Metadata-Version: 2.1
Name: fluidml
Version: 0.3.1
Summary: FluidML is a lightweight framework for developing machine learning pipelines. Focus only on your tasks and not the boilerplate!
Home-page: https://github.com/fluidml/fluidml/
Author: Lars Hillebrand, Rajkumar Ramamurthy
Author-email: hokage555@web.de
License: Apache-2.0
Download-URL: https://github.com/fluidml/fluidml/
Description: <div align="center">
        <img src="logo/fluid_ml_logo.png" width="400px">
        
        _Develop ML pipelines fluently with no boilerplate code. Focus only on your tasks and not the boilerplate!_
        
        ---
        
        <p align="center">
          <a href="#key-features">Key Features</a> •
          <a href="#getting-started">Getting Started</a> •
          <a href="#functionality">Functionality</a> •
          <a href="#examples">Examples</a> •
          <a href="#citation">Citation</a>
        </p>
        
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        [![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/fluidml/fluidml/blob/main/CODE_OF_CONDUCT.md)
        
        [//]: # (?style=flat-square)
        
        </div>
        
        ---
        
        **FluidML** is a lightweight framework for developing machine learning pipelines.
        
        <div align="center">
        <img src="logo/fluidml_example.gif" width="70%" />
        </div>
        
        Developing machine learning models is a challenging process, with a wide range of sub-tasks: 
        data collection, pre-processing, model development, hyper-parameter tuning and deployment. 
        Each of these tasks is iterative in nature and requires lot of iterations to get it right with good performance.
        
        Due to this, each task is generally developed sequentially, with artifacts from one task being fed as inputs to the subsequent tasks. 
        For instance, raw datasets are first cleaned, pre-processed, featurized and stored as iterable datasets (on disk), which are then used for model training. 
        However, this type of development can become messy and un-maintenable quickly for several reasons:
        
        - pipeline code may be split across multiple scripts whose dependencies are not modeled explicitly
        - each of this task contains boilerplate code to collect results from previous tasks (eg: reading from disk)
        - hard to keep track of task artifacts and their different versions
        - hyper-parameter tuning adds further complexity and boilerplate code
        
        FluidML attempts to solve all of the above issues without restricting the user's flexibility.
        
        ## Key Features
        
        FluidML provides following functionalities out-of-the-box:
        
        - **Task Graphs** - Create ML pipelines as a directed task graph using simple APIs
        - **Results Forwarding** - Results from tasks are automatically forwarded to downstream tasks based on dependencies
        - **Parallel Processing** - Execute the task graph parallely with multi-processing
        - **Grid Search** - Extend the task graph by enabling grid search on tasks with just one line of code
        - **Result Caching** - Task results are persistently cached in a results store (e.g.: Local File Store or a MongoDB Store) and made available for subsequent runs without executing the tasks again and again
        - **Flexibility** - Provides full control on your task implementations. You are free to choose any framework of your choice (Sklearn, TensorFlow, Pytorch, Keras, or any of your favorite library)
        
        ---
        
        ## Getting Started
        
        ### Installation
        
        #### 1. From Pip
        Simply execute:  
        ```bash
        $ pip install fluidml
        ```
        
        #### 2. From Source
        1. Clone the repository,
        2. Navigate into the cloned directory (contains the setup.py file),
        3. Execute `$ pip install .`
        
        **Note:** To run demo examples, execute `$ pip install fluidml[examples]` (Pip) or `$ pip install .[examples]` (Source) to install the additional requirements.
        
        ### Minimal Example
        
        This minimal toy example showcases how to get started with FluidML.
        For real machine learning examples, check the "Examples" section below.
        
        #### 1. Define Tasks
        
        First, we define some toy machine learning tasks. A Task can be implemented as a function or as a class inheriting from our `Task` class.
        
        In case of the class approach, each task must implement the `run()` method, which takes some inputs and performs the desired functionality. 
        These inputs are actually the results from predecessor tasks and are automatically forwarded by FluidML based on registered task dependencies. 
        If the task has any hyper-parameters, they can be defined as arguments in the constructor. 
        Additionally, within each task, users have access to special `Task` methods and attributes like 
        `self.save()` and `self.resource` to save a result and access task resources (more on that later).
        
        ```Python
        from fluidml import Task
        
        
        class MyTask(Task):
            def __init__(self, config_param_1, config_param_2):
                ...
            def run(self, predecessor_result_1, predecessor_result_2):
                ...
        ```
        
        or
        
        ```Python
        def my_task(predecessor_result_1, predecessor_result_2, config_param_1, config_param_2, task: Task):
            ...
        ```
        
        In the case of defining the task as callable, an extra task object is provided to the task,
        which makes important internal attributes and functions like `task.save()` and `task.resource` available to the user.
        
        Below, we define standard machine learning tasks such as dataset preparation, pre-processing, featurization and model training using Task classes.
        Notice that:
        
        - Each task is implemented individually and it's clear what the inputs are (check arguments of `run()` method)
        - Each task saves its results using `self.save(...)` by providing the object to be saved and a unique name for it. 
          This unique name corresponds to input names in successor task definitions.
        
        ```Python
        class DatasetFetchTask(Task):
            def run(self):
                ...           
                self.save(obj=data_fetch_result, name="data_fetch_result")
        
        
        class PreProcessTask(Task):
            def __init__(self, pre_processing_steps: List[str]):
                super().__init__()
                self._pre_processing_steps = pre_processing_steps
        
            def run(self, data_fetch_result):
                ...
                self.save(obj=pre_process_result, name="pre_process_result")
        
        
        class TFIDFFeaturizeTask(Task):
            def __init__(self, min_df: int, max_features: int):
                super().__init__()
                self._min_df = min_df
                self._max_features = max_features
        
            def run(self, pre_process_result):
                ...
                self.save(obj=tfidf_featurize_result, name="tfidf_featurize_result")
        
        
        class GloveFeaturizeTask(Task):
            def run(self, pre_process_result):
                ...
                self.save(obj=glove_featurize_result, name="glove_featurize_result")
        
        
        class TrainTask(Task):
            def __init__(self, max_iter: int, balanced: str):
                super().__init__()
                self._max_iter = max_iter
                self._class_weight = "balanced" if balanced else None
        
            def run(self, tfidf_featurize_result, glove_featurize_result):
                ...
                self.save(obj=train_result, name="train_result")
        
        
        class EvaluateTask(Task):
            def run(self, train_result):
                ...
                self.save(obj=evaluate_result, name="evaluate_result")
        ```
        
        #### 2. Task Specifications
        
        Next, we can create the defined tasks with their specifications. 
        We now only write their specifications, later these are used to create real instances of tasks by FluidML.
        
        Note the `config` argument holds the configuration of the task (i.e. hyper-parameters).
        
        
        ```Python
        from fluidml import TaskSpec
        
        
        dataset_fetch_task = TaskSpec(task=DatasetFetchTask)
        pre_process_task = TaskSpec(task=PreProcessTask, config={"pre_processing_steps": ["lower_case", "remove_punct"]})
        featurize_task_1 = TaskSpec(task=GloveFeaturizeTask)
        featurize_task_2 = TaskSpec(task=TFIDFFeaturizeTask, config={"min_df": 5, "max_features": 1000})
        train_task = TaskSpec(task=TrainTask, config={"max_iter": 50, "balanced": True})
        evaluate_task = TaskSpec(task=EvaluateTask)
        ```
        
        #### 3. Registering task dependencies
        
        Here we create the task graph by registering dependencies between the tasks. 
        In particular, for each task specifier, you can register a list of predecessor tasks using the `requires()` method.
        
        ```Python
        pre_process_task.requires(dataset_fetch_task)
        featurize_task_1.requires(pre_process_task)
        featurize_task_2.requires(pre_process_task)
        train_task.requires(dataset_fetch_task, featurize_task_1, featurize_task_2)
        evaluate_task.requires(dataset_fetch_task, featurize_task_1, featurize_task_2, train_task)
        ```
        
        #### 4. Configure Logging
        
        FluidML internally utilizes Python's `logging` library. However, we refrain from configuring a logger object with handlers
        and formatters since each user has different logging needs and preferences. Hence, if you want to use FluidML's logging
        capability, you just have to do the configuration yourself. For convenience, we provide a simple utility function which
        configures a visually appealing logger (using a specific handler from the [rich](https://github.com/willmcgugan/rich) library).
        
        We highly recommend to enable logging in your fluidml application in order to benefit from console progress logging.
        
        ```python
        from fluidml import configure_logging
        
        
        configure_logging()
        ```
        
        #### 5. Run tasks using Flow
        
        Now that we have all the tasks specified, we can just run the task graph. 
        For that, we create the task flow by passing all tasks to the `Flow()` class.
        Subsequently, we execute the task graph by calling `flow.run()`.
        
        ```Python
        from fluidml import Flow
        
        
        tasks = [dataset_fetch_task, pre_process_task, featurize_task_1,
                 featurize_task_2, train_task, evaluate_task]
        
        flow = Flow(tasks=tasks)
        results = flow.run()
        ```
        
        ---
        
        ## Functionality
        
        The following sections highlight the most important features and options when specifying and executing a task pipeline.
        For a complete documentation of all available options we refer to the [API documentation](https://fluidml.readthedocs.io/en/latest/).
        
        ### Grid Search - Automatic Task Expansion
        
        We can easily enable grid search for our tasks with just one line of code. 
        We just have to provide the `expand` argument with the `product` and `zip` expansion option to the `TaskSpec` constructor. 
        Automatically, all `List` elements in the provided config are recursively unpacked and taken into account for expansion.
        If a list itself is an argument and should not be expanded, it has to be wrapped again in a list. 
        
        ```Python
        train_task = TaskSpec(
          task=TrainTask,
          config={"max_iter": [50, 100], "balanced": [True, False], "layers": [[50, 100, 50]]},
          expand="product",  # or 'zip'
        )
        ```
        
        That's it! Flow expands this task specification into 4 tasks with provided cross product combinations of `max_iter` and `balanced`. 
        Alternatively, using `zip` the expansion method would result in 2 expanded tasks, 
        with the respective `max_iter` and `balanced` combinations of `(50, True), (100, False)`. 
        
        Note `layers` is not considered for different grid search realizations since it will be unpacked and the actual list 
        value will be passed to the task.
        Further, any successor tasks (for instance, evaluate task) in the task graph will also be automatically expanded. 
        Therefore, in our example, we would have 4 evaluate tasks, each one corresponding to the 4 train tasks.
        
        For more advanced Gird Search Expansion options we refer to the documentation.
        
        
        ### Model Selection
        
        Running a complete machine learning pipeline usually yields trained models for many grid search parameter combinations.
        A common goal is then to automatically determine the best hyper-parameter setup and the best performing model.
        FluidML enables just that by providing a `reduce=True` argument to the `TaskSpec` class. Hence, to automatically 
        compare the 4 evaluate tasks and select the best performing model, we implement an additional `ModelSelectionTask`
        which gets wrapped by our `TaskSpec` class.
        
        ```Python
        class ModelSelectionTask(Task):
            def run(self, train_result: List[Sweep]):
                # from all trained models/hyper-parameter combinations, determine the best performing model
                ...
        
        model_selection_task = TaskSpec(task=ModelSelectionTask, reduce=True)
        
        model_selection_task.requires(evaluate_task)
        ```
        
        The important `reduce=True` argument enables that a single `ModelSelectionTask` instance gets the training results
        from all grid search expanded predecessor tasks.
        `train_result` is of type `List[Sweep]` and holds the results and configs of all specified grid search parameter combination. For example:
        
        ```Python
        train_result = [
            Sweep(value=value_1, config={...}),  # first unique parameter combination config
            Sweep(value=value_2, config={...}),  # second unique parameter combination config
            ...
        ]
        ```
        
        ### Result Store
        
        FluidML provides the `ResultStore` interface to efficiently save, load and delete task results. Internally, the result store is used
        to automatically collect saved predecessor results and pass the collected results as inputs to defined successor tasks.
        
        By default, results of tasks are stored in an `InMemoryStore`, which might be impractical for large datasets/models or long running tasks since the results are not persistent. 
        To have persistent storage, FluidML provides two fully implemented `ResultsStore` namely `LocalFileStore` and `MongoDBStore`.
        
        Additionally, users can provide their own results store to `Flow.run()` by inheriting from the `ResultsStore` interface 
        and implementing `load()`, `save()`, `delete()`, `delete_run()` and `get_context()` methods. 
        Note these methods rely on task name and its config parameters, which act as lookup-key for results. 
        In this way, tasks are skipped by FluidML when task results are already available for the given config. 
        But users can override and force execute tasks by passing `force` parameter to the `Flow.run()` methods. 
        For details check the API documentation.
        
        ```Python
        class MyResultsStore(ResultsStore):
            def load(self, name: str, task_name: str, task_unique_config: Dict, **kwargs) -> Optional[Any]:
                """ Query method to load an object based on its name, task_name and task_config if it exists """
                raise NotImplementedError
        
            def save(self, obj: Any, name: str, type_: str, task_name: str, task_unique_config: Dict, **kwargs):
                """ Method to save/update any artifact """
                raise NotImplementedError
        
            def delete(self, name: str, task_name: str, task_unique_config: Dict):
                """ Method to delete any artifact """
                raise NotImplementedError
        
            def delete_run(self, task_name: str, task_unique_config: Dict):
                """Method to delete all task results from a given run config"""
                raise NotImplementedError
        
            def get_context(self, task_name: str, task_unique_config: Dict) -> StoreContext:
                """Method to get store specific storage context, e.g. the current run directory for Local File Store"""
                raise NotImplementedError
        ```
        
        We can instantiate for example a `LocalFileStore`
        
        ```python
        results_store = LocalFileStore(base_dir="/some/dir")
        ```
        
        and use it to enable persistent results storing via `flow.run(results_store=results_store)`.
        
        ### Multiprocessing
        
        FluidML automatically infers the optimal number of worker processes based on the expanded task graph and the number of available CPUs
        in your system. If the resulting number is greater than 1, `Flow` will automatically run the graph in parallel using multiprocessing.
        If 1 worker is optimal and no multiprocessing is needed, the task graph will be executed in the main process without multiprocessing.
        
        You can manually control the number of workers by providing the `num_workers` argument to `flow.run()`.
        
        ### Logging
        
        Internally, FluidML makes use of Python's `logging` library to visualize and log the progress of the task pipeline execution
        in the console. We recommend to configure `logging` in your fluidml application for a better user experience.
        For convenience, we provide a simple utility function `configure_logging()` which configures a visually appealing logger 
        (using a specific handler from the [rich](https://github.com/willmcgugan/rich) library). For different logging options 
        we refer to the documentation.
        
        In the case of executing the task graph in parallel with multiple workers using multiprocessing, the console output might become
        garbled and unreadable. In that scenario you can turn on [tmux](https://github.com/tmux/tmux/wiki) logging py providing the `log_to_tmux` argument:
        `flow.run(log_to_tmux=True)`. In addition to the standard console, a `tmux` terminal session with `num_worker` panes is automatically started.
        Each worker process logs to a dedicated pane in the tmux session so that the console output is nicely readable.
        
        Note `log_to_tmux=True` requires the [installation](https://github.com/tmux/tmux/wiki/Installing) of tmux.
        
        
        ### Visualization
        
        FluidML provides functions to visualize the original task specification graph as well as the (potentially expanded) task graph, which facilitates debugging.
        After instantiating the `Flow` object we have access to the task specification graph `flow.task_spec_graph` and the
        expanded task graph `flow.task_graph`.
        
        Both graphs can be visualized in the console `visualize_graph_in_console` or in the browser or a jupyter notebook `visualize_graph_interactive`.
        
        ```Python
        from fluidml.visualization import visualize_graph_in_console, visualize_graph_interactive
        
        
        flow = Flow(tasks=tasks)
        
        visualize_graph_in_console(graph=flow.task_spec_graph)
        visualize_graph_interactive(graph=flow.task_graph, browser="firefox")
        ```
        When using console visualization the default arguments `use_pager=True` and `use_unicode=False` will render the graph in ascii within a pager for horizontal scrolling support. 
        If `use_pager=False` the graph is simply printed and if `use_unicode=True` a visually more appealing unicode character set is used for console rendering. 
        However not every console supports unicode characters.
        
        See below the console visualization of the task specification graph and the expanded task graph from our minimal example:
        
        <div align="center">
        <img src="logo/task_spec_graph.png" width="500px">
        </div>
        
        <div align="center">
        <img src="logo/task_graph.png">
        </div>
        
        When using interactive visualization the default output is to a running jupyter notebook.
        If you want the graph to be rendered in a browser, provide the `browser` argument to `visualize_graph_interactive()`, e.g. 
        `visualize_graph_interactive(graph=flow.task_graph, browser="chrome")`. You might receive a `webbrowser` error: 
        `webbrowser.Error: could not locate runnable browser` which means that you have to register the browser manually so that
        Python's `webbrowser` library can find it. Registering can be done via
        
        ```python
        import webbrowser
        webbrowser.register(
          "chrome", None, webbrowser.BackgroundBrowser("/path/to/chrome/executable")
        )
        ```
        
        ---
        
        ## Examples
        
        For real machine learning pipelines including grid search implemented with FluidML, check our
        Jupyter Notebook tutorials:
        
        - [Transformer based Sequence to Sequence Translation (PyTorch)](https://github.com/fluidml/fluidml/blob/main/examples/pytorch_transformer_seq2seq_translation/transformer_seq2seq_translation.ipynb)
        - [Multi-class Text Classification (Sklearn)](https://github.com/fluidml/fluidml/blob/main/examples/sklearn_text_classification/sklearn_text_classification.ipynb)
        
        ---
        
        ## Citation
        
        ```
        @article{fluid_ml,
          title = {FluidML - a lightweight framework for developing machine learning pipelines},
          author = {Hillebrand, Lars and Ramamurthy, Rajkumar},
          year = {2020},
          publisher = {GitHub},
          journal = {GitHub repository},
          howpublished = {\url{https://github.com/fluidml/fluidml}},
        }
        ```
        
Keywords: pipelines,machine-learning,parallel,deep-learning
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Environment :: Console
Classifier: Framework :: IPython
Classifier: Intended Audience :: Developers
Classifier: Natural Language :: English
Classifier: Operating System :: Microsoft :: Windows
Classifier: Operating System :: MacOS
Classifier: Operating System :: POSIX :: Linux
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Classifier: Programming Language :: Python :: 3.9
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Typing :: Typed
Requires-Python: >=3.7.0
Description-Content-Type: text/markdown
Provides-Extra: examples
Provides-Extra: mongo-store
Provides-Extra: tests
