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)
Does it solve the problem?
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 answer is "B. No".
Increasing the size of the compute's memory may help to some extent in handling larger datasets in memory. However, it does not guarantee that the "Out of memory" error will be resolved.
The issue is that the current approach loads the entire CSV file into memory as a pandas dataframe object. Doubling the memory will increase the available memory to 32GB, but if the CSV file size is 2GB, it will still consume a significant portion of the memory.
To resolve this issue, we can consider using a different approach that involves loading the data in batches, processing the data in smaller chunks, and then aggregating the results. This approach can help to reduce memory usage and avoid the "Out of memory" error.
We can also consider using Azure Data Lake Storage to store and process large datasets. Azure Data Lake Storage provides scalable storage and processing capabilities for big data scenarios, allowing us to store and process data of any size.
In conclusion, simply doubling the size of the compute's memory may not be enough to solve the "Out of memory" error when loading large CSV files into memory. It's essential to consider alternative approaches such as processing data in batches or using Azure Data Lake Storage.