How to Upload a Folder to Google Colab
Google Colaboratory is a free Jupyter notebook surroundings that runs on Google'due south cloud servers, letting the user leverage backend hardware like GPUs and TPUs. This lets you practise everything you lot can in a Jupyter notebook hosted in your local machine, without requiring the installations and setup for hosting a notebook in your local motorcar.
Colab comes with (about) all the setup you need to start coding, just what it doesn't take out of the box is your datasets! How exercise you access your information from within Colab?
In this commodity we will talk virtually:
- How to load data to Colab from a multitude of data sources
- How to write back to those data sources from inside Colab
- Limitations of Google Colab while working with external files
Directory and file operations in Google Colab
Since Colab lets you do everything which yous can in a locally hosted Jupyter notebook, you can besides use shell commands like ls, dir, pwd, cd, true cat, echo, et cetera using line-magic (%) or bash (!).
To browse the directory structure, yous can use the file-explorer pane on the left.
How to upload files to and download files from Google Colab
Since a Colab notebook is hosted on Google's cloud servers, there's no direct access to files on your local bulldoze (unlike a notebook hosted on your auto) or any other environment by default.
Nonetheless, Colab provides various options to connect to virtually any data source you can imagine. Let u.s.a. encounter how.
Accessing GitHub from Google Colab
You can either clone an unabridged GitHub repository to your Colab surroundings or access individual files from their raw link.
Clone a GitHub repository
You tin can clone a GitHub repository into your Colab environment in the same style as you would in your local car, using git clone. Once the repository is cloned, refresh the file-explorer to browse through its contents.
And so you lot tin simply read the files as y'all would in your local car.
Load individual files direct from GitHub
In case you just have to work with a few files rather than the entire repository, y'all can load them directly from GitHub without needing to clone the repository to Colab.
To do this:
- click on the file in the repository,
- click on View Raw,
- re-create the URL of the raw file,
- use this URL every bit the location of your file.
Accessing Local File Organisation to Google Colab
You can read from or write to your local file system either using the file-explorer, or Python code:
Access local files through the file-explorer
Uploading files from local file system through file-explorer
You can either use the upload selection at the top of the file-explorer pane to upload any file(s) from your local file system to Colab in the present working directory.
To upload files directly to a subdirectory y'all need to:
1. Click on the three dots visible when you lot hover above the directory
ii. Select the "upload" option.
iii. Select the file(s) yous wish to upload from the "File Upload" dialog window.
four. Wait for the upload to complete. The upload progress is shown at the bottom of the file-explorer pane.
Once the upload is consummate, you can read from the file as you would normally.
Downloading files to local file system through file-explorer
Click on the three dots which are visible while hovering in a higher place the filename, and select the "download" pick.
Accessing local file system using Python lawmaking
This step requires you to first import the files module from the google.colab library:
from google.colab import files Uploading files from local file system using Python lawmaking
You use the upload method of the files object:
uploaded = files.upload()
Running this opens the File Upload dialog window:
Select the file(southward) you wish to upload, and and so await for the upload to complete. The upload progress is displayed:
The uploaded object is a dictionary having the filename and content as it'south fundamental-value pairs:
Once the upload is complete, you lot can either read it every bit any other file from colab:
df4 = pd.read_json("News_Category_Dataset_v2.json", lines=True) Or read it straight from the uploaded dict using the io library:
import io df5 = pd.read_json(io.BytesIO(uploaded['News_Category_Dataset_v2.json']), lines=True) Brand certain that the filename matches the name of the file you lot wish to load.
Downloading files from Colab to local file system using Python lawmaking:
The download method of the files object tin can exist used to download whatsoever file from colab to your local drive. The download progress is displayed, and once the download completes, you lot tin can cull where to relieve it in your local motorcar.
Accessing Google Drive from Google Colab
You can use the drive module from google.colab to mount your entire Google Bulldoze to Colab past:
one. Executing the beneath code which will provide you with an hallmark link
from google.colab import drive drive.mount('/content/gdrive') 2. Open the link
3. Cull the Google business relationship whose Drive y'all want to mount
4. Let Google Drive Stream access to your Google Account
5. Copy the code displayed, paste information technology in the text box as shown below, and press Enter
Once the Bulldoze is mounted, you lot'll get the message "Mounted at /content/gdrive", and you lot'll be able to scan through the contents of your Drive from the file-explorer pane.
Now you lot tin can interact with your Google Drive as if it was a binder in your Colab environs. Whatsoever changes to this binder will reverberate direct in your Google Bulldoze. Yous tin read the files in your Google Drive equally whatever other file.
Y'all can fifty-fifty write straight to Google Bulldoze from Colab using the usual file/directory operations.
!touch "/content/gdrive/My Drive/sample_file.txt" This will create a file in your Google Drive, and will exist visible in the file-explorer pane once you lot refresh it:
Accessing Google Sheets from Google Colab
To admission Google Sheets:
ane. Y'all need to get-go cosign the Google account to exist linked with Colab past running the lawmaking below:
from google.colab import auth auth.authenticate_user() ii. Executing the above lawmaking volition provide yous with an hallmark link. Open the link,
3. Choose the Google business relationship which you lot want to link,
iv. Allow Google Cloud SDK to access your Google Business relationship,
v. Finally re-create the lawmaking displayed and paste information technology in the text box shown, and hit Enter.
To collaborate with Google Sheets, you demand to import the preinstalled gspread library. And to qualify gspread access to your Google account, you need the GoogleCredentials method from the preinstalled oauth2client.client library:
import gspread from oauth2client.customer import GoogleCredentials gc = gspread.qualify(GoogleCredentials.get_application_default()) One time the above code is run, an Awarding Default Credentials (ADC) JSON file will exist created in the present working directory. This contains the credentials used by gspread to access your Google business relationship.
In one case this is done, yous tin can now create or load Google sheets directly from your Colab environment.
Creating/updating a Google Canvass in Colab
1. Use the gc object'southward create method to create a workbook:
wb = gc.create('demo') 2. One time the workbook is created, you lot can view it in sheets.google.com.
3. To write values to the workbook, first open a worksheet:
ws = gc.open('demo').sheet1 4. Then select the cell(southward) you want to write to:
5. This creates a list of cells with their index (R1C1) and value (currently blank). You lot can modify the private cells by updating their value aspect:
vi. To update these cells in the worksheet, use the update_cells method:
7. The changes will now be reflected in your Google Sheet.
Downloading data from a Google Sheet
1. Utilise the gc object's open method to open a workbook:
wb = gc.open('demo') 2. Then read all the rows of a specific worksheet by using the get_all_values method:
3. To load these to a dataframe, you tin can apply the DataFrame object's from_record method:
Accessing Google Cloud Storage (GCS) from Google Colab
You lot demand to have a Google Cloud Project (GCP) to use GCS. You can create and access your GCS buckets in Colab via the preinstalled gsutil command-line utility.
ane. Starting time specify your project ID:
project_id = '<project_ID>' 2. To admission GCS, y'all've to authenticate your Google account:
from google.colab import auth auth.authenticate_user() 3. Executing the above code will provide you with an authentication link. Open the link,
4. Choose the Google account which you want to link,
5. Allow Google Cloud SDK to access your Google Account,
vi. Finally re-create the lawmaking displayed and paste it in the text box shown, and hit Enter.
7. Then you configure gsutil to employ your project:
!gcloud config set project {project_id} 8. Yous can brand a saucepan using the brand bucket (mb) command. GCP buckets must have a universally unique name, so use the preinstalled uuid library to generate a Universally Unique ID:
import uuid bucket_name = f'sample-bucket-{uuid.uuid1()}' !gsutil mb gs://{bucket_name} ix. Once the bucket is created, you lot can upload a file from your colab environment to information technology:
!gsutil cp /tmp/to_upload.txt gs://{bucket_name}/ x. Once the upload has finished, the file will be visible in the GCS browser for your project: https://panel.cloud.google.com/storage/browser?project=<project_id>
!gsutil cp gs://{bucket_name}/{filename} {download_location} Once the download has finished, the file will be visible in the Colab file-explorer pane in the download location specified.
Accessing AWS S3 from Google Colab
You need to accept an AWS account, configure IAM, and generate your access key and secret access fundamental to be able to access S3 from Colab. You besides demand to install the awscli library to your colab surround:
ane. Install the awscli library
!pip install awscli
ii. Once installed, configure AWS by running aws configure:
- Enter your
access_keyandsecret_access_keyin the text boxes, and press enter.
Then yous can download whatsoever file from S3:
!aws s3 cp s3://{bucket_name} ./{download_location} --recursive --exclude "*" --include {filepath_on_s3} filepath_on_s3 can point to a single file, or match multiple files using a pattern.
You will be notified in one case the download is complete, and the downloaded file(southward) volition exist available in the location you specified to be used as you wish.
To upload a file, just contrary the source and destination arguments:
!aws s3 cp ./{upload_from} s3://{bucket_name} --recursive --exclude "*" --include {file_to_upload} file_to_upload can bespeak to a single file, or match multiple files using a pattern.
Y'all will be notified one time the upload is consummate, and the uploaded file(southward) will exist available in your S3 bucket in the folder specified: https://s3.panel.aws.amazon.com/s3/buckets/{bucket_name}/{folder}/?region={region}
Accessing Kaggle datasets from Google Colab
To download datasets from Kaggle, you lot first demand a Kaggle account and an API token.
1. To generate your API token, go to "My Business relationship", then "Create New API Token".
ii. Open the kaggle.json file, and copy its contents. Information technology should be in the form of {"username":"########", "key":"################################"}.
three. Then run the beneath commands in Colab:
!mkdir ~/.kaggle !echo '<PASTE_CONTENTS_OF_KAGGLE_API_JSON>' > ~/.kaggle/kaggle.json !chmod 600 ~/.kaggle/kaggle.json !pip install kaggle 4. Once the kaggle.json file has been created in Colab, and the Kaggle library has been installed, you can search for a dataset using
!kaggle datasets list -south {KEYWORD} 5. And then download the dataset using
!kaggle datasets download -d {DATASET Proper name} -p /content/kaggle/ The dataset will be downloaded and volition be available in the path specified (/content/kaggle/ in this case).
Accessing MySQL databases from Google Colab
i. Yous need to import the preinstalled sqlalchemy library to piece of work with relational databases:
import sqlalchemy 2. Enter the connection details and create the engine:
HOSTNAME = 'ENTER_HOSTNAME' USER = 'ENTER_USERNAME' Countersign = 'ENTER_PASSWORD' DATABASE = 'ENTER_DATABASE_NAME' connection_string = f'mysql+pymysql://{MYSQL_USER}:{MYSQL_PASSWORD}@{MYSQL_HOSTNAME}/{MYSQL_DATABASE}' engine = sqlalchemy.create_engine(connection_string) iii. Finally, just create the SQL query, and load the query results to a dataframe using pd.read_sql_query():
query = f"SELECT * FROM {DATABASE}.{Tabular array}" import pandas as pd df = pd.read_sql_query(query, engine) Limitations of Google Colab while working with Files
Ane important caveat to remember while using Colab is that the files you lot upload to it won't exist available forever. Colab is a temporary surroundings with an idle timeout of ninety minutes and an absolute timeout of 12 hours. This means that the runtime will disconnect if information technology has remained idle for xc minutes, or if it has been in apply for 12 hours. On disconnection, you lose all your variables, states, installed packages, and files and will be connected to an entirely new and clean environment on reconnecting.
Too, Colab has a deejay infinite limitation of 108 GB, of which only 77 GB is available to the user. While this should be enough for nigh tasks, keep this in mind while working with larger datasets like image or video information.
Decision
Google Colab is a slap-up tool for individuals who want to harness the ability of loftier-finish calculating resource like GPUs, without beingness restricted by their cost.
In this commodity, we take gone through near of the means you can supercharge your Google Colab feel by reading external files or data in Google Colab and writing from Google Colab to those external information sources.
Depending on your use-case, or how your data architecture is set-upward, y'all can easily employ the in a higher place-mentioned methods to connect your data source directly to Colab, and start coding!
Other resource
- Getting Started with Google CoLab | How to utilize Google Colab
- External data: Local Files, Drive, Sheets and Deject Storage
- Importing Information to Google Colab — the Make clean Way
- Get Started: 3 Ways to Load CSV files into Colab | by A Apte
- Downloading Datasets into Google Drive via Google Colab | by Kevin Luk
READ Adjacent
How to Apply Google Colab for Deep Learning – Complete Tutorial
9 mins read | Author Harshit Dwivedi | Updated June 8th, 2021
If you're a programmer, you want to explore deep learning, and need a platform to assistance you do information technology – this tutorial is exactly for you.
Google Colab is a great platform for deep learning enthusiasts, and information technology can too exist used to exam basic motorcar learning models, proceeds experience, and develop an intuition nearly deep learning aspects such every bit hyperparameter tuning, preprocessing data, model complexity, overfitting and more.
Permit'south explore!
Introduction
Colaboratory by Google (Google Colab in short) is a Jupyter notebook based runtime environment which allows yous to run code entirely on the cloud.
This is necessary because it means that y'all can train large scale ML and DL models even if you lot don't have admission to a powerful automobile or a high speed cyberspace access.
Google Colab supports both GPU and TPU instances, which makes it a perfect tool for deep learning and data analytics enthusiasts because of computational limitations on local machines.
Since a Colab notebook can be accessed remotely from any automobile through a browser, it's well suited for commercial purposes as well.
In this tutorial you volition acquire:
- Getting around in Google Colab
- Installing python libraries in Colab
- Downloading big datasets in Colab
- Training a Deep learning model in Colab
- Using TensorBoard in Colab
Continue reading ->
Source: https://neptune.ai/blog/google-colab-dealing-with-files
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