Deep learnin on Google Colab: loading large image dataset is very long, how to accelerate the process?

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I'm working on a Deep Learning model using Keras and to speed up the computation I'd like to use the GPU available on google colab.

My image files are already loaded on my google drive. I have 24'000 images for training on 4'000 for testing my model.

However when I load my images into an array, it takes a very long time (almost 2h) So it is not very convenient to do that every time I use google colab notebook.

Would you know how to accelerate the process ? This is my current code:

TRAIN_DIR  = "Training_set/X"
TRAIN_DIR_Y = "Training_set/Y"
IMG_SIZE = 128

def parse_img_data(path):
    X_train = []
    index_train = []
    img_ind = []
    for img in tqdm(os.listdir(path)):
        path = os.path.join(TRAIN_DIR,img)
        img = cv2.imread(path,cv2.IMREAD_COLOR)
        img = cv2.resize(img, (IMG_SIZE,IMG_SIZE))
    return np.array(img_ind), np.array(X_train)

ind_train, X_train = parse_img_data(TRAIN_DIR)

I'd be very grateful if you would help me.


Not sure if you solve the issue. I was having the same problem. After I use os.listdir to the particular data folder before I ran CNN and worked.

print(os.listdir("./drive/My Drive/Colab Notebooks/dataset"))

Speed up your image training on Google Colab, Training times range from “very long” to “aborted because of Drive timeout”. Deep Learning Explained in 7 Steps - Updated | Data Driven Investor performance by loading the dataset into a fastai databunch and running the learning In lots of cases, the process simply aborted with a “drive timeout” error. Deep learning is computationally very intensive. For decades, training neural networks was limited by hardware. Even relatively smaller models had to be trained for days, and training large architectures on huge datasets was impossible. However, with the appearance of general computing GPU programming, deep learning exploded.

from numpy import savez_compressed trainX, trainy = parse_img_data('/content/drive/My Drive/Training_set/') savez_compressed('dataset.npz', trainX, train)

for the first time you can load and save the data then Use it over and over again

import numpy as np data=np.load('/content/drive/My Drive/dataset.npz') trainX,trainy=data['arr_0'],data['arr_1']

How to work with large training dataset in Google Colab platform, Google Colab platform offers GPU and 12 GB of memory free for One constant in training a Deep Neural Network is the need for large training dataset. So, we will need to load the dataset to Colab while we connect to a (In the original zip file, the cat and dog images are in their respective folders). Google Colaboratory (a.k.a. Colab) is a cloud service based on Jupyter Notebooks for disseminating machine learning education and research. It provides a runtime fully configured for deep learning

You can try to mount your Google Drive folder (you can find the code snippet from Examples menu) and use ImageDataGenerator with flow_from_directory(). Check documentation here

4 Awesome Ways Of Loading ML Data In Google Colab, Loading data in Google Colab with various APIs and packages. there have to be some awesome ways of loading machine learning data right from your local machine to the Cloud. !unzip /content/images/ You can upload very large datasets to Long Live Business Science! Deep learning is a vast field so we’ll narrow our focus a bit and take up the challenge of solving an Image Classification project. Additionally, we’ll be using a very simple deep learning architecture to achieve a pretty impressive accuracy score.

I have been trying, and for those curious, it has not been possible for me to use flow from directory with a folder inside google drive. The collab file environment does not read the path and gives a "Folder does not exist" error. I have been trying to solve the problem and searching stack, similar questions have been posted here Google collaborative and here Google Colab can't access drive content , with no effective solution and for some reason, many downvotes to those who ask.

The only solution I find to reading 20k images in google colab, is uploading them and then processing them, wasting two sad hours to do so. It makes sense, google identifies things inside the drive with ids, flow from directory requires it to be identified both the dataset, and the classes with folder absolute paths, not being compatible with google drives identification method. Alternative might be using a google cloud enviroment instead I suppose and paying.We are getting quite a lot for free as it is. This is my novice understanding of the situation, please correct me if wrong.

edit1: I was able to use flow from directory on google collab, google does identify things with path also, the thing is that if you use os.getcwd(), it does not work properly, if you use it it will give you that the current working directory is "/content", when in truth is "/content/drive/My Drive/foldersinsideyourdrive/...../folderthathasyourcollabnotebook/. If you change in the traingenerator the path so that it includes this setting, and ignore os, it works. I had however, problems with the ram even when using flow from directory, not being able to train my cnn anyway, might be something that just happens to me though.

Make sure to execute

from google.colab import drive

so that the notebook recognizes the paths

3 Essential Google Colaboratory Tips & Tricks, Google Colaboratory is a promising machine learning research platform. In a 3 step process, first invoke a file selector within your notebook with this: Now, load the contents of the file into a Pandas DataFrame using the I have lots of image data and rather than upload it into colab, I uploaded it into my Google Drive  Blindly accepting learning rate suggestion is not always the best option: the lr_find() for the ResNet50 produces a long plateau and the method suggested a very low learning rate value in a small valley at the left end of the graphic. When we trained the network with this value, it oscillated and did not produce a good result (90% accuracy only).

Google colab limitations, This article will get you started with Google Colab, a free GPU cloud service Here is an overview of the setup process together with a sample notebook large data (even just a couple GB to load) you can't load from google drive as datasets designed to make deep learning more accessible and accelerate ML research. Image classification with Keras and deep learning. 2020-05-13 Update: This blog post is now TensorFlow 2+ compatible! This blog post is part two in our three-part series of building a Not Santa deep learning classifier (i.e., a deep learning model that can recognize if Santa Claus is in an image or not):

"10 Minutes" uses Google Colab to build an image classification model, “Building a deep learning model in a few minutes? In fact, you don't need to work for Google or other large technology companies to use deep learning data sets, Manually checking and classifying images is a very tedious process. Install Google Colab; Import library; Load and preprocess data (3  🤔 loading a Dataset from TFRecords using TFRecordDataset; Please take a moment to go through this checklist in your head. In a nutshell. If all the terms in bold in the next paragraph are already known to you, you can move to the next exercise. If your are just starting in deep learning then welcome, and please read on.

8 Tips For Google Colab Notebooks To Take Advantage Of Their , kriyeng profile image It provides a runtime fully configured for deep learning and This is why is so important to speed up the time you need to run your On Colab notebooks you can access your Google Drive as a network a lot of files in your root folder can affect the process of mapping the unit. Deep learning models excel at learning from a large number of labeled examples, but typically do not generalize to… The list of CNN models from the PyTorch website and their performance on the ImageNet dataset is available here.