Metadata-Version: 2.1
Name: ploomber
Version: 0.12.6
Summary: A convention over configuration workflow orchestrator. Develop locally (Jupyter or your favorite editor), deploy to Airflow or Kubernetes. 
Home-page: https://github.com/ploomber/ploomber
Author: Eduardo Blancas
Author-email: hello@ploomber.io
License: UNKNOWN
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        **Notebooks are hard to maintain.** Teams often prototype projects in notebooks, but maintaining them is an error-prone process that slows progress down. Ploomber overcomes the challenges of working with `.ipynb` files allowing teams to develop collaborative, production-ready pipelines using JupyterLab or any text editor.
        
        ## Main Features
        
        1. **Scripts as notebooks.** Open `.py` files as notebooks, then execute them from the terminal and generate an output notebook to review results.
        2. **Dependency resolution.** Quickly build a DAG by referring to previous tasks in your code; Ploomber infers execution order and orchestrates execution.
        3. **Incremental builds.** Speed up iterations by skipping tasks whose source code hasn't changed since the last execution.
        4. **Production-ready.** Deploy to [Kubernetes](https://soopervisor.readthedocs.io/en/latest/tutorials/kubernetes.html) (via Argo Workflows), [Airflow](https://soopervisor.readthedocs.io/en/latest/tutorials/airflow.html), and [AWS Batch](https://soopervisor.readthedocs.io/en/latest/tutorials/aws-batch.html) without code changes.
        5. **Parallelization.** Run independent tasks in parallel.
        6. **Testing.** Import pipelines in any testing frameworks and test them with any CI service (e.g. GitHub Actions).
        7. **Flexible.** Use Jupyter notebooks, Python scripts, R scripts, SQL scripts, Python functions or a combination of them as pipeline tasks.
        
        ![repo-lab-example](https://ploomber.io/repo-lab-example.png)
        
        ## Community
        
        * [Join us on Slack](http://community.ploomber.io)
        * [Newsletter](https://www.getrevue.co/profile/ploomber)
        * [Contact the development team](https://forms.gle/Xf9h1Q2TGoSk15NEA)
        
        ## Resources
        
        * [Documentation](https://ploomber.readthedocs.io/)
        * [Examples (Machine Learning pipeline, ETL, among others)](https://github.com/ploomber/projects)
        * [Blog](https://ploomber.io/)
        * [Guest blog post on the official Jupyter blog](https://blog.jupyter.org/ploomber-maintainable-and-collaborative-pipelines-in-jupyter-acb3ad2101a7)
        * [Comparison with other tools](https://ploomber.io/posts/survey)
        * [JupyterCon 2020 talk](https://www.youtube.com/watch?v=M6mtgPfsA3M)
        * [Argo Community Meeting talk](https://youtu.be/FnpXyg-5W_c)
        * [Pangeo Showcase talk (AWS Batch demo)](https://youtu.be/XCgX1AszVF4)
        
        ## Installation
        
        *Compatible with Python 3.6 and higher.*
        
        Install with `pip`:
        
        ```sh
        pip install ploomber
        ```
        
        Or with `conda`:
        
        ```sh
        conda install ploomber -c conda-forge
        ```
        
        ## Getting started
        
        Use Binder to try out Ploomber without setting up an environment:
        
        [![image](https://mybinder.org/badge_logo.svg)](https://mybinder.org/v2/gh/ploomber/binder-env/main?urlpath=git-pull%3Frepo%3Dhttps%253A%252F%252Fgithub.com%252Fploomber%252Fprojects%26urlpath%3Dlab%252Ftree%252Fprojects%252Fspec-api-python%252FREADME.ipynb%26branch%3Dmaster)
        
        Or run an example locally:
        
        ```sh
        # ML pipeline example
        ploomber examples --name ml-basic
        cd ml-basic
        
        # if using pip
        pip install -r requirements.txt
        
        # if using conda
        conda env create --file environment.yml
        conda activate ml-basic
        
        # run pipeline
        ploomber build
        ```
        
        Pipeline output saved in the `output/` folder. Check out the pipeline definition
        in the `pipeline.yaml` file.
        
        To get a list of examples, run `ploomber examples`.
        
        # CHANGELOG
        
        ## 0.12.6 (2021-09-02)
        * Fixes look up of conda root when running `ploomber install` when conda binary is inside the `Library` directory (Windows)
        * No longer looking up pip inside conda when running `ploomber install` and `setup.py` does not exist
        * Adds `--use-lock/-l` option to `ploomber install` to install using lock files
        
        ## 0.12.5 (2021-08-16)
        
        * Simplifies serializer and unserializer creation with new decorators
        * Adds guide on serializer/unserializer decorators to the documentation
        
        ## 0.12.4 (2021-08-12)
        
        * Clearer error message when failing to import function
        * Better error message when `tasks` in YAML spec is not a list
        * Fixes an issue that caused dag plot to fail when using `.svg`
        * Fixes duplicated log entries when viewing a file in Jupyter
        
        ## 0.12.3 (2021-08-03)
        
        * Fixes cell injection when using the `--notebook-dir` during Jupyter initialization
        * Reduces verbosity in Jupyter logs (#314)
        * Adds `tasks[*].params.resources_` to track changes in external files
        * Minor bug fixes
        
        ## 0.12.2 (2021-07-26)
        
        * Lazy load for ``serializer``, ``unserialize``, DAG clients, Task clients, Product clients, and task hooks, which allows the Jupyter plugin to work even if the Jupyter process does not have the dependencies required to import such dotted paths
        * CLI `--help` message shows if `ENTRY_POINT` environment variable is defined
        * `ploomber scaffold` now takes a `-e/--entry-point` optional argument
        *  Fixes error that caused the `{{here}}` placeholder not to work if an `env.yaml` exists
        *  Adds `--empty` option to `ploomber scaffold` to create a `pipeline.yaml` with no tasks
        
        ## 0.12.1 (2021-07-09)
        
        - Allowing `pipeline.yaml` at project root if setup.py but `src/*/pipeline.yaml` is missing
        - Fixes bug in `EnvDict.find` that caused the `{{here}}` placeholder to point to the `env.yaml` file instead of its parent
        - `DAGSpec._find_relative` returns relative path to spec
        - Fixes error that missed `env.yaml` loading when initializing DAGSpecPartial
        
        ## 0.12 (2021-07-08)
        
        - Changes the logic that determines project root: only considers `pipeline.yaml` and `setup.py` (instead of `environment.yml` or `requirements.txt`)
        - Adds configuration and scaffold user guides
        - Updates Jupyter user guide
        - Deletes conda user guide
        - Renames internal modules for consistency (this should not impact end-users)
        - Fixes error that caused Files generated from TaskGroups in the spec API not to resolve to their absolute values
        - Fixes error that caused metadata not to delete on when saving files in Jupyter if using a source in more than one task
        - `DAGSpec` loads an `env.{name}.yaml` file when loading a `pipeline.{name}.yaml` if one exists
        - `ploomber plot` saves to `pipeline.{name}.png`
        - Override `env.yaml` to load using `PLOOMBER_ENV_FILENAME` environment variable
        - `EnvDict` init no longer searches recursively, moved that logic to `EnvDict.find`. `with_env` decorator now uses the latter to prevent breaking the API
        - `PostgresCopyFrom` compatible with `psycopg>=2.9`
        - `jupyter_hot_reload=True` by default
        - `PythonCallableSource` finds the location of a dotted path without importing any of the submodules
        - Jupyter integration lazily loads DAGs (no need to import callable tasks)
        - CLI no longer showing `env.yaml` parameters when initializing from directory or pattern
        
        ## 0.11.1 (2021-06-08)
        
        - Task's `metadata.params` stores `null` if any parameter isn't serializable
        - Task status ignores `metadata.params` if they are `null`
        - Fixes unserialization when an upstream task produces a `MetaProduct`
        
        ## 0.11 (2021-05-31)
        
        - Adds `remote` parameter to `DAG.render` to check status against remote storage
        - `NotebookSource` no longer includes the injected cell in its `str` representation
        - `Metadata` uses task params to determine task status
        - Support for wildcards when building dag partially
        - Support to skip upstream dependencies when building partially
        - Faster `File` remote metadata downloads using multi-threading during `DAG.render`
        - Faster upstream dependencies parallel download using multi-threading during `Task.build`
        - Suppresses papermill `FutureWarning` due to importing a deprecated `pyarrow` module
        - Fixes error that caused a warning due to unused env params when using `import_tasks_from`
        - Other bug fixes
        
        ## 0.10.4 (2021-05-22)
        
        - `DAGSpec.find` exposes `starting_dir` parameter
        - `ploomber install` supports `pip`'s `requirements.txt` files
        - `ploomber install` supports non-packages (i.e., no `setup.py`)
        - `ploomber scaffold` flags to use conda (`--conda`) and create package (`--package`)
        
        ## 0.10.3 (2021-05-17)
        
        - `ParamGrid` supports initialization from a list
        - Adds `tasks[*].grid` to generate multiple tasks at once
        - Support for using wildcards to declare dependencies (e.g., `task-*`)
        - Fixes to `ploomber scaffold` and `ploomber install`
        - `PythonCallable` creates parent directories before execution
        - Support for the parallel executor in Spec API
        - `DagSpec.find` exposes `lazy_import` argument
        - `TaskGroup` internal API changes
        
        ## 0.10.2 (2021-05-05)
        
        - `GCloudStorageClient` loads credentials relative to the project root
        - Adds `ploomber install`
        - Adds `S3Client`
        
        ## 0.10.1 (2021-04-17)
        
        - `DAGSpec` warns if parameter declared in env but unused
        - Implements `{SQLDump, NotebookRunner, PythonCallable}.load()`
        - `File.client` downloads during task execution instead of render
        - Adds `ploomber.OnlineModel`, which provides a simpler API than `OnlineDAG` for models that implement a `.predict()` method
        - Adds function to find package name if using standard layout
        
        ## 0.10 (2021-03-13)
        
        - Changes `extract_product` default in spec API to False
        - Tasks get a default name equal to the filename without extension (e.g., plot.py -> plot)
        - `File` saves metadata in a `.{filename}.metadata` file instead of `{filename}.source`
        - Adds `ploomber examples` command
        - Adds Deployment guide to documentation
        - `EnvDict` loads `env.yaml` and uses values as defaults when passing a custom dict
        - Simpler repr for SQL products
        - Improved Spec API docs
        - Adds `ploomber.tasks.TaskGroup.from_params` to create multiple tasks at once
        
        ## 0.9.5 (2021-03-07)
        - Changes a lot of error messages for clarity
        - Clearer `__repr__` for `Placeholder`, `File`, and `MetaProduct`
        - Default placeholders can be used in `pipeline.yaml` without defining `env.yaml`
        - Better formatting for displaying DAG build and render errors
        - Spec API initializes task spec as `SQLDump` if product has suffix `.csv` or `.parquet`
        - Coloring CLI error traceback
        - Spec API skips `SourceLoader` if passing an absolute path
        - `DAG.clients` validates keys (using `DAGClients`)
        - `params` available as hook argument
        - Rewritten Spec API documentation
        
        ## 0.9.4 (2021-02-15)
        - Better display of errors when building or rendering a DAG (layout and colors)
        - `File` implements the `os.PathLike` interface (this works now: `pandas.read_parquet(File('file.parquet'))`)
        - Several error messages refactored for clarity
        - Adds `DAGSpec.find()` to automatically find `pipeline.yaml`
        
        
        ## 0.9.3 (2021-02-13)
        - Adds `OnlineDAG` to convert `DAG` objects for in-memory inference
        - Spec API (`pipeline.yaml`) supports DAG-level and Task-level `serializer` and `serializer`
        - CLI looks for `src/{pkg}/pipeline.yaml` if `pipeline.yaml` doesn't exist
        - Adds `{{cwd}}` placeholder for `env.yaml` that expands to current working directory
        
        
        ## 0.9.2 (2021-02-11)
        
        - Support for Python 3.9
        - `SQLAlchemyClient` now accepts an argument to pass custom parameters to `sqlalchemy.create_engine`
        - Temporarily pins papermill version due to an incompatibility with jupytext and nbformat (jupytext does not support cell ids yet)
        - Adds `--on-finish/-of` to `ploomber task` to execute the `on_finish` hook
        - DAGs with R notebooks can render even if the ir kernel is not installed
        
        ## 0.9.1 (2021-02-01)
        
        - `File` now supports a `client` argument to upload products to cloud
            storage
        - Adds `GCloudStorageClient`
        - Fixes error that caused jupyter to fail to initialize the dag when
            adding a function to a module already included in the YAML spec
        - Fixes IPython namespace errors when using `ploomber interact`
        - Adds `ploomber.testing.sql.assert_no_duplicates_in_column` to check
            for record duplicates and optionally show duplicates statistics
        - Deprecates a few internal methods: `Table.save`, `DAG.to_dict()`,
            `Task.to_dict()`
        - Improvements to SQL static analyzer to warn when relations created
            by a SQL script do not match `Product`
        - A few changes to `Metadata` (internal API) to cover some edge cases
        - Warning when `Product` metadata is corrupted
        - Adds new `meta.import_tasks_from` option in YAML specs to import
            tasks from another file
        
        
        ## 0.9 (2021-01-18)
        
        - Deprecates `ploomber new` and `ploomber add`
        - Adds `ploomber scaffold`
        - Jupyter plugin now exports functions as notebooks using
            `jupyter_functions_as_notebooks` in `pipeline.yaml`
        
        ## 0.8.6 (2021-01-08)
        
        - `ploomber add` generates template tasks and functions if they don't exist
        - Jupyter plugin now shows PythonCallable tasks as notebooks
        
        ## 0.8.5 (2020-12-14)
        
        - Documentation tutorials re-organization and CSS fixes
        - Improvements to the `InMemoryDAG` API
        - Minor bug fixes
        - `File.__repr__` shows a relative path whenever possible
        
        ## 0.8.4 (2020-11-21)
        
        - Adds support for passing glob-like patterns in `ploomber build` (via
            `DAGSpec.from_directory`)
        
        ## 0.8.3 (2020-11-15)
        
        - Full Windows compatibility
        - Adds documentation to show how to customize notebook output using
            `nbconvert`
        - Improvements to introductory tutorials
        - Adds `--debug/-d` option to `ploomber build` to drop a debugger if
            an exception happens
        - Ensuring all dag-level, task-level and product-level clients are
            closed after `dag.build()` is done
        - Minor bug fixes
        
        ## 0.8.2 (2020-10-31)
        
        - Removes `matplotlib` from dependencies, now using `IPython.display`
            for inline plotting
        - Fixes bug that caused custom args to
            `{PythonCallable, NotebookRunner}.develop(args="--arg=value")` not
            to be sent correctly to the subprocess
        - `NotebookRunner` (initialized from ipynb) only considers the actual
            code as its source, ignores the rest of the JSON contents
        - Fixes bug when `EnvDict` was initialized from another `EnvDict`
        - `PythonCallableSource` can be initialized with dotted paths
        - `DAGSpec` loads `env.yaml` when initialized with a YAML spec and
            there is a `env.yaml` file in the spec parent folder
        - `DAGSpec` converts relative paths in sources to be so to the
            project's root folder
        - Adds `lazy_import` to `DAGspec`, to avoid importing `PythonCallable`
            sources (passes the dotted paths as strings instead)
        
        ## 0.8.1 (2020-10-18)
        
        - `ploomber interact` allows to switch DAG parameters, just like
            `ploomber build`
        - Adds `PythonCallable.develop()` to develop Python functions
            interactively
        - `NotebookRunner.develop()` to develop now also works with Jupyter
            lab
        
        ## 0.8 (2020-10-15)
        
        - Dropping support for Python 3.5
        - Removes `DAGSpec.from_file`, loading from a file is now handled
            directly by the `DAGSpec` constructor
        - Performance improvements, DAG does not fetch metadata when it doesn't need to
        - Factory functions: Bool parameters with default values are now
            represented as flags when called from the CLI
        - CLI arguments to replace values from `env.yaml` are now
            built with double hyphens instead of double underscores
        - `NotebookRunner` creates parent folders for output file if they don't exist
        - Bug fixes
        
        ## 0.7.5 (2020-10-02)
        
        - NotebookRunner.develop accepts passing arguments to jupyter notebook
        - Spec API now supports PythonCallable (by passing a dotted path)
        - Upstream dependencies of PythonCallables can be inferred via the
            `extract_upstream` option in the Spec API
        - Faster `DAG.render(force=True)` (avoid checking metadata when
            possible)
        - Faster notebook rendering when using the extension thanks to the
            improvement above
        - `data_frame_validator` improvement: `validate_schema` can now
            validate optional columns dtypes
        - Bug fixes
        
        ## 0.7.4 (2020-09-14)
        
        - Improved `__repr__` methods in PythonCallableSource and
            NotebookSource
        - Improved output layout for tables
        - Support for nbconvert>=6
        - Docstrings are parsed from notebooks and displayed in DAG status table (#242)
        - Jupyter extension now works for DAGs defined via directories (via
            `ENTRY_POINT` env variable)
        - Adds Jupyter integration guide to documentation
        - Several bug fixes
        
        ## 0.7.3 (2020-08-19)
        
        - Improved support for R notebooks (`.Rmd`)
        - New section for `testing.sql` module in the documentation
        
        ## 0.7.2 (2020-08-17)
        
        - New guides: parametrized pipelines, SQL templating, pipeline testing
            and debugging
        - `NotebookRunner.debug(kind='pm')` for post-mortem debugging
        - Fixes bug in Jupyter extension when the pipeline has a task whose
            source is not a file (e.g. SQLDump)
        - Fixes a bug in the CLI custom arg parser that caused dynamic params
            not to show up
        - `DAGspec` now supports `SourceLoader`
        - Docstring (from dotted path entry point) is shown in the CLI summary
        - Customized sphinx build to execute guides from notebooks
        
        ## 0.7.1 (2020-08-06)
        
        - Support for R
        - Adding section on R pipeline to the documentation
        - Construct pipeline from a directory (no need to write a
            `pipeline.yaml` file)
        - Improved error messages when DAG fails to initialize (jupyter
            notebook app)
        - Bug fixes
        - CLI accepts factory function parameters as positional arguments,
            types are inferred using type hints, displayed when calling `--help`
        - CLI accepts env variables (if any), displayed when calling `--help`
        
        ## 0.7 (2020-07-30)
        
        - Simplified CLI (breaking changes)
        - Refactors internal API for notebook conversion, adds tests for
            common formats
        - Metadata is deleted when saving a script from the Jupyter notebook
            app to make sure the task runs in the next pipeline build
        - SQLAlchemyClient now supports custom tokens to split source
        
        ## 0.6.3 (2020-07-24)
        
        - Adding `--log` option to CLI commands
        - Fixes a bug that caused the `dag` variable not to be
            exposed during interactive sessions
        - Fixes `ploomber task` forced run
        - Adds SQL pipeline tutorial to get started docs
        - Minor CSS changes to docs
        
        ## 0.6.2 (2020-07-22)
        
        - Support for `env.yaml` in `pipeline.yaml`
        - Improved CLI. Adds `plot`, `report` and `task` commands`
        
        ## 0.6.1 (2020-07-20)
        
        - Changes `pipeline.yaml` default (extract_product: True)
        - Documentation re-design
        - Simplified `ploomber new` generated files
        - Ability to define `product` in SQL scripts
        - Products are resolved to absolute paths to avoid ambiguity
        - Bug fixes
        
        ## 0.6 (2020-07-08)
        
        - Adds Jupyter notebook extension to inject parameters when opening a
            task
        - Improved CLI `ploomber new`, `ploomber add` and `ploomber entry`
        - Spec API documentation additions
        - Support for `on_finish`, `on_failure` and `on_render` hooks in spec API
        - Improved validation for DAG specs
        - Several bug fixes
        
        ## 0.5.1 (2020-06-30)
        
        - Reduces the number of required dependencies
        - A new option in DBAPIClient to split source with a custom separator
        
        ## 0.5 (2020-06-27)
        
        - Adds CLI
        - New spec API to instantiate DAGs using YAML files
        - NotebookRunner.debug() for debugging and .develop() for interacive
            development
        - Bug fixes
        
        ## 0.4.1 (2020-05-19)
        
        - PythonCallable.debug() now works in Jupyter notebooks
        
        ## 0.4.0 (2020-05-18)
        
        - PythonCallable.debug() now uses IPython debugger by default
        - Improvements to Task.build() public API
        - Moves hook triggering logic to Task to simplify executors
            implementation
        - Adds DAGBuildEarlyStop exception to signal DAG execution stop
        - New option in Serial executor to turn warnings and exceptions
            capture off
        - Adds Product.prepare_metadata hook
        - Implements hot reload for notebooks and python callables
        - General clean ups for old `__str__` and `__repr__` in several modules
        - Refactored ploomber.sources module and ploomber.placeholders
            (previously ploomber.templates)
        - Adds NotebookRunner.debug() and NotebookRunner.develop()
        - NotebookRunner: now has an option to run static analysis on render
        - Adds documentation for DAG-level hooks
        - Bug fixes
        
        ## 0.3.5 (2020-05-03)
        
        - Bug fixes #88, #89, #90, #84, #91
        - Modifies Env API: Env() is now Env.load(), Env.start() is now Env()
        - New advanced Env guide added to docs
        - Env can now be used with a context manager
        - Improved DAGConfigurator API
        - Deletes logger configuration in executors constructors, logging is
            available via DAGConfigurator
        
        ## 0.3.4 (2020-04-25)
        
        - Dependencies cleanup
        - Removed (numpydoc) as dependency, now optional
        - A few bug fixes: #79, #71
        - All warnings are captured and shown at the end (Serial executor)
        - Moves differ parameter from DAG constructor to DAGConfigurator
        
        ## 0.3.3 (2020-04-23)
        
        - Cleaned up some modules, deprecated some rarely used functionality
        - Improves documentation aimed to developers looking to extend
            ploomber
        - Introduces DAGConfigurator for advanced DAG configuration
            [Experimental API]
        - Adds task to upload files to S3 (ploomber.tasks.UploadToS3),
            requires boto3
        - Adds DAG-level on_finish and on_failure hooks
        - Support for enabling logging in entry points (via `--logging`)
        - Support for starting an interactive session using entry points (via
            python -i -m)
        - Improved support for database drivers that can only send one query
            at a time
        - Improved repr for SQLAlchemyClient, shows URI (but hides password)
        - PythonCallable now validates signature against params at render time
        - Bug fixes
        
        ## 0.3.2 (2020-04-07)
        
        - Faster Product status checking, now performed at rendering time
        - New products: GenericProduct and GenericSQLRelation for Products
            that do not have a specific implementation (e.g. you can use Hive
            with the DBAPI client + GenericSQLRelation)
        - Improved DAG build reports, subselect columns, transform to
            pandas.DataFrame and dict
        - Parallel executor now returns build reports, just like the Serial
            executor
        
        ## 0.3.1 (2020-04-01)
        
        - DAG parallel executor
        - Interact with pipelines from the command line (entry module)
        - Bug fixes
        - Refactored access to Product.metadata
        
        ## 0.3 (2020-03-20)
        
        - New Quickstart and User Guide section in documentation
        - DAG rendering and build now continue until no more tasks can
            render/build (instead of failing at the first exception)
        - New `@with_env` and `@load_env` decorators for managing environments
        - Env expansion ({{user}} expands to the current, also {{git}} and
            {{version}} available)
        - `Task.name` is now optional when Task is initialized with a source
            that has `__name__` attribute (Python functions) or a name
            attribute (like Placeholders returned from SourceLoader)
        - New Task.on_render hook
        - Bug fixes
        - A lot of new tests
        - Now compatible with Python 3.5 and higher
        
        ## 0.2.1 (2020-02-20)
        
        - Adds integration with pdb via PythonCallable.debug
        - Env.start now accepts a filename to look for
        - Improvements to data_frame_validator
        
        ## 0.2 (2020-02-13)
        
        - Simplifies installation
        - Deletes BashCommand, use ShellScript
        - More examples added
        - Refactored env module
        - Renames SQLStore to SourceLoader
        - Improvements to SQLStore
        - Improved documentation
        - Renamed PostgresCopy to PostgresCopyFrom
        - SQLUpload and PostgresCopy have now the same API
        - A few fixes to PostgresCopy (#1, #2)
        
        ## 0.1
        
        - First release
        
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Developers
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: Apache Software License
Classifier: Operating System :: MacOS :: MacOS X
Classifier: Operating System :: Unix
Classifier: Operating System :: POSIX
Classifier: Operating System :: Microsoft :: Windows
Classifier: Programming Language :: Python
Classifier: Programming Language :: Python :: 3
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
Provides-Extra: all
Provides-Extra: dev
