AutoMl Dataset too large

[Mirko]

Hello community,

i have the following problem: I am using automl to solve a regression model, but in the preprocessing my dataset is sampled to ~30% of the original amount.

I am using runtime 14.2 ML 

Driver: Standard_DS4_v2 28GB Memory 8 cores

Worker: Standard_DS4_v2 28GB Memory 8 cores (min 1, max 2)

i allready set spark.task.cpus = 8, but my dataset is still down sampled 😞

Catalog says that my Table got the folowing size:

Size:264.5MiB, 8 files

I dont know how it still doesnt fit.

Any help would be appreciated

Mirko

[Kaniz_Fatma · Accepted solution]

Hi @Mirko, I understand the challenge you’re facing with your AutoML regression model and the downsampling of your dataset.

Let’s break down the situation and explore potential solutions:

1. Dataset Size and Downsampling:

   • Your dataset is currently downsampled to approximately 30% of its original size during preprocessing.
   • The catalog indicates that your table size is 264.5 MiB and consists of 8 files.

2. AutoML Behavior:

   • With Databricks Runtime 9.1 LTS ML or above, AutoML automatically samples your dataset if it’s too l...¹.
   • Downsampling is a common technique to handle large datasets, especially when memory constraints exist.

3. Potential Solutions:

   • Let’s explore a few steps to address this issue:
     • Increase Worker Resources:
       • You’re currently using Standard_DS4_v2 workers with 28GB memory and 8 cores. Consider increasing the worker resources (both memory and cores) to accommodate a larger dataset.
     • Check Memory Usage:
       • Verify that your dataset isn’t consuming excessive memory during preprocessing. Sometimes, other operations or transformations might be using memory beyond the dataset itself.
     • Partitioning and Parallelism:
       • Ensure that your data is properly partitioned. If your dataset is not partitioned, consider repartitioning it to optimize parallelism during processing.
     • Sample Size and Quality:
       • Assess whether a smaller sample still provides representative data for your regression task. If so, downsampling might be acceptable.
     • Time-Based Split:
       • If your data has a time component (e.g., timestamps), consider splitting it into training, validation, and test sets based on time. This can help maintain temporal consistency.
     • Class Weights (for Imbalanced Data):
       • If your regression problem involves imbalanced data, AutoML uses class weights to address this. These weights are inversely related to the degree of downsampling for each class¹.
     • Review Preprocessing Steps:
       • Double-check any additional preprocessing steps (e.g., feature engineering, missing value imputation) to ensure they’re not inadvertently affecting the dataset size.

4. Next Steps:

   • I recommend:
     • Inspecting Memory Usage: Check if memory is being used efficiently during preprocessing.
     • Reviewing Partitioning: Ensure proper data partitioning.
     • Assessing Sample Size: Evaluate whether downsampling impacts model performance significantly.
     • Exploring Class Weights: If applicable, consider class weights for imbalanced data.

Remember that AutoML aims to simplify the model-building process, but understanding its behavior and tuning parameters can lead to better results. If you encounter any specific issues during these steps, feel free to ask for further assistance! 😊

[Mirko]

Thank you for your detailed answer. I followed your sugestions with the following result:

- repartioing of the data didnt change anything

- i checked the metrics of the workers and the memory is indeed nearly fully used (10gig is used, nearly 17gig is cached)

- i do not fully understand why my relativ small dataset creates such a big memory demand, maybe it results in the amount of categorial features. One hot encoding could result in many "extra columns"

[Mirko]

I am pretty sure that i know what the problem was. I had a timestamp column (with second precision) as a feature. If they get one hot encoded, the dataset can get pretty large.