Do I need to remain connected with Colab and the internet when training a dataset model (Darknet) for Object detection on Google Colab? As the training is going on Colab and connected to my drive, here Weight files will be saved on my google drive folder. So, can I disconnect my internet and exit colab?
this is the last screen shown when I started my training process on COLAB and now I am waiting for my weight files to be saved in my drive
You absolutely must remain connected to colab for ensuring that your code continues to run. Just because your weight files are being saved to Google Drive does not mean that you can disconnect/close the browser and it will continue to run. Keep in mind that Google Drive is just mounted for the sake of storage space and is not an alternative to an active Colab session.
However, if your Colab session gets disconnected suddenly due to internet/server issues, it'll automatically try to reconnect after a "short" while and continue execution from the interruption point (if you're back online). However, if the time to start after the timeout is too long, you have to run all cells from the start and it can't continue it's most recent operation.
Note that this does not apply if you have exhausted the permitted usage limit for a give time (supposed to be 12 hrs). In this case, you may have to wait for many hours before being allowed to use Colab again
Related
I'm first time using Google Earth engine via this repo:
https://github.com/kratzert/Caravan/blob/main/code/Caravan_part1_Earth_Engine.ipynb
All is working good but at a moment I forgot to dowload finished batch on my pc to free some space on google Drive.
So 2 batch has fail du to lack of memory space.
Can I just resume this 2 specific failed batchs?
Or I have to run again the code to download all batch? (7 days)?
If I run again, can I stopthe process after the number of batch missing and use the other batch of the first try??
It's generally a good practice to chunk out larger tasks so that you can pick up where you left off / avoid mysterious fails that seem common w massive requests.
That's been my experience, anyways.
There is no way to resume a task
I am getting an error with a Sagemaker training job with the error message "OverflowError: signed integer is greater than maximum". This is an image identification problem with code written in keras and tensorflow. The input for this is a large npy file stored in an s3 bucket.
The code works fine when run the Sagemaker notebook cells but errors out when submitted as a Training job using boto3 request.
I am using the same role in both places. what could be the cause for this error? I am using ml.g4dn.16xlarge instance in both cases
Couple of things I would check is
Framework Versions used in your notebook instance vs the training job
Instance Storage volume for the training job, since you are using G4dn it comes with attached SSD which ideally should be good enough.
This seems like a bug, Requests and urllib3 should only ask for a maximum number of bytes and it is capable of handling at once.
I've looked for some questions and mostly they discussed dataset uploading, but one did state that google colab only use our internet connection to run the code. I am confused with this, does this mean our internet speed also affects the training time of our model? or what matters is that once we ran our code, the google server takes care of it, and do not need our connection?
I think yes, Google Colab's speed is affected by our Internet connection. I'm not absolutely sure why, but you can check in this link. Obviously, when the model is being trained, the Internet data usage rises considerably. My guess is that our computer, as a client, needs to save some hidden internal information relevant to the running state. Therefore, the Colab server has to send this information every time 1 line of code is executed, and the next line of code can only be executed when this information reaches the client. So if the Internet connection is slow, it will take more time for the client to receive the information and the whole process will be slow.
There is also another proof to see if the Internet connection really affects Google Colab's speed. With the coffee Internet, which is significantly stronger than that of my house, the same block of code is executed more than 2 times faster than when using my house's wifi.
The program by which the day is for picture learning of AI using Google Colaboratory, continuously.
It was being carried out, time which made them learn was total of 3-about 4 hours, the one of the program
When trying to execute other programs during execution, the following message was output.
It can't be connected to gpu at present because I reached the amount of consumption upper limit in colab.
Would there be a point that you pay attention by use of Google Colaboratory to avoid the symptom the following symptom generated after message output?
It couldn't be reconnected any more this daytime.
Even if a program wasn't executed, one after the next day just connected to GPU and reached the gpu amount of consumption upper limit.
When GPU could be connected, during executing a program, I reach the amount of consumption upper limit by a short time.
I am starting to try out Google's Colaboratory 🎉 (it is very cool!) -- is it expected with this system that you'd re-install any packages each time you return to the doc / is there a known time-out I should expect?
Yes, this is the expected behavior.
Currently, each user gets assigned a new VM, and that VM is reclaimed when the user is idle for a given period. (That period is currently 90 minutes, but may change in the future.) A single user with multiple notebooks open will all share a single backend VM; no two users will ever be assigned to the same VM.