Memory Clear

[Evelio Olivo]

Accordingto the user manual, Warm Boot RAM and Memory Clear have opposite effects. It is tough particularly with ASUS as usually the manuals are poorly translated to English.

I agree that Memory Clear probably zeroes the memoryso that there are no left-overs in RAM from the previous boot.This is a security measure to ensure that sensitive data,such as password, cannot be retrieved by a virus immediately after the boot terminates.It will of course slow down the boot in proportion to the amount of installed RAM.

Warm Boot RAM [Enabled]

Allows you to enable or disable the re-use of data in the RAM after a warm boot to speed-up the boot process.

Configuration options: [Enabled] [Disabled]

I believe that Memory Clear is "the opposite" as regarding boot-speed,slowing down the boot which Warm Boot RAM is supposed to speed-up.In any case, it destroys the RAM that Warm Boot RAM is supposed not to destroy(although preserving it is useless to anything other than a virus).

I am training PyTorch deep learning models on a Jupyter-Lab notebook, using CUDA on a Tesla K80 GPU to train. While doing training iterations, the 12 GB of GPU memory are used. I finish training by saving the model checkpoint, but want to continue using the notebook for further analysis (analyze intermediate results, etc.).

When you have an error in a notebook environment, the ipython shell stores the traceback of the exception so you can access the error state with %debug. The issue is that this requires holding all variables that caused the error to be held in memory, and they aren't reclaimed by methods like gc.collect(). Basically all your variables get stuck and the memory is leaked.

Usually, causing a new exception will free up the state of the old exception. So trying something like 1/0 may help. However things can get weird with Cuda variables and sometimes there's no way to clear your GPU memory without restarting the kernel.

Context: I have pytorch running in Jupyter Lab in a Docker container and accessing two GPU's [0,1]. Two notebooks are running. The first is on a long job while the second I use for small tests. When I started doing this, repeated tests seemed to progressively fill the GPU memory until it maxed out. I tried all the suggestions: del, gpu cache clear, etc. Nothing worked until the following.

Note that I don't actually use numba for anything except clearing the GPU memory. Also I have selected the second GPU because my first is being used by another notebook so you can put the index of whatever GPU is required. Finally, while this doesn't kill the kernel in a Jupyter session, it does kill the tf session so you can't use this intermittently during a run to free up memory.

If you have a variable called model, you can try to free up the memory it is taking up on the GPU (assuming it is on the GPU) by first freeing references to the memory being used with del model and then calling torch.cuda.empty_cache().

I am trying to get the output of a neural network which I have already trained. The input is an image of the size 300x300. I am using a batch size of 1, but I still get a CUDA error: out of memory error after I have successfully got the output for 25 images.

Every time, I am sending a new image in the network for computation. So, I don't really need to store the previous computation results in the GPU after every iteration in the loop. Is there any way to achieve this?

Basically, what PyTorch does is that it creates a computational graph whenever I pass the data through my network and stores the computations on the GPU memory, in case I want to calculate the gradient during backpropagation. But since I only wanted to perform a forward propagation, I simply needed to specify torch.no_grad() for my model.

Answering exactly the question How to clear CUDA memory in PyTorch.In google colab I tried torch.cuda.empty_cache().But it didn't help me.And using this code really helped me to flush GPU:

ThunderSTORM: a comprehensive ImageJ plugin for SMLM data analysis and super-resolution imaging - GitHub - zitmen/thunderstorm: ThunderSTORM: a comprehensive ImageJ plugin for SMLM data analysis an...

Do you mean it frees the memory when not running in batch mode, but still has the issue when running with batch mode? That seems to me like an ImageJ issue indeed. @Wayne did you observe similar behavior in the past?

Yes, this is absolutely how things are supposed to work - but I have also run into similar issues before and found this addition does help a little, even in situations where garbage collection should already have been taken care of.

torch.cuda.empty_cache() (EDITED: fixed function name) will release all the GPU memory cache that can be freed.

If after calling it, you still have some memory that is used, that means that you have a python variable (either torch Tensor or torch Variable) that reference it, and so it cannot be safely released as you can still access it.

The variables prec1[0] and prec5[0] still hold reference to tensors. These should be replaced with prec1[0].item() and prec5[0].item() respectively. This is due to the fact that the accuracy method part of the pytorch imagenet training example code returns a tensor and can cause memory leak.

If you see increasing memory usage, you might accidentally store some tensors with the an attached computation graph. E.g. if you store the loss for printing or debugging purposes, you should save loss.item() instead.

Running empty_cache at the beginning of your process is not useful as nothing is allocated yet.

When you restart the kernel, you force all memory to be deallocated.

So if you still run out of memory it is simply because your program requires more than what you have. You will most likely have to reduce the batch size or the size of your model.

I have a data analysis module that contains functions which call on the matplotlib.pyplot API multiple times to generate up to 30 figures in each run. These figures get immediately written to disk after they are generated, and so I need to clear them from memory.

If you are using a MacOS system along with its default backend (referred as 'MacOSX'), this does NOT work (at least in Big Sur). The only solution I have found is to switch to other of the well-known backends, such as TkAgg, Cairo, etc. To do it, just type:

Late answer, but this worked for me. I had a long sequential code generating many plots, and it would always end up eating all the RAM by the end of the process.Rather than calling fig.close() after each figure is complete, I have simply redefined the plt.figure function as follows, so that it is done automatically:

When the first model is loaded it pre-allocates the entire GPU memory (which I want for working through the first batch of data). But it doesn't unload memory when it's finished. When the second model is loaded, using both tf.reset_default_graph() and with tf.Graph().as_default() the GPU memory still is fully consumed from the first model, and the second model is then starved of memory.

currently the Allocator in the GPUDevice belongs to the ProcessState, which is essentially a global singleton. The first session using GPU initializes it, and frees itself when the process shuts down.

So if you would call the function run_tensorflow() within a process you created and shut the process down (option 1), the memory is freed. If you just run run_tensorflow() (option 2) the memory is not freed after the function call.

Based on what Yaroslav Bulatov said in their answer (that tf deallocates GPU memory when the object is destroyed), I surmised that it could just be that the garbage collector hadn't run yet. Forcing it to collect freed me up, so that might be a good way to go.

Now there seem to be two ways to resolve the iterative training model or if you use future multipleprocess pool to serve the model training, where the process in the pool will not be killed if the future finished. You can apply two methods in the training process to release GPU memory meanwhile you wish to preserve the main process.

I am figuring out which option is better in the Jupyter Notebook. Jupyter Notebook occupies the GPU memory permanently even a deep learning application is completed. It usually incurs the GPU Fan ERROR that is a big headache. In this condition, I have to reset nvidia_uvm and reboot the linux system regularly. I conclude the following two options can remove the headache of GPU Fan Error but want to know which is better.

Put the following code at the end of the cell. The kernel immediately ended upon the application runtime is completed. But it is not much elegant. Juputer will pop up a message for the died ended kernel.

The following code can also end the kernel with Jupyter Notebook. I do not know whether numba is secure. Nvidia prefers the "0" GPU that is the most used GPU by personal developer (not server guys). However, both Neil G and mradul dubey have had the response: This leaves the GPU in a bad state.

It is not such the problem to automatically release the GPU memory in the environment of Anaconda by direct executing "$ python abc.py". However, I sometimes need to use Jyputer Notebook to handle .ipynb application.

GPU memory allocated by tensors is released (back into TensorFlow memory pool) as soon as the tensor is not needed anymore (before the .run call terminates). GPU memory allocated for variables is released when variable containers are destroyed. In case of DirectSession (ie, sess=tf.Session("")) it is when session is closed or explicitly reset (added in 62c159ff)

I have trained my models in a for loop for different parameters when I got this error after 120 models trained. Afterwards I could not even train a simple model if I did not kill the kernel.I was able to solve my issue by adding the following line before building the model:

I think, thant Oliver Wilken's user (Jun 30, 2017 at 8:30) is the best solution.But in tensorflow 2 there is eager execution instead of sessions.So you just need to import all the tensorflow and keras stuff inside the run_tensorflow(...) function.

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