I am working on a remote jupyter notebook located on internal server. I want to save my findings/insight on my local computer to make further analysis.
Example:
Suppose this is the final data I have after analysis
I want to write it to my local disk.
But the code below writes the data to the server's D:\ drive
data.to_csv(r'D:\Team.csv')
This is the sample code for savinng the png file of the dataset which I have train.
plotting data for saving img to local library
plot_model(model, to_file="model.png", show_shapes=True, show_layer_names=True)
Related
I would compare a local list of csv files with another remote list of csv file on online server. I would use R for this, how I can do ?
I'm alreday connect via R to the online server and now I would like to compare the local folder with the online without download the online
I am wondering my script takes 29h on my current machine.
I have access to a remote Ubuntu machine with a more powerful CPU which might speed up the calculations.
Is there an easy way to transfer the results of the RScript run on the remote machine to my local machine session?
I can only think of saving the results to a csv file and then importing that csv file again locally.
I have an R script file currently saved in my local computer. This is computationally intensive script which download data from Internet and save data in csv files in local disk.
I was thinking if I can run this script within Kaggle and save the data in csv files in Google drive. I also intend to run that script everyday at scheduled time.
Can you please guide me if this is a good idea to implement? How can I deploy this script in Kaggle and have it run based on scheduled time?
Your pointer will be highly appreciated.
s
I have an excel file that sits in a shared drive (MS One drive) and I would like to run an R script that updates some data in that file.
Is there a control in R to force close any open instances of that file so that the data update runs ok?
I have tried the close() and file() functions but without success.
Any ideas?
Thank you
I'm pretty new to cluster computing, so not sure if this is even possible.
I am successfully creating a spark_context in Rstudio (using sparklyr) to connect to our local Spark cluster. Using copy_to I can upload data frames from R to Spark, but I am trying to upload a locally stored CSV file directly to the Spark cluster using spark_read_csv without importing it into the R environment first (it's a big 5GB file). It's not working (even prefixing location with file:///), and it seems that it can only upload files that are ALREADY stored in the cluster.
How do I upload a local file directly to spark without loading it into R first??
Any tips appreciated.
You cannot. File has to be reachable from each machine in your cluster either as a local copy or placed on distributed files system / object storage.
You can upload the files from local to spark by using spark_read_csv() method. Please pass the path properly.
Note: It is not necessary to load the data first into R environment.