I want to train the model with binary logistic regression model,with a dataset of 3000 data points. while creating the pipeline , it fails at the training model step.
Please help me in training the model with large dataset or retrain the model continuously.
Also Do pipelines have any limitation on the dataset? if so, what is the limit
I haven't seen there is a limitation for training dataset size. May I know how you do the pipeline? If you are using Azure Machine Learning Designer, could you please try the enterprise version? https://learn.microsoft.com/en-us/azure/machine-learning/concept-ml-pipelines#building-pipelines-with-the-designer
Also, I have attached a tutorial here for large data pipeline: https://learn.microsoft.com/en-us/azure/machine-learning/tutorial-pipeline-batch-scoring-classification
Related
I am currently using SageMaker to train BERT and trying to improve the BERT training time. I use PyTorch and Huggingface on AWS g4dn.12xlarge instance type.
However when I run parallel training it is far from achieving linear improvement. I'm looking for some hints on distributed training to improve the BERT training time in SageMaker.
You can use SageMaker Distributed Data Parallel (SMDDP) to run training on a multinode and multigpu setup. Please refer to the below links for BERT based training example
https://github.com/aws/amazon-sagemaker-examples/blob/main/training/distributed_training/pytorch/data_parallel/bert/pytorch_smdataparallel_bert_demo.ipynb
This is with HuggingFace - https://github.com/aruncs2005/pytorch-ddp-sm-example
please refer to the documentation here for step by step instructions.
https://docs.aws.amazon.com/sagemaker/latest/dg/data-parallel-modify-sdp-pt.html
I am currently training a model using an azure ML pipeline that i build with sdk. I am trying to add cross-validation to my ml step. I have noticed that you can add this in the parameters when you configure the autoML. My dataset consists of 30% label 0 and 70% label 1.
My question is, does azure autoML stratify data when performing the cross-validation? If not i would have to do the split/stratify myself before passing it to autoML.
Auto ML can stratify the data when performing cross-validation. The following procedure needs to be followed to perform cross-validation
Create the workspace resource.
After giving all the details, click on create
Launch the Studio and go to AutoML and click on New Automated ML job
Upload the dataset from here and give the basic details required.
Dataset uploaded with some basic categories
After uploading dataset use that dataset for the prediction model performance
Here for prediction, we can choose the k-fold cross validation for validation type and number of cross validations as 5. There is no split we are performing. The model will perform according to the validation requirements.
I am creating Azure ML experienment to predict multiple values. but in azure ml we can not train a model to predict multiple values. my question is how to bring multiple trained models in single experienment and create webout put that gives me multiple prediction.
You would need to manually save the trained models (right click the module output and save to your workspace) from your training experiment and then manually create the predictive experiment unlike what is done in this document. https://learn.microsoft.com/en-us/azure/machine-learning/studio/walkthrough-5-publish-web-service
Regards,
Jaya
We are using Azure Machine Learning Studio for building Trained Model and for that we have used Two Class Bayes Point Machine Algorithm.
For sample data , we have imported .CSV file that contains columns such as: Tweets and Label.
After deploying the web service, we got improper output.
We want our algorithm to predict the result of Label as 0 or 1 on the basis of different types tweets, that are already stored in the dataset.
While testing it with the tweets that are there in the dataset, it gives proper result, but the problem occurs while testing it with other tweets(that are not there in the dataset).
You can view our experiment over here:
Experiment
Are you planning to do a binary classification based on the textual data on tweets? If so you should try doing feature hashing before doing the classification.
I am using scikit-learn to build text classifiers. As part of the preprocessing I use a tf-ifd transformer. I have had difficulty when trying to validate a model against an unseen dataset as the vocabulary is different. How can a pipeline be applied to unseen data that needs to be used for prediction at a later time?
Thanks