memory / storage check

code:size of data

import sys

# Check memory usage of the raw_eeg_data object

print(f"Size of raw_eeg_data: {sys.getsizeof(raw_eeg_data)} bytes")

code: Is Preload

print(f"Is the data preloaded (in memory)? {raw_eeg_data.preload}")

code:memory usage

import psutil

# Get memory usage of current Python process

process = psutil.Process()

memory_info = process.memory_info()

print(f"Memory usage: {memory_info.rss / (1024 ** 2):.2f} MB")

how to conserve memory

1. Stream the Data Directly from GCS:

Instead of downloading large files to your GCP instance, you can stream the data directly from GCS into memory. This reduces the need for local disk space and can help minimize resource usage. The Python gcsfs library allows you to work with files on GCS as if they were local.

code:eg

Install gcsfs

pip install gcsfs

import mne

import gcsfs

# Initialize GCS FileSystem

fs = gcsfs.GCSFileSystem()

# Open the file directly from GCS without downloading

with fs.open(f'gs://{bucket_name}/{vhdr_path}') as f:

raw = mne.io.read_raw_brainvision(f)

4. Optimize Your Data Handling Workflow:

You might consider breaking down your data into smaller chunks if possible, so you're only loading parts of the EEG dataset at a time. This can prevent overwhelming the system's memory.

code:eg

For example:

python

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# Load EEG data in smaller chunks or segments

raw = mne.io.read_raw_brainvision(local_vhdr, preload=False)

raw.crop(tmin=0, tmax=60) # Load the first 60 seconds of data

5. Mount GCS Bucket to GCP Using gcsfuse:

You can mount your GCS bucket as a file system on your GCP instance using gcsfuse, which allows you to access GCS files as if they were on a local disk. This way, you don't need to explicitly download the files.

Install gcsfuse:

bash

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sudo apt-get install gcsfuse

Mount the GCS bucket to a directory:

bash

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mkdir /path/to/mount

gcsfuse your-bucket-name /path/to/mount

Access files in the mounted directory without fully downloading them to local disk.