Sagemaker hyperparameters. The main github repository for Sagemaker examples is here.

hyperparameters. A model's training step has two parameter input types: the parameters and the model's hyperparameters. The number of required clusters. For convenience, this accepts other types for keys and values, but str() will be called to convert them before training. The value for this parameter should be about the same as the prediction_length. The thing is, in the workshop they teach you how to use HyperparameterTuner using the ready-made XGBoost image from AWS, while most of my pipelines are using Scikit-Learn models such as GradientBoostingClassifier or RandomForest Dec 7, 2022 · However, we’re not creating a single training job. To just get the hyperparameters with the SageMaker Python SDK (v1. We’re excited to give developers access to this scalable, highly accurate forecasting algorithm that drives mission-critical Automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many jobs that test a range of hyperparameters on your dataset. . Oct 31, 2023 · Any hyperparameters provided by the training job are passed to the entry point as script arguments. Save the training artifacts and run the evaluation on the test set if the current node is the primary. You can use LightGBM as an Amazon SageMaker built-in algorithm. The following tables list the hyperparameters supported by the Amazon SageMaker semantic segmentation algorithm for network architecture, data inputs, and training. SageMaker takes the content under /opt/ml/model/ to create a tarball that is used to deploy the model to SageMaker for hosting. The ParameterRanges field has three subfields: categorical, integer, and continuous. Initialize a parameter range Apr 8, 2021 · This function runs the following steps: Register the custom dataset to Detectron2’s catalog. Jan 8, 2020 · My question is about using the same script for running one SageMaker hyper-parameter tuning job, and two training jobs, with slightly different logics that could be modulate with custom parameters. import sagemaker. Feb 16, 2021 · To start a tuning job, we create a similar file run_sagemaker_tuner. Starting with the main guard, use a parser to read the hyperparameters passed to your Amazon SageMaker estimator when creating the training job. The number of principal components to compute. In our example, the SageMaker training job took 20,632 seconds, which is about 5. The hyperparameters are made accessible as a dict[str, str] to the training code on SageMaker. You choose the objective metric from the Configure hyperparameters. The hyperparameters that you can tune depend on the algorithm that you are training. Save the trained model at the local container path /opt/ml/model/. Set up a cluster with multiple instances or GPUs. Jan 25, 2019 · Yes. The managed Scikit-learn environment is an Amazon-built Docker container that executes functions defined in the supplied entry_point Python script. 751. TrainingInput instead of sagemaker. A user only needs to select the hyperparameters to tune, a range for each parameter to explore, and the total number of training jobs to budget. IntegerParameterRange. The following table contains the hyperparameters for the Factorization Machines algorithm. The constructor has the following signature: HyperparameterTuner(estimator, objective_metric_name, hyperparameter_ranges, metric_definitions=None, strategy='Bayesian', objective_type='Maximize', max_jobs=1, max_parallel_jobs=1, tags=None, base_tuning_job_name=None) apacker pushed a commit to apacker/sagemaker-python-sdk that referenced this issue Nov 15, 2018 Merge pull request aws#224 from awslabs/arpin_pca_mnist_payload_size … fb52f25 Feb 14, 2024 · SOLVED. Oct 6, 2021 · In this blog post, we are going to take a heuristic approach of finding the most optimized hyperparameters using SageMaker automatic model tuning. s3_inputs worked to get that code cell functioning. The Amazon SageMaker Python SDK provides framework estimators and generic estimators to train your model while orchestrating the machine learning (ML) lifecycle accessing the SageMaker features for training and the AWS infrastructures, such as Amazon Elastic Container Registry (Amazon ECR), Amazon Elastic Compute Cloud (Amazon EC2), Amazon Simple Storage Service (Amazon S3). Dec 31, 2020 · Using sagemaker. Network Architecture Hyperparameters. For more information about how k-means clustering works, see How K-Means Clustering Works. Then I manually copy and paste and hyperparameters into xgboost model in the Python app For more information about these and other hyperparameters see XGBoost Parameters. Jan 17, 2024 · In SageMaker Studio, navigate to the Llama-2-13b Neuron model. Aug 4, 2021 · In [PyTorch Estimator for SageMaker] [1], it says as below. Here xgboost has a set of optimized hyperparameters obtained from SageMaker. The model also receives lagged inputs from the target, so context_length can be much smaller than typical seasonalities. Amazon SageMaker supports various frameworks and interfaces such as TensorFlow, Apache MXNet, PyTorch, scikit-learn 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. [8-32] The following table lists the hyperparameters for the Amazon SageMaker RCF algorithm. Distributed training with SageMaker Training Compiler is an extension of single-GPU training with additional steps. During optimization, the computational complexity of a hyperparameter tuning job depends on the following: The number of hyperparameters. For a list of hyperparameters for each training algorithm provided by SageMaker, see Algorithms. You need to create a new instance using PyTorchModel() then register it. This example shows how to create a new notebook for configuring and launching a hyperparameter tuning job. See for information on image classification hyperparameter tuning. SageMaker Experiments offers a single interface where you can visualize your in-progress training jobs, share experiments within your team, and deploy models directly from an experiment. fit is invoked: ``` – Feb 29, 2024 · Set hyperparameters for the training algorithm. For more information, including recommendations on how to choose hyperparameters, see How RCF Works. Based on the problem type, SageMaker Data Wrangler provides a model summary, feature summary, and confusion matrix to quickly give you insight so you can iterate on your data preparation flows. The training of your script is invoked when you call fit on a HuggingFace Estimator. The hyperparameter, num_trees, sets the number of trees used in the RCF model. For more information on how to open JumpStart in Studio, see Open and use JumpStart in Studio. Mar 13, 2024 · Today, we’re excited to announce that the Gemma model is now available for customers using Amazon SageMaker JumpStart . You can use the new release of the XGBoost algorithm as either: A Amazon SageMaker built-in algorithm. In the Estimator you define, which fine-tuning script should be used as entry_point, which instance_type should be used, which hyperparameters are passed in, you can find all possible HuggingFace Apr 4, 2019 · We are excited to introduce two highly requested features to automatic model tuning in Amazon SageMaker: random search and hyperparameter scaling. For example, assume you're using the learning rate Jun 22, 2018 · Your arguments when initializing the HyperparameterTuner object are in the wrong order. ArgumentParser instance. Set to -1 to use full validation set (if bleu is chosen as Jul 12, 2018 · You can also specify algorithm-specific hyperparameters that are used to help estimate the parameters of the model from a training dataset. If you don't already know the optimal values for these hyperparameters, which maximize per-word log-likelihood and produce an accurate LDA model, automatic May 3, 2019 · Although the hyperparameters at SageMaker has a maximum length of 256 (that is not documented). The right solution to adapt ourselves to sagemaker's contract with hyperparameters is: keeping track of all possible key/values and their types in a custom image, just to parse it back to their expected format. Get inferences from large datasets. 7 hours. Associate input records with inferences to help with the interpretation of results. This post introduces a method for HPO using Optuna and its reference architecture in Amazon SageMaker. Here you can choose the instance name, the instance type, elastic inference (scales your instance size according to demand and usage), and other security Tuning a Semantic Segmentation Model. Use case 2: Use code to develop machine learning models with more flexibility and control. We use the automatic model tuning capability of SageMaker through the use of a hyperparameter tuning job. It provides a UI experience for running ML workflows that makes SageMaker ML tools available across multiple integrated Jun 7, 2018 · In the past this was a painstakingly manual process. Note that there is some "magic" in SageMaker Python SDK that pass the parameters to the script when . Use case 1: Develop a machine learning model in a low-code or no-code environment. Apr 25, 2018 · We also specify algorithm-specific hyperparameters. Automatic model tuning, also known as hyperparameter tuning, finds the best version of a model by running many jobs that test a range of hyperparameters on your dataset. attach('your-tuning-job-name') job_desc = tuner. HyperparameterTuner. Used during training for computing bleu and used during inference. json file, and if you've utilized the sagemaker-training-toolkit, read in and made available as environment variables to your script/entry point. Amazon SageMaker is a fully managed machine learning (ML) service. A SageMaker notebook to launch hyperparameter tuning jobs for xgboost. For a list of hyperparameters available for a SageMaker built-in algorithm, find them listed in Hyperparameters under the algorithm link in Use Amazon SageMaker Built-in Algorithms or Pre-trained Models. These are parameters that are set by users to facilitate the estimation of model parameters from data. xgboost (auc of validation set): 0. retrieve_default(region=None, model_id=None, model_version=None, hub_arn=None, instance_type=None, include_container_hyperparameters=False, tolerate_vulnerable_model=False, tolerate_deprecated_model=False, sagemaker_session=<sagemaker. Both hyperparameters, alpha0 and num_topics, can affect the LDA objective metric (test:pwll). They are estimated or learned from data. Run inference when you don't need a persistent endpoint. hyperparameters specifies training The variety of hyperparameters that you can fine-tune. sagemaker. The complete list of SageMaker hyperparameters is available here. To run a model tuning job, you need to provide Amazon SageMaker with hyperparameter ranges rather than fixed values, so that it can explore the hyperparameter space and automatically May 8, 2024 · SageMaker automatic model tuning (ATM), also known as hyperparameter tuning, finds the best version of a model by running many training jobs on your dataset using the algorithm and ranges of hyperparameters that you specify. The following table lists the hyperparameters for the k-means training algorithm provided by Amazon SageMaker. In this code example, the objective metric for the hyperparameter tuning job finds the hyperparameter configuration that maximizes validation:auc. Note Automatic model tuning for XGBoost 0. Training is started by calling fit () on this Estimator. Distribute input data to all workers. Input dimension. Tune an Amazon SageMaker BlazingText Word2Vec model with the following hyperparameters. The number of data points to be sampled from the training data set. The number of passes done over the training data. parameter. For more information about how object training works, see How Object Detection Works. inputs. session Here is what I have now: A binary classification app fully built with Python, with xgboost being the ML model. AWS Collective Join the discussion. tar. Each training job will get a different set of hyperparameters and so your train() function's responsibility is to simply read the file and use the values therein to k-NN Hyperparameters. SageMaker built-in algorithms automatically write the objective metric to CloudWatch Logs. These algorithms provide high-performance, scalable machine learning and are optimized for speed, scale, and accuracy. See Tune an Object Detection - TensorFlow model for information on hyperparameter tuning. Sep 2, 2021 · The training script saves the model artifacts in the /opt/ml/model once the training is completed. The main github repository for Sagemaker examples is here. The following table contains the subset of hyperparameters that are required or most commonly used for the Amazon SageMaker CatBoost algorithm. They values define the skill of the model on your problem. ParameterRange (min_value, max_value, scaling_type = 'Auto') ¶ Bases: object. The following table lists the hyperparameters for the PCA training algorithm provided by Amazon SageMaker. Length of the beam for beam search. It is an appropriate solution, though there may be another approach. You can use XGBoost for regression, classification (binary and multiclass), and ranking problems. hyperparameters (dict) – Hyperparameters that will be used for training (default: None). To train on multiple GPUs or instances, we create a SageMaker PyTorch Estimator that ingests the DINO training script, the image and metadata file paths, and the training hyperparameters: Jul 18, 2018 · For issue #2, tuner. To deploy JumpStart foundation models, navigate to a model detail card in the Studio UI. describe() job_desc['HyperParameterRanges'] # returns a dictionary with your tunable hyperparameters. estimator import Estimator. The number of time-points that the model gets to see before making the prediction. from sagemaker. gz and save it to the S3 location specified to output_path Estimator parameter. The algorithm detects the type of classification problem based on the number of labels in your data. Then, follow the steps in Deploy models with Aug 31, 2021 · The hyperparameters you define in the Estimator are passed in as named arguments. HyperParameters (dict) – Algorithm-specific parameters that influence the quality of the model. Sagemaker is not for the heart fainted or who likes proper documentation. The following section describes how to use LightGBM with the SageMaker Python SDK. Each tree learns a separate model from a subsample of the input training data and outputs an I created this custom sagemaker estimator using the Framework class of the sagemaker estimator. Fit the training dataset to the chosen object detection architecture. There are several parameters you should define in the Estimator: entry_point specifies which fine-tuning script to use. The Gemma family consists of two sizes: a 7 billion parameter model and a 2 billion parameter model. This is used to define what hyperparameters to tune for an Amazon SageMaker hyperparameter tuning job and to verify hyperparameters for Marketplace Algorithms. You set hyperparameters before you start the learning process. Jun 11, 2020 · 1. Therefore, any convergence issue in single-GPU training propagates to distributed training Hyperparameters. Each hyperparameter is a key-value pair. See Tune an Image Classification - TensorFlow model for information on hyperparameter tuning. The Estimator handles end-to-end SageMaker training. Using these algorithms you can train on petabyte-scale data. Number of instances to pick from validation dataset to decode and compute bleu score during training. The two primary hyperparameters available in the Amazon SageMaker RCF algorithm are num_trees and num_samples_per_tree. Hyperparameters control the behavior of the model/algorithm, while model parameters are learned from data. HyperparameterTuner() If you reached this point of the post and still is a bit loss on this whole hyperparam tuning thing, it's my fault. It then chooses the hyperparameter values that result in a model that performs the best, as measured by a metric that you DeepAR Hyperparameters. For Hyperparameter configuration, choose ranges for the tunable hyperparameters that you want the tuning job to search, and set static values for hyperparameters that you want to remain constant in all training jobs that the hyperparameter tuning job launches. Nov 1, 2019 · Head over to your AWS dashboard and find SageMaker, and on the left sidebar, click on `Notebook instances`. Jul 3, 2024 · Hyperparameter tuning is crucial for selecting the right machine learning model and improving its performance. After navigating to the model detail page of your choice, choose Deploy in the upper right corner of the Studio UI. They are often not set manually by the practitioner. The optional hyperparameters that can be set are listed next Jul 13, 2021 · Customers can add a model tuning step (TuningStep) in their SageMaker Pipelines which will automatically invoke a hyperparameter tuning job. – Learn about how the hyperparameters used to facilitate the estimation of model parameters from data with the Amazon SageMaker XGBoost algorithm. The batch size for training. They can then The following table lists the hyperparameters for the Amazon SageMaker IP Insights algorithm. They are required by the model when making predictions. Any hyperparameters provided by the training job are passed to the entry point as script arguments. The following hyperparameters are supported by the Amazon SageMaker built-in Image Classification algorithm. The SageMaker Python SDK uses this feature to pass special hyperparameters to the training job, including sagemaker_program and sagemaker_submit_directory. They are designed to provide up to 10x the performance of the other […] Mar 6, 2020 · amazon-sagemaker; hyperparameters; or ask your own question. Recommended Ranges or Values. class sagemaker. tuner import IntegerParameter, HyperparameterTuner, ContinuousParameter. Parameter Name. After training, artifacts PDF RSS. The number of entity vector representations (entity embedding vectors) to train. You choose the objective metric from the metrics that 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. The hyperparameters are made accessible as a dict [str, str] to the training code on SageMaker. Each entity in the training set is randomly assigned to one of these vectors using a hash function. There are 10 classes (one for each of the 10 digits). Users set these parameters to facilitate the estimation of model parameters from data. Refer here for a complete list of instance types. Number of rows in a mini-batch. For information on how to use LightGBM from the Amazon SageMaker Studio Classic UI, see Train, deploy, and evaluate pretrained models with SageMaker JumpStart. May 16, 2021 · sagemaker. Jan 30, 2023 · I hope the code walkthough shows just how easy it is to tune hyperparameters using the Sagemaker sdk and that there is a lot to be gained in model development by using it. def model_fn(features, labels, mode, hyperparameters=None): if Creates a SKLearn Estimator for Scikit-learn environment. import boto3 from sagemaker. I found a solution on gokul-pv github. Tunable LDA Hyperparameters. 766 SageMaker best model (auc of validation set):0. We first configure the training jobs the hyperparameter tuning job will launch by initiating an estimator, which includes: The container image for the algorithm (XGBoost) Configuration for the output of the training jobs The Estimator handles end-to-end Amazon SageMaker training. Mini batch size for gradient descent. Model tuning is completely agnostic to the actual model algorithm. I've made comments on a related issue #65 that offers some additional details. Valid values: classifier for classification or regressor for regression. The tuning job uses the Use the XGBoost algorithm with Amazon SageMaker to train a model to predict whether a customer will enroll for a term deposit at a bank after being contacted by phone. For doing more comparisons, go with what Oliver_Cruchant posted. Jun 5, 2023 · Using LoRA and quantization makes fine-tuning BLOOMZ-7B to our task affordable and efficient with SageMaker. You can use the SageMaker API to define hyperparameter ranges. GridSearchCV and RandomSearchCV are systematic ways to search for optimal hyperparameters. It then chooses the hyperparameter values that result in a model that performs the best, as measured by an objective metric that you choose. The following are the main uses cases for training ML models within SageMaker. warm_start_config ( sagemaker. The following table lists the hyperparameters provided by Amazon SageMaker for training the object detection algorithm. On the Deploy tab, you can point to the Amazon Simple Storage Service (Amazon S3) bucket containing the training and validation datasets for fine-tuning. Jun 26, 2020 · @uwaisiqbal The hyperparameters should be available. tuner. Feb 27, 2020 · And get a best model with the following set of hyperparameters: Out of curiosity, I hooked up these hyperparameters into xgboost python package, as such: I retrained the model and realized the results I got from the latter is better than that from SageMaker. The number of features in the data set. For convenience, this accepts other types for keys and values, but str () will be called to convert Jun 29, 2020 · Hyperparameters are passed to your script as arguments and can be retrieved with an argparse. should've mentioned about it earlier but better later then never right. This process is an essential part of machine learning, and choosing appropriate hyperparameter values is crucial for success. 65. fit(), the fit() function will kick off a SageMaker Tuning job that will run multiple SageMaker Training jobs - each of which will call your train() function. These hyperparameters are made available as arguments to your input script. You can specify a maximum of 100 hyperparameters. 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. The SageMaker CatBoost algorithm is an implementation of the open-source CatBoost package. 90 is only available from the Amazon SageMaker SDKs, not from the SageMaker console. You choose the tunable hyperparameters, a range of values for each, and an objective metric. WarmStartConfig) – A WarmStartConfig object that has been initialized with the configuration defining the nature of warm start tuning job. In this example, we are using SageMaker Python SDK to set up and manage the hyperparameter tuning job. estimator import Framework class ScriptModeTensorFlow(Framework): """This class is temporary until the final version of Script Mode is released. Jupyter Notebooks for using the hyperparameter tuner are available here and here. Jun 21, 2024 · You can also track parameters, metrics, datasets, and other artifacts related to your model training jobs. You specify Semantic Segmentation for training in the AlgorithmName of the CreateTrainingJob request. It also shows how to use SageMaker Automatic Model Tuning to select appropriate hyperparameters in order to get the best model. When using SageMaker training jobs, you only pay for GPUs for the duration of model training. Although you can simultaneously specify up to 30 hyperparameters, limiting your search to a smaller number can reduce computation time. The required hyperparameters that must be set are listed first, in alphabetical order. Jun 20, 2018 · The hyperparameters will be made available as arguments to our input script in the training container. The default hyperparameters are based on example datasets in the LightGBM sample notebooks. The hyperparameters that have the greatest impact on Word2Vec objective metrics are: mode, learning_rate , window_size, vector_dim, and negative_samples. SageMaker provides useful properties about the training environment through various environment variables, including the following: SM_MODEL_DIR – A string that represents the path where the training job writes the model artifacts to. Synchronize the model updates from all workers. instance_type specifies an Amazon instance to launch. Base class for representing parameter ranges. This question is in a collective: a subcommunity defined by Hyperparameters are parameters that are set before a machine learning model begins learning. 0+): tuner = sagemaker. Here, we look for hyperparameters like batch size, epochs, learning rate, momentum, etc. May 28, 2020 · Preferred Networks (PFN) released the first major version of their open-source hyperparameter optimization (HPO) framework Optuna in January 2020, which has an eager API. For example, for a hyper-parameter needed in your model_fn: DEFAULT_LEARNING_RATE = 1e-3. Use case 3: Develop machine learning models at scale with maximum flexibility and control. Gemma is a family of language models based on Google’s Gemini models, trained on up to 6 trillion tokens of text. Jan 8, 2018 · Today we are launching Amazon SageMaker DeepAR as the latest built-in algorithm for Amazon SageMaker. However, thanks to the work of some very talented researchers we can use SageMaker to eliminate almost all of the manual overhead. (If you use the Random Cut Forest estimator, this value is calculated for you Jul 1, 2021 · In this step you run an Amazon SageMaker automatic model tuning job to find the best hyperparameters and improve upon the training accuracy obtained in Step 6. This post describes these features, explains when and how to enable them, and shows how they can improve your search for hyperparameters that perform well. You can set Estimator metric_definitions parameter to extract model metrics from the training logs. The LightGBM algorithm detects the type of classification problem based on the number of labels in The dataset is split into 60,000 training images and 10,000 test images. In addition, you can configure deployment configuration, hyperparameters, and security settings for fine-tuning. The default hyperparameters are based on example datasets in the AutoGluon-Tabular sample notebooks. The number of nearest neighbors. Valid values: positive integer. Accessors to retrieve hyperparameters for training jobs. By default, the SageMaker AutoGluon-Tabular algorithm automatically chooses an evaluation metric based on the type of classification problem. DeepAR is a supervised learning algorithm for time series forecasting that uses recurrent neural networks (RNN) to produce both point and probabilistic forecasts. For more information about how PCA works, see How PCA Works. Hyperparameters directly control model structure, function, and performance. A framework to run training scripts in your local environments. You choose the objective metric from the metrics that the algorithm computes. Parameter Type. Nov 29, 2023 · I'm following an Amazon Sagemaker workshop to try and leverage several of Sagemaker's utilities instead of running everything off a Notebook as I'm currently doing. This tutorial will show how to train and test an MNIST model on SageMaker using PyTorch. Tune a linear learner model. And undoing the str() applied by sagemaker, casting every hyperparameter that is not a string is quite time consuming. Description. . The type of inference to use on the data labels. We start by defining a training script that accepts the hyperparameters as input for the specified model algorithm, and then implement the model training and evaluation steps. batch_size. By default, the SageMaker LightGBM algorithm automatically chooses an evaluation metric and objective function based on the type of classification problem. To learn about SageMaker Experiments, see Manage Nov 8, 2018 · Amazon SageMaker provides several built-in machine learning (ML) algorithms that you can use for a variety of problem types. You use the low-level SDK for Python (Boto3) to Jul 25, 2017 · A model parameter is a configuration variable that is internal to the model and whose value can be estimated from data. The number of features in the input data. Create the configuration node for training. Hyperparameters are parameters that are set before a machine learning model begins learning. Aug 16, 2023 · With these adjustments, we are ready to train DINO models on BigEarthNet-S2 using SageMaker. Specify the names of hyperparameters and ranges of values in the ParameterRanges field of the HyperParameterTuningJobConfig parameter that you pass to the CreateHyperParameterTuningJob operation. Because of hash collisions, it might be possible to have multiple Automatic model tuning searches the hyperparameters chosen to find the combination of values that result in the model that optimizes the evaluation metric. SageMaker archives the artifacts under /opt/ml/model into model. The SageMaker service makes these available in a hyperparameters. If we don’t define their values in our SageMaker estimator call, they’ll take on the defaults we’ve provided. It will execute an Scikit-learn script within a SageMaker Training Job. With SageMaker, data scientists and developers can quickly and confidently build, train, and deploy ML models into a production-ready hosted environment. To create an instance, click the orange button that says `Create notebook instance`. So now I will have to edit my Dockerfile to accept around 30 arguments with suboptions for minimum and maximum for each. It seems that you can't use the same PyTorch model for training and registration for some reason. The range of values that Amazon SageMaker has to search. Hyperparameter tuning allows data scientists to tweak model performance for optimal results. The following hyperparameters are supported by the Amazon SageMaker built-in Image Classification - TensorFlow algorithm. You can also specify algorithm-specific HyperParameters as string-to-string maps. The hyperparameter tuning finds the best version of a model by running many training jobs on the dataset using the algorithm and the ranges of hyperparameters specified by the customer. The following hyperparameters are supported by the Amazon SageMaker built-in Object Detection - TensorFlow algorithm. early_stopping_type ( str) – Specifies whether early stopping is enabled for the job. For more information on all the hyperparameters that you can tune, refer to Perform Automatic Model Tuning with SageMaker. You can tune the following hyperparameters for the LDA algorithm. py, where we also first define an Estimator object, and give it as input to another object of class HyperparameterTuner: from sagemaker. feature_dim. Sequence-to-Sequence Hyperparameters. Can be either ‘Auto’ or ‘Off’ (default: ‘Off’). In a few steps, SageMaker Data Wrangler splits and trains an XGBoost model with default hyperparameters. nq dl iq dv jv mf nh yd jc ca