I want to train a network with mBART model in google colab , but I got the message of RuntimeError: CUDA out of memory. Tried to allocate 886.00 MiB (GPU 0; 15.90 GiB total capacity; 13.32 GiB already allocated; 809.75 MiB free; 14.30 GiB reserved in total by PyTorch) I subscribed with GPU in colab. I tried to use 128 or 64 for The maximum total input sequence length. Kindly, What can I do to fix the problem?

I have a colab pro+ subscription and like many, I'm finding that the GPU allowance is rather small compared to the price. While I wait for GPU access I was wondering if the TPU VM would be a substitute. It's running now and seems slower. I have not adjusted my code. Is there any point in this? To be honest, I'm not quite clear on the difference between a TPU and a GPU. I ran lscpu in the console and …

I have referred to various articles on Stack Overflow and external sources but somehow am unable to get answer for this. I would like to read a table from MySQL workbench database into a dataframe in colab. $1^{st}$ Method: In this method, first line of code is successfully executed. Note: I have hidden database, table and password name for security reasons. Source - USER = 'root' PASSWORD = 'PASSWORD' DATABASE = 'DATABASE' TABLE = 'TABLE' connection_string = f'mysql+pymysql://root:PASSWORD/DATABASE' engine = …

I have a code in which it downloads images from Google image according to a query. All those queries are cars (different branch, model and year). Since it is difficult to label each image with a given label (branch_model_year), I would like to first identify the car inside the image and then label it as (branch_model_year). I've used Yolov3 + Darknet to identify the object within the object. See image. How can I change this label for a future classification? …

I've made a neural network in google colab. The original data contained 12 000 rows (csv 350KB). 4 features and 2 labels. Each epoch would run within a few ms. I tried to increase the amount of data to see if the algorithm would yield better results. All I did was update the file, which now contains 326 000 rows of data (12MB). But the algorithm is now incredibly slow, taking several seconds to run a single epoch making it …

I have to train vgg16 from scratch on a digit dataset as my college project. I require more computational power. Can anyone suggest to me how can I do that other than Google Colab? Currently, it is taking approx 17 hours to run one epoch? Like on cloud or anything? and it should be cost-efficient too as I'm just a student.

I have a time series data with the date and temperature records of a city. Following are my observations from the time series analysis: By plotting the graph of date vs temperature seasonality is observed. Performing adfuller test we find that the data is already stationary, so d=0. Perform Partial Autocorrelation and Autocorrelation with First Seasonal Difference and found p=2 and q=10 respectively. Code to Train Model model=sm.tsa.statespace.SARIMAX(df['temperature'],order=(1, 1, 1),seasonal_order=(2,0,10,12)) results=model.fit() This fit function runs indefinitely and does not reach …

As best as I could find, this question was not asked before. I'm using colab, and I use its variable inspector. I'm trying to do the FastAI exercices, and I noticed that when doing them, the variable inspector stops showing variables. For instance - I open a new notebook and I start creating variables in cells. x=5, a=6 and so forth. These variables are shown in the inspector. But, once I run the line: from fastbook import * the variable …

I have millions of sensor readings taken over time. I'd like to create an interactive plot in Google colab where I can easily zoom in on sections. I've done something similar with the Plotly package. However, it fails when the amount of data gets over ~ 700,000. Are there interactive plotting packages that work with Google colab and handle millions of data points? Thank you.

I am trying to implement an autoencoder for prediction of multiple labels using Keras. This is a snippet: input = Input(shape=(768,)) hidden1 = Dense(512, activation='relu')(input) compressed = Dense(256, activation='relu', activity_regularizer=l1(10e-6))(hidden1) hidden2 = Dense(512, activation='relu')(compressed) output = Dense(768, activation='sigmoid')(hidden2) # sigmoid is used because output of autoencoder is a set of probabilities model = Model(input, output) model.compile(optimizer='adam', loss='categorical_crossentropy') # categorical_crossentropy is used because it's prediction of multiple labels history = model.fit(x_train, x_train, epochs=100, batch_size=50, validation_split=0.2) I ran this in Jupyter Notebook …

I am working concurrently with multiple very large datasets (10s-100s of GBs)). I signed up for Colab Pro+ thinking it is the best option. However, I face a significant bottleneck in getting data into Colab. My options all seem very bad: Downloading from AWS (where the data is located) - very slow. Uploading data to Google Drive and mounting Drive using below code. This is also surprisingly very slow. from google.colab import drive drive.mount('/content/drive') Paying for a persistent server. Something …

I know that we can use Kaggle's api directly in google colab which downloads the dataset. The commands are : !mkdir .kaggle !echo '{"username":"somename","key":"apikey"}' > /root/.kaggle/kaggle.json !chmod 600 /root/.kaggle/kaggle.json !kaggle competitions download -c bluebook-for-bulldozers -p /content But I need to do this process of making .kaggle file and pass the apikey in google colab gpu everytime. And sometimes the echo command run saying no file called .kaggle but after say 2 mins without restarting the kernal, it works. It sounds …

I have a big doubt. I see a lot of blog posts where they say that you can use the Colab front-end to edit a local Jupiter Notebook However, I don't see the point. the actual advantage would be to use something like DataSpell or some local IDE, on a remote Notebook on Colab, and use the Colab Resources to do the computations, so you have: IDE level of suggestions (Colab is pretty slow compared to local IDE) cloud computing …

What are the major differences between Kaggle notebook and Google Colab notebook? To work on a dataset my first step is to start a Kaggle notebook but then I cant help thinking what could be the advantage of using Colab notebook instead. I know few differences, correct me if I'm mistaken about any: Kaggle has a console and Colab doesn't (but I still don't know what to do with the console). Kaggle notebook allows collaboration with other users on Kaggle's …

So I am currently trying to sort through a data frame containing attribute classes and values of teams. However, my data has multiple rows of different classes and values of the same Team ID/Attribute ID. I was wondering if there was a faster way to get just the last row of each of the same Team IDs/Attribute IDs.

I have recently discovered Google Colab and I am wondering if there is an option to permanently authorize Google Colab to access and mount my Google Drive. from google.colab import drive drive.mount('/content/drive') Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=xxx Enter your authorization code: ·········· Mounted at /content/drive Right now, I need to all the time insert manually the authorization code and I want when I start a new Google Colab to have my drive mounted. Is there an option …

I have a question, that maybe is very simple, but I can't do it: I have 3 neural networks which are trained with 100 epochs, and I need to save all the history's train displayed at the end in a .txt file (i.e: time: 00s 0ms/step loss:... accuracy:... recall:... etc.), maybe it's easy, but also I need to plot each metric and its val_metric from each epoch and save that plots too, as images I guess (I know that maybe …

Initially, only x_train was reshape, and an error occurred, so x_test was also reshape. Then, I have another error. It seems to be an error caused by inconsistency with y data, but modifying the code does not solve the error. This is my code import tensorflow as tf (x_train, y_train), (x_test, y_test) = tf.keras.datasets.mnist.load_data() print(x_train.shape) # (60000, 28, 28) print(y_train.shape) # (60000, ) import matplotlib.pyplot as plt print("Y[0] : ", y_train[0]) plt.imshow(x_train[0], cmap=plt.cm.gray_r, interpolation = "nearest") x_train = x_train.reshape(-1, 28*28) …

I'm trying to install python packages in COLAB using the following setup.py file. from setuptools import setup setup( name="gym_robot", version="0.3", url="https://github.com/zoraiz-ali/gym.git", author="Zoraiz Ali", license="MIT", packages=["gym_robot", "gym_robot.envs", "gym_robot.envs.helper", ], include_package_data=True, install_requires=["gym", "numpy", "opencv-python", "pillow"] ) I execute the following command ! python /content/gym/setup.py install This returns the following error error: package directory 'gym_robot' does not exist I do not find any solution, anyone knows how to install packages on Goole COlAB?

I am following steps mentioned in below link for installing Rapids in Google Colab. However as soon as I run cell no 4, the folders I see in file section disappear and I see "Connecting to a runtime to enable file browsing." in Files section. https://colab.research.google.com/drive/1rY7Ln6rEE1pOlfSHCYOVaqt8OvDO35J0#forceEdit=true&offline=true&sandboxMode=true How can I fix this?