the dataset that i want 1 file from. I want to get one file from it - 'application_train.csv'.
I did pip install kaggle. The official page gives me one liner to download the entire dataset.
kaggle competitions download -c home-credit-default-risk
But i want only 1 file.
something kaggle competitions download -c home-credit-default-risk application_train.csv, but it s not working
what's the command to download one file from kaggle?
the official instructions does have the answer
get the kaggle.json from Account tab under your name
place under your Windows/username in windows dir.
in jupyter notebook - run command kaggle competitions files home-credit-default-risk to list files
to download 1 file run kaggle competitions download home-credit-default-risk -f application_train.csv.7z'
Related
I am a newbie Blender user. I made my first animation yesterday and tried to render it in Google Colab. I ran a code which worked for a Youtuber who is running Blender2.91-linux version, but the same code showed error when I ran it.
I am currently using Windows 10 and really new at Blender. I need a working code that can successfully render Animation made with blender in Colab.
This is the code that I found online and ran. Please help :(
#Download Blender from Repository
!wget http://download.blender.org/release/Blender2.93/blender-2.93.0-linux-x64.tar.xz
#Install Blender
!tar xf blender-2.93.0-linux-x64.tar.xz
#Connect Google Drive
from google.colab import drive
drive.mount('/gdrive')
#Set Paths to Blender files
filename = '/gdrive/MyDrive/SHIP IN WATER With Particles.blend'
#Render an animation
!sudo ./blender-2.93.0-linux-x64/blender -b $filename -noaudio -E 'Cycles' -o '//image_####' -s 0 -e 72 -a -- --cycles-device OpenCL
The output of the last line came :
sudo: ./blender-2.93.0-linux-x64/blender: command not found
In short, I want a working code that can help me render Animation made in Blender in Google Colab.
Thank you in advance.... :)
The problem with your code is that the folder name is not the same after extracting from the tar file. Here is what you can do to fix your issue:
#Download Blender from Repository
!wget http://download.blender.org/release/Blender2.93/blender-2.93.0-linux-x64.tar.xz
#Install Blender
!tar xf blender-2.93.0-linux-x64.tar.xz
#Connect Google Drive
from google.colab import drive
drive.mount('/gdrive')
#Set Paths to Blender files
filename = '/gdrive/MyDrive/SHIP IN WATER With Particles.blend'
After that, add this command: !ls – you will get list files and folders in the current directory. Copy the extracted folder name from there and replace the old folder name with it:
OLD
./blender-2.93.0-linux-x64/blender
NEW
./NewFoldeName/blender
I was wondering if anyone could confirm the best practice for downloading kaggle datasets to our colab notebooks?
I have seen code examples like the one below where we download the API token file and upload it to the environment, is that the best practice or is there a different/simpler/better approach?
Thanks in advance!
Jacob
from google.colab import files
!pip install -q kaggle
files.upload()
!mkdir -p ~/.kaggle
!cp kaggle.json ~/.kaggle/
!chmod 600 /root/.kaggle/kaggle.json
!kaggle datasets download -d alxmamaev/flowers-recognition
There are 2 approaches that are both convenient:
1) Save your kaggle.json in GDrive. Then mount by just clicking (in the left pane). Then, copy it here.
!mkdir -p ~/.kaggle
!cp "drive/My Drive/kaggle.json" ~/.kaggle/
# the rest is the same
2) Embed the kaggle.json in Colab itself.
!mkdir ~/.kaggle
!echo '{"username":"korakot","key":"8db2xxx"}' > ~/.kaggle/kaggle.json
# the rest is the same
If you are worried, use the first which is more secure.
If you are lazy, use the second.
I tried to train the custom object according to https://github.com/AlexeyAB/darknet#how-to-train-to-detect-your-custom-objects and I got an error.
Please tell me any good solutions.
I am going with google colaboratory.
I changed the directory, but it does not change.
dir
--content/
┠darknet-master/
┠build/
┠darknet/
┠x64/
┠data/
┠obj.data
┠
%%bash
cd /content/darknet-master./darknet detector train data/obj.data yolo-obj.cfg darknet53.conv.74 > train_log.txt
Couldn't open file: data/obj.dat
%cd /content/darknet-master/build/darknet/x64
!./darknet detect cfg/yolov3.cfg yolov3.weights data/person.jpg
/content/darknet-master/build/darknet/x64
/bin/bash: ./darknet: No such file or directory
Use %cd or os.chdir rather than %%bash cd...
The reason is that %%bash run commands in a sub-shell. But, I believe what you want to do is to change the working directory of the Python backend running your code.
I am using Google Colab to create a deep learning model, and I face an issue when I run this code at the first time.
!p7zip -d filename.7z
I get the following message:
/usr/bin/p7zip: cannot read filename.7z
But when I re-run the same cell again, the code works.
Do you know what is the reason of this issue?
First, you have to specify path before the file name
in my case
!mkdir ~/data
!cd ~/data
!mkdir planet
!cd planet
# -c: competition name
# -f: which file you want to download
# -p: path to where the file should be saved
!kaggle competitions download -c planet-understanding-the-amazon-from-space -f train-jpg.tar.7z -p ~/data/planet/
# Unzip the 7zip files
# -d: which file to un7zip
!p7zip -d ~/data/planet/test-jpg.tar.7z
I don't understand how colab works with directories, I created a notebook, and colab put it in /Google Drive/Colab Notebooks.
Now I need to import a file (data.py) where I have a bunch of functions I need. Intuition tells me to put the file in that same directory and import it with:
import data
but apparently that's not the way...
I also tried adding the directory to the set of paths but I am specifying the directory incorrectly..
Can anyone help with this?
Thanks in advance!
Colab notebooks are stored on Google Drive. But it is run on another virtual machine. So, you need to copy your data.py there too. Do this to upload data.py through Colab.
from google.colab import files
files.upload()
# choose the file on your computer to upload it then
import data
Now google is officially providing support for accessing and working with Gdrive at ease.
You can use the below code to mount your drive to Colab:
from google.colab import drive
drive.mount('/gdrive')
%cd /gdrive/My\ Drive/{location you want to move}
To easily upload a local file you can use the new Google Colab feature:
click on right arrow on the left of your screen (below the Google
Colab logo)
select Files tab
click Upload button
It will open a popup to choose file to upload from your local filesystem.
To upload Local files from system to collab storage/directory.
from google.colab import files
def getLocalFiles():
_files = files.upload()
if len(_files) >0:
for k,v in _files.items():
open(k,'wb').write(v)
getLocalFiles()
So, here is how I finally solved this. I have to point out however, that in my case I had to work with several files and proprietary modules that were changing all the time.
The best solution I found to do this was to use a FUSE wrapper to "link" colab to my google account. I used this particular tool:
https://github.com/astrada/google-drive-ocamlfuse
There is an example of how to set up your environment there, but here is how I did it:
# Install a Drive FUSE wrapper.
!apt-get install -y -qq software-properties-common python-software-properties module-init-tools
!add-apt-repository -y ppa:alessandro-strada/ppa 2>&1 > /dev/null
!apt-get update -qq 2>&1 > /dev/null
!apt-get -y install -qq google-drive-ocamlfuse fuse
# Generate auth tokens for Colab
from google.colab import auth
auth.authenticate_user()
# Generate creds for the Drive FUSE library.
from oauth2client.client import GoogleCredentials
creds = GoogleCredentials.get_application_default()
import getpass
!google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret} < /dev/null 2>&1 | grep URL
vcode = getpass.getpass()
!echo {vcode} | google-drive-ocamlfuse -headless -id={creds.client_id} -secret={creds.client_secret}
At this point you'll have installed the wrapper and the code above will generate a couple of links for you to authorize access to your google drive account.
The you have to create a folder in the colab file system (remember this is not persistent, as far as I know...) and mount your drive there:
# Create a directory and mount Google Drive using that directory.
!mkdir -p drive
!google-drive-ocamlfuse drive
print ('Files in Drive:')
!ls drive/
the !ls command will print the directory contents so you can check it works, and that's it. You now have all the files you need and you can make changes to them with no further complications. Remember that you may need to restar the kernel to update the imports and variables.
Hope this works for someone!
you can write following commands in colab to mount the drive
from google.colab import drive
drive.mount('/content/gdrive')
and you can download from some external url into the drive through simple linux command wget like this
!wget 'https://dataverse.harvard.edu/dataset'