Google Colab can be a useful platform for training deep learning models, including tasks like detecting sickle cell disease from images. Here are some benefits of using Google Colab for your project:

Despite its advantages, there are some considerations and potential drawbacks to using Google Colab, particularly when dealing with a vast volume of data:

Considering the volume of data you are working with, it might be beneficial to explore alternative solutions, such as cloud-based platforms or high-performance computing clusters. These solutions can provide more extensive computational resources, efficient data storage, and scalability.

Cloud platforms like Google Cloud Platform (GCP) or Amazon Web Services (AWS) offer services specifically designed for deep learning, such as Google Cloud AI Platform or AWS Deep Learning AMIs. These platforms provide scalable computing power, storage options, and additional tools for managing large-scale deep learning projects.

Ultimately, the choice of platform depends on your specific needs, available resources, and expertise. It may be worth considering a combination of tools and services, leveraging the advantages of Google Colab for prototyping and smaller-scale experiments while utilizing more scalable infrastructure for large-scale training and deployment.

Google Colab can be a suitable choice for detecting sickle cell disease through deep learning, even with a large volume of data. Its benefits include easy access to powerful GPUs, free cloud storage, and pre-installed libraries for deep learning tasks. However, Colab has limitations such as session time restrictions and limited GPU memory, which may impact training on large datasets. To overcome these limitations, you can utilize strategies like data augmentation, batch processing, or leveraging external storage options. Alternatively, you can consider using other cloud-based platforms like AWS or Azure, which offer more flexibility and resources for handling massive datasets.

Google Colab is a beneficial platform for your deep learning project on detecting sickle cell disease, especially considering the large volume of data you're working with. Some advantages of using Colab include its free availability, easy accessibility through a web browser, and the ability to utilize powerful GPU resources for training deep learning models. Colab also provides a collaborative environment for sharing and collaborating on code. However, there are a few drawbacks to consider, such as limited computational resources, session time restrictions, and the need to frequently upload and preprocess large datasets. To address these limitations, you can explore alternative solutions such as utilizing cloud-based services like Google Cloud Platform or Amazon Web Services, which offer more scalability and dedicated resources for handling large-scale deep learning tasks. Additionally, setting up your own on-premises deep learning infrastructure might be a viable option if you have the necessary resources and expertise.

Thank you, everyone, I will definitely explore these options. I have been thinking of going with AWS.

Hi Mariya,

Almost all of my personal deep learning projects were done with Google Colab, I think it should be able to handle that many images if you're strategic about it. For example, don't let your laptop go to sleep on you or undergo a system update when you're 8 hours in. Also, if you don't already you might want to pay for extra compute or Colab Pro for a project that big - you get access to high-RAM and better GPU/TPU machines for extended periods.

Good luck!

Hi Mariya,

If you're dealing with millions of images, Google Colab might not be the best fit due to sits resource limitations and potential difficulties handling such large volumes of data.

I would prefer a cloud-based machine learning platform like AWS Sagemaker, Google Cloud AI Platform, or Microsoft Azure ML might be a better.

Howerver, you should try colab + first.

Good luck!

Cheers, A

Probably you need to establish a workflow that works.

I would work with "raw" Python like Pycharm on a low level GPU (I have a mere GTX 3050) in order to have full understanding, debugging of what's going on , not just copy/paste and hoping for a decent result.

Preprocessing, choosing the "matching" model, eliminating all these little things that affect more or less the procedure..etc.

When you have done all you homework you can rent a robust DL platform and based on a good working workflow go for the final step.

Hello, I am conducting several research projects in the field of peripheral blood disease detection based on cell images (you can review some of my works). Initially, I started with Google Colab, but the problem with performing algorithms on this platform is the limited GPU capacity for data calculations in model training. Analyzing databases with thousands of images, prediction architectures consume computational resources that are constrained in Colab. While training a model, server disconnections can occur that would require restarting the training process. You can use it as an initial platform since it provides pre-installed libraries for machine and deep learning. Afterwards, you can consider renting a cloud service from Google, Amazon, or Azure. You can even think about Lambda. Finally, if economic availability is an issue, there are low-cost cards that allow training deep learning algorithms, although their limitation lies in optimizing computational resources.

I think it is possible to simplify the task by processing not the images themselves, but their spectra. Then millions of images - it will be a lot more, but still a lot is acceptable, not too much. This tutorial describes spectral automated system-cognitive image analysis: http://lc.kubagro.ru/aidos/aidos18_LLS/aidos18_LLS.pdf