhost:6789

The DRUM prediction server provides the following routes. You may provide the environment variable URL_PREFIX. Note that URLs must end with /.
For complete API specification in Openapi 3.0 format check here drum_server_api.yaml, you can also open it rendered in the Swagger Editor.

  • Status routes:
    A GET URL_PREFIX/ and URL_PREFIX/ping/ routes, shows server status - if the server is alive.
    Example: GET http://localhost:6789/
   {"message": "OK"}
  • Health route:
    A GET URL_PREFIX/health/ route, shows functional health. E.g. model is loaded and functioning properly.
    Example: GET http://localhost:6789/health/

    • Success:
      {"message": "OK"}
    • Error:
        "message": "ERROR: \n\nRunning environment language: Python.\n Failed loading hooks from [/tmp/model/python3_sklearn/] : No module named 'andas'"
  • Info route:
    A GET URL_PREFIX/info/ route, shows information about running model (metadata, paths, predictor type, etc.).
    Example: GET http://localhost:6789/info/

       "codeDir": "/tmp/model/python3_sklearn",
       "drumServer": "flask",
       "drumVersion": "1.5.3",
       "language": "python",
       "modelMetadata": {
         "environmentID": "5e8c889607389fe0f466c72d",
         "inferenceModel": {
           "targetName": "Grade 2014"
         "modelID": "5f1f15a4d6111f01cb7f91fd",
         "name": "regression model",
         "targetType": "regression",
         "type": "inference",
         "validation": {
           "input": "../../../tests/testdata/juniors_3_year_stats_regression.csv"
       "predictor": "scikit-learn",
       "targetType": "regression"
  • Statistics route:
    A GET URL_PREFIX/stats/ route, shows running model statistics (memory).
    Example: GET http://localhost:6789/stats/
    mem_info::drum_rss represent a sum of drum_info::mem values.

      "drum_info": [{
          "cmdline": [
          "mem": 256.71484375,
          "pid": 342391
      "mem_info": {
          "avail": 17670.828125,
          "container_limit": null,
          "container_max_used": null,
          "container_used": null,
          "drum_rss": 256.71484375,
          "free": 312.33203125,
          "nginx_rss": 0,
          "total": 31442.73046875
      "time_info": {
        "run_predictor_total": {
          "avg": 0.0165,
          "max": 0.023,
          "min": 0.013
  • Capabilities route:
    A GET URL_PREFIX/capabilities/ route, shows payload formats supported by the running model.
    Example: GET http://localhost:6789/capabilities/

  • Structured predictions routes:
    A POST URL_PREFIX/predict/ and URL_PREFIX/predictions/ routes, which returns predictions on data.
    Example: POST http://localhost:6789/predict/; POST http://localhost:6789/predictions/
    For these routes data can be posted in two ways:

    • as form data parameter with a key:value pair, where:
      key = X
      value = filename of the csv/arrow/mtx format, that contains the inference data.
    • as binary data; in case of arrow or mtx formats, mimetype application/x-apache-arrow-stream or text/mtx must be set.
  • Structured transform route (for Python predictor only):
    A POST URL_PREFIX/transform/ route, which returns transformed data.
    Example: POST http://localhost:6789/transform/;
    For this route data can be posted in two ways:

    • as form data parameter with a key:value pair, where:
      key = X.
      value = filename of the csv/arrow/mtx format, that contains the inference data.

      optionally a second key, y, can be passed with value = a second filename containing target data.

      if y is passed, the route will return both X.transformed and y.transformed keys, along with out.format indicating the format of the transformed X output. This will take a value of csv, sparse or arrow. y.transformed is never sparse.

      an arrow_version key may also be passed if you desire to use arrow format for X.transformed or y.transformed. this is used to ensure that the endpoint returns data that can be opened by the caller's version of arrow. without this key, all dense data returned will default to csv format.

    • as binary data; in case of arrow or mtx formats, mimetype application/x-apache-arrow-stream or text/mtx must be set.

  • Unstructured predictions routes:
    A POST URL_PREFIX/predictUnstructured/ and URL_PREFIX/predictionsUnstructured/ routes, which returns predictions on data.
    Example: POST http://localhost:6789/predictUnstructured/; POST http://localhost:6789/predictionsUnstructured/
    For these routes data is posted as binary data. Provide mimetype and charset to properly handle the data. For more detailed information please go here.

Starting drum as prediction server in production mode.

DRUM prediction server can be started in production mode which has nginx and uwsgi as the backend. This provides better stability and scalability - depending on how many CPUs are available several workers will be started to serve predictions.
--max-workers parameter can be used to limit number of workers.
E.g. drum server --code-dir ~/user_code_dir --address localhost:6789 --production --max-workers 2

Note: uwsgi is an extra dependency for DRUM. Install it using: pip install datarobot-drum[uwsgi] or pip install uwsgi
Note: Production mode may not be available on Windows-based systems out ot the box, as uwsgi installation requires special handling. Docker container based Linux environment can be used for such cases.

Fit mode

Note: Running fit inside of DataRobot is currently in alpha. Check back soon for the opportunity to test out this functionality yourself.

DRUM can run your training model to make sure it can produce a trained model artifact before adding the training model into DataRobot.

You can try this out on our sklearn classifier model template this this command:

drum fit --code-dir task_templates/3_pipelines/python3_sklearn_binary --target-type binary --target Species --input \
tests/testdata/iris_binary_training.csv --output . --positive-class-label Iris-setosa \
--negative-class-label Iris-versicolor

Note: If you don't provide class label, DataRobot tries to autodetect the labels for you.

You can also use DRUM on regression datasets, and soon you will also be able to provide row weights. Checkout the drum fit --help output for further details.

Running inside a docker container

In every mode, DRUM can be run inside a docker container by providing the option --docker <image_name/directory_path>. The container should implement an environment required to perform desired action. DRUM must be installed as a part of this environment.
The following is an example on how to run DRUM inside of container:
drum score --code-dir ~/user_code_dir/ --target-type <target type> --input dataset.csv --docker <container_name>
drum perf-test --code-dir ~/user_code_dir/ --target-type <target type> --input dataset.csv --docker <container_name>

Alternatively, the argument passed through the --docker flag may be a directory containing the unbuilt contents of an image. The DRUM tool will then attempt to build an image using this directory and run your model inside the newly built image.

If the argument passed to --docker is a docker context directory, and code dir contains dependencies file requirements.txt, DRUM will try to install the packages during the image build.
To skip dependencies installation you can use --skip-deps-install flag.

Drum Push

Starting in version 1.1.4, drum includes a new verb called push. When the user writes drum push -cd /dirtopush/ the contents of that directory will be submitted as a custom model to DataRobot. However, for this to work, you must create two types of configuration.

  1. DataRobot client configuration push relies on correct global configuration of the client to access a DataRobot server. There are two options for supplying this configuration, through environment variables or through a config file which is read by the DataRobot client. Both of these options will include an endpoint and an API token to authenticate the requests.
  • Option 1: Environment variables. Example:
    export DATAROBOT_API_TOKEN=<yourtoken>
  • Option 2: Create this file, which we check for: ~/.config/datarobot/drconfig.yaml
    token: <yourtoken>
  1. Model Metadata push also relies on a metadata file, which is parsed on DRUM to create the correct sort of model in DataRobot. This metadata file includes quite a few options. You can read about those options or see an example.

Project details

Release history Release notifications | RSS feed

Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distributions

No source distribution files available for this release.See tutorial on generating distribution archives.

Built Distribution

datarobot_drum-1.11.5-py3-none-any.whl (10.5 MB view hashes)

Uploaded Python 3

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page