From 8a0ebd8cebc8a8de56948841a0c7f4a63d364463 Mon Sep 17 00:00:00 2001 From: tarekabouchakra Date: Wed, 24 Jan 2024 15:30:46 +0100 Subject: [PATCH 01/11] Closes #52: summary_csv compatible with development_stage_id --- neps/api.py | 14 +++++++++++--- 1 file changed, 11 insertions(+), 3 deletions(-) diff --git a/neps/api.py b/neps/api.py index d035a5b5..e1579624 100644 --- a/neps/api.py +++ b/neps/api.py @@ -98,8 +98,11 @@ def write_loss_and_config(file_handle, loss_, config_id_, config_): def run( run_pipeline: Callable, root_directory: str | Path, - pipeline_space: dict[str, Parameter | CS.ConfigurationSpace] | str | Path | - CS.ConfigurationSpace | None = None, + pipeline_space: dict[str, Parameter | CS.ConfigurationSpace] + | str + | Path + | CS.ConfigurationSpace + | None = None, overwrite_working_directory: bool = False, post_run_summary: bool = False, development_stage_id=None, @@ -277,13 +280,18 @@ def run( pre_load_hooks=pre_load_hooks, ) - if post_run_summary: + if post_run_summary and development_stage_id: + optimization_dir = Path(root_directory) / f"dev_{development_stage_id}" + post_run_csv(optimization_dir, logger) + elif post_run_summary: post_run_csv(root_directory, logger) def _run_args( searcher_info: dict, pipeline_space: dict[str, Parameter | CS.ConfigurationSpace] + | str + | Path | CS.ConfigurationSpace | None = None, max_cost_total: int | float | None = None, From 9dd412cd19076e3e501d510314b787dd17be0f6b Mon Sep 17 00:00:00 2001 From: tarekabouchakra Date: Tue, 6 Feb 2024 09:22:12 +0100 Subject: [PATCH 02/11] Adjusting summary_csv with task_id and dev_stage_id args --- neps/api.py | 84 +++++++++++++++++++++++-------------------- neps/metahyper/api.py | 7 ---- 2 files changed, 46 insertions(+), 45 deletions(-) diff --git a/neps/api.py b/neps/api.py index e1579624..2584fcbe 100644 --- a/neps/api.py +++ b/neps/api.py @@ -98,11 +98,13 @@ def write_loss_and_config(file_handle, loss_, config_id_, config_): def run( run_pipeline: Callable, root_directory: str | Path, - pipeline_space: dict[str, Parameter | CS.ConfigurationSpace] - | str - | Path - | CS.ConfigurationSpace - | None = None, + pipeline_space: ( + dict[str, Parameter | CS.ConfigurationSpace] + | str + | Path + | CS.ConfigurationSpace + | None + ) = None, overwrite_working_directory: bool = False, post_run_summary: bool = False, development_stage_id=None, @@ -115,17 +117,19 @@ def run( loss_value_on_error: None | float = None, cost_value_on_error: None | float = None, pre_load_hooks: Iterable | None = None, - searcher: Literal[ - "default", - "bayesian_optimization", - "random_search", - "hyperband", - "priorband", - "mobster", - "asha", - "regularized_evolution", - ] - | BaseOptimizer = "default", + searcher: ( + Literal[ + "default", + "bayesian_optimization", + "random_search", + "hyperband", + "priorband", + "mobster", + "asha", + "regularized_evolution", + ] + | BaseOptimizer + ) = "default", searcher_path: Path | str | None = None, **searcher_kwargs, ) -> None: @@ -262,6 +266,11 @@ def run( "skipped. Accurate continuation of runs can no longer be guaranteed!" ) + if task_id is not None: + root_directory = Path(root_directory) / f"task_{task_id}" + if development_stage_id is not None: + root_directory = Path(root_directory) / f"dev_{development_stage_id}" + metahyper_run( run_pipeline, searcher_instance, @@ -270,8 +279,6 @@ def run( max_evaluations_total=max_evaluations_total, max_evaluations_per_run=max_evaluations_per_run, continue_until_max_evaluation_completed=continue_until_max_evaluation_completed, - development_stage_id=development_stage_id, - task_id=task_id, logger=logger, post_evaluation_hook=_post_evaluation_hook_function( loss_value_on_error, ignore_errors @@ -280,36 +287,37 @@ def run( pre_load_hooks=pre_load_hooks, ) - if post_run_summary and development_stage_id: - optimization_dir = Path(root_directory) / f"dev_{development_stage_id}" - post_run_csv(optimization_dir, logger) - elif post_run_summary: + if post_run_summary: post_run_csv(root_directory, logger) def _run_args( searcher_info: dict, - pipeline_space: dict[str, Parameter | CS.ConfigurationSpace] - | str - | Path - | CS.ConfigurationSpace - | None = None, + pipeline_space: ( + dict[str, Parameter | CS.ConfigurationSpace] + | str + | Path + | CS.ConfigurationSpace + | None + ) = None, max_cost_total: int | float | None = None, ignore_errors: bool = False, loss_value_on_error: None | float = None, cost_value_on_error: None | float = None, logger=None, - searcher: Literal[ - "default", - "bayesian_optimization", - "random_search", - "hyperband", - "priorband", - "mobster", - "asha", - "regularized_evolution", - ] - | BaseOptimizer = "default", + searcher: ( + Literal[ + "default", + "bayesian_optimization", + "random_search", + "hyperband", + "priorband", + "mobster", + "asha", + "regularized_evolution", + ] + | BaseOptimizer + ) = "default", searcher_path: Path | str | None = None, **searcher_kwargs, ) -> tuple[BaseOptimizer, dict]: diff --git a/neps/metahyper/api.py b/neps/metahyper/api.py index a3f167ca..c89879c9 100644 --- a/neps/metahyper/api.py +++ b/neps/metahyper/api.py @@ -421,8 +421,6 @@ def metahyper_run( max_evaluations_total=None, max_evaluations_per_run=None, continue_until_max_evaluation_completed=False, - development_stage_id=None, - task_id=None, logger=None, post_evaluation_hook=None, overwrite_optimization_dir=False, @@ -432,11 +430,6 @@ def metahyper_run( if logger is None: logger = logging.getLogger("metahyper") - if task_id is not None: - optimization_dir = Path(optimization_dir) / f"task_{task_id}" - if development_stage_id is not None: - optimization_dir = Path(optimization_dir) / f"dev_{development_stage_id}" - optimization_dir = Path(optimization_dir) if overwrite_optimization_dir and optimization_dir.exists(): logger.warning("Overwriting working_directory") From 01fe73f92916cd245a35b9bd8204f8d1842149e2 Mon Sep 17 00:00:00 2001 From: Daniel <63580393+danrgll@users.noreply.github.com> Date: Sat, 10 Feb 2024 23:56:57 +0100 Subject: [PATCH 03/11] Big Documentation Update + align naming of configspace for yaml usage --- README.md | 26 +-- docs/README.md | 41 +++-- docs/getting_started.md | 101 ++++++++++++ docs/installation.md | 24 +++ docs/neps_run.md | 100 +++++++++++ .../README.md => docs/optimizers.md | 34 ++-- docs/parallelization.md | 5 +- docs/pipeline_space.md | 99 ++++++----- docs/run_pipeline.md | 156 ++++++++++++++++-- mkdocs.yml | 8 +- neps/api.py | 14 +- neps/search_spaces/search_space.py | 8 +- neps_examples/efficiency/multi_fidelity.py | 5 + .../config_including_unknown_types.yaml | 2 +- .../config_including_wrong_types.yaml | 2 +- .../correct_config.yaml | 2 +- .../correct_config_including_priors.yml | 2 +- .../correct_config_including_types.yaml | 2 +- .../inconsistent_types_config.yml | 2 +- .../inconsistent_types_config2.yml | 2 +- .../incorrect_config.txt | 2 +- .../missing_key_config.yml | 2 +- .../not_allowed_key_config.yml | 2 +- 23 files changed, 525 insertions(+), 116 deletions(-) create mode 100644 docs/installation.md rename neps/optimizers/README.md => docs/optimizers.md (81%) diff --git a/README.md b/README.md index 3d4145b4..cd95279f 100644 --- a/README.md +++ b/README.md @@ -50,9 +50,10 @@ pip install neural-pipeline-search Using `neps` always follows the same pattern: -1. Define a `run_pipeline` function that evaluates architectures/hyperparameters for your problem -1. Define a search space `pipeline_space` of architectures/hyperparameters -1. Call `neps.run` to optimize `run_pipeline` over `pipeline_space` +1. Define a `run_pipeline` function capable of evaluating different architectural and/or hyperparameter configurations + for your problem. +2. Define a search space named `pipeline_space` of those Parameters e.g. via a dictionary +3. Call `neps.run` to optimize `run_pipeline` over `pipeline_space` In code, the usage pattern can look like this: @@ -69,20 +70,20 @@ def run_pipeline( model = MyModel(architecture_parameter) # Train and evaluate the model with your training pipeline - validation_error, test_error = train_and_eval( + validation_error, training_error = train_and_eval( model, hyperparameter_a, hyperparameter_b ) return { # dict or float(validation error) "loss": validation_error, "info_dict": { - "test_error": test_error + "training_error": training_error # + Other metrics }, } -# 2. Define a search space of hyperparameters; use the same names as in run_pipeline +# 2. Define a search space of parameters; use the same names for the parameters as in run_pipeline pipeline_space = dict( hyperparameter_b=neps.IntegerParameter( lower=1, upper=42, is_fidelity=True @@ -111,20 +112,19 @@ if __name__ == "__main__": ## Examples Discover how NePS works through these practical examples: +* **[Pipeline Space via YAML](neps_examples/basic_usage/defining_search_space)**: Explore how to define the `pipeline_space` using a + YAML file instead of a dictionary. -* **Hyperparameter Optimization (HPO)**: Learn the essentials of hyperparameter optimization with NePS. [View Example](neps_examples/basic_usage/hyperparameters.py) +* **[Hyperparameter Optimization (HPO)](neps_examples/basic_usage/hyperparameters.py)**: Learn the essentials of hyperparameter optimization with NePS. -* **Defining Search Space with YAML**: Explore how to define the search space for your neural network models using a YAML file. [View Example](neps_examples/basic_usage/defining_search_space) +* **[Architecture Search with Primitives](neps_examples/basic_usage/architecture.py)**: Dive into architecture search using primitives in NePS. -* **Architecture Search with Primitives**: Dive into architecture search using primitives in NePS. [View Example](neps_examples/basic_usage/architecture.py) +* **[Multi-Fidelity Optimization](neps_examples/efficiency/multi_fidelity.py)**: Understand how to leverage multi-fidelity optimization for efficient model tuning. -* **Multi-Fidelity Optimization**: Understand how to leverage multi-fidelity optimization for efficient model tuning. [View Example](neps_examples/efficiency/multi_fidelity.py) - -* **Utilizing Expert Priors for Hyperparameters**: Learn how to incorporate expert priors for more efficient hyperparameter selection. [View Example](neps_examples/efficiency/expert_priors_for_hyperparameters.py) +* **[Utilizing Expert Priors for Hyperparameters](neps_examples/efficiency/expert_priors_for_hyperparameters.py)**: Learn how to incorporate expert priors for more efficient hyperparameter selection. * **[Additional NePS Examples](neps_examples/)**: Explore more examples, including various use cases and advanced configurations in NePS. - ## Documentation For more details and features please have a look at our [documentation](https://automl.github.io/neps/latest/) diff --git a/docs/README.md b/docs/README.md index 4495ccd5..25a3db2d 100644 --- a/docs/README.md +++ b/docs/README.md @@ -1,18 +1,33 @@ -# Installation +# Neural Pipeline Search (NePS) -## Install from pip +[![PyPI version](https://img.shields.io/pypi/v/neural-pipeline-search?color=informational)](https://pypi.org/project/neural-pipeline-search/) +[![Python versions](https://img.shields.io/pypi/pyversions/neural-pipeline-search)](https://pypi.org/project/neural-pipeline-search/) +[![License](https://img.shields.io/pypi/l/neural-pipeline-search?color=informational)](LICENSE) +[![Tests](https://github.com/automl/neps/actions/workflows/tests.yaml/badge.svg)](https://github.com/automl/neps/actions) -```bash -pip install neural-pipeline-search -``` +Welcome to NePS, a powerful and flexible Python library for hyperparameter optimization (HPO) and neural architecture search (NAS) with its primary goal: enable HPO adoption in practice for deep learners! -## Install from source +NePS houses recently published and some more well-established algorithms that are all capable of being run massively parallel on any distributed setup, with tools to analyze runs, restart runs, etc. -!!! note - We use [poetry](https://python-poetry.org/docs/) to manage dependecies. -```bash -git clone https://github.com/automl/neps.git -cd neps -poetry install --no-dev -``` \ No newline at end of file +## Key Features + +In addition to the common features offered by traditional HPO and NAS libraries, NePS stands out with the following key features: + +1. [**Hyperparameter Optimization (HPO) With Prior Knowledge:**](neps_examples/template/priorband_template.py) + - NePS excels in efficiently tuning hyperparameters using algorithms that enable users to make use of their prior knowledge within the search space. This is leveraged by the insights presented in: + - [PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning](https://arxiv.org/abs/2306.12370) + - [πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization](https://arxiv.org/abs/2204.11051) + +2. [**Neural Architecture Search (NAS) With Context-free Grammar Search Spaces:**](neps_examples/basic_usage/architecture.py) + - NePS is equipped to handle context-free grammar search spaces, providing advanced capabilities for designing and optimizing architectures. this is leveraged by the insights presented in: + - [Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars](https://arxiv.org/abs/2211.01842) + +3. [**Easy Parallelization:**](docs/parallelization.md) + - NePS simplifies the parallelization of optimization tasks. Whether experiments are running on a single machine or a distributed computing environment. + +4. [**Resume Runs After Termination:**](docs/parallelization.md) + - NePS allows users to easily resume optimization runs after termination, providing a convenient and efficient workflow for long-running experiments. + +5. [**Seamless User Code Integration:**](neps_examples/template/) + - NePS's modular design ensures flexibility and extensibility. Integrate NePS effortlessly into existing machine learning workflows. diff --git a/docs/getting_started.md b/docs/getting_started.md index e69de29b..7f87e21b 100644 --- a/docs/getting_started.md +++ b/docs/getting_started.md @@ -0,0 +1,101 @@ +# Getting Started + +Getting started with NePS involves a straightforward yet powerful process, centering around its three main components. +This approach ensures flexibility and efficiency in evaluating different architecture and hyperparameter configurations +for your problem. + +## The 3 Main Components +1. **Define a [`run_pipeline`](https://automl.github.io/neps/latest/run_pipeline) Function**: This function is essential +for evaluating different configurations. You'll implement the specific logic for your problem within this function. +For detailed instructions on initializing and effectively using `run_pipeline`, refer to the guide. + +2. **Establish a [`pipeline_space`](https://automl.github.io/neps/latest/pipeline_space)**: Your search space for +defining parameters. You can structure this in various formats, including dictionaries, YAML, or ConfigSpace. +The guide offers insights into defining and configuring your search space. + +3. **Execute with [`neps.run`](https://automl.github.io/neps/latest/neps_run)**: Optimize your `run_pipeline` over +the `pipeline_space` using this function. For a thorough overview of the arguments and their explanations, +check out the detailed documentation. + +By following these steps and utilizing the extensive resources provided in the guides, you can tailor NePS to meet +your specific requirements, ensuring a streamlined and effective optimization process. + +## Basic Usage +In code, the usage pattern can look like this: + +```python +import neps +import logging + + +# 1. Define a function that accepts hyperparameters and computes the validation error +def run_pipeline( + hyperparameter_a: float, hyperparameter_b: int, architecture_parameter: str +) -> dict: + # insert here your own model + model = MyModel(architecture_parameter) + + # insert here your training/evaluation pipeline + validation_error, training_error = train_and_eval( + model, hyperparameter_a, hyperparameter_b + ) + + return { # dict or float(validation error) + "loss": validation_error, + "info_dict": { + "training_error": training_error + # + Other metrics + }, + } + + +# 2. Define a search space of the parameters of interest; ensure that the names are consistent with those defined +# in the run_pipeline function +pipeline_space = dict( + hyperparameter_b=neps.IntegerParameter( + lower=1, upper=42, is_fidelity=True + ), # Mark 'is_fidelity' as true for a multi-fidelity approach. + hyperparameter_a=neps.FloatParameter( + lower=0.001, upper=0.1, log=True + ), # If True, the search space is sampled in log space. + architecture_parameter=neps.CategoricalParameter( + ["option_a", "option_b", "option_c"] + ), +) + +if __name__ == "__main__": + # 3. Run the NePS optimization + logging.basicConfig(level=logging.INFO) + neps.run( + run_pipeline=run_pipeline, + pipeline_space=pipeline_space, + root_directory="path/to/save/results", # Replace with the actual path. + max_evaluations_total=100, + searcher="hyperband" # Optional specifies the search strategy, + # otherwise NePs decides based on your data. + ) +``` + +## Examples + +Discover the features of NePS through these practical examples: + +* **[Hyperparameter Optimization (HPO)]( +https://github.com/automl/neps/blob/master/neps_examples/template/basic_template.py)**: Learn the essentials of +hyperparameter optimization with NePS. + +* **[Architecture Search with Primitives]( +https://github.com/automl/neps/tree/master/neps_examples/basic_usage/architecture.py)**: Dive into architecture search +using primitives in NePS. + +* **[Multi-Fidelity Optimization]( +https://github.com/automl/neps/tree/master/neps_examples/efficiency/multi_fidelity.py)**: Understand how to leverage +multi-fidelity optimization for efficient model tuning. + +* **[Utilizing Expert Priors for Hyperparameters]( +https://github.com/automl/neps/blob/master/neps_examples/template/priorband_template.py)**: +Learn how to incorporate expert priors for more efficient hyperparameter selection. + +* **[Additional NePS Examples]( +https://github.com/automl/neps/tree/master/neps_examples/)**: Explore more examples, including various use cases and +advanced configurations in NePS. diff --git a/docs/installation.md b/docs/installation.md new file mode 100644 index 00000000..90066f37 --- /dev/null +++ b/docs/installation.md @@ -0,0 +1,24 @@ +# Installation + +## Prerequisites + +Ensure you have Python version 3.8, 3.9, 3.10, or 3.11 installed. NePS installation will automatically handle +any additional dependencies via pip. + +## Install from pip + +```bash +pip install neural-pipeline-search +``` +> Note: As indicated with the `v0.x.x` version number, NePS is early stage code and APIs might change in the future. + +## Install from source + +!!! note + We use [poetry](https://python-poetry.org/docs/) to manage dependecies. + +```bash +git clone https://github.com/automl/neps.git +cd neps +poetry install --no-dev +``` diff --git a/docs/neps_run.md b/docs/neps_run.md index e69de29b..886ff91c 100644 --- a/docs/neps_run.md +++ b/docs/neps_run.md @@ -0,0 +1,100 @@ +# Configuring and Running Optimizations + +The `neps.run` function is the core of the NePS optimization process, where the search for the best hyperparameters +and architectures takes place. This document outlines the arguments and options available within this function, +providing a detailed guide to customize the optimization process to your specific needs. + +## Search Strategy +At default NePS intelligently selects the most appropriate search strategy based on your defined configurations in +`pipeline_space`. +The characteristics of your search space, as represented in the `pipeline_space`, play a crucial role in determining +which optimizer NePS will choose. This automatic selection process ensures that the strategy aligns perfectly +with the specific requirements and nuances of your search space, thereby optimizing the effectiveness of the +hyperparameter and/or architecture optimization. You can also manually select a specific or custom optimizer that better +matches your specific needs. For more information, refer [here](https://automl.github.io/neps/latest/optimizers). + +## Arguments + +### Mandatory Arguments +- **`run_pipeline`** (function): The objective function, targeted by NePS for minimization, by evaluation various + configurations. It requires these configurations as input and should return either a dictionary or a sole loss + value as the +output. For correct setup instructions, refer to [here](https://automl.github.io/neps/latest/run_pipeline) +- **`pipeline_space`** (dict | yaml | configspace): This defines the search space for the configurations from which the + optimizer samples. It accepts either a dictionary with the configuration names as keys, a path to a YAML + configuration file, or a configSpace.ConfigurationSpace object. For comprehensive information and examples, + please refer to the detailed guide available [here](https://automl.github.io/neps/latest/pipeline_space) + +- **`root_directory`** (str): The directory path where the information about the optimization and its progress gets + stored. This is also used to synchronize multiple calls to run(.) for parallelization. + +- **Budget**: +To define a budget, provide either or both of the following parameters: + + - **`max_evaluations_total`** (int, default: None): Specifies the total number of evaluations to conduct before + halting the optimization process. + - **`max_cost_total`** (int, default: None): Prevents the initiation of new evaluations once this cost + threshold is surpassed. This requires adding a cost value to the output of the `run_pipeline` function, + for example, return {'loss': loss, 'cost': cost}. For more details, please refer + [here](https://automl.github/io/neps/latest/run_pipeline) + +### Optional Arguments +##### Further Monitoring Options + - **`overwrite_working_directory`** (bool, default: False): When set to True, the working directory + specified by + `root_directory` will be + cleared at the beginning of the run. This is e.g. useful when debugging a `run_pipeline` function. + - **`post_run_summary`** (bool, default: False): When enabled, this option generates a summary CSV file + upon the + completion of the + optimization process. The summary includes details of the optimization procedure, such as the best configuration, + the number of errors occurred, and the final performance metrics. + - **`development_stage_id`** (int | float | str, default: None): An optional identifier used when working with + multiple development stages. Instead of creating new root directories, use this identifier to save the results + of an optimization run in a separate dev_id folder within the root_directory. + - **`task_id`** (int | float | str, default: None): An optional identifier used when the optimization process + involves multiple tasks. This functions similarly to `development_stage_id`, but it creates a folder named + after the task_id instead of dev_id, providing an organized way to separate results for different tasks within + the `root_directory`. +##### Parallelization Setup + - **`max_evaluations_per_run`** (int, default: None): Limits the number of evaluations for this specific call of + `neps.run`. + - **`continue_until_max_evaluation_completed`** (bool, default: False): In parallel setups, pending evaluations + normally count towards max_evaluations_total, halting new ones when this limit is reached. Setting this to + True enables continuous sampling of new evaluations until the total of completed ones meets max_evaluations_total, + optimizing resource use in time-sensitive scenarios. + +For an overview and further resources on how NePS supports parallelization in distributed systems, refer to +the [Parallelization Overview](#parallelization). +##### Handling Errors + - **`loss_value_on_error`** (float, default: None): When set, any error encountered in an evaluated configuration + will not halt the process; instead, the specified loss value will be used for that configuration. + - **`cost_value_on_error`** (float, default: None): Similar to `loss_value_on_error`, but for the cost value. + - **`ignore_errors`** (bool, default: False): If True, errors encountered during the evaluation of configurations + will be ignored, and the optimization will continue. Note: This error configs still count towards + max_evaluations_total. +##### Search Strategy Customization + - **`searcher`** (Literal["bayesian_optimization", "hyperband",..] | BaseOptimizer, default: "default"): Specifies + manually which of the optimization strategy to use. Provide a string identifying one of the built-in + search strategies or an instance of a custom `BaseOptimizer`. + - **`searcher_path`** (Path | str, default: None): A path to a custom searcher implementation. + - **`**searcher_kwargs`**: Additional keyword arguments to be passed to the searcher. + + For more information about the available searchers and how to customize your own, refer +[here](https://automl.github.io/neps/latest/optimizers). +##### Others + - **`pre_load_hooks`** (Iterable, default: None): A list of hook functions to be called before loading results. + +## Parallelization + +`neps.run` can be called multiple times with multiple processes or machines, to parallelize the optimization process. +Ensure that `root_directory` points to a shared location across all instances to synchronize the optimization efforts. +For more information [look here](https://automl.github.io/neps/latest/parallelization) + +## Customization + +The `neps.run` function allows for extensive customization through its arguments, enabling to adapt the +optimization process to the complexities of your specific problems. + +For a deeper understanding of how to use `neps.run` in a practical scenario, take a look at our +[examples and templates](https://github.com/automl/neps/tree/master/neps_examples). diff --git a/neps/optimizers/README.md b/docs/optimizers.md similarity index 81% rename from neps/optimizers/README.md rename to docs/optimizers.md index 4d2a0a47..c6155ed6 100644 --- a/neps/optimizers/README.md +++ b/docs/optimizers.md @@ -1,50 +1,60 @@ ## Optimizer Configuration Options -Before running the optimizer for your AutoML tasks, you have several configuration options to tailor the optimization process to your specific needs. These options allow you to customize the optimizer's behavior according to your preferences and requirements. +Before running the optimizer for your AutoML tasks, you have several configuration options to tailor the optimization +process to your specific needs. These options allow you to customize the optimizer's behavior according to your +preferences and requirements. ### 1. Automatic Optimizer Selection -If you prefer not to specify a particular optimizer for your AutoML task, you can simply pass `default` or `None` for the neps searcher. NePS will automatically choose the best optimizer based on the characteristics of your search space. This provides a hassle-free way to get started quickly. +If you prefer not to specify a particular optimizer for your AutoML task, you can simply pass `"default"` or `None` +for the neps searcher. NePS will automatically choose the best optimizer based on the characteristics of your search +space. This provides a hassle-free way to get started quickly. -The optimizer selection is based on the following characteristics of your search space: +The optimizer selection is based on the following characteristics of your `pipeline_space`: - If it has fidelity: `hyperband` - If it has both fidelity and a prior: `priorband` - If it has a prior: `pibo` - If it has neither: `bayesian_optimization` -For example, running the following format, without specifying a searcher will choose an optimizer depending on the `pipeline_space` passed. +For example, running the following format, without specifying a searcher will choose an optimizer depending on +the `pipeline_space` passed. ```python neps.run( run_pipeline=run_function, pipeline_space=pipeline_space, root_directory="results/", - max_evaluations_total=25, + max_evaluations_total=25, # no searcher specified ) ``` ### 2. Choosing one of NePS Optimizers -We have also prepared some optimizers with specific hyperparameters that we believe can generalize well to most AutoML tasks and use cases. For more details on the available default optimizers and the algorithms that can be called, please refer to the next section on [SearcherConfigs](#Searcher-Configurations). +We have also prepared some optimizers with specific hyperparameters that we believe can generalize well to most AutoML +tasks and use cases. For more details on the available default optimizers and the algorithms that can be called, +please refer to the next section on [SearcherConfigs](#searcher-configurations). ```python neps.run( run_pipeline=run_function, pipeline_space=pipeline_space, root_directory="results/", - max_evaluations_total=25, + max_evaluations_total=25, # searcher specified, along with an argument searcher="bayesian_optimization", initial_design_size=5, ) ``` -For more optimizers, please refer [here](#List-Available-Searchers) . +For more optimizers, please refer [here](#list-available-searchers) . ### 3. Custom Optimizer Configuration via YAML -For users who want more control over the optimizer's hyperparameters, you can create your own YAML configuration file. In this file, you can specify the hyperparameters for your preferred optimizer. To use this custom configuration, provide the path to your YAML file using the `searcher_path` parameter when running the optimizer. The library will then load your custom settings and use them for optimization. +For users who want more control over the optimizer's hyperparameters, you can create your own YAML configuration file. +In this file, you can specify the hyperparameters for your preferred optimizer. To use this custom configuration, +provide the path to your YAML file using the `searcher_path` parameter when running the optimizer. +The library will then load your custom settings and use them for optimization. Here's the format of a custom YAML (`custom_bo.yaml`) configuration using `Bayesian Optimization` as an example: @@ -68,7 +78,7 @@ neps.run( run_pipeline=run_function, pipeline_space=pipeline_space, root_directory="results/", - max_evaluations_total=25, + max_evaluations_total=25, # searcher specified, along with an argument searcher_path = "custom/path/to/directory" # `custom_bo.yaml` should be in `searcher_path` @@ -85,11 +95,11 @@ neps.run( run_pipeline=run_function, pipeline_space=pipeline_space, root_directory="results/", - max_evaluations_total=25, + max_evaluations_total=25, # searcher specified, along with an argument searcher_path = "custom/path/to/directory" # `custom_bo.yaml` should be in `searcher_path` - searcher="custom_bo", + searcher="custom_bo", initial_design_size=5, # overrides value in custom_bo.yaml random_interleave_prob: 0.25 # overrides value in custom_bo.yaml ) diff --git a/docs/parallelization.md b/docs/parallelization.md index d8acf593..9a265a40 100644 --- a/docs/parallelization.md +++ b/docs/parallelization.md @@ -1,6 +1,9 @@ # Parallelization and Resuming Runs -NePS utilizes files as a means of communication for implementing parallelization and resuming runs. As a result, when `neps.run` is called multiple times with the same `root_directory` in the file system, NePS will automatically load the optimizer state, allowing seamless parallelization of the run across different processes or machines. This concept also applies to resuming runs even after termination. +NePS utilizes files as a means of communication for implementing parallelization and resuming runs. As a result, +when `neps.run` is called multiple times with the same `root_directory` in the file system, NePS will automatically +load the optimizer state, allowing seamless parallelization of the run across different processes or machines. +This concept also applies to resuming runs even after termination. Example: diff --git a/docs/pipeline_space.md b/docs/pipeline_space.md index 1ae2f63d..2978a215 100644 --- a/docs/pipeline_space.md +++ b/docs/pipeline_space.md @@ -1,16 +1,23 @@ -# Initializing the Search Space +# Initializing the Pipeline Space -In NePS, defining the Search Space is one of two essential tasks. You can define it either through a Python dictionary -,YAML file or ConfigSpace. This section provides examples and instructions for both methods. +In NePS, a pivotal step is the definition of the search space, termed `pipeline_space`. This space can be structured +through various approaches, including a Python dictionary, a YAML file, or ConfigSpace. Each of these methods allows +you to specify a set of Parameter types, ranging from Float and Categorical to specialized Architecture Parameters. +Whether you choose a dictionary, YAML file, or ConfigSpace, your selected method serves as a container or framework +within which these parameters are defined and organized. This section not only guides you through the process of +setting up your `pipeline_space` using these methods but also provides detailed instructions and examples on how to +effectively incorporate various Parameter types, ensuring that NePS can utilize them in the optimization process. -## Option 1: Using a Python Dictionary -To define the Search Space using a Python dictionary, follow these steps: +## Methods for Defining the NePS Pipeline Space +### Option 1: Using a Python Dictionary + +To define the `pipeline_space` using a Python dictionary, follow these steps: Create a Python dictionary that specifies the parameters and their respective ranges. For example: ```python -search_space = { +pipeline_space = { "learning_rate": neps.FloatParameter(lower=0.00001, upper=0.1, log=True), "num_epochs": neps.IntegerParameter(lower=3, upper=30, is_fidelity=True), "optimizer": neps.CategoricalParameter(choices=["adam", "sgd", "rmsprop"]), @@ -18,19 +25,19 @@ search_space = { } ``` -## Option 2: Using a YAML File +### Option 2: Using a YAML File -Create a YAML file (e.g., search_space.yaml) with the parameter definitions following this structure. +Create a YAML file (e.g., pipeline_space.yaml) with the parameter definitions following this structure. ```yaml -search_space: # important to start with +pipeline_space: # important to start with learning_rate: lower: 2e-3 upper: 0.1 log: true num_epochs: - type: int # or "integer" + type: int # or "integer", optional if u want to manually set this lower: 3 upper: 30 is_fidelity: True @@ -43,13 +50,19 @@ search_space: # important to start with ... ``` -Ensure your YAML file starts with `search_space:`. +Ensure your YAML file starts with `pipeline_space:`. This is the root key under which all parameter configurations are defined. -## Option 3: Using ConfigSpace +!!! note "Note" + The various types of parameters displayed in the Dictionary of Option 1 are here automatically determined by the + data. If desired, you have the option to define them manually by providing the argument `type`. For more details, + refer to the section on [Supported Hyperparameter Types](#supported-hyperparameter-types). + -For users familiar with the ConfigSpace library, can also define the Search Space through -ConfigurationSpace() +### Option 3: Using ConfigSpace + +For users familiar with the ConfigSpace library, can also define the `pipeline_space` through +ConfigurationSpace(). ```python from configspace import ConfigurationSpace, UniformFloatHyperparameter @@ -60,10 +73,9 @@ configspace.add_hyperparameter( ) ``` -For additional information on ConfigSpace and its features, please visit the following link: -https://github.com/automl/ConfigSpace - -## Supported Hyperparameter Types using a YAML File +For additional information on ConfigSpace and its features, please visit the following +[link](https://github.com/automl/ConfigSpace). +## Supported Hyperparameter Types ### Float/Integer Parameter @@ -72,41 +84,49 @@ https://github.com/automl/ConfigSpace - `upper`: The maximum value of the parameter. - Accepted Values: Int or Float depending on the specific parameter type one wishes to use. - **Optional Arguments:** - - `type`: Specifies the data type of the parameter. - - Accepted Values: 'int', 'integer', or 'float'. - - Note: If type is not specified e notation gets converted to float - `log`: Boolean that indicates if the parameter uses a logarithmic scale (default: False) - - [Details on how YAML interpret Boolean Values](#important-note-on-yaml-string-and-boolean-interpretation) + - [Details on how YAML interpret Boolean Values](#important-note-on-yaml-data-type-interpretation) - `is_fidelity`: Boolean that marks the parameter as a fidelity parameter (default: False). - `default`: Sets a prior central value for the parameter (default: None). - - Note: Currently, if you define a prior for one parameter, you must do so for all your variables. + > Note: Currently, if you define a prior for one parameter, you must do so for all your variables. - `default_confidence`: Specifies the confidence level of the default value, indicating how strongly the prior - should be considered (default: "low"). + should be considered (default: 'low'). - Accepted Values: 'low', 'medium', or 'high'. + - `type`: Specifies the data type of the parameter. + - Accepted Values: 'int', 'integer', or 'float'. + > Note: If type is not specified e notation gets converted to float + + !!! note "YAML Method Specific:" + The type argument, used to specify the data type of parameters as 'int', 'integer', or 'float', + is unique to defining the pipeline_space with a YAML file. This explicit specification of the parameter + type is not required when using a Python dictionary or ConfigSpace, as these methods inherently determine + the data types based on the syntax and structure of the code. ### Categorical Parameter - **Expected Arguments:** - - `choices`: A list of discrete options(int | float | str) that the parameter can take. + - `choices`: A list of discrete options (int | float | str) that the parameter can take. - **Optional Arguments:** - - `type`: Specifies the data type of the parameter. - - Accepted Values: 'cat' or 'categorical'. - `is_fidelity`: Marks the parameter as a fidelity parameter (default: False). - - [Details on how YAML interpret Boolean Values](#important-note-on-yaml-string-and-boolean-interpretation) + - [Details on how YAML interpret Boolean Values](#important-note-on-yaml-data-type-interpretation) - `default`: Sets a prior central value for the parameter (default: None). - - Note: Currently, if you define a prior for one parameter, you must do so for all your variables. + > Note: Currently, if you define a prior for one parameter, you must do so for all your variables. - `default_confidence`: Specifies the confidence level of the default value, indicating how strongly the prior should be considered (default: "low"). + - `type`: Specifies the data type of the parameter. + - Accepted Values: 'cat' or 'categorical'. + > Note: Yaml Method Specific ### Constant Parameter - **Expected Arguments:** - - `value`: The fixed value(int | float | str) for the parameter. + - `value`: The fixed value (int | float | str) for the parameter. - **Optional Arguments:** - `type`: Specifies the data type of the parameter. - Accepted Values: 'const' or 'constant'. + > Note: Yaml Method Specific - `is_fidelity`: Marks the parameter as a fidelity parameter (default: False). ### Important Note on YAML Data Type Interpretation @@ -131,9 +151,8 @@ When working with YAML files, it's essential to understand how the format interp 3. **Numbers:** - Unquoted numeric values are interpreted as integers or floating-point numbers, depending on their format. - - Example: `123` is an integer, `4.56` is a float, `1e3` can be either an integer or a floating-point number, - depending on the type specified by the user. By default, 1e3 is treated as a floating-point number. - This interpretation is unique to our system. + - By default, when the 'type' is not specified, any number in scientific notation (e.g., 1e3) is interpreted as a + floating-point number. This interpretation is unique to our system. 4. **Empty Strings:** @@ -146,13 +165,17 @@ When working with YAML files, it's essential to understand how the format interp Remember to use appropriate quotes and formats to ensure values are interpreted as intended. -## Supported ArchitectureParameter Types - -**Note**: The definition of Search Space from a YAML file is limited to supporting only Hyperparameter Types. +## Supported Architecture parameter Types -If you are interested in exploring Architecture, particularly Hierarchical parameters, you can find detailed examples and usage in the following resources: +!!! note "Note" + The definition of `pipeline_space` from a YAML file is currently limited to supporting only Hyperparameter Types. +!!! note "Note" + A comprehensive documentation for the Architecture parameter will be available soon. +If you are interested in exploring Architecture, particularly Hierarchical parameters, you can find detailed examples +and usage in the following resources: - [Basic Usage Examples](https://github.com/automl/neps/tree/master/neps_examples/basic_usage) - Basic usage examples that can help you understand the fundamentals of Architecture parameters. -- [Experimental Examples](https://github.com/automl/neps/tree/master/neps_examples/experimental) - For more advanced and experimental use cases, including Hierarchical parameters, check out this collection of examples. +- [Experimental Examples](https://github.com/automl/neps/tree/master/neps_examples/experimental) - For more advanced +and experimental use cases, including Hierarchical parameters, check out this collection of examples. diff --git a/docs/run_pipeline.md b/docs/run_pipeline.md index 249bc49e..ee7aa5cd 100644 --- a/docs/run_pipeline.md +++ b/docs/run_pipeline.md @@ -1,10 +1,124 @@ # The run_pipeline Function -The `run_pipeline` function is crucial for NePS. It encapsulates the `objective function` to be minimized, which could be a regular equation or a neural network. +The `run_pipeline` function is crucial for NePS. It encapsulates the objective function to be minimized, which could +range from a regular equation to a full training and evaluation pipeline for a neural network. -This function receives the configuration to be utilized from the parameters defined in the search space. Consequently, it executes the same set of instructions or equations based on the provided configuration to minimize the objective function. +This function receives the configuration to be utilized from the parameters defined in the search space. Consequently, +it executes the same set of instructions or equations based on the provided configuration to minimize the objective +function. -The `run_pipeline` function will look similar to the following: +We will show some basic usages and some functionalites this function would require for successful implementation. + +## General Implementation + +Assuming the `pipeline_space` was already created (see +[here](https://automl.github.io/neps/latest/pipeline_space/) for details). The `run_pipeline` function will resemble the +following: + +```python +def run_pipeline( + **config, # The hyperparameters to be used in the pipeline +): + element_1 = config["element_1"] + element_2 = config["element_2"] + element_3 = config["element_3"] + + loss = element_1 - element_2 + element_3 + + return loss +``` + +## Expected Returns + +In this section, we will outline the key variables that are expected to be returned when utilizing the `run_pipeline` +function. + +### Loss + +One crucial return variable is the `loss`. This metric serves as a fundamental indicator for the optimizer. + +!!! note + + Loss can be any value that is to be minimized by the objective function. We have chosen to label that as `loss`, + because of it's natural integration with neural networks common terminology. + +Additionally, the function can be modified to include other data in its return. One option is to return a dictionary +with the `loss` as a key, along with other user-chosen metrics. + +```python +def run_pipeline( + **config, # The hyperparameters to be used in the pipeline +): + + element_1 = config["element_1"] + element_2 = config["element_2"] + element_3 = config["element_3"] + + loss = element_1 - element_2 + element_3 + reverse_loss = element_2 - element_1 - element_3 + + return { + "loss": loss, + "info_dict": { + "reverse_loss": reverse_loss + ... + } + } +``` + +### Cost + +Along with the return of the `loss`, the `run_pipeline` function would optionally need to return a `cost` in certain +cases. Specifically when the `max_cost_total` parameter is being utilized in the `neps.run` function. + + +!!! note + + `max_cost_total` sums the cost from all returned configuration results and checks whether the maximum allowed cost + has been reached (if so, the search will come to an end). + +```python +import neps +import logging + + +def run_pipeline( + **config, # The hyperparameters to be used in the pipeline +): + + element_1 = config["element_1"] + element_2 = config["element_2"] + element_3 = config["element_3"] + + loss = element_1 - element_2 + element_3 + cost = 2 + + return { + "loss": loss, + "cost": cost, + } + +if __name__ == "__main__": + logging.basicConfig(level=logging.INFO) + neps.run( + run_pipeline=run_pipeline, + pipeline_space=pipeline_space, # Assuming the pipeline space is defined + root_directory="results/bo", + max_cost_total=10, + searcher="bayesian_optimization", + ) +``` + +Each evaluation carries a cost of 2. Hence in this example, the Bayesian optimization search is set to perform 5 +evaluations. + +## Arguments for Convenience + +NePS also provides the `pipeline_directory` and the `previous_pipeline_directory` as arguments in the `run_pipeline` +function for user convenience. + +Regard an example to be run with a multi-fidelity searcher, some checkpointing would be advantageos such that one does +not have to train the configuration from scratch when the configuration qualifies to higher fidelity brackets. ```python def run_pipeline( @@ -12,17 +126,37 @@ def run_pipeline( previous_pipeline_directory, # The directory of the immediate lower fidelity config **config, # The hyperparameters to be used in the pipeline ): + # Assume element3 is our fidelity element + element_1 = config["element_1"] + element_2 = config["element_2"] + element_3 = config["element_3"] - element_1 = config[element_1] - element_2 = config[element_2] - element_3 = config[element_3] + # Load any saved checkpoints + checkpoint_name = "checkpoint.pth" + start_element_3 = 0 - loss = element_1 - element_2 + element_3 + if previous_pipeline_directory is not None: + # Read in state of the model after the previous fidelity rung + checkpoint = torch.load(previous_pipeline_directory / checkpoint_name) + prev_element_3 = checkpoint["element_3"] + else: + prev_element_3 = 0 - return loss -``` + start_element_3 += prev_element_3 -The `run_pipeline` function should be replaced with the user's specific objective function. It is invoked by `neps.run` without any arguments, as these arguments are automatically handled by NePS. Additionally, NePS provides the pipeline directory and the previous pipeline directory for user convenience (mainly useful for searches that require fidelities). + loss = 0 + for i in range(start_element_3, element_3): + loss += element_1 - element_2 -Have a look at our examples and templates [here](https://github.com/automl/neps/tree/master/neps_examples) to see how we use this function in different scenarios. + torch.save( + { + "element_3": element_3, + }, + pipeline_directory / checkpoint_name, + ) + + return loss +``` +This could allow the proper navigation to the trained models and further train them on higher fidelities without +repeating the entire training process. diff --git a/mkdocs.yml b/mkdocs.yml index be71054c..dda28b7f 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -1,4 +1,4 @@ -site_name: Neural Pipeline Search +site_name: NePS docs_dir: docs repo_url: https://github.com/automl/neps repo_name: automl/neps @@ -8,7 +8,8 @@ theme: name: material icon: repo: fontawesome/brands/github - palette: + + palette: # Palette toggle for light mode - scheme: default toggle: @@ -36,11 +37,12 @@ extra: provider: mike nav: - - Introduction and Installation: 'README.md' + - Installation: 'installation.md' - Getting Started: 'getting_started.md' - The run_pipeline Function: 'run_pipeline.md' - The pipeline_space: 'pipeline_space.md' - The neps.run Function: 'neps_run.md' + - Optimizers: 'optimizers.md' - Analysing Runs: 'analyse.md' - Parallelization: 'parallelization.md' - Citations: 'citations.md' diff --git a/neps/api.py b/neps/api.py index e1579624..d035a5b5 100644 --- a/neps/api.py +++ b/neps/api.py @@ -98,11 +98,8 @@ def write_loss_and_config(file_handle, loss_, config_id_, config_): def run( run_pipeline: Callable, root_directory: str | Path, - pipeline_space: dict[str, Parameter | CS.ConfigurationSpace] - | str - | Path - | CS.ConfigurationSpace - | None = None, + pipeline_space: dict[str, Parameter | CS.ConfigurationSpace] | str | Path | + CS.ConfigurationSpace | None = None, overwrite_working_directory: bool = False, post_run_summary: bool = False, development_stage_id=None, @@ -280,18 +277,13 @@ def run( pre_load_hooks=pre_load_hooks, ) - if post_run_summary and development_stage_id: - optimization_dir = Path(root_directory) / f"dev_{development_stage_id}" - post_run_csv(optimization_dir, logger) - elif post_run_summary: + if post_run_summary: post_run_csv(root_directory, logger) def _run_args( searcher_info: dict, pipeline_space: dict[str, Parameter | CS.ConfigurationSpace] - | str - | Path | CS.ConfigurationSpace | None = None, max_cost_total: int | float | None = None, diff --git a/neps/search_spaces/search_space.py b/neps/search_spaces/search_space.py index 8f7335ed..0280f14c 100644 --- a/neps/search_spaces/search_space.py +++ b/neps/search_spaces/search_space.py @@ -118,10 +118,10 @@ def pipeline_space_from_yaml( f"The file at {str(yaml_file_path)} is not a valid YAML file." ) from e - # check for init key search_space - if "search_space" not in config: + # check for init key pipeline_space + if "pipeline_space" not in config: raise KeyError( - "The YAML file is incorrectly constructed: the 'search_space:' " + "The YAML file is incorrectly constructed: the 'pipeline_space:' " "reference is missing at the top of the file." ) @@ -129,7 +129,7 @@ def pipeline_space_from_yaml( pipeline_space = {} # Iterate over the items in the YAML configuration - for name, details in config["search_space"].items(): + for name, details in config["pipeline_space"].items(): # get parameter type param_type = deduce_and_validate_param_type(name, details) diff --git a/neps_examples/efficiency/multi_fidelity.py b/neps_examples/efficiency/multi_fidelity.py index 4239aee7..0731b1b5 100644 --- a/neps_examples/efficiency/multi_fidelity.py +++ b/neps_examples/efficiency/multi_fidelity.py @@ -37,6 +37,11 @@ def get_model_and_optimizer(learning_rate): return model, optimizer +# Important: Include the "pipeline_directory" and "previous_pipeline_directory" arguments +# in your run_pipeline function. This grants access to NePS's folder system and is +# critical for leveraging efficient multi-fidelity optimization strategies. + + def run_pipeline(pipeline_directory, previous_pipeline_directory, learning_rate, epoch): model, optimizer = get_model_and_optimizer(learning_rate) checkpoint_name = "checkpoint.pth" diff --git a/tests/test_yaml_search_space/config_including_unknown_types.yaml b/tests/test_yaml_search_space/config_including_unknown_types.yaml index 62abc0d9..790f473d 100644 --- a/tests/test_yaml_search_space/config_including_unknown_types.yaml +++ b/tests/test_yaml_search_space/config_including_unknown_types.yaml @@ -1,4 +1,4 @@ -search_space: +pipeline_space: learning_rate: type: numerical lower: 0.00001 diff --git a/tests/test_yaml_search_space/config_including_wrong_types.yaml b/tests/test_yaml_search_space/config_including_wrong_types.yaml index 206f3a5c..df3a4602 100644 --- a/tests/test_yaml_search_space/config_including_wrong_types.yaml +++ b/tests/test_yaml_search_space/config_including_wrong_types.yaml @@ -1,4 +1,4 @@ -search_space: +pipeline_space: learning_rate: type: int lower: 0.00001 diff --git a/tests/test_yaml_search_space/correct_config.yaml b/tests/test_yaml_search_space/correct_config.yaml index 5f0c641c..895efa38 100644 --- a/tests/test_yaml_search_space/correct_config.yaml +++ b/tests/test_yaml_search_space/correct_config.yaml @@ -1,4 +1,4 @@ -search_space: +pipeline_space: param_float1: lower: 0.00001 upper: 0.1 diff --git a/tests/test_yaml_search_space/correct_config_including_priors.yml b/tests/test_yaml_search_space/correct_config_including_priors.yml index 2b6d2e80..a73f2157 100644 --- a/tests/test_yaml_search_space/correct_config_including_priors.yml +++ b/tests/test_yaml_search_space/correct_config_including_priors.yml @@ -1,4 +1,4 @@ -search_space: +pipeline_space: learning_rate: lower: 0.00001 upper: 0.1 diff --git a/tests/test_yaml_search_space/correct_config_including_types.yaml b/tests/test_yaml_search_space/correct_config_including_types.yaml index 870d9574..60fc4995 100644 --- a/tests/test_yaml_search_space/correct_config_including_types.yaml +++ b/tests/test_yaml_search_space/correct_config_including_types.yaml @@ -1,4 +1,4 @@ -search_space: +pipeline_space: param_float1: type: float lower: 0.00001 diff --git a/tests/test_yaml_search_space/inconsistent_types_config.yml b/tests/test_yaml_search_space/inconsistent_types_config.yml index 5c3182a2..797658c4 100644 --- a/tests/test_yaml_search_space/inconsistent_types_config.yml +++ b/tests/test_yaml_search_space/inconsistent_types_config.yml @@ -1,4 +1,4 @@ -search_space: +pipeline_space: learning_rate: lower: "string" # Lower is now a string upper: 1e3 diff --git a/tests/test_yaml_search_space/inconsistent_types_config2.yml b/tests/test_yaml_search_space/inconsistent_types_config2.yml index 5f205e92..d126b52e 100644 --- a/tests/test_yaml_search_space/inconsistent_types_config2.yml +++ b/tests/test_yaml_search_space/inconsistent_types_config2.yml @@ -1,4 +1,4 @@ -search_space: +pipeline_space: learning_rate: type: int lower: 2.3 # float diff --git a/tests/test_yaml_search_space/incorrect_config.txt b/tests/test_yaml_search_space/incorrect_config.txt index c80774ee..cff7baa4 100644 --- a/tests/test_yaml_search_space/incorrect_config.txt +++ b/tests/test_yaml_search_space/incorrect_config.txt @@ -1,4 +1,4 @@ -search_space # : is missing +pipeline_space # : is missing learning_rate: lower: 0.00001 upper: 0.1 diff --git a/tests/test_yaml_search_space/missing_key_config.yml b/tests/test_yaml_search_space/missing_key_config.yml index d6dbeb26..db0f6361 100644 --- a/tests/test_yaml_search_space/missing_key_config.yml +++ b/tests/test_yaml_search_space/missing_key_config.yml @@ -1,4 +1,4 @@ -search_space: +pipeline_space: learning_rate: lower: 0.00001 log: true diff --git a/tests/test_yaml_search_space/not_allowed_key_config.yml b/tests/test_yaml_search_space/not_allowed_key_config.yml index b6703282..59887401 100644 --- a/tests/test_yaml_search_space/not_allowed_key_config.yml +++ b/tests/test_yaml_search_space/not_allowed_key_config.yml @@ -1,4 +1,4 @@ -search_space: +pipeline_space: float_name1: lower: 3e-5 upper: 0.1 From e605256e1319af647bba90530e265d6cfc834f7b Mon Sep 17 00:00:00 2001 From: tarekabouchakra Date: Mon, 12 Feb 2024 09:41:39 +0100 Subject: [PATCH 04/11] Documentation iteration + minor tblogger example change --- docs/analyse.md | 36 ++++++++++++++---- .../tensorboard/tblogger_hparam1.jpg | Bin 205592 -> 207727 bytes .../tensorboard/tblogger_hparam2.jpg | Bin 286538 -> 321199 bytes .../tensorboard/tblogger_hparam3.jpg | Bin 240540 -> 138426 bytes .../doc_images/tensorboard/tblogger_image.jpg | Bin 181448 -> 184767 bytes .../tensorboard/tblogger_scalar.jpg | Bin 236698 -> 204856 bytes neps/plot/tensorboard_eval.py | 6 ++- .../convenience/neps_tblogger_tutorial.py | 2 +- 8 files changed, 34 insertions(+), 10 deletions(-) diff --git a/docs/analyse.md b/docs/analyse.md index 5ff59616..cd78a14d 100644 --- a/docs/analyse.md +++ b/docs/analyse.md @@ -17,6 +17,28 @@ ROOT_DIRECTORY ├── best_loss_trajectory.txt └── best_loss_with_config_trajectory.txt ``` +## Summary CSV + +The argument `post_run_summary` in `neps.run` allows for the automatic generation of CSV files after a run is complete. The new root directory after utilizing this argument will look like the following: + +``` +ROOT_DIRECTORY +├── results +│ └── config_1 +│ ├── config.yaml +│ ├── metadata.yaml +│ └── result.yaml +├── summary_csv +│ ├── config_data.csv +│ └── run_status.csv +├── all_losses_and_configs.txt +├── best_loss_trajectory.txt +└── best_loss_with_config_trajectory.txt +``` + +- *`config_data.csv`*: Contains all configuration details in CSV format, ordered by ascending `loss`. Details include configuration hyperparameters, any returned result from the `run_pipeline` function, and metadata information. + +- *`run_status.csv`*: Provides general run details, such as the number of sampled configs, best configs, number of failed configs, best loss, etc. ## TensorBoard Integration @@ -37,9 +59,9 @@ The `tblogger.log` function is invoked within the model's training loop to facil tblogger.log( loss: float, current_epoch: int, - write_summary_incumbent: bool = False, write_config_scalar: bool = False, write_config_hparam: bool = True, + write_summary_incumbent: bool = False, extra_data: dict | None = None ) ``` @@ -47,9 +69,9 @@ tblogger.log( - **Parameters:** - `loss` (float): The loss value to be logged. - `current_epoch` (int): The current epoch or iteration number. - - `write_summary_incumbent` (bool, optional): Set to `True` for a live incumbent trajectory. - `write_config_scalar` (bool, optional): Set to `True` for a live loss trajectory for each configuration. - `write_config_hparam` (bool, optional): Set to `True` for live parallel coordinate, scatter plot matrix, and table view. + - `write_summary_incumbent` (bool, optional): Set to `True` for a live incumbent trajectory. - `extra_data` (dict, optional): Additional data to be logged, provided as a dictionary. ### Extra Custom Logging @@ -104,15 +126,15 @@ You can find this example [here](https://github.com/automl/neps/blob/master/neps !!! info "Important" We have optimized the example for computational efficiency. If you wish to replicate the exact results showcased in the following section, we recommend the following modifications: - 1- Increase maximum epochs [here](https://github.com/automl/neps/blob/master/neps_examples/convenience/neps_tblogger_tutorial.py#L260) from 2 to 10 + 1- Increase maximum epochs from 2 to 10 - 2- Set the `write_summary_incumbent` argument [here](https://github.com/automl/neps/blob/master/neps_examples/convenience/neps_tblogger_tutorial.py#L300) to `True` + 2- Set the `write_summary_incumbent` argument to `True` - 3- Change the searcher [here](https://github.com/automl/neps/blob/master/neps_examples/convenience/neps_tblogger_tutorial.py#L357) from `random_search` to `bayesian_optimization` + 3- Change the searcher from `random_search` to `bayesian_optimization` - 4- Increase the maximum evaluations [here](https://github.com/automl/neps/blob/master/neps_examples/convenience/neps_tblogger_tutorial.py#L362) from 2 to 14 + 4- Increase the maximum evaluations before disabling `tblogger` from 2 to 14 - 5- Increase the maximum evaluations [here](https://github.com/automl/neps/blob/master/neps_examples/convenience/neps_tblogger_tutorial.py#L391) from 3 to 15 + 5- Increase the maximum evaluations after disabling `tblogger` from 3 to 15 ### Visualization Results diff --git a/docs/doc_images/tensorboard/tblogger_hparam1.jpg b/docs/doc_images/tensorboard/tblogger_hparam1.jpg index c2bbcaec8089d761d72ec6665aff5239fd0bd860..19ea6f112b1ce23d1d6f8ea2434598190a960a49 100644 GIT binary patch literal 207727 zcmdpeXIN9+(l$*x0-}Ia6#)@xDufP#B1J$!DI#4!>AeLAy+{-3(o{fHdap_7ND)MO z4OI{Vp@z_sZ*k6fo^#Io{`r1>*SD`r_cqykueD~)+%t2}#Hhzw59z4csELS(=+sp2 z>ktu9K#7P*dnw7mCwjAs7r}|lO-b!3B{=*jUq*s|Uv__B;I8Xr?e1mnYDHw@=;UA} z}qB1{@Tg$%G1{lRz#XkSHx~#QL(bPa$EHF-7BI} z(zkC*-xa^2{NU*=y6#LOqANsd_wPOR&P31n_+1(~C2WuNP4hyo6SpFGqDguXUny07 zE>Kh6v^9QJT;uV;uGqoY)~>qvgsjk`mim*P&>O#J?XeFYNLZB@5ffWc$xOJVrXQY) zb!!Skf&$4Rn{whG-c4WbORtC2ryghTN_B>@n5>^G^CCrJIW%}2-?Ph)OZ`;_4K$Z(_rvI4IVo&0q!*+9bw|mwlfdx@+zio?k;5v!r(jg@W%572 zOwc*{U~o3r*5q-JsH8O4Q(H;D0Xnd%ULMPrGU~vEpR$9Rb{m zVO<9_-gUq>V$RT#DwZRstO!>;MKzG=oaFmd_$Q1O5ocpjy~+sl+tAb~gNR&hu)jHI zh2bbiaFmxp?%I;H6it1rcCcTjrOuYE4TnCh{DpZQI`YkHq1AxZ2E+GB(1roObu|;7 z5wEp^hH5Wa$G(IOg#-U?^$=f@0iy3lxb z)@mT3RMslGu7sa=&0(+=x36xC?$_LM3RbJv6DJ{erY^AIz5KHJ$;WtW%;kA8(_wHA zDUXtzH^`~2o5v{xRnwvrIaT z<=xyY7yG?eOWu9t4vC|NLhv~VxU0W}?jq7dX3$F4 zu1TA10($*0IE`+KiNl*GC_D8XfuPkfakw$Knfz~|L~;Z&etXz+2>XnSWv8zzMjkBB z5}+}m5JxhZ=d)VRm49vGNO-E{2g4Fw+QP_=j5Q}WauNxT^{+*nwBQ{RYGDVA2F1Qi z_*G2ZJPFY>JmaHM;47yWft6lNh<@dkomB2#(XA{$Vn-L@!I7MHiZ2g-%STimH}3RH zmvg%tx9ehDYM)OT3PB$qa6{jvVBj5+)5hO)o{`?|ZS6TznSo5jZKGPIRfX{3LKE4W z=A{YmUtk+5-tMb@^`6O`VW%2+uLy*N{mdKab?V1iNogJ1VgEBldJM^lj1&J883ylg zLaC{RC490T63#>IUm^+{pyjDK%Ed{KmB&A=wSD3KXX>J6)34*J`NH_b0sj3R8_{2E z39c=sH{Roha;I9j%acCB+HI$hJ^LL_GeHv{I9u*G@tth*w2%6SBgbBm_`}{9Ri%FF zwruve=`e)H+o{hInDoGCv;?E&4ee8*{+bc0URoF>3?sNzEyKz4l%C(~&S1V1Q*ZV; 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z*IjB{&CKG0bW>nSQL~@L%AjL>cwi~2TCHzIF!qK+rjOq8saWJgiQ=xRY#(u@~T=6Oc9CC31B^&-sE;~d#r=dezVjr5D?zCQgUv&J!cJ(VJTq;Y?W z-PM9mYeM=5c)3>cAXSM`oqhs4~b@fOQDtSZ~F8 zI+E4ko-DKPX`Xjo8yg#E($|{izcvz#2qzUf_2da&LXp?w@nSJ3F>2TEnGgTdq`LoA z(p|>&zczM8xw66NOBCyu{U)uMx)BZ-FDzDQyAu9(Qp%oP#EGq3pY81}O1oiIMq1>o zjm48AlhKEsG98lIo6pqJvKY{zz(g7yy4Tr|K;t7&&1;G*gCf1P{i0eLc3)k;$q6nb z>4i1auq<(jz<|gjt#LSn?!c!JyKNmmXvR@5@3;6S3*2NwgkWS4eI6?Gt4?0|9beB@ z8l1!i3i*CJb_{S4R$~4m9xAS}*I}$VuVj^d|hH-(CRb2WbRYKLM8h#Eu=1a4Mbq5pLQ9P^sa`bX7IM z5yoROMw78N8J`eCATi`7V;5pE!O@>;`IAk!6Z2xagmies>uQLOzSg?OH zj%O)Fys*GiG*|srm4Fj_i9IPObu2zZ+t|K${&F<( Date: Mon, 12 Feb 2024 14:41:03 +0100 Subject: [PATCH 05/11] Update pyproject.toml --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 26a5cce9..061afd0d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -47,7 +47,7 @@ packages = [ [tool.poetry.dependencies] python = ">=3.8,<3.12" -ConfigSpace = "^0.6" +ConfigSpace = "^0.7" grakel = "^0.1" numpy = "^1" pandas = "^2" From d40afea86e54383a8e58acafc9f34d06e401883b Mon Sep 17 00:00:00 2001 From: Daniel <63580393+danrgll@users.noreply.github.com> Date: Tue, 13 Feb 2024 22:12:28 +0100 Subject: [PATCH 06/11] Doc link fixes + some minor text changes --- README.md | 11 +++++------ docs/README.md | 15 +++++++-------- docs/pipeline_space.md | 29 ++++++++++++++--------------- 3 files changed, 26 insertions(+), 29 deletions(-) diff --git a/README.md b/README.md index cd95279f..a83ac11d 100644 --- a/README.md +++ b/README.md @@ -25,13 +25,12 @@ In addition to the common features offered by traditional HPO and NAS libraries, - NePS is equipped to handle context-free grammar search spaces, providing advanced capabilities for designing and optimizing architectures. this is leveraged by the insights presented in: - [Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars](https://arxiv.org/abs/2211.01842) -3. [**Easy Parallelization:**](docs/parallelization.md) - - NePS simplifies the parallelization of optimization tasks. Whether experiments are running on a single machine or a distributed computing environment. +3. [**Easy Parallelization and Resumption of Runs:**](docs/parallelization.md) + - NePS simplifies the process of parallelizing optimization tasks both on individual computers and in distributed + computing environments. It also allows users to conveniently resume these optimization tasks after completion to + ensure a seamless and efficient workflow for long-running experiments. -4. [**Resume Runs After Termination:**](docs/parallelization.md) - - NePS allows users to easily resume optimization runs after termination, providing a convenient and efficient workflow for long-running experiments. - -5. [**Seamless User Code Integration:**](neps_examples/template/) +4. [**Seamless User Code Integration:**](neps_examples/template/) - NePS's modular design ensures flexibility and extensibility. Integrate NePS effortlessly into existing machine learning workflows. ## Getting Started diff --git a/docs/README.md b/docs/README.md index 25a3db2d..28f2f0dd 100644 --- a/docs/README.md +++ b/docs/README.md @@ -14,20 +14,19 @@ NePS houses recently published and some more well-established algorithms that ar In addition to the common features offered by traditional HPO and NAS libraries, NePS stands out with the following key features: -1. [**Hyperparameter Optimization (HPO) With Prior Knowledge:**](neps_examples/template/priorband_template.py) +1. [**Hyperparameter Optimization (HPO) With Prior Knowledge:**](https://github.com/automl/neps/tree/master/neps_examples/template/priorband_template.py) - NePS excels in efficiently tuning hyperparameters using algorithms that enable users to make use of their prior knowledge within the search space. This is leveraged by the insights presented in: - [PriorBand: Practical Hyperparameter Optimization in the Age of Deep Learning](https://arxiv.org/abs/2306.12370) - [πBO: Augmenting Acquisition Functions with User Beliefs for Bayesian Optimization](https://arxiv.org/abs/2204.11051) -2. [**Neural Architecture Search (NAS) With Context-free Grammar Search Spaces:**](neps_examples/basic_usage/architecture.py) +2. [**Neural Architecture Search (NAS) With Context-free Grammar Search Spaces:**](https://github.com/automl/neps/tree/master/neps_examples/basic_usage/architecture.py) - NePS is equipped to handle context-free grammar search spaces, providing advanced capabilities for designing and optimizing architectures. this is leveraged by the insights presented in: - [Construction of Hierarchical Neural Architecture Search Spaces based on Context-free Grammars](https://arxiv.org/abs/2211.01842) -3. [**Easy Parallelization:**](docs/parallelization.md) - - NePS simplifies the parallelization of optimization tasks. Whether experiments are running on a single machine or a distributed computing environment. +3. [**Easy Parallelization and Resumption of Runs:**](https://automl.github.io/neps/latest/parallelization) + - NePS simplifies the process of parallelizing optimization tasks both on individual computers and in distributed + computing environments. It also allows users to conveniently resume these optimization tasks after completion to + ensure a seamless and efficient workflow for long-running experiments. -4. [**Resume Runs After Termination:**](docs/parallelization.md) - - NePS allows users to easily resume optimization runs after termination, providing a convenient and efficient workflow for long-running experiments. - -5. [**Seamless User Code Integration:**](neps_examples/template/) +4. [**Seamless User Code Integration:**](https://github.com/automl/neps/tree/master/neps_examples/template/) - NePS's modular design ensures flexibility and extensibility. Integrate NePS effortlessly into existing machine learning workflows. diff --git a/docs/pipeline_space.md b/docs/pipeline_space.md index 2978a215..ab49a221 100644 --- a/docs/pipeline_space.md +++ b/docs/pipeline_space.md @@ -2,11 +2,11 @@ In NePS, a pivotal step is the definition of the search space, termed `pipeline_space`. This space can be structured through various approaches, including a Python dictionary, a YAML file, or ConfigSpace. Each of these methods allows -you to specify a set of Parameter types, ranging from Float and Categorical to specialized Architecture Parameters. +you to specify a set of parameter types, ranging from Float and Categorical to specialized architecture parameters. Whether you choose a dictionary, YAML file, or ConfigSpace, your selected method serves as a container or framework within which these parameters are defined and organized. This section not only guides you through the process of setting up your `pipeline_space` using these methods but also provides detailed instructions and examples on how to -effectively incorporate various Parameter types, ensuring that NePS can utilize them in the optimization process. +effectively incorporate various parameter types, ensuring that NePS can utilize them in the optimization process. ## Methods for Defining the NePS Pipeline Space @@ -82,7 +82,7 @@ For additional information on ConfigSpace and its features, please visit the fol - **Expected Arguments:** - `lower`: The minimum value of the parameter. - `upper`: The maximum value of the parameter. - - Accepted Values: Int or Float depending on the specific parameter type one wishes to use. + - Accepted values: int or float depending on the specific parameter type one wishes to use. - **Optional Arguments:** - `log`: Boolean that indicates if the parameter uses a logarithmic scale (default: False) - [Details on how YAML interpret Boolean Values](#important-note-on-yaml-data-type-interpretation) @@ -92,9 +92,9 @@ For additional information on ConfigSpace and its features, please visit the fol - `default_confidence`: Specifies the confidence level of the default value, indicating how strongly the prior should be considered (default: 'low'). - - Accepted Values: 'low', 'medium', or 'high'. + - Accepted values: 'low', 'medium', or 'high'. - `type`: Specifies the data type of the parameter. - - Accepted Values: 'int', 'integer', or 'float'. + - Accepted values: 'int', 'integer', or 'float'. > Note: If type is not specified e notation gets converted to float !!! note "YAML Method Specific:" @@ -116,7 +116,7 @@ For additional information on ConfigSpace and its features, please visit the fol indicating how strongly the prior should be considered (default: "low"). - `type`: Specifies the data type of the parameter. - - Accepted Values: 'cat' or 'categorical'. + - Accepted values: 'cat' or 'categorical'. > Note: Yaml Method Specific ### Constant Parameter @@ -125,7 +125,7 @@ For additional information on ConfigSpace and its features, please visit the fol - `value`: The fixed value (int | float | str) for the parameter. - **Optional Arguments:** - `type`: Specifies the data type of the parameter. - - Accepted Values: 'const' or 'constant'. + - Accepted values: 'const' or 'constant'. > Note: Yaml Method Specific - `is_fidelity`: Marks the parameter as a fidelity parameter (default: False). @@ -168,14 +168,13 @@ Remember to use appropriate quotes and formats to ensure values are interpreted ## Supported Architecture parameter Types !!! note "Note" - The definition of `pipeline_space` from a YAML file is currently limited to supporting only Hyperparameter Types. + The configuration of `pipeline_space` from a YAML file does not currently support architecture parameter types. !!! note "Note" A comprehensive documentation for the Architecture parameter will be available soon. -If you are interested in exploring Architecture, particularly Hierarchical parameters, you can find detailed examples -and usage in the following resources: + If you are interested in exploring architecture parameters, you can find detailed + examples and usage in the following resources: -- [Basic Usage Examples](https://github.com/automl/neps/tree/master/neps_examples/basic_usage) - Basic usage - examples that can help you understand the fundamentals of Architecture parameters. - -- [Experimental Examples](https://github.com/automl/neps/tree/master/neps_examples/experimental) - For more advanced -and experimental use cases, including Hierarchical parameters, check out this collection of examples. + - [Basic Usage Examples](https://github.com/automl/neps/tree/master/neps_examples/basic_usage) - Basic usage + examples that can help you understand the fundamentals of Architecture parameters. + - [Experimental Examples](https://github.com/automl/neps/tree/master/neps_examples/experimental) - For more advanced + and experimental use cases, including Hierarchical parameters, check out this collection of examples. From 3041412d3b4578a317f411ad21bb67a526428882 Mon Sep 17 00:00:00 2001 From: tarekabouchakra Date: Wed, 14 Feb 2024 09:37:22 +0100 Subject: [PATCH 07/11] Updating docs for citations --- docs/citations.md | 22 ++++++++++++---------- mkdocs.yml | 1 - 2 files changed, 12 insertions(+), 11 deletions(-) diff --git a/docs/citations.md b/docs/citations.md index 907c0b7d..b697c49f 100644 --- a/docs/citations.md +++ b/docs/citations.md @@ -1,31 +1,33 @@ -# Citation of The Software +# Citations + +## Citation of The Software For citing NePS, please refer to the following: -## APA Style +### APA Style ```apa -Stoll, D., Mallik, N., Schrodi, S., Janowski, M., Garibov, S., Abou Chakra, T., Hvarfner, C., Bergman, E., Binxin, R., Kober, N., Vallaeys, T., & Hutter, F. (2023). Neural Pipeline Search (NePS) (Version 0.10.0) [Computer software]. https://github.com/automl/neps +Stoll, D., Mallik, N., Schrodi, S., Janowski, M., Garibov, S., Abou Chakra, T., Rogalla, D., Bergman, E., Hvarfner, C., Binxin, R., Kober, N., Vallaeys, T., & Hutter, F. (2023). Neural Pipeline Search (NePS) (Version 0.11.0) [Computer software]. https://github.com/automl/neps ``` -## BibTex Style +### BibTex Style ```bibtex @software{Stoll_Neural_Pipeline_Search_2023, -author = {Stoll, Danny and Mallik, Neeratyoy and Schrodi, Simon and Janowski, Maciej and Garibov, Samir and Abou Chakra, Tarek and Hvarfner, Carl and Bergman, Eddie and Binxin, Ru and Kober, Nils and Vallaeys, Théophane and Hutter, Frank}, +author = {Stoll, Danny and Mallik, Neeratyoy and Schrodi, Simon and Janowski, Maciej and Garibov, Samir and Abou Chakra, Tarek and Rogalla, Daniel and Bergman, Eddie and Hvarfner, Carl and Binxin, Ru and Kober, Nils and Vallaeys, Théophane and Hutter, Frank}, month = oct, title = {{Neural Pipeline Search (NePS)}}, url = {https://github.com/automl/neps}, -version = {0.10.0}, +version = {0.11.0}, year = {2023} } ``` -# Citation of Papers +## Citation of Papers -If you have used [PriorBand](https://openreview.net/forum?id=uoiwugtpCH) as the optimizer, please use the bibtex below: +### PriorBand -## PriorBand +If you have used [PriorBand](https://openreview.net/forum?id=uoiwugtpCH) as the optimizer, please use the bibtex below: ```bibtex @inproceedings{mallik2023priorband, @@ -37,7 +39,7 @@ keywords = {} } ``` -## Hierarchichal NAS with Context-free Grammars +### Hierarchichal NAS with Context-free Grammars If you have used the context-free grammar search space and the graph kernels implemented in NePS for the paper [Hierarchical NAS](https://openreview.net/forum?id=Hpt1i5j6wh), please use the bibtex below: diff --git a/mkdocs.yml b/mkdocs.yml index dda28b7f..5afc9246 100644 --- a/mkdocs.yml +++ b/mkdocs.yml @@ -47,7 +47,6 @@ nav: - Parallelization: 'parallelization.md' - Citations: 'citations.md' - Alternatives: 'alternatives.md' - - Citations: 'citations.md' - Contributing: - Introduction: "contributing/README.md" - Installation for Contributors: "contributing/installation.md" From 5d091293c571159320c11ece6b65b2ccc78768e3 Mon Sep 17 00:00:00 2001 From: tarekabouchakra Date: Fri, 16 Feb 2024 15:27:15 +0100 Subject: [PATCH 08/11] Updating run_pipeline docs --- docs/run_pipeline.md | 54 +++++++++++++++++--------------------------- 1 file changed, 21 insertions(+), 33 deletions(-) diff --git a/docs/run_pipeline.md b/docs/run_pipeline.md index ee7aa5cd..fb0e9bed 100644 --- a/docs/run_pipeline.md +++ b/docs/run_pipeline.md @@ -1,19 +1,18 @@ # The run_pipeline Function -The `run_pipeline` function is crucial for NePS. It encapsulates the objective function to be minimized, which could -range from a regular equation to a full training and evaluation pipeline for a neural network. +## Introduction -This function receives the configuration to be utilized from the parameters defined in the search space. Consequently, -it executes the same set of instructions or equations based on the provided configuration to minimize the objective -function. +The `run_pipeline` function is crucial for NePS. It encapsulates the objective function to be minimized, which could range from a regular equation to a full training and evaluation pipeline for a neural network. + +This function receives the configuration to be utilized from the parameters defined in the search space. Consequently, it executes the same set of instructions or equations based on the provided configuration to minimize the objective function. We will show some basic usages and some functionalites this function would require for successful implementation. -## General Implementation +## Types of Returns + +### 1. Single Value -Assuming the `pipeline_space` was already created (see -[here](https://automl.github.io/neps/latest/pipeline_space/) for details). The `run_pipeline` function will resemble the -following: +Assuming the `pipeline_space` was already created (have a look at [pipeline space](https://automl.github.io/neps/latest/pipeline_space/) for more details). A `run_pipeline` function with an objective of minimizing the loss will resemble the following: ```python def run_pipeline( @@ -28,22 +27,17 @@ def run_pipeline( return loss ``` -## Expected Returns +### 2. Dictionary -In this section, we will outline the key variables that are expected to be returned when utilizing the `run_pipeline` -function. +In this section, we will outline the special variables that are expected to be returned when the `run_pipeline` function returns a dictionary. -### Loss +#### Loss -One crucial return variable is the `loss`. This metric serves as a fundamental indicator for the optimizer. +One crucial return variable is the `loss`. This metric serves as a fundamental indicator for the optimizer. One option is to return a dictionary with the `loss` as a key, along with other user-chosen metrics. !!! note - Loss can be any value that is to be minimized by the objective function. We have chosen to label that as `loss`, - because of it's natural integration with neural networks common terminology. - -Additionally, the function can be modified to include other data in its return. One option is to return a dictionary -with the `loss` as a key, along with other user-chosen metrics. + Loss can be any value that is to be minimized by the objective function. ```python def run_pipeline( @@ -55,7 +49,7 @@ def run_pipeline( element_3 = config["element_3"] loss = element_1 - element_2 + element_3 - reverse_loss = element_2 - element_1 - element_3 + reverse_loss = -loss return { "loss": loss, @@ -66,16 +60,14 @@ def run_pipeline( } ``` -### Cost +#### Cost -Along with the return of the `loss`, the `run_pipeline` function would optionally need to return a `cost` in certain -cases. Specifically when the `max_cost_total` parameter is being utilized in the `neps.run` function. +Along with the return of the `loss`, the `run_pipeline` function would optionally need to return a `cost` in certain cases. Specifically when the `max_cost_total` parameter is being utilized in the `neps.run` function. !!! note - `max_cost_total` sums the cost from all returned configuration results and checks whether the maximum allowed cost - has been reached (if so, the search will come to an end). + `max_cost_total` sums the cost from all returned configuration results and checks whether the maximum allowed cost has been reached (if so, the search will come to an end). ```python import neps @@ -109,16 +101,13 @@ if __name__ == "__main__": ) ``` -Each evaluation carries a cost of 2. Hence in this example, the Bayesian optimization search is set to perform 5 -evaluations. +Each evaluation carries a cost of 2. Hence in this example, the Bayesian optimization search is set to perform 5 evaluations. ## Arguments for Convenience -NePS also provides the `pipeline_directory` and the `previous_pipeline_directory` as arguments in the `run_pipeline` -function for user convenience. +NePS also provides the `pipeline_directory` and the `previous_pipeline_directory` as arguments in the `run_pipeline` function for user convenience. -Regard an example to be run with a multi-fidelity searcher, some checkpointing would be advantageos such that one does -not have to train the configuration from scratch when the configuration qualifies to higher fidelity brackets. +Regard an example to be run with a multi-fidelity searcher, some checkpointing would be advantageos such that one does not have to train the configuration from scratch when the configuration qualifies to higher fidelity brackets. ```python def run_pipeline( @@ -158,5 +147,4 @@ def run_pipeline( return loss ``` -This could allow the proper navigation to the trained models and further train them on higher fidelities without -repeating the entire training process. +This could allow the proper navigation to the trained models and further train them on higher fidelities without repeating the entire training process. From 7de9db25d67865b0012e630a3acea20fbfa6d066 Mon Sep 17 00:00:00 2001 From: Neeratyoy Mallik Date: Mon, 19 Feb 2024 15:17:13 +0100 Subject: [PATCH 09/11] Bump version from v0.11.0 to v0.11.1 --- CITATION.cff | 2 +- pyproject.toml | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/CITATION.cff b/CITATION.cff index f03b5639..fde949e5 100644 --- a/CITATION.cff +++ b/CITATION.cff @@ -28,6 +28,6 @@ authors: - family-names: Hutter given-names: Frank title: "Neural Pipeline Search (NePS)" -version: 0.11.0 +version: 0.11.1 date-released: 2023-10-25 url: "https://github.com/automl/neps" diff --git a/pyproject.toml b/pyproject.toml index 061afd0d..66be36bb 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -1,6 +1,6 @@ [tool.poetry] name = "neural-pipeline-search" -version = "v0.11.0" +version = "v0.11.1" description = "Neural Pipeline Search helps deep learning experts find the best neural pipeline." authors = [ "Danny Stoll ", From 2ab70032e98174e3497acf0745c8b26ba5edfc10 Mon Sep 17 00:00:00 2001 From: tarekabouchakra Date: Mon, 4 Mar 2024 09:00:24 +0100 Subject: [PATCH 10/11] Adding sleep time to inaccessible locks for summary_csv and sampler_info --- neps/metahyper/api.py | 2 ++ neps/status/status.py | 3 +++ 2 files changed, 5 insertions(+) diff --git a/neps/metahyper/api.py b/neps/metahyper/api.py index c89879c9..1dc9c3bf 100644 --- a/neps/metahyper/api.py +++ b/neps/metahyper/api.py @@ -164,6 +164,8 @@ def _process_sampler_info( finally: decision_locker.release_lock() should_break = True + else: + time.sleep(2) def _load_sampled_paths(optimization_dir: Path | str, serializer, logger): diff --git a/neps/status/status.py b/neps/status/status.py index 93208819..e7bc99d3 100644 --- a/neps/status/status.py +++ b/neps/status/status.py @@ -1,6 +1,7 @@ from __future__ import annotations import logging +import time from pathlib import Path from typing import Any @@ -313,6 +314,8 @@ def _save_data_to_csv( finally: locker.release_lock() should_break = True + else: + time.sleep(2) def post_run_csv(root_directory: str | Path, logger=None) -> None: From cf34bdc0de35d848b132b189e01181a3154023ca Mon Sep 17 00:00:00 2001 From: Daniel <63580393+danrgll@users.noreply.github.com> Date: Mon, 4 Mar 2024 13:40:32 +0100 Subject: [PATCH 11/11] fix example --- .../basic_usage/defining_search_space/search_space_example.yaml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/neps_examples/basic_usage/defining_search_space/search_space_example.yaml b/neps_examples/basic_usage/defining_search_space/search_space_example.yaml index 27bd37c1..cdcdee66 100644 --- a/neps_examples/basic_usage/defining_search_space/search_space_example.yaml +++ b/neps_examples/basic_usage/defining_search_space/search_space_example.yaml @@ -1,4 +1,4 @@ -search_space: +pipeline_space: float_name1: lower: 3e-5 upper: 0.1

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