Doug works What is SageMaker HyperPod? AmazonSageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing machine learning (ML) infrastructure for training foundation models (FMs), reducing training time by up to 40%. 199. . Bases: object. Jul 17, 2023 · Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning July 17, 2023 Recent years have shown amazing growth in deep learning neural networks (DNNs). A ConditionStep allows SageMaker Pipelines to support conditional running in your pipeline DAG based on the condition of step properties. HyperbandStrategyConfig can use two parameters: max_resource (optional) for the maximum number of iterations to be used for a training job to achieve the objective, and min_resource – the minimum number of iterations to be used by a training job before stopping the training. Dec 1, 2017 · SageMaker Python SDK is an open source library for training and deploying machine learning models on Amazon SageMaker. For more information about strategies, see Penyetelan Model Otomatis SageMaker juga mendukung Hyperband, sebuah strategi pencarian baru. These are parameters that are set by users to facilitate the estimation of model parameters from data. The platform lets you quickly build, train and deploy machine learning models. 0 documentation. I am new to AWS Sagemaker. Oct 16, 2018 · In TensorFlow, you allow for hyper-parameters to be specified by SageMaker via the addition of the hyperparameters argument to the functions you need to specify in the entry point file. Amazon SageMaker HyperPod offers advanced training tools to help you accelerate scalable, reliable, and secure generative AI application development. Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs. Jan 5, 2024 · Here’s how to set one up: Create a Notebook Instance: In the SageMaker dashboard, click on ‘Notebook instances’, then ‘Create notebook instance’. May 4, 2023 · To start exploring the GPT-2 model demo in JumpStart, complete the following steps: On JumpStart, search for and choose GPT 2. SageMaker HyperPod is pre-configured with SageMaker’s distributed training libraries that enable Oct 26, 2022 · Grid search will cover every combination of the specified hyperparameter values and yield reproducible tuning results. The following describes the requirements of each step type and provides an example implementation of the step. You can also train and deploy models with Amazon algorithms , which are scalable implementations of core machine Dec 15, 2020 · This paper presents Amazon SageMaker Automatic Model Tuning (AMT), a fully managed system for black-box. Jan 28, 2021 · SageMaker is a fully managed service that provides developers and data scientists the ability to build, train, and deploy ML models quickly. Because the Hyperband strategy has its own advanced internal early stopping mechanism, TrainingJobEarlyStoppingType must be OFF to use Hyperband. Open the sample notebooks from the Advanced Functionality section in your notebook instance or from GitHub using the provided links. Connect to data sources. In this post, we show how automatic model tuning with Hyperband can provide faster hyperparameter tuning—up to three times as fast. Amazon SageMaker 自動モデルチューニングが新しい検索戦略として Hyperband を使用した、最大で 3 倍速いハイパーパラメータチューニングの提供を開始; AutoGluon を利用した新しい「アンサンブル」トレーニングモードで Amazon SageMaker Autopilot の実験が最大 8 倍高速に Jul 18, 2023 · Amazon SageMaker is an end-to-end machine learning (ML) platform with wide-ranging features to ingest, transform, and measure bias in data, and train, deploy, and manage models in production with best-in-class compute and services such as Amazon SageMaker Data Wrangler, Amazon SageMaker Studio, Amazon SageMaker Canvas, Amazon SageMaker Model Registry, Amazon SageMaker Feature Store, Amazon Oct 25, 2023 · To avoid incurring unwanted charges after a methane monitoring job has completed, ensure that you terminate the SageMaker instance and delete any unwanted local files. Feb 12, 2024 · SageMaker Clarify enables you to generate model explainability reports using Shapley Additive exPlanations (SHAP) when training your models on SageMaker, supporting both global and local model interpretability. Choose the SageMaker Examples tab for a list of all SageMaker example notebooks. Select Add another request if you have class sagemaker. I used custom inference. After configuring the estimator class, use the class method fit() to start a training job. When selecting automatic scaling (the Auto setting), Amazon SageMaker uses log scaling or reverse logarithmic scaling whenever the appropriate choice is clear from the hyperparameter ranges. About the authors. Linear. (If you use the Random Cut Forest estimator, this value is calculated for you Our predictions from xgboost yield continuous values between 0 and 1, and we force them into the binary classes that we began with. The managed Scikit-learn environment is an Amazon-built Docker container that executes functions defined in the supplied entry_point Python script. SageMaker removes the heavy lifting from each step of the ML process to make it easier to develop high-quality models. optimization at scale. "objective":"quantile")? Simply by not giving this hyperparameter a range and hard coding it May 20, 2021 · 1. It uses a convolutional neural network that can be trained from scratch or trained using transfer learning when a large number Feb 26, 2020 · We had two ideas of how to resolve. 0) does not Use batch transform when you need to do the following: Preprocess datasets to remove noise or bias that interferes with training or inference from your dataset. workflow. Nov 16, 2022 · Make sure you install the SageMaker library as part of the first notebook cell and restart the kernel before you run the rest of the notebook cells. early_stopping_type ( str) – Specifies whether early stopping is enabled for the job. References Creates a SKLearn Estimator for Scikit-learn environment. In order to align the HPT run with our previous examples, we will use the recently announced SageMaker HPT support for the HyperBand algorithm with a similar configuration. Amazon SageMaker Automatic Model Tuning allows you to tune and find the most accurate version of a machine learning model by searching for the optimal set of hyperparameter configurations for your dataset using various search Aug 31, 2021 · This sample uses the Hugging Face transformers and datasets libraries with SageMaker to fine-tune a pre-trained transformer model on binary text classification and deploy it for inference. instance_count ( int or PipelineVariable) – Number of EC2 instances to use. Anyway, I want to use this SDK/API instead - more precisely the HyperparameterTuner. The user can specify the tuning strategy, the metric to SageMaker Studio Lab is a service built on AWS and uses many of the same core services as Amazon SageMaker Studio, such as Amazon S3 and Amazon EC2. SageMaker images contain the latest Amazon SageMaker Amazon SageMaker provides a suite of built-in algorithms, pre-trained models, and pre-built solution templates to help data scientists and machine learning practitioners get started on training and deploying machine learning models quickly. Get inferences from large datasets. tuner import IntegerParameter, HyperparameterTuner, ContinuousParameter. To create a pipeline schedule, specify a single type using the at, rate, or cron parameters. Feb 27, 2023 · With Amazon SageMaker automatic model tuning, you can find the best version of your model by running training jobs on your dataset with several search strategies, such as Bayesian, Random search, Grid search, and Hyperband. Feb 10, 2021 · SageMaker is a highly flexible platform, allowing you to bring your own HPO tool, which we illustrated using the popular open-source tool Ray Tune. use_attr and setting the list of trials of the same bracket as its attribute for samplers to get access to the list of With Amazon SageMaker, you can build ML models to detect suspicious transactions before they occur and alert your customers in a timely fashion. Dec 16, 2022 · Today, we’re happy to announce updates to our Amazon SageMaker Experiments capability of Amazon SageMaker that lets you organize, track, compare and evaluate machine learning (ML) experiments and model versions from any integrated development environment (IDE) using the SageMaker Python SDK or boto3, including local Jupyter Notebooks. Amazon SageMaker Python SDK. compile ()” as a placeholder in reference to the PyTorch 2 update — note that this is an upcoming feature in Ray and is not currently supported. The configuration for a training job launched by a hyperparameter tuning job. Nov 30, 2022 · SageMaker Studio now includes a new Getting Started notebook that walks you through the basics of how to use SageMaker Studio. Hyperband adalah strategi tuning berbasis multi-fidelity yang secara dinamis merealokasi sumber daya. When SageMaker Data Wrangler helps you understand your data and identify potential errors and extreme values with a set of robust preconfigured visualization templates. SageMaker Autopilot can automatically select the training method based on the dataset size, or you can select it manually. 2xlarge). 1_invoke_sagemaker_endpoint. The documentation for the SMP library v1. How would I specify StaticHyperParameters (e. import sagemaker. SageMaker hyperparameter tuning chooses the best scale for the hyperparameter. Parameters. For smaller training jobs using less runtime, use either random search or Bayesian optimization . feature_dim. To learn more, visit Perform Automatic Model Tuning with SageMaker. estimator import Estimator. Hyperparameter tuning searches the values in the hyperparameter range by using a linear scale. For example, you might want to import tables from a data warehouse in Amazon Redshift, or you might want to import Google Analytics data. Hyperband can also reallocate resources towards well-utilized hyperparameter configurations and run parallel jobs. PipelineSchedule(name=None, enabled=True, start_date=None, at=None, rate=None, cron=None) ¶. With the SDK, you can train and deploy models using popular deep learning frameworks Apache MXNet and TensorFlow . Now that your experiment has completed, you can choose the best tuning model and deploy the model to an endpoint managed by Amazon SageMaker. So, I'm able to generate predictions on json input, which contains url to the image, the code is quite straigtforward: Setting a random seed and using the same seed later for the same tuning job will allow hyperparameter optimization to find more a consistent hyperparameter configuration between the two runs. Amazon SageMaker Clarify can detect potential bias during data preparation, after model training, and in your deployed May 12, 2020 · Step 6. Hyperband dapat menemukan set hyperparameter yang optimal hingga tiga kali lebih cepat dibandingkan pencarian Bayesian untuk model skala besar, seperti jaringan neural dalam yang mengatasi masalah penglihatan komputer. triggers. If not, it falls back to linear scaling. On-demand Serverless Inference is ideal for workloads which have idle periods between traffic spurts and can tolerate cold starts. Apr 4, 2019 · This tells Amazon SageMaker to internally apply the transformation log(1. SageMaker provides built-in ML algorithms, such as Random Cut Forrest and XGBoost, that you can use to train and deploy fraud detection models. Can be either ‘Auto’ or ‘Off’ (default: ‘Off’). SageMaker is a fully managed service that allows you to build, train, deploy, and monitor machine learning (ML) models. Use case 2: Use code to develop machine learning models with more flexibility and control. Hyperband juga dapat mengalokasikan kembali sumber daya ke konfigurasi hyperparameter yang dimanfaatkan dengan baik dan menjalankan pekerjaan paralel. Type: Integer. Conclusion. I have custom CV PyTorch model locally and deployed it to Sagemaker endpoint. In this session, experience how to train a large language model (LLM) in diverse, representative data and learn how to utilize the latest SageMaker model training tools to troubleshoot convergence issues and improve the model performance. To find the best combination for your dataset, ensemble mode runs 10 trials with different model May 15, 2019 · SageMaker is for data scientists/developers and Studio is designed for citizen data scientists. SageMaker already makes each of those steps easy with access to powerful Jupyter notebook instances, built-in algorithms, and model training within Jul 9, 2024 · SageMaker Automated Model Tuning is a serverless parameter search orchestrator that launches multiple training jobs on your behalf, according to a search logic that can be random, Bayesian, or HyperBand. Amazon SageMaker provides prebuilt Docker images that include deep learning frameworks and other dependencies needed for training and inference. c5. But, Studio does also support a Jupyter Notebook interface, making it possible that data scientists could also use Studio and the cloud infrastructure for Azure Machine Learning Services to also accomplish what SageMaker offers on top of Amazon cloud Step 9: Define a condition step to verify model accuracy. A class for handling creating and interacting with Amazon SageMaker transform jobs. Machine learning (ML) is an iterative process. Before we define and run our tuner object, let’s recap our understanding from an architecture perspective. It provides implementations of several state-of-the-art global optimizers, such as Bayesian optimization, Hyperband, and population-based training. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose. Hyperband uses both intermediate and final results of training jobs to re-allocate epochs to well-utilized hyperparameter configurations and automatically stops those that underperform. warm_start_config ( sagemaker. Nov 10, 2023 · Hyperband; We further describe these strategies and equip you with some guidance to choose one later in this post. For SageMaker hosting instance, choose your instance (for this post, we use ml. This post demonstrates how to do the following: Dec 3, 2019 · Update September 30, 2021 – This post has been edited to remove broken links. In this blog post, we will take a look at what SageMaker HyperPod is a capability of SageMaker that provides an always-on machine learning environment on resilient clusters. Regrettably, as of the time of this writing, the SageMaker SDK (version 2. In the DeployModel section, expand Deployment Configuration. py, where we also first define an Estimator object, and give it as input to another object of class HyperparameterTuner: from sagemaker. To learn more about bringing other algorithms such as genetic algorithms to SageMaker HPO, see Bring your own hyperparameter optimization algorithm on Amazon SageMaker. Jul 25, 2023 · Welcome! Log into your account. These are not working implementations because they don't provide the resource and inputs needed. For more advanced use cases, use Hyperband , which evaluates objective metrics for training jobs after every epoch. The number of features in the data set. The notebook Feb 8, 2023 · TBH I do not even know if this is an old way of doing things or a different SDK - very confusing Sagemaker sometimes. Pipeline Schedule trigger type used to create EventBridge Schedules for SageMaker Pipelines. To enable PyTorch DDP: Jun 21, 2024 · Encrypt Your SageMaker Canvas Data with AWS KMS; Store SageMaker Canvas application data in your own SageMaker space; Grant Your Users Permissions to Build Custom Image and Text Prediction Models; Grant Your Users Permissions to Perform Time Series Forecasting; Grant Users Permissions to Fine-tune Foundation Models; Update SageMaker Canvas for The estimator initiates the SageMaker-managed Hugging Face environment by using the pre-built Hugging Face Docker container and runs the Hugging Face training script that user provides through the entry_point argument. Note: For more information, see the Choose and deploy the best model. g. Jan 30, 2023 · Sagemaker’s HyperparameterTuner makes running hyperparameter jobs easy to maintain and cost effective. Amazon SageMaker Serverless Inference is a purpose-built inference option that enables you to deploy and scale ML models without configuring or managing any of the underlying infrastructure. Training is started by calling fit () on this Estimator. Use case 3: Develop machine learning models at scale with maximum flexibility and control. Jul 13, 2023 · Animations, Music, And Videos Digital Assets » Effectively solve distributed training convergence issues with Amazon SageMaker Hyperband Automatic Model Tuning Uri Rosenberg AWS Machine Learning Blog Sep 20, 2022 · Amazon SageMaker Automatic Model Tuning introduces Hyperband, a multi-fidelity technique to tune hyperparameters as a faster and more efficient way to find an optimal model. On the Requests panel for Request 1, select the Region, the resource Limit to increase and the New Limit value you are requesting. 近年来，深度学习神经网络（DNN）取得了惊人的增长。这种增长体现在更准确的模型上，甚至通过生成型人工智能带来了新的可能性：合成自然语言的大型语言模型（LLM）、文本到图像生成器等。 Hyperband. Walkthrough overview. Configure the Instance: Name your Training modes. Histograms, scatter plots, box and whisker plots, line plots, and bar charts are all built in for applying to your data. This guide shows metrics and validation techniques that you can use to measure machine learning model performance. More advanced ML-specific visualizations (such as bias report For information about using the updated Studio experience, see Amazon SageMaker Studio. The following are the main uses cases for training ML models within SageMaker. Specifies whether to use early stopping for training jobs launched by the hyperparameter tuning job. 0 - value) to all values. This class takes a Sagemaker estimator — the base class for running machine learning training jobs in AWS — and configures a tuning job based on arguments provided by the user. Follow these steps to choose the best tuning job and deploy the model. On the Create case page, choose Service limit increase. Hyperband menggunakan hasil menengah dan akhir dari pekerjaan pelatihan untuk mengalokasikan kembali zaman ke konfigurasi hyperparameter yang digunakan dengan baik dan secara otomatis menghentikan yang berkinerja buruk. Invoke a SageMaker endpoint – Run the notebook STEP1. Amazon SageMaker Python SDK is an open source library for training and deploying machine-learned models on Amazon SageMaker. model_name ( str or PipelineVariable) – Name of the SageMaker model being used for the transform job. Sep 16, 2022 · SageMaker Automatic Model Tuning now supports Hyperband, a new search strategy that can find the optimal set of hyperparameters up to 3x faster than Bayesian search for large-scale models such as deep neural networks that address computer vision problems. Open the notebook instance you created. Amazon SageMaker Autopilot produces metrics that measure the predictive quality of machine learning model candidates. your username. Run inference when you don't need a persistent endpoint. In addition to model explainability reports, SageMaker Clarify supports running analyses for pre-training bias metrics, post-training Feb 16, 2021 · To start a tuning job, we create a similar file run_sagemaker_tuner. The choices are as follows: Ensembling – Autopilot uses the AutoGluon library to train several base models. Unlike the other services, customers will not need an AWS account. The Llama 3 models are a collection of pre-trained and fine-tuned generative text models. With the SDK, you can train and deploy models using popular deep learning frameworks, algorithms provided by Amazon, or your own algorithms built into SageMaker-compatible Docker images. Description. Associate input records with inferences to help with the interpretation of results. In this case, you only want to register a model package if the accuracy of that model exceeds the required value. Doug Mbaya is a Senior Partner Solution architect with a focus in data and analytics. Oct 6, 2021 · In this blog post, we are going to walk through the steps for building a highly scalable, high-accuracy, machine learning pipeline, with the k-fold cross-validation method, using Amazon Simple Storage Service (Amazon S3), Amazon SageMaker Pipelines, SageMaker automatic model tuning, and SageMaker training at scale. AMT finds the best version of a machine learning model by Nov 17, 2022 · In the code block below, we show how to configure and run a SageMaker HPT job. Deploy the best model. Apr 18, 2024 · Today, we are excited to announce that Meta Llama 3 foundation models are available through Amazon SageMaker JumpStart to deploy, run inference and fine tune. Nov 19, 2021 · Today we announce the general availability of Syne Tune, an open-source Python library for large-scale distributed hyperparameter and neural architecture optimization. ipynb to invoke and test the SageMaker model inference endpoint created in the previous notebook. def model_fn(features, labels, mode, hyperparameters=None): if Metrics and validation. py code to define model_fn, input_fn, output_fn and predict_fn methods. Apr 12, 2023 · I put in the code “model. For someone who is new to SageMaker, choosing the right algorithm for your particular use case can be a Jul 14, 2023 · We saw that SageMaker AMT using Hyperband addressed the main concerns that optimizing data parallel distributed training introduced: convergence (which improved by more than 10%), operational efficiency (the tuning job took 50% less time than a sequential, non-optimized job would have taken) and cost-efficiency (30 vs. For example, for a hyper-parameter needed in your model_fn: DEFAULT_LEARNING_RATE = 1e-3. However, because a customer that churns is expected to cost the company more than proactively trying to retain a customer who we think might churn, we should consider lowering this cutoff. Initialize a Transformer. The model demoed here is DistilBERT —a small, fast, cheap, and light transformer model based on the BERT architecture. One is adding bracket index to trials. WarmStartConfig) – A WarmStartConfig object that has been initialized with the configuration defining the nature of warm start tuning job. The configuration for the Hyperband optimization strategy. The Amazon SageMaker image classification algorithm is a supervised learning algorithm that supports multi-label classification. By combining SageMaker geospatial capabilities with open geospatial data sources you can implement your own highly customized remote monitoring solutions at scale. Use case 1: Develop a machine learning model in a low-code or no-code environment. the 90 billable minutes Issue #, if available: Description of changes: That's a fix of the Hyperband strategy support for the HPO Testing done: extended unit tests + test this SDK installed locally and that was successful warm_start_config ( sagemaker. Typically, you choose this if the range of all values from the lowest to the highest is relatively small (within one order of magnitude). To open a notebook, choose its Use tab, then choose Create copy. In Amazon SageMaker Canvas, you can import data from a location outside of your local file system through an AWS service, a SaaS platform, or other databases using JDBC connectors. Hyperband has an early stopping mechanism to stop under-performing jobs. You can use these clusters to run any machine learning workloads for developing state-of-the-art machine learning models such as large language models (LLMs) and diffusion models. Nov 29, 2023 · SageMaker HyperPod removes the undifferentiated heavy lifting involved in building and optimizing ML infrastructure for training FMs. Hyperband memiliki mekanisme penghentian dini untuk menghentikan pekerjaan yang berkinerja buruk. You may also explore bringing your own algorithm, as explained in Bring your own hyperparameter optimization algorithm on Amazon SageMaker . Nov 29, 2023 · At its re:Invent conference today, Amazon’s AWS cloud arm announced the launch of SageMaker HyperPod, a new purpose-built service for training and fine-tuning large language models (LLMs The following table lists the hyperparameters for the Amazon SageMaker RCF algorithm. The notebook covers everything from the fundamentals of JupyterLab to a practical walkthrough of training an ML model. Required: No. This page also gives information about the format needed to create the ARN for each image. On the Case details panel, select SageMaker Automatic Model Tuning [Hyperparameter Optimization] for the Limit type. Valid Range: Minimum value of 0. Step types. Today, we’re extremely happy to launch Amazon SageMaker Autopilot to automatically create the best classification and regression machine learning models, while allowing full control and visibility. SageMaker HyperPod is pre-configured with SageMaker’s distributed training libraries that enable customers to automatically split training workloads across thousands of accelerators, so workloads can be processed in parallel for improved model performance. Sep 11, 2020 · The adaptability of Amazon SageMaker allows you to manage more tasks with fewer resources, resulting in a faster, more efficient workload. tuner. from sagemaker. PDF RSS. Oct 26, 2022 · Amazon SageMaker Automatic Model Tuning workflows (AMT) With Amazon SageMaker automatic model tuning, you can find the best version of your model by running training jobs on your dataset with several search strategies, such as Bayesian, Random search, Grid search, and Hyperband. Instead, they will create an SageMaker Studio Lab specific account with an email address. In addition, SageMaker provides a set of solutions for The following table contains the subset of hyperparameters that are required or most commonly used for the Amazon SageMaker XGBoost algorithm. The Llama 3 Instruct fine-tuned models are optimized for dialogue use cases and are available on Nov 10, 2023 · Hyperband; We further describe these strategies and equip you with some guidance to choose one later in this post. For a complete list of the prebuilt Docker images managed by SageMaker, see Docker Registry Paths and Example Code. Untuk pekerjaan besar, menggunakan strategi tuning Hyperband dapat mengurangi waktu komputasi. Its modular design lets you pick and choose the features that suit your use cases at Dec 7, 2022 · Hyperband search – Uses both intermediate and final results of training jobs to reallocate epochs to well-utilized hyperparameter configurations, and automatically stops those that underperform. your password Nov 13, 2018 · Amazon SageMaker is a managed machine learning service (MLaaS). In 1959, Arthur Samuel defined machine learning as the ability for computers to learn without being […] A hyperparameter tuning job finds the best version of a model by running many training jobs on your dataset using the algorithm you choose and values for hyperparameters within ranges that you specify. SageMaker Studio Lab is an ideal platform for learning and experimenting with data science and machine learning tools. Sep 16, 2022 · Using Hyperband in SageMaker also allows you to specify the minimum and maximum resource in the HyperbandStrategyConfig parameter for further runtime controls. For more information, including recommendations on how to choose hyperparameters, see How RCF Works. 114. Hyperband is a multi-fidelity based tuning strategy that dynamically reallocates resources. Additionally, it supports constrained and multi-objective optimization, and 使用亚马逊SageMaker Hyperband自动模型调优，解决分布式训练收敛问题. It takes an image as input and outputs one or more labels assigned to that image. x is archived and available at Run distributed training with the SageMaker model parallelism library in the Amazon SageMaker User Guide , and the SMP v1 API reference is available in the SageMaker Python SDK v2. Jun 7, 2018 · Model Tuning in the Machine Learning Process. Choose Bayesian for Bayesian optimization, and Random for random search optimization. StrategyConfig. Automatic scaling. If you are a first-time user of SageMaker Studio, this is the perfect starting place. Jul 13, 2023 · Next, we provide the configuration for the Hyperband strategy and the tuner object configuration using the SageMaker SDK. This page lists the SageMaker images and associated kernels that are available in Amazon SageMaker Studio Classic. The metrics calculated for candidates are specified using an array of MetricDatumtypes. Weights and Biases Hyperband. It will execute an Scikit-learn script within a SageMaker Training Job. Nov 2, 2022 · Starting today, SageMaker Autopilot will use a new multi-fidelity hyperparameter optimization (HPO) strategy that employs the state-of-the-art hyperband tuning algorithm on datasets that are greater than 100 MB with 100 or more trials while continuing to leverage the Bayesian optimization strategy for data sets lesser than 100MB. The required hyperparameters that must be set are listed first, in alphabetical order. A developer’s typical machine learning process comprises 4 steps: exploratory data analysis (EDA), model design, model training, and model evaluation. We covered the architectural overview of SageMaker AMT in our last post and reproduce an excerpt of it here for convenience. Parameter Name. ap ba mz rw eu xa cy mo qh ni