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Set of Machine Learning versioning helpers

Project description


The Machine Learning Versioning Tools.
mlvtools version 2.1.1 is the last version supporting dvc<=0.94.1.


To install mlvtools with pip from PyPI:

$ pip install mlvtools

To install it from sources for development:

$ git clone
$ cd mlvtools
$ pip install -e .[dev]


A tutorial is available to showcase how to use the tools. See mlvtools tutorial.


Step Metadata: in this document it refers to the first code cell when it is used to declare metadata such as parameters, dvc inputs/outputs, etc.

Working Directory: the project's working directory. Files specified in the user configuration are relative to this directory. The --working-directory (or -w) flag is used to specify the Working Directory.


ipynb_to_python: this command converts a Jupyter Notebook to a parameterized and executable Python script (see specific syntax in section below).

$ ipynb_to_python -n [notebook_path] -o [python_script_path]

gen_dvc: this command creates a DVC command which calls the Python script generated by ipynb_to_python.

$ gen_dvc -i [python_script] --out-py-cmd [python_command] --out-bash-cmd [dvc_command]

export_pipeline: this command exports the pipeline corresponding to the given DVC meta file into a bash script. Pipeline steps are called sequentially in dependency order. Only for local steps.

$ export_pipeline --dvc [DVC target meta file] -o [pipeline script]

ipynb_to_dvc: this command converts a Jupyter Notebook to a parameterized and executable Python script and a DVC command. It is the combination of ipynb_to_python and gen_dvc. It only works with a configuration file.

$ ipynb_to_dvc -n [notebook_path]

check_script_consistency and check_all_scripts_consistency: those commands ensure consitency between a Jupyter notebook and its generated python script. It is possible to use them as git hook or in the project's Continuous Integration. The consistency check ignores blank lines and comments.

$ check_script_consistency -n [notebook_path] -s [script_path]
$ check_all_scripts_consistency -n [notebook_directory]
# Works only with a configuration file (provided or auto-detected)


A configuration file can be provided, but it is not mandatory. Its default location is [working_directory]/.mlvtools. Use the flag --conf-path (or -c) on the command line to specify a specific configuration file path.

The configuration file format is JSON.

    "python_script_root_dir": "[path_to_the_script_directory]",
    "dvc_cmd_root_dir": "[path_to_the_dvc_cmd_directory]",
    "dvc_metadata_root_dir": "[path_to_the_dvc_metadata_directory] (optional)"
  "ignore_keys": ["keywords", "to", "ignore"],
  "dvc_var_python_cmd_path": "MLV_PY_CMD_PATH_CUSTOM",
  "dvc_var_python_cmd_name": "MLV_PY_CMD_NAME_CUSTOM",
  "docstring_conf": "./docstring_conf.yml"

All given paths must be relative to the Working Directory.

  • path_to_the_script_directory: the directory where Python scripts will be generated using ipynb_to_script commands. The generated Python script names are based on the notebook names.

    $ ipynb_to_script -n ./data/My\ Notebook.ipynb

    Generated script: [path_to_the_script_directory]/

  • path_to_the_dvc_cmd_directory: the directory where DVC commands will be generated using gen_dvc command. The generated command names are based on the Python script names.

    $ gen_dvc -i ./scripts/

    Generated command: [path_to_the_python_cmd_directory]/my_notebook_dvc

  • path_to_the_dvc_metadata_directory: the directory where DVC metadata files will be generated when executing gen_dvc commands. This value is optional, by default DVC metadata files will be saved in the Working Directory. The generated DVC metadata file names are based on the Python 3 script names.

    Generated file: [path_to_the_dvc_metadata_directory]/my_notebook.dvc

  • ignore_keys: list of keywords use to discard a cell. Default value is ['# No effect ]. (See "Discard cell" section)

  • dvc_var_python_cmd_path, dvc_var_python_cmd_name, dvc_var_meta_filename: allow to customize variable names which can be used in dvc-cmd Docstring parameters.

    They respectively correspond to the variables holding the Python command file path, the file name and the variable holding the DVC default meta file name.

    Default values are MLV_PY_CMD_PATH, MLV_PY_CMD_NAME and MLV_DVC_META_FILENAME. (See DVC Command/Complex cases section for usage.)

  • docstring_conf: the path to the docstring configuration used for Jinja templating (see DVC templating section). This parameter is optional.

Jupyter Notebook syntax

The Step Metadata cell is used to declare script parameters and DVC outputs and dependencies. This can be done using basic Docstring syntax. This Docstring must be the first statement is this cell, only comments can be writen above.

Good practices

Avoid using relative paths in your Jupyter Notebook because they are relative to the notebook location which is not the same when it will be converted to a script.

Python Script Parameters

Parameters can be declared in the Jupyter Notebook using basic Docstring syntax. This parameters description is used to generate configurable and executable Python scripts.

Parameters declaration in Jupyter Notebook:

Jupyter Notebook: process_files.ipynb

#:param [type]? [param_name]: [description]?
:param str input_file: the input file
:param output_file: the output_file
:param rate: the learning rate
:param int retry:

Generated Python script:

def process_file(input_file, output_file, rate, retry):

Script command line parameters: -h

usage: my_cmd [-h] --input-file INPUT_FILE --output-file OUTPUT_FILE --rate RATE --retry RETRY

Command for script [script_name]

optional arguments:
  -h, --help            show this help message and exit
  --input-file INPUT_FILE
                        the input file
  --output-file OUTPUT_FILE
                        the output_file
  --rate RATE           the rate
  --retry RETRY

All declared arguments are required.

DVC command

A DVC command is a wrapper over a dvc run command called on a Python script generated with the ipynb_to_python command. It is a step of a pipeline.

It is based on data declared in the Notebook's Step Metadata.

Two modes are available:

  • describe only input/output for simple cases (recommended)
  • describe full command for complex cases

Simple cases


:param str input_csv_file: Path to input file
:param str output_csv_file_1: Path to output file 1
:param str output_csv_file_2: Path to output file 2

[:dvc-extra: {python_other_param}]?

:dvc-in: ./data/filter.csv
:dvc-in input_csv_file: ./data/info.csv
:dvc-out: ./data/train_set_1.csv
:dvc-out output_csv_file_1: ./data/test_set_1.csv
:dvc-out-persist: ./data/train_set_2.csv
:dvc-out-persist output_csv_file_2: ./data/test_set_2.csv
:dvc-extra: --mode train --rate 12
  • {file_path} path can be absolute or relative to the Working Directory.
  • {related_param} is a parameter of the corresponding Python script, it is filled in for the python script call
  • dvc-extra allows to declare parameters which are not dvc outputs or dependencies. Those parameters are provided to the call of the Python command.
pushd /working-directory


dvc run \
-d ./data/filter.csv\
-o ./data/train_set_1.csv\
--outs-persist ./data/train_set_2.csv\
--outs-persist $OUTPUT_CSV_FILE_2\
gen_src/ --mode train --rate 12
        --input-csv-file $INPUT_CSV_FILE
        --output-csv-file-1 $OUTPUT_CSV_FILE_1
        --output-csv-file-2 $OUTPUT_CSV_FILE_2

Complex cases


:dvc-cmd: {dvc_command}

:dvc-cmd: dvc run -o ./out_train.csv -o ./out_test.csv
    "$MLV_PY_CMD_PATH -m train --out ./out_train.csv &&
     $MLV_PY_CMD_PATH -m test --out ./out_test.csv"

This syntax allows to provide the full dvc command to generate. All paths can be absolute or relative to the Working Directory. The variables $MLV_PY_CMD_PATH and $MLV_PY_CMD_NAME are available. They correspond to the path and the name of the corresponding Python command, respectively. The variable $MLV_DVC_META_FILENAME contains the default name of the DVC meta file.

pushd /working-directory

dvc run -f $MLV_DVC_META_FILENAME -o ./out_train.csv \
    -o ./out_test.csv \
    "$MLV_PY_CMD_PATH -m train --out ./out_train.csv && \
    $MLV_PY_CMD_PATH -m test --out ./out_test.csv"

DVC templating

It is possible to use Jinja2 templates in the DVC Docstring parts. For example, it can be useful to declare all steps dependencies, outputs and extra parameters.


# Docstring in Jupyter notebook
:dvc-in: {{ conf.train_data_file_path }}
:dvc-out: {{ conf.model_file_path }}
:dvc-extra: --rate {{ conf.rate }}
# Docstring configuration file (Yaml format): ./dc_conf.yml
train_data_file_path: ./data/trainset.csv
model_file_path: ./data/model.pkl
rate: 45
# DVC command generation
gen_dvc -i ./ --docstring-conf ./dc_conf.yml

The Docstring configuration file can be provided through the main configuration or using the --docstring-conf argument. This feature is only available for gen_dvc command.

Discard cell

Some cells in Jupyter Notebook are executed only to watch intermediate results. In a Python script those are statements with no effect. The comment # No effect allows to discard a whole cell content to avoid waste of time running those statements. It is possible to customize the list of discard keywords, see the Configuration section.


We happily welcome contributions to mlvtools. Please see our contribution guide for details.

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