mig | Distributed & real time digital forensics at the speed | Security library

It is possibly because there are errors (exceptions) when the rendering is running. Have you checked whether there is an exception? For example, if you are using error-reporting tools like Sentry, go to its webpage to see. If you do not have one, try to see logs. Or, setup error handling (e.g. simply print it) following the official guide: https://flutter.dev/docs/testing/errors.

If you cannot find out any clues, try to setup error handling and put all logs here and I can try to see it.

EDIT

With more info, I can explain what happens.

You need to create a JavaBean for Recipe to use it with your ArrayList, so instead of ArrayList you would probably have ArrayList.

For this example I will create a JavaBean named RecipeBean which will contain the Strings recipeName and recipeDescription

You could use a multidimensional array nodes[$1,$2] and print the values in the END part.

run with CUDA_VISIBLE_DEVICES="-1" ./your_code.py if using python script or import os; os.environ['CUDA_VISIBLE_DEVICES'] = '-1'` in the code.

If you experienced significant change in nvidia-smi and/or speed/duration of the training, then you were using GPU in the first place. ( having `CUDA_VISIBLE_DEVICES="0" ( or "0,1,2" if on multi-gpu setting)

• Make sure you are importing and using tf.keras.
• Make sure you have installed tensorflow-gpu
• Watch GPU utilization with watch -n 1 nvidia-smi while .fit is running.
• Check version compatibility table. This is important.
• Ignore the cuda version shown in nvidia-smi, as it is the version of cuda, your driver came with. The installed cuda version is shown with nvcc -V.

The model is getting loaded to GPU. So, it is not related to your GPU utilization issue. It is possible that your train_gen and val_gen takes time or they are buggy. Try without performing any specific augmentation to make sure the problem is not related to *_gen.

You should iterate through the planes first, then make a getter for the passenger list inside the plane and iterate through those.

If we look at the following article link, we see that scaling in (by default) doesn't occur until the signal that caused the scale out has ceased 10 minutes ago. I read this as:

If at 9:00am your signal (pub/sub) was breached then assuming the condition is no longer present immediately, scale in won't happen until at least 9:10am.

I seem to see that the scaling in looks for the signal in the last 10 minute window. Given also that you are looking a GCP monitoring metrics and these don't get updated in real time but instead have latencies, even though the trigger may no longer be present immediately, it may be 9:05am before monitoring reports all is now well. This means it might be 9:15am (9:05am + 10 minutes) before scale in occurs.

Looking at the docs again, we seem to see that we can change the scale-in policy using the --scale-in-control flag of gcloud. See scale-in-control flag documentation. Looking closely, it has a parameter called time-window that is documented as:

How long back autoscaling should look when computing recommendations. The autoscaler will not resize below the maximum allowed deduction subtracted from the peak size observed in this period. Measured in seconds.

This seems to allow us to over-ride the default 10 minutes period.

I'm answering my own question.

PyTorch pip wheels and Conda binaries ship with the CUDA runtime. But CUDA does not normally come with NVCC, and requires to install separately from conda-forge/cudatoolkit-dev, which is very troublesome during the installation.

So, what I did is that I install NVCC from Nvidia CUDA toolkit.

In that post you linked, in the UPDATE section of my answer, I indicated that the GPU scheduler has changed in Pascal and beyond (your Tesla P100 is a Pascal GPU).

MPS is supported on all current NVIDIA GPUs.

The results you got are expected (in the non-MPS case) because the GPU scheduler allows both kernels to run, in a time-sliced fashion. All currently supported CUDA GPUs support multiprocessing (in Default compute mode). However the older GPUs (e.g. Kepler) would run the kernel from one process, then the kernel from the other process. Pascal and newer GPUs will run the kernel from one process for a period of time, then the other process for a period of time, then the first process, etc in a round-robin time-sliced fashion.

Solved by reinstalling the pytorch in my Conda Env.

You may try reinstalling the Pytorch or create a new Conda Environment to do it again.