ml-versioning-tools 2.0.1
Set of Machine Learning versioning helpers
Keywords: peopledoc,machine learning,versioning,mlvtools

Machine Learning Versioning Tools - MLV-tools
=============================================
Public repository for versioning machine learning data.

Installing
----------

MLV-tools can be installed from **PyPi**:

    pip install ml-versioning-tools

It is also possible to install it directly from sources:

    git clone https://github.com/peopledoc/ml-versioning-tools.git
    cd ml-versioning-tools

        make develop
    OR
        make package
        pip install ./package/*.whl

Tutorial
--------

A tutorial is available to showcase how to use the tools.
See [MLV-tools tutorial](https://github.com/peopledoc/mlv-tools-tutorial).

Keywords
--------

**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.

Tools
-----

**ipynb_to_python**: this command converts a given *Jupyter Notebook* to a
parameterized and executable *Python3 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 call the 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 a dependency order. Only for local steps.

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


**ipynb_to_dvc**: this command converts a given *Jupyter Notebook* to a
parameterized and executable *Python3 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 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)

Configuration
-------------

A configuration file can be provided, but it is not mandatory.  Its default location is
`[working_dir]/.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

```json
{
  "path":
  {
    "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*: is the directory where **Python 3** script will be generated using
**ipynb_to_script** command. The **Python 3** script name is based on the notebook name.

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

        Generated script: `[path_to_the_script_directory]/my_notebook.py`

- *path_to_the_dvc_cmd_directory*: is the directory where **DVC** commands will be generated using
**gen_dvc** command. Generated command names are based on **Python 3** script names.

        gen_dvc -i ./scripts/my_notebook.py

        Generated commands: `[path_to_the_python_cmd_directory]/my_notebook_dvc`

- *path_to_the_dvc_metadata_directory*: is 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**.
Generated **DVC** metadata file names are based on **Python 3** script names.

        ./[path_to_the_python_cmd_directory]/my_notebook_dvc

        Generated files: `[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*: they allow to customize variable names which
can be used in **dvc-cmd** Docstring parameter. 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 not mandatory.


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 **Python3 script**:

    [...]
    def process_file(input_file: str, output_file, rate, retry:int):
        """
         ...
        """
    [...]

Script command line parameters:

    my_script.py -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 **dvc run** command called on a **Python 3** script generated
with **ipynb_to_python** command. It is a step of a pipeline.

It is based on data declared in **notebook metadata**,
 2 modes are available:
    - describe only input/output for simple cases (recommended)
    - describe full command for complex cases

#### Simple cases

Syntax

    :param str input_csv_file: Path to input file
    :param str output_csv_file: Path to output file
    [...]

    [:dvc-[in|out][\s{related_param}]?:[\s{file_path}]?]*
    [:dvc-extra: {python_other_param}]?

    :dvc-in: ./data/filter.csv
    :dvc-in input_csv_file: ./data/info.csv
    :dvc-out: ./data/train_set.csv
    :dvc-out output_csv_file: ./data/test_set.csv
    :dvc-extra: --mode train --rate 12

Provided **{file_path}** path can be absolute or relative to the working directory.

The **{related_param}** is a parameter of the corresponding **Python 3** script,
 it is filled in for the python script call

The **dvc-extra** allows to declare parameters which are not dvc outputs or dependencies.
Those parameters are provided to the call of the **Python 3** command.

    pushd /working-directory

    INPUT_CSV_FILE="./data/info.csv"
    OUTPUT_CSV_FILE="./data/test_set.csv"

    dvc run \
    -d ./data/filter.csv\
    -d $INPUT_CSV_FILE\
    -o ./data/train_set.csv\
    -o $OUTPUT_CSV_FILE\
    gen_src/python_script.py --mode train --rate 12
            --input-csv-file $INPUT_CSV_FILE
            --output-csv-file $OUTPUT_CSV_FILE



#### Complex cases

Syntax

    :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 respectively contains the path and the name
 of the corresponding python command.
The variable $MLV_DVC_META_FILENAME contains the default name of the **DVC** meta file.

    pushd /working-directory
    MLV_PY_CMD_PATH="gen_src/python_script.py"
    MLV_PY_CMD_NAME="python_script.py"

    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"
    popd


### DVC templating

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

Example:

    # 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 ./python_script.py --docstring-conf ./dc_conf.yml

The *Docstring configuration file* can be provided through the main configuration or using **--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 3** 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 *Configuration* section.


Contributing
------------

We happily welcome contributions to MLV-tools. Please see our [contribution](./CONTRIBUTING.md) guide for details.