I am trying to run this code and facing a UnimplementedError while training. I haven't changed anything. Can anyone help?
Git repo: https://github.com/ravasconcelos/spoken-digits-recognition
Google Colab link : https://colab.research.google.com/drive/1-WYSPMgfRQpsBFZRf-xxsmwnBfKmfIlj?usp=sharing
I am not sure if it's a gpu issue or a dtype issue
I am trying to train Facebook's prophet on timeseries data.
Data I used, one column named ds and another y
During fitting however I get this error first :
first error screenshot
followed by
second error screenshot
Does anyone know why this is the case? Thanks!
I tried searching for the problem online and could not find a solution. I tried installing cmdstanpy version 0.9.5 and reinstalling prophet but it did not work so I am a bit stuck. I am running python 3.7.12 version.
When using 'OneNote(Business)'.GetSectionsInNotebook() in Power Apps, one Notebook returns the error:
Error; unknown error code encountered when attempting to retrieve
notebook details. Code: 20143
The Notebook key comes directly from OneNote(Business).GetNotebooks().
All other Notebooks works fine.
Where can I start my troubleshooting?
I don't have access to the Azure OneNote Logs
PowerApps Monitor shows nothing more than the error posted
I wrote a lengthy code in a new notebook with print output commands. Later when I tried to open the notebook, it was just an empty notebook page and am unable to see my codes. when I refresh, I get to see a glimpse of my full code. however, it disappeared. Did anyone face a similar kind of issue? Is it because of the print statement?
I am a new learner. I recently started learning Google Colab. Whenever I close my Colab and reopen it, all the code start executing from beginning. Is there any way to restore the local variable, code outputs and all the previous program data?
It is really time-consuming to load the dataset every time.
Unfortunately No (by this answer posted date), you cannot restore to previous runtime. Everything restarts on a new runtime session on a different virtual machine. Notebooks run by connecting to virtual machines that have maximum lifetimes that can be as much as 12 hours. And Colab Pro says to provide around 24hrs of runtime. This is necessary for Colab to be able to offer computational resources for free.
However you can apply good practices to help you work faster. Some of them are:
Save your datasets and trained models on your Google Drive; Mount it and use it as required. Only runtime local variables and program data for that session are destroyed.
Use pre-trained models to implement Transfer Learning to save training time.
Use "Connect to hosted runtime" and "Manage Sessions" to use the free resources effectively.
Sadly, it's just part of the workflow with colab, but there are ways to make life easier. To persist data you'd want to connect to google drive and pull/save files from there:
from google.colab import drive
drive.mount('/content/drive')
Then follow instructions - click the link, copy/paste the auth token.
After connecting to google drive - copy files that are stored on the drive using command !cp. For example, these commands copy files stored on the drive to local notebook environment:
!cp "/content/drive/My Drive/Colab Notebooks/trainer.py" "trainer.py"
!cp "/content/drive/My Drive/Colab Notebooks/data.pkl" "data.pkl"
To copy files and folders from notebook environment to drive use the same !cp command:
!cp "model" "/content/drive/My Drive/Colab Notebooks/my-fancy-model"
Assuming you want to see previous ouputs of the code. You could use File > Save and Pin Revision to save revision history including revision name. That way it will store previous outputs including code changes. Now going to File > Revision History, it will show difference between two version. Clicking on three dot on right side it will show option to restore version, open, or name it.