For your ML experiments, you need to process CSV data files.
Size of your files is about 10GB each.
Your training script loads the ingested data to a pandas dataframe object.
During the runs, you get an “Out of memory” error.
You decide to convert the files to Parquet format and process it partially, i.e.
loading only the columns relevant from the modelling point of view.
Does it solve the problem?
Click on the arrows to vote for the correct answerA. B.
Option A is CORRECT because the data loaded from a CSV file can expand significantly when loaded into a dataframe in memory.
Converting it to the columnar Parquet format is a viable solution because it enables loading selected columns which are necessary for the training process.
Option B is incorrect because using the columnar Parquet format instead of CSV can be used to optimize memory consumption, therefore it is a good solution.
The proposed solution of converting CSV files to Parquet format and loading only the relevant columns can potentially solve the "Out of memory" error during the ML experiments. Here is a detailed explanation:
CSV files store data in a plain-text format where each row represents a record and each column is separated by a delimiter (e.g., comma or tab). In contrast, Parquet is a columnar storage format that stores data in a binary format optimized for query performance. Parquet can be more efficient than CSV when working with large datasets because it minimizes data duplication, reduces I/O and network traffic, and allows for column-level compression and encoding.
Pandas is a popular Python library for data manipulation and analysis. It provides a powerful data structure called a DataFrame that allows users to store and manipulate tabular data. However, loading large CSV files into a pandas dataframe object can consume a lot of memory and lead to "Out of memory" errors.
To address the "Out of memory" error, the proposed solution is to convert the CSV files to Parquet format and load only the columns relevant for modelling. This approach can help reduce the amount of data that needs to be loaded into memory, which can lower the memory footprint of the ML experiment.
It's important to note that converting CSV files to Parquet format and loading only relevant columns is not a panacea for all memory-related issues in ML experiments. Depending on the complexity of the modelling task, the size and structure of the data, and the available hardware resources, there may still be situations where the memory demands exceed the available resources. Therefore, it's essential to carefully monitor the memory usage during the ML experiment and optimize the solution accordingly.
In conclusion, the proposed solution of converting CSV files to Parquet format and loading only relevant columns can potentially solve the "Out of memory" error during ML experiments, but it's important to consider the limitations and monitor the memory usage.