How to increase the speed of loading large datasets in google collab

There are several ways to increase the speed of loading large datasets in Google Colab:

1. Use a faster internet connection: A faster internet connection can significantly increase the speed of loading large datasets.
2. Use Google Drive: If your dataset is stored on Google Drive, you can use the PyDrive library to access the dataset directly, rather than downloading it to the local runtime.
3. Use Google Cloud Storage: Google Cloud Storage is a more powerful and scalable storage solution than Google Drive. You can use the gcsfs library to access the dataset directly, rather than downloading it to the local runtime.
4. Use a faster runtime: Colab offers a number of runtime types, including GPU and TPU. Using a faster runtime type can increase the speed of loading large datasets.
5. Compress the dataset: Compressing the dataset can reduce the amount of data that needs to be transferred, which can increase the speed of loading the dataset.
6. Use data-generator: If your dataset is too big to fit into memory, you can use a data generator to load it in chunks.

If your problem truly is the network speed between Collab and Drive, you should try uploading the files directly to the Google Collab instance, rather than accessing them from Drive.

Doing this will save the files directly to your Collab instance, allowing your code to access the files locally. However, I'd suspect that there might be other problems besides the network latency – perhaps your model has lots of parameters, or somehow there was a bug in the code to get CUDA going. Sometimes I would forget to change my runtime to a GPU runtime under the "Runtime" menu tab, "Change Runtime Type".

Hope this helps!