I am trying to run the AdaIN-style model in Google Colab. In order to do this, I need to install Lua. I saw this question and tried the solution given (reproduced below for convenience), both with the given distro and the official torch distro.
%cd /content/
!git clone https://github.com/nagadomi/distro.git torch --recursive
import os
os.chdir('./torch/')
!bash install-deps
!./install.sh
!. ./install/bin/torch-activate
Now it should work using absolute path to th:
!/content/torch/install/bin/th neural_style.lua -style_image examples/inputs/picasso_selfport1907.jpg -content_image examples/inputs/brad_pitt.jpg
However, the last line fails, giving me the below error
/bin/bash: . ./install/bin/torch-activate: No such file or directory
However, the binary for th seems to exist at the given filepath. Thus, I try to run the given command
th test.lua -content input/content/cornell.jpg -style input/style/woman_with_hat_matisse.jpg
but I get the following error
/content/torch/install/bin/luajit: /content/torch/install/share/lua/5.1/trepl/init.lua:389: module 'cudnn' not found:No LuaRocks module found for cudnn
I tried looking this up further, but wasn't able to make much progress.
I also tried this method, which seems similar to the above, but I get the same errors.
Any pointers would be appreciated.
Error that I'm struggling with
I'm trying to use MarsExplorer (https://github.com/dimikout3/MarsExplorer) in google colab, but I'm struggling with properly importing it. I was able to successfully clone the repo, but when I try to run the demo mars_explorer isn't imported. How can I fix this?
Indeed #Dr.Snoopy makes a great point here. This is the line in test.py where it is expected to work:
from mars_explorer.envs.settings import DEFAULT_CONFIG as conf
Now, notice that this is being called from your runtime path, usually root in Colab's case (I suppose), then it should return an error.
Now, if installed, for instance with pip install -e you can both import the module and edit it as you go.
Check out these docs.-,6.1.2.%20The%20Module%20Search%20Path,-%C2%B6), they may be helpful to understand this error.
apply these two commands in colab cell:
!git clone https://github.com/dimikout3/GeneralExplorationPolicy.git
the above command will save GeneralExplorationPolicy folders in your colab and inside this folder you can find mars-explorer folder then make copy of the path of the mars-explorer folder for example. /content/GeneralExplorationPolicy/mars-explorer
then apply this command for install mars-explorer:
!pip install -e /content/GeneralExplorationPolicy/mars-explorer
I have been using a google colab template for iterative LQR that uses the Pydrake, however, it seems like the code repository is removed and I can't reinstall it on google Colab:
try:
import pydrake
import underactuated
except ImportError:
!curl -s https://raw.githubusercontent.com/RussTedrake/underactuated/master/scripts/setup/jupyter_setup.py > jupyter_setup.py
from jupyter_setup import setup_underactuated
setup_underactuated()
# Setup matplotlib backend (to notebook, if possible, or inline).
from underactuated.jupyter import setup_matplotlib_backend
plt_is_interactive = setup_matplotlib_backend()
File "/content/jupyter_setup.py", line 1
404: Not Found
^
SyntaxError: invalid syntax
I tried clicking this link https://raw.githubusercontent.com/RussTedrake/underactuated/master/scripts/setup/jupyter_setup.py, and the page is not found... everything was working fine yesterday
Sorry. You're correct... I updated it this morning, and don't have a good deprecation policy in place on that repo, and this setup script is two versions ago. The path you want is https://raw.githubusercontent.com/RussTedrake/underactuated/master/setup/jupyter_setup.py
(remove the script from the directory). But if you look at that file, you'll see that even that is pointing to an updated setup script which you might want to point to.
This is actually all good news... we are on the path to a much better solution. You can now just pip install drake on colab (see the drake installation guide). Once I land the pip install underactuated (probably in time for my Spring offering of the class), then all of that nasty setup will be gone.
What are the common ways to import private data into Google Colaboratory notebooks? Is it possible to import a non-public Google sheet? You can't read from system files. The introductory docs link to a guide on using BigQuery, but that seems a bit... much.
An official example notebook demonstrating local file upload/download and integration with Drive and sheets is available here:
https://colab.research.google.com/notebooks/io.ipynb
The simplest way to share files is to mount your Google Drive.
To do this, run the following in a code cell:
from google.colab import drive
drive.mount('/content/drive')
It will ask you to visit a link to ALLOW "Google Files Stream" to access your drive. After that a long alphanumeric auth code will be shown that needs to be entered in your Colab's notebook.
Afterward, your Drive files will be mounted and you can browse them with the file browser in the side panel.
Here's a full example notebook
Upload
from google.colab import files
files.upload()
Download
files.download('filename')
List directory
files.os.listdir()
step 1- Mount your Google Drive to Collaboratory
from google.colab import drive
drive.mount('/content/gdrive')
step 2- Now you will see your Google Drive files in the left pane (file explorer). Right click on the file that you need to import and select çopy path.
Then import as usual in pandas, using this copied path.
import pandas as pd
df=pd.read_csv('gdrive/My Drive/data.csv')
Done!
Simple way to import data from your googledrive - doing this save people time (don't know why google just doesn't list this step by step explicitly).
INSTALL AND AUTHENTICATE PYDRIVE
!pip install -U -q PyDrive ## you will have install for every colab session
from pydrive.auth import GoogleAuth
from pydrive.drive import GoogleDrive
from google.colab import auth
from oauth2client.client import GoogleCredentials
# 1. Authenticate and create the PyDrive client.
auth.authenticate_user()
gauth = GoogleAuth()
gauth.credentials = GoogleCredentials.get_application_default()
drive = GoogleDrive(gauth)
UPLOADING
if you need to upload data from local drive:
from google.colab import files
uploaded = files.upload()
for fn in uploaded.keys():
print('User uploaded file "{name}" with length {length} bytes'.format(name=fn, length=len(uploaded[fn])))
execute and this will display a choose file button - find your upload file - click open
After uploading, it will display:
sample_file.json(text/plain) - 11733 bytes, last modified: x/xx/2018 - %100 done
User uploaded file "sample_file.json" with length 11733 bytes
CREATE FILE FOR NOTEBOOK
If your data file is already in your gdrive, you can skip to this step.
Now it is in your google drive. Find the file in your google drive and right click. Click get 'shareable link.' You will get a window with:
https://drive.google.com/open?id=29PGh8XCts3mlMP6zRphvnIcbv27boawn
Copy - '29PGh8XCts3mlMP6zRphvnIcbv27boawn' - that is the file ID.
In your notebook:
json_import = drive.CreateFile({'id':'29PGh8XCts3mlMP6zRphvnIcbv27boawn'})
json_import.GetContentFile('sample.json') - 'sample.json' is the file name that will be accessible in the notebook.
IMPORT DATA INTO NOTEBOOK
To import the data you uploaded into the notebook (a json file in this example - how you load will depend on file/data type - .txt,.csv etc. ):
sample_uploaded_data = json.load(open('sample.json'))
Now you can print to see the data is there:
print(sample_uploaded_data)
The simplest way I've made is :
Make repository on github with your dataset
Clone Your repository with ! git clone --recursive [GITHUB LINK REPO]
Find where is your data ( !ls command )
Open file with pandas as You do it in normal jupyter notebook.
This allows you to upload your files through Google Drive.
Run the below code (found this somewhere previously but I can't find the source again - credits to whoever wrote it!):
!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
from google.colab import auth
auth.authenticate_user()
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}
Click on the first link that comes up which will prompt you to sign in to Google; after that another will appear which will ask for permission to access to your Google Drive.
Then, run this which creates a directory named 'drive', and links your Google Drive to it:
!mkdir -p drive
!google-drive-ocamlfuse drive
If you do a !ls now, there will be a directory drive, and if you do a !ls drive you can see all the contents of your Google Drive.
So for example, if I save my file called abc.txt in a folder called ColabNotebooks in my Google Drive, I can now access it via a path drive/ColabNotebooks/abc.txt
The Best and easy way to upload data / import data into Google colab GUI way is click on left most 3rd option File menu icon and there you will get upload browser files as you get in windows OS .Check below the images for better easy understanding.After clicking on below two options you will get upload window box easy. work done.
from google.colab import files
files=files.upload()
On the left bar of any colaboratory there is a section called "Files".
Upload your files there and use this path
"/content/YourFileName.extension"
ex: pd.read_csv('/content/Forbes2015.csv');
Quick and easy import from Dropbox:
!pip install dropbox
import dropbox
access_token = 'YOUR_ACCESS_TOKEN_HERE' # https://www.dropbox.com/developers/apps
dbx = dropbox.Dropbox(access_token)
# response = dbx.files_list_folder("")
metadata, res = dbx.files_download('/dataframe.pickle2')
with open('dataframe.pickle2', "wb") as f:
f.write(res.content)
The simplest solution I have found so far which works perfectly for small to mid-size CSV files is:
Create a secret gist on gist.github.com and upload (or copy-paste the content of) your file.
Click on the Raw view and copy the raw file URL.
Use the copied URL as the file address when you call pandas.read_csv(URL)
This may or may not work for reading a text file line by line or binary files.
For those who, like me, came from Google for the keyword "upload file colab":
from google.colab import files
uploaded = files.upload()
Just two lines of code in Colab. Very easy way:
Load all your files in one zip archive to Google Drive.
Make it visible for everyone with a link.
Copy ID from this link. ( For example: In this link https://drive.google.com/open?id=29PGh8XCts3mlMP6zRphvnIcbv27boawn ID is 29PGh8XCts3mlMP6zRphvnIcbv27boawn)
Enter in Colab:
!gdown --id 29PGh8XCts3mlMP6zRphvnIcbv27boawn
And last step to enter in Colab:
! unzip file_name.zip
Voilà! All needed files are ready to be used in Colab in /content/file_name.csv
For this easy way to get files from Drive to Colab I thank Gleb Mikhaylov.
You can mount to google drive by running following
from google.colab import drive
drive.mount('/content/drive')
Afterwards For training copy data from gdrive to colab root folder.
!cp -r '/content/drive/My Drive/Project_data' '/content'
where first path is gdrive path and second is colab root folder.
This way training is faster for large data.
I created a small chunk of code that can do this in multiple ways. You can
Use already uploaded file (useful when restarting kernel)
Use file from Github
Upload file manually
import os.path
filename = "your_file_name.csv"
if os.path.isfile(filename):
print("File already exists. Will reuse the same ...")
else:
use_github_data = False # Set this to True if you want to download from Github
if use_github_data:
print("Loading fie from Github ...")
# Change the link below to the file on the repo
filename = "https://github.com/ngupta23/repo_name/blob/master/your_file_name.csv"
else:
print("Please upload your file to Colab ...")
from google.colab import files
uploaded = files.upload()
You can also use my implementations on google.colab and PyDrive at https://github.com/ruelj2/Google_drive which makes it a lot easier.
!pip install - U - q PyDrive
import os
os.chdir('/content/')
!git clone https://github.com/ruelj2/Google_drive.git
from Google_drive.handle import Google_drive
Gd = Google_drive()
Then, if you want to load all files in a Google Drive directory, just
Gd.load_all(local_dir, drive_dir_ID, force=False)
Or just a specific file with
Gd.load_file(local_dir, file_ID)
in google colabs
if this is your first time,
from google.colab import drive
drive.mount('/content/drive')
run these codes and go through the outputlink
then past the pass-prase to the box
when you copy you can copy as follows,
go to file right click and copy the path
***don't forget to remove " /content "
f = open("drive/My Drive/RES/dimeric_force_field/Test/python_read/cropped.pdb", "r")
As mentioned by #Vivek Solanki, I also uploaded my file on the colaboratory dashboard under "File" section.
Just take a note of where the file has been uploaded. For me,
train_data = pd.read_csv('/fileName.csv') worked.
It has been solved, find details here and please use the function below:
https://stackoverflow.com/questions/47212852/how-to-import-and-read-a-shelve-or-numpy-file-in-google-colaboratory/49467113#49467113
from google.colab import files
import zipfile, io, os
def read_dir_file(case_f):
# author: yasser mustafa, 21 March 2018
# case_f = 0 for uploading one File and case_f = 1 for uploading one Zipped Directory
uploaded = files.upload() # to upload a Full Directory, please Zip it first (use WinZip)
for fn in uploaded.keys():
name = fn #.encode('utf-8')
#print('\nfile after encode', name)
#name = io.BytesIO(uploaded[name])
if case_f == 0: # case of uploading 'One File only'
print('\n file name: ', name)
return name
else: # case of uploading a directory and its subdirectories and files
zfile = zipfile.ZipFile(name, 'r') # unzip the directory
zfile.extractall()
for d in zfile.namelist(): # d = directory
print('\n main directory name: ', d)
return d
print('Done!')
Here is one way to import files from google drive to notebooks.
open jupyter notebook and run the below code and do complete the authentication process
!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
from google.colab import auth
auth.authenticate_user()
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}
once you done with above code , run the below code to mount google drive
!mkdir -p drive
!google-drive-ocamlfuse drive
Importing files from google drive to notebooks (Ex: Colab_Notebooks/db.csv)
lets say your dataset file in Colab_Notebooks folder and its name is db.csv
import pandas as pd
dataset=pd.read_csv("drive/Colab_Notebooks/db.csv")
I hope it helps
if you want to do this without code it's pretty easy.
Zip your folder in my case it is
dataset.zip
then in Colab right click on the folder where you want to put this file and press Upload and upload this zip file. After that write this Linux command.
!unzip <your_zip_file_name>
you can see your data is uploaded successfully.
If the Data-set size is less the 25mb, The easiest way to upload a CSV file is from your GitHub repository.
Click on the data set in the repository
Click on View Raw button
Copy the link and store it in a variable
load the variable into Pandas read_csv to get the dataframe
Example:
import pandas as pd
url = 'copied_raw_data_link'
df1 = pd.read_csv(url)
df1.head()
Another simple way to do it with Dropbox would be:
Put your data into dropbox
Copy the file sharing link of your file
Then do wget in colab.
Eg:
! wget - O filename filelink(like- https://www.dropbox.com/.....)
And you're done. The data will start appearing in your colab content folder.
You can use the below function. I am assuming that you are trying to upload a data frame sort of file (.csv, .xlsx)
def file_upload():
file = files.upload()
path = f"/content/{list(file.keys())[0]}"
df = pd.read_excel(path)
return df
#your file will be saved in the variable: dataset
dataset = file_upload()
This is in case you have not changed the directory of the google collab then this is the easiest way
from google.colab import drive
drive.mount('/content/drive')
import pandas as pd
dv=pd.read_csv('/content/drive/MyDrive/Diana/caso/Data_Caso_Propuesto.csv')
dv.info()
Following several instructions on the web, I could install Opencv3.0 and tensorflow on my Ubuntu 16.04. Each tutorial recommends using virtual environment. Although I agree that, problem is that I just followed the tutorials and created separate environments.
** For minor information, Tensorflow installation was easy, but Opencv3.0 was hard.
I used virtualenv for Tensorflow with the name tf, and virtualenvwrapper for Opencv with the name cv, i.e., I activate tf by $ source ~/project/tf/bin/activate, and cv by $ workon cv.
In this case, what is the best way of using both?
Should I activate both always?
Should I enter one environment and install the other again?
Should I symlink site-package/cv.so to tf environment?
I think cv is now in the python site-package folder. I create tf with --site-package option, but it was before installing cv. I am so confusing. Please help.
Yes, I had the same issue and the sym-link between tensorflow and openCV will not work after making the cv.so inside the tf virtualenv after some struggle I got them both to work in the same environment but I suggest uninstalling openCV and then install it back without using a virtual environment for better results.
Cheers.