Hyperparameter tuning frameworks. Reload to refresh your session.

Deep Learning Specialization by Andrew Ng on Coursera. Jul 19, 2020 · Module 3: Hyperparameter tuning, Batch Normalization and Programming Frameworks. Master the process of hyperparameter tuning; Assignment of Week 3. It can be a computationally involved process depending on the number of parameters, search space and model architectures. If selected by the user they can be specified as explained on the tutorial page on learners – simply pass them to makeLearner(). The aim of hyperparameter optimization in machine learning is to find the hyperparameters of a given machine learning algorithm that return the best performance as measured on a validation set. How we tune hyperparameters is a question not only about which tuning methodology we use but also about how we evolve hyperparameter learning phases until we find the final and best. Click Hyperparameter tuning. Apr 20, 2018 · Robust and efficient optimization algorithms are needed for hyperparameter tuning. While the process of manually tuning hyperparameters can be tedious and time-consuming, there are many hyperparameter optimization frameworks available that can help automate the process. May 23, 2022 · 5. Optuna is a software framework for automating the optimization process of these hyperparameters. Most importantly, we will do this in a similar way to how top Kagglers tune their LGBM models that achieve impressive results. . Then, the research focuses on major optimization algorithms and their applicability, covering their efficiency and Nov 20, 2020 · It surveys common HPO libraries and frameworks for practical use. Jun 18, 2024 · This formalization not only clarifies the goal of hyperparameter tuning but also provides a structured framework for achieving it. GA's are a good solution if you have less than 50 hyperparameters or so. , classifier and svm_c) through multiple trials (e. How To Use Tune With BayesOpt. The fields of the tuning strategy - called tuning hyperparameters - are those tuning parameters specific to the strategy that do not refer to specific models or specific model hyperparameters. Optuna. Apr 20, 2018 · In this paper we present an automated parallel derivative-free optimization framework called Autotune, which combines a number of specialized sampling and search methods that are very effective in tuning machine learning models despite these challenges. Unexpected token < in JSON at position 4. Hyperparameters are thus typically set before model training begins and are not modified within the training process in the way that parameters are. Three phases of parameter tuning along feature engineering. Batch normalization makes your hyperparameter search problem much easier, makes your neural network much more robust. How To Use Tune With HyperOpt. Oct 12, 2020 · Hyperopt. Hyperparameter optimization finds a tuple of hyperparameters that yields an optimal Mar 26, 2024 · Typically, hyperparameter tuning in machine learning is performed by following the steps mentioned below-Step 1: Select the model type based on the data type. Hyperopt has four important features you If we have limited computational resources, we must restrict ourselves to hyperparameter tuning on a single model over several hours/days. Conclusion. Mar 9, 2023 · In conclusion, hyperparameter optimization is an essential step in building accurate and performant machine learning models. Hyperparameter tuning allows data scientists to tweak model performance for optimal results. Available guides. Especially dependencies between different hyperparameters produce new challenges. 4 days ago · Configure the Optimizer. Caviar Batch Normalization Normalizing Activations in Oct 31, 2020 · A hyperparameter is a parameter whose value is set before the learning process begins. Jan 1, 2023 · 1. Model parameters are learned during training. Reload to refresh your session. Oct 7, 2023 · Due to the lack of inherent explainability of DL models, the hyperparameter optimization (HPO) or tuning specific to each model is a combination of art, science, and experience. suggest. The goal of a study is to find out the optimal set of hyperparameter values (e. This can be thought of geometrically as an n-dimensional volume, where each hyperparameter represents a different dimension and the scale of the dimension are the values that the hyperparameter Nov 7, 2020 · The result of the tuning process is the optimal values of hyperparameters which is then fed to the model training stage. Please refer to sample code below. In this article, we have explored various existing methods or ways to identify the optimal set of values for the hyperparameters specific to the DL models along with Week 3 Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks If searching among a large number of hyperparameters, you should try values in a grid rather than random values, so that you can carry out the search more systematically and not rely on chance. Handling failed trials in KerasTuner. com. Hyperparameter tuning is the process of selecting the optimal values for a machine learning model’s hyperparameters. md at master · muhac/coursera-deep-learning-solutions Aug 23, 2022 · Image taken from the blog post: Scalable Hyperparameter Tuning for AutoML, ARM research. - deep-learning-coursera/Improving Deep Neural Networks Hyperparameter tuning, Regularization and Optimization/Week 3 Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks. For example, assume you're using the learning rate Dec 29, 2022 · The practical adoption of existing hyperparameter tuning frameworks in production is hindered due to several factors, such as inflexible architecture, limitations of search algorithms, software Feb 4, 2024 · Explore TensorFlow for neural network development, hyperparameter tuning, and batch normalization. Optuna is a framework designed for the automation and the acceleration of the optimization studies. Often simple things like choosing a different learning rate or changing a network layer size can have a dramatic impact on your model performance. 4. Vertex AI keeps track of the results of each trial and makes adjustments for subsequent trials. For example, we would define a list of values to try for both n Jul 9, 2024 · How hyperparameter tuning works. Start TensorBoard and click on "HParams" at the top. 00 ©2019 IEEE scenarios like fine-tuning an already trained model. You will use the Pima Indian diabetes dataset. Some may have little or no effect, while others could be critical to the model’s viability. Autotune is a product within SAS® Visual Data Min-ing and Machine Learning [27], and operates on SAS® Viya® [24], which is designed to enable distributed analytics and to support cloud computing. Distributed hyperparameter tuning with KerasTuner. SIATune is an open-source deep learning model hyperparameter tuning toolbox especially for OpenMMLab's model frameworks such as mmdetection and mmsegmentation. Later, you will learn about top frameworks like Scikit, Hyperopt, Optuna, NNI, and DEAP to implement hyperparameter tuning. md at master · Kulbear/deep-learning-coursera Hyperparameter tuning, Batch Normalization, Programming Frameworks \n 1. Hyperparameters determine how well your neural network learns and processes information. When coupled with cross-validation techniques, this results in training more robust ML models. LLM hyperparameter tuning is the process of adjusting different hyperparameters during the training process with the goal of finding the combination that generates the optimal output. mixed variable types (continuous, discrete) make hyperparameter tuning more difficult. tune . Run a hyperparameter tuning job on Vertex AI This lab uses custom training via a custom container on Google Container Registry, but you can also run a hyperparameter tuning job with a Vertex AI Pre-built container. Hyperparameters are data variables that govern the training process itself. algorithm=tpe. Namun, ada jenis parameter lain yang Mar 8, 2022 · In both cases, the tuning is done via random search. . This means that Hyperopt will use the ‘ Tree of Parzen Estimators’ (tpe) which is a Bayesian approach. Solutions of Deep Learning Specialization by Andrew Ng on Coursera - coursera-deep-learning-solutions/B - Improving Deep Neural Networks/week 3/Hyperparameter_tuning_Batch_Normalization_Programming_Frameworks. This means that you can define the hyperparameter search space as a Python dictionary where the keys are the parameter names of the Jan 29, 2020 · In fact, many of today’s state-of-the-art results, such as EfficientNet, were discovered via sophisticated hyperparameter optimization algorithms. Getting started with KerasTuner. Step 5: Repeat steps 2 – 4 for the specified number of trial runs. Many machine learning algorithms have hyperparameters that need to be set. Manual tuning takes time away from important steps of the machine learning pipeline like feature engineering and interpreting results. This process is an essential part of machine learning, and choosing appropriate hyperparameter values is crucial for success. Optuna You can tune estimators of almost any ML, DL package/framework, including Sklearn, PyTorch, TensorFlow, Keras, XGBoost, LightGBM, CatBoost, etc with a real-time Web Dashboard called optuna-dashboard. Hyperopt By bridging the gap between multi-objective decision-making and machine learning model tuning, this research offers valuable insights and opportunities for further advancements in both fields. Hyperparameters don't just include the model's parameters but could also include parameters related to preprocessing Mar 13, 2020 · Before starting the tuning process, we must define an objective function for hyperparameter optimization. Hyperparameters are settings that control the learning process of the model, such as the learning rate, the number of neurons in a neural network, or the kernel size in a support vector machine. Through these techniques and considerations, hyperparameter tuning emerges not just as a task, but as an art and science critical to the success of machine learning models. In order to support job scheduling and resource management, SIATune adopts Ray and Ray. With this technique, we simply build a model for each possible combination of all of the hyperparameter values provided, evaluating each model, and selecting the architecture which produces the best results. Dive into deep learning frameworks and let TensorFlow handle backpropagation. We include many practical recommendations w. Beyond RayTune’s core features, there are two primary reasons why researchers and developers prefer RayTune over other existing hyperparameter tuning frameworks: scale and flexibility. The dataset corresponds to a classification problem on which you need to make predictions on the basis of whether a person is to suffer diabetes given the 8 features in the dataset. GridSearchCV and RandomSearchCV are systematic ways to search for optimal hyperparameters. performance evaluation, how to combine HPO with ML pipelines, runtime improvements and parallelization. Jul 9, 2019 · Image courtesy of FT. 02522v1 [cs. So, for example, a default resolution to be used in a grid search is a hyperparameter of Grid , but the resolution to be applied to a specific May 22, 2023 · Automated Hyperparameter Tuning Libraries: Several libraries and frameworks provide automated hyperparameter tuning functionality, such as scikit-learn’s GridSearchCV and RandomizedSearchCV, Optuna, Hyperopt, and Ray Tune. Optuna offers three distinct features that make it an optimal hyperparameter optimization framework: Eager search spaces: automated search for optimal hyperparameters. t. Jul 7, 2021 · Given a complex model with many hyperparameters, effective hyperparameter tuning may drastically improve performance. Something went wrong and this page crashed! If the issue persists, it's likely a problem on our side. Hyperparameter tuning is a final step in the process of applied machine learning before presenting results. md at master · gmortuza/Deep-Learning-Specialization Jun 24, 2018 · Hyperparameter Optimization. The criteria we propose include (1) define-by-run API that allows users to construct the parameter search space dynamically, (2) efficient implementation of both searching and pruning strategies, and (3) easy-to-setup, versatile architecture that can be deployed for various You signed in with another tab or window. Tailor the search space. Hyperparameter tuning with Ray Tune¶. Mar 15, 2020 · Step #2: Defining the Objective for Optimization. Here you can find detailed examples on each of our integrations: How To Use Tune With Ax. In this paper we present an automated parallel derivative-free optimization framework called \textbf {Autotune}, which combines a number of specialized sampling and search methods that are very effective in tuning machine learning models despite these challenges. Thanks to our define-by-run API, the code written with Optuna enjoys high modularity, and the user of Optuna can dynamically construct the search spaces for the hyperparameters. Grid and random search are hands-off, but Week 3 Quiz - Hyperparameter tuning, Batch Normalization, Programming Frameworks. SyntaxError: Unexpected token < in JSON at position 4. Kamu dapat menyesuaikan parameter model dengan melatih model menggunakan data yang ada. DL models-based Aug 18, 2019 · RayTune supports any machine learning framework, including PyTorch, TensorFlow, XGBoost, LightGBM, scikit-learn, and Keras. However, even for a testing procedure, we need to choose some ( k ) numbers of nodes. Comet Optimizer offers you an easy-to-use interface for model tuning which supports any ml framework and can easily be integrated in any of your workflows. I will be using the Titanic dataset from Kaggle for comparison. Aug 20, 2019 · Beyond Tune’s core features, there are two primary reasons why researchers and developers prefer Tune over other existing hyperparameter tuning frameworks: scale and flexibility. By Coding Studio Team / December 23, 2021. This is the fourth article in my series on fully connected (vanilla) neural networks. The speedup factor certainly depends on how many nodes you have in your cluster. Ray Tune integrates with state-of-the-art hyperparameter search algorithms and supports distributed training, making it a powerful tool for optimizing machine In this section, we describe the derivative-free optimization frame-work Autotune, the search methods incorporated, and its default search method. Python Libraries for Hyperparameter Optimization I found these 10 Python libraries for hyperparameter optimization. surrogate optimization is an efficient approach, it can accelerate the search, and. Jul 3, 2018 · 23. Such frameworks not only help fit an accurate model but can help boost Data scientists’ efficiency to the next level. Hyperparameter tuning is the process of discovering a set of performant parameter values for our model. arXiv:1911. there is a need for hyperparameter tuning, 2. Wu and Li [ 2 ] also explore the idea of reusing samples collected by “one configuration to estimate the value of a different configuration to improve Aug 17, 2020 · With several packages developed and still in progress, it has become a tough choice to pick one. They are configuration variables that control how the algorithm operates. r. It can optimize a model with hundreds of parameters on a large scale. , 2019), is used to optimize the parameters of the velocity deficit model. True or False? \n \n; False \n \n Jun 21, 2023 · This is the third and last week of the second course of DeepLearning. Let me now introduce Optuna, an optimization library in Python that can be employed for hyperparameter optimization. You switched accounts on another tab or window. AI’s Deep Learning Specialization offered on Coursera. The first section introduces the key hyper-parameters related to model training and structure, and discusses their importance and methods to define the value range. 2. Jul 25, 2019 · This methods automatically conducts hyperparameter tuning using the optuna framework [1], which uses Bayesian optimization. We are going to use XGBoost to model the housing price. Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. Model matematika yang berisi sejumlah parameter yang harus dipelajari dari data disebut sebagai model machine learning. An optimization procedure involves defining a search space. We are going to use Tensorflow Keras to model the housing price. The main set-up step is to define the tuning configuration for Optimizer inside a configuration dictionary. Optuna serves as a specialized framework for hyperparameter optimization, aimed at finding the ideal set of parameters for machine learning, such as the learning rates and number of hidden layers in a Feb 2, 2020 · Since all of these 100 models run in parallel on different nodes, we can save a lot of time when doing random hyperparameter search. This means that you can use it with any machine learning or deep learning framework. 3. Across all compute budget levels, µTransfer is about an order of magnitude Nov 29, 2018 · The most common framework for this is most likely the k-fold cross-validation. Ray Tune includes the latest hyperparameter search algorithms, integrates with TensorBoard and other analysis libraries, and natively supports distributed training through Ray’s distributed machine learning engine. g. Master the art of tuning, normalize activations, and understand Softmax classification. Sep 29, 2023 · Step 3: Run an ML experiment for the selected set of hyperparameters and their values, and evaluate and log its performance metric. In this article, we will be optimizing a neural network and performing hyperparameter tuning in order to obtain a high-performing model on the Beale function — one of many test functions commonly used for studying the effectiveness of various optimization techniques. Some of the popular hyperparameter tuning techniques are discussed below. Dec 23, 2021 · Kenali Hyperparameter Tuning dalam Machine Learning. (Hyperparameters, in contrast to model parameters, are set by the machine learning engineer before training. Figure 5 illustrates a Pareto frontier (opens in new tab) of the relative tuning compute budget compared with the tuned model quality (BLEU score) on IWSLT14 De-En (opens in new tab), a machine translation dataset. Quiz 3: Hyperparameter tuning, Batch Normalization, Programming Frameworks; Programming Assignment: Tensorflow; Course Certificate. Refresh. To start, navigate to the Training section in the Vertex section of your Cloud console: Step 1: Configure training job. It is a popular optimized distributed library, which implements machine learning algorithms under the Gradient Boosting framework. Hyperparameter tuning is an essential process in Jul 25, 2019 · The purpose of this study is to introduce new design-criteria for next-generation hyperparameter optimization software. Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization/week 3/quiz/Hyperparameter tuning, Batch Normalization, Programming Frameworks. , n_trials=100). The world's cleanest AutoML library - Do hyperparameter tuning with the right pipeline abstractions to write clean deep learning production pipelines. Welcome to the grand finale of the Rasa NLU in Depth series 🎉. It supports various machine learning frameworks, including PyTorch, TensorFlow, and Keras. Hyperopt is a powerful Python library for hyperparameter optimization developed by James Bergstra. %tensorboard --logdir logs/hparam_tuning. Can be extended easily, documentation is somewhat lacking. Compatible with Scikit-Learn, TensorFlow, and most other libraries, frameworks and MLOps enviro… Sep 18, 2020 · This is called hyperparameter optimization, hyperparameter tuning, or hyperparameter search. In machine learning, hyperparameter optimization [1] or tuning is the problem of choosing a set of optimal hyperparameters for a learning algorithm. These libraries simplify the process of hyperparameter tuning and offer various algorithms and strategies. The HParams dashboard can now be opened. islands with migration/pollination, crossovers, etc. keyboard_arrow_up. Specify the algorithm: # set the hyperparam tuning algorithm. The purpose of this article to explore how the performance and the computational time of the random forest model are changing with various hyperparameter tuning methods. It uses a form of Bayesian optimization for parameter tuning that allows you to get the best parameters for a given model. Jul 30, 2023 · Solving hyperparameter tuning by framing it as a multiarmed bandit problem makes the problem shift into RL space, gives a different perspective, and opens other opportunities to explore it. Autotune provides significantly improved models over using default hyperparameter settings A hyperparameter optimization framework, named Optuna (Akiba et al. Jul 3, 2024 · Hyperparameter tuning is crucial for selecting the right machine learning model and improving its performance. content_copy. Keras Tuner makes it easy to define a search Feb 27, 2024 · Hyperparameter tuning can be done using various tools and frameworks, such as Optuna, Ray Tune, Hyperopt, or Scikit-Optimize. General Hyperparameter Tuning Strategy 1. This week’s topics are: Hyperparameter Tuning Tuning Process Random Search Coarse-to-fine Grained Search Using an Appropriate Scale when Searching Python Implementation Hyperparameter Tuning in Practice: Pandas vs. Here I am showing how a recent popular framework Optuna can be used to get the best parameters for any Scikit-learn model. In this survey paper, we begin with a comprehensive introduction of the common optimization techniques used in ML hyper-parameter tuning problems. LG] 6 Nov 2019 Hyperparameter optimization. Let your pipeline steps have hyperparameter spaces. It is a deep learning neural networks API for Python. Unlike these parameters, hyperparameters must be set before the training process starts. Multi-Objective Hyperparameter Optimization with Multi-Armed Bandits Learn essential techniques for tuning hyperparameters to enhance the performance of your neural networks. In this three-piece blog post series we shared our best practices and experiences about the open-source framework Rasa NLU which we gained in our work with the Rasa community and customers all over the world. You signed out in another tab or window. 4 days ago · Ray Tune is a Python library for experiment execution and hyperparameter tuning at any scale. Aug 30, 2023 · Choosing the correct hyperparameters for machine learning or deep learning models is one of the best ways to extract the last juice out of your models. It features an imperative, define-by-run style user API. Tuning machine learning hyperparameters is a tedious yet crucial task, as the performance of an algorithm can be highly dependent on the choice of hyperparameters. Step 4: After the experiment, the surrogate function is updated with the last experiment’s results. Hyperparameters directly control model structure, function, and performance. Mar 12, 2020 · This paper provides a review of the most essential topics on HPO. In this tutorial, we will show you how to integrate Ray Tune into your PyTorch training workflow. These tools can help automate the tuning process, parallelize the Dec 21, 2021 · In lines 1 and 2, we import GridSearchCV from sklearn. Keras documentation. Example Optimizer specs viewed from the Comet UI. If searching among a large number of hyperparameters, you should try values in a grid rather than random values, so that you can carry out the search more systematically and not rely on chance. For example, a gradient boosting classifier has many different parameters to fine-tune, each uniquely changing the model’s performance. For me, I had 100 machines at my disposal, so I got ~ 100x speedup. 1. Specifically, the following parameters are optimized: learning rate Ray Tune includes the latest hyperparameter search algorithms, integrates with TensorBoard and other analysis libraries, and natively supports distributed training through Ray’s distributed machine learning engine. In this article, I will show you some of the best ways to do hyperparameter tuning that are available today. ; Step 2: Select the appropriate Feb 28, 2019 · Rasa NLU in Depth: Part 3 – Hyperparameter Tuning. Tuning Hyperparameters. Design steps in your pipeline like components. A hyperparameter is a parameter whose value is used to control the learning process. However, if we have sufficient computational resources, we can afford to try out different hyperparameter settings on models in parallel, and choose the one that works best. Before starting the tuning process, we must define an objective function for hyperparameter optimization. Hyperparameter tuning works by running multiple trials of your training application with values for your chosen hyperparameters, set within limits you specify. It discusses the open challenges and research directions of the HPO research domain. Let’s see how batch normalization works. Apr 27, 2024 · Download Citation | HyperTuner: a cross-layer multi-objective hyperparameter auto-tuning framework for data analytic services | Hyperparameters optimization (HPO) is vital for machine learning models. The primary objectives of module 3 are: To master the process of hyperparameter tuning; To familiarize yourself with the concept of Batch Normalization; Much like the first module, this is further divided into three sections: Part I: Hyperparameter tuning SmartML is demonstrated, a meta learning-based framework for automated selection and hyperparameter tuning for machine learning algorithms that outperforms the-state-of-the-art techniques in the domain of automated machine learning frameworks. Jul 29, 2022 · Individual chapters are also dedicated to the three main groups of hyperparameter tuning methods: exhaustive search, heuristic search, Bayesian optimization, and multi-fidelity optimization. Keras Tuner is an easy-to-use, distributable hyperparameter optimization framework that solves the pain points of performing a hyperparameter search. Dec 7, 2023 · Hyperparameter Tuning. However, this inevitably can involve considerable trial and error: meticulously tracking the application of each hyperparameter and recording the corresponding Oct 23, 2017 · Hyperparameter parameters Tips for hyperparam-tuning. In line 3, the hyperparameter values are defined as a dictionary where keys are the hyperparameter name and a list of values containing hyperparameter values we want to try. Grid Search Cross Mar 23, 2023 · TPOT optimizes a sequence of feature preprocessors and machine learning models to enhance the classification accuracy by making use of GA for hyperparameter tuning 52. Deep-Learning-Specialization is maintained by Jun 12, 2023 · The values are determined after iterating through different combinations of hyperparameter values with a model and comparing the metrics/evaluation results. Tune hyperparameters in your custom training loop. Dec 13, 2019 · 1. Hyperparameters control the behavior of the model/algorithm, while model parameters are learned from data. Nov 5, 2021 · Here, ‘hp. Jan 22, 2024 · Advanced methods can include more sophisticated techniques like Bayesian optimization or automated hyperparameter tuning frameworks. The left pane of the dashboard provides filtering capabilities that are active across all the views in the HParams dashboard: Optuna is an automatic hyperparameter optimization software framework, particularly designed for machine learning. propulate - various genetic algorithm variants, e. Jan 16, 2023 · After a general introduction of hyperparameter optimization, we review important HPO methods such as grid or random search, evolutionary algorithms, Bayesian optimization, Hyperband and racing. Week 3: Hyperparameter tuning, Batch Normalization and Programming Frameworks Key Concepts of Week 3. try random values. reason: difficule to know which hyperparam May 6, 2024 · These tools and frameworks facilitate the hyperparameter-tuning process by offering efficient optimization techniques, algorithms, and user-intuitive interfaces. The following formula may give you a starting point: Nov 2, 2017 · Grid search is arguably the most basic hyperparameter tuning method. randint’ assigns a random integer to ‘n_estimators’ over the given range which is 200 to 1000 in this case. Visualize the hyperparameter tuning process. To guide you better on this, we have created a list of most popular Hyper-parameter tuning tools and frameworks: Apr 21, 2023 · Optuna is a hyperparameter tuning library that is specifically designed to be framework agnostic. Tobias Wochinger. Tune Hyperparameter Optimization Framework Examples #. Explore and run machine learning code with Kaggle Notebooks | Using data from No attached data sources. When the job is finished, you can get a summary of all I built my own genetic algorithm for tuning. Tuning process Many hyperparams to tune, mark importance by colors (red > yellow > purple): How to select set of values to explore ? Do NOT use grid search (grid of n * n) — this was OK in pre-DL era. model_selection and define the model we want to perform hyperparameter tuning on. Often suitable parameter values are not obvious and it is preferable to tune the hyperparameters, that is Jan 6, 2022 · Visualize the results in TensorBoard's HParams plugin. The choice of hyperparameters is a much bigger range of hyperparameters that work well, and will also enable you to much more easily train even very deep networks. Sep 3, 2021 · Then, we will see a hands-on example of tuning LGBM parameters using Optuna — the next-generation bayesian hyperparameter tuning framework. Hyperparameter tuning can make the difference between an average model and a highly accurate one. Tune integrates with a wide variety of hyperparameter optimization frameworks and their respective search algorithms. Due to the increasing success of machine learning techniques, nowadays, thay have been widely utilized in almost every domain such as financial Also, hyperparameter tuning is often highly coupled with the respective framework and it is difficult to use for other 978-1-7281-0858-2/19/$31. Additional to its capacity to work on distributed computing frameworks, Mango is also compatible with the scikit-learn API. py aw ga hm zc va up ww pu jl