Handling Large CSV Data Files with Pandas and Memory Optimization
Question
For your ML experiments, you need to process CSV data files.
Size of your files is about 2GB each.
Your training script loads the ingested data to a pandas dataframe object.
During the first run, you get an “Out of memory” error.
You decide to double the size of the compute's memory (which is 16GB currently)
Is this a possible solution to the problem?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B.Answer: A.
Option A is CORRECT because the data loaded from a CSV file can expand even as much as 10 times when loaded into a dataframe in memory.
It is recommended to set the size of the memory at least two times the size of the input data.
Option B is incorrect because a typical reason for “Out of memory” errors during this process is that data loaded from a CSV file expands significantly when loaded to a dataframe.
Extending the compute memory is one possible solution.
Reference:
The solution of doubling the size of the compute's memory from 16GB to 32GB might help to solve the "Out of memory" error, but it depends on the specific requirements of the machine learning experiment and the size of the dataset.
A pandas dataframe object loads the entire data into memory, so the size of the dataset should be smaller than the memory available in the compute for the program to run without running out of memory. Doubling the memory will increase the available memory and give more space for the program to run.
However, it's important to consider that increasing the memory might not be a permanent solution. If the dataset grows further or the complexity of the model increases, the program may still run out of memory. In such cases, it's recommended to consider other solutions, such as using a distributed computing platform like Azure Databricks or using a data storage solution like Azure Blob Storage that can handle large datasets efficiently.
Overall, the solution of doubling the compute's memory size can help to solve the "Out of memory" error in the short term, but it's important to evaluate the requirements of the machine learning experiment and consider other solutions to handle large datasets efficiently in the long run.
