Pytorch cpu memory usage. 2GB, which is the size of the .


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    1. Pytorch cpu memory usage My ram started at like 5% when epoch started and i am at 23000th I am using PyTorch on NVIDIA Jetson TX2 (GPU and CPU have shared memory), and have only about 2 Gb of free memory. If you use profile decorator, the PyTorch Forums CPU memory allocation when using a GPU. Train a model on CPU with PyTorch `` DistributedDataParallel``(DDP) functionality¶ For small scale models or memory-bound models, such as DLRM, training on CPU is also a good Before moving to GPU: The model uses a significant amount of CPU memory during the loading and preparation stages. Thanks in advance. While training the gpu usage seems not to be stable. Let’s say that I have a PyTorch tensor that I’m loading onto CPU. cuda. I solve most of my problems with memory using these commands. e. So the size of a tensor a in memory (cpu . It seems like for every GPU there is additional cuda initialization overhead. But there is no such option for CPU. batched matmul pre-expands all “batch” dimensions to same sizes, so w tensor is replicated 1000 times. During an epoch run, memory keeps constantly increasing. 8Mb image. rss” to get the memory utilization. We still rely on the I've run the same code on a different machine and there's no memory leak whatsoever. 2. 7. 8. I tried to remove unnecessary tensor and clear cache. I would like to add how you can load a previously trained model on the cpu (examples taken from the pytorch docs). e. Making these transfers non-blocking results in significant speed increases (almost 2x). device('cuda:0') the memory usage of the same comes down out of the GPU, and most of it comes down out of the system RAM as well. 4. cpu() Also! One reason why a lot of people are running out of vram is because they are trying to keep their total loss without detaching the graph or moving the tensor to cpu. This indicates that the demand for CPU resources exceeds the available physical cores, causing contention and competition among processes for CPU time. I’m not sure how the CPU memory allocation works in Python and PyTorch in particular. valgrind to find memory leaks in an application. Note: make sure that all the data inputted into the model also is on the cpu. After moving to GPU: The memory usage on the CPU doesn't drop much. to('cpu', non_blocking=True). During this process, I am looking to better understand and monitor the inter-GPU communication, the transfer of parameters and operators, as well as the usage of GPU memory and CPU memory. cpu()],dim=1) In this case, I've already moved every tensor in the list of outs to the cpu, but it still takes up a lot of memory. When I am training the network, the CPU memory usage keeps building up even though I am doing all the training on GPU(I move the model, datasets and all parameters to ‘cuda’) until at some the Hi, I am noticing a ~3Gb increase in CPU RAM occupancy after the first . This happens on a cluster where the submission of jobs is done with HT Condor. Filename: implemented_model. Modified 4 years, 9 months ago. nvidia-smi Hi community! I am trying to use neural network to learn a black box dynamics model that can predict the dynamics of a system based on the current state and input. init()" would consume about 2Gb of memory. path. 4 LTS Processor: Intel® Xeon® Gold 6338N CPU @ 2. While the memory usage certainly decreased by a factor of 2, the overall runtime seems to be the same? I ran some testing with profiler and it seems like the gradient scaling step takes over 300ms of CPU time? Seems like gradient scaling defeats the purpose of all the speed up we receive from AMP? Also, while I observed similar Hi, I am trying to calculate the peak memory utilization in pyych. However, I have little knowledge about CS things (processes, threads, etc. If I use only the CPU, the memory overhead would be only 180 Mb of memory. I use the PyTorch Lightning library. 6. Learn the Basics. For more details: https://pytorch. Btw I changed this to actually copy the models there: I tried AMP on my training pipeline. load(model_path, map_location="cpu"), strict=False) model. How to prevent memory use growth when updating weights and biases in a Pytorch model. And I had read quite a few discussions regarding similar issues, but none fixed my problem. cat([all_outs. If you use master instead of 0. Allocating a tensor to CPU by Tensor. 10. Module’s gpu/cpu memory resource consumption. Share. In a nutshell, I want to train several different models in order to compare their performance, but I cannot run more than 2-3 on my machine without the kernel crashing for lack of RAM (top I am trying to train a model written specifically in pytorch that requires a lot of memory and my CPU has more memory and can handle a larger batch size, but the GPU is much faster but limited in memory. rand((256, 256)). Expected behavior is low memory usage as in pytorch 1. I wolud like to know how pytorch works with a bit more detail so I can use it optimally, A more efficient memory allocator, operator fusion, memory layout format optimization by Intel® Extension for PyTorch* improve Memory Bound. Attempting to split the A memory usage of ~10GB would be expected for a ResNet50 with the specified input shape. 4 LTS Processor: Intel® Xeon(R) W-2223 CPU @ 3. Improve this answer. I don’t know, if your prints worked correctly, as you would only use ~4MB, which is quite small for Hi, I’ve been using PyTorch (Lightning) almost for a year. Thanks in advance for the kind help and efforts. I am on Windows 10. I am training a model related to video processing and would like to increase the batch size. Initially, I was spinning off a thread that recorded Why does . In order to debug this issue,I install python memory Hi Code : train_dataleak. memory_stats (device = None) [source] ¶ Return a dictionary of XPU memory allocator statistics for a given device. But there aren’t many resources out there that explain everything that affects memory usage at various stages of You could try to use e. If you use the torch. The latter is quite I collected and organized several PyTorch tricks and tips to maximize the efficiency of memory usage and minimize the run time. Intro to PyTorch - YouTube Series High CPU Utilization: By using the htop command, you can observe that the CPU utilization is consistently high, often reaching or exceeding its maximum capacity. mem_get_info: It returns a tuple where the first element is the free memory usage and the second is the total available memory. Improve this question. 56 Kb have been released. Below is my for training step. PyTorch CPU memory leak but only when running on a specific machine. I’ve noticed this behavior in my power edge: OS: Ubuntu 20. empty_cache). The CPU Besides the size of the model the number and type of layers would also matter for the speed. Key deep learning primitives, such as convolution, matrix multiplication, dot-product, etc have been well optimized by Intel® Extension for PyTorch* and oneDNN library, improving Core Bound. Rewriting the above code to minimize the usage of Indeed, this answer does not address the question how to enforce a limit to memory usage. I have a basic question that I could not find a straight answer for anywhere. I have used memory profiler to trace the leakage location. cpu() for out in outs] all_outs = torch. The GPU utilization is quite bad and depending on the num_workers I have set, each worker “works” with maximum 1/num_workers %. Is there a way to reclaim some/most of CPU RAM that was originally allocated for loading/initialization after moving my modules to GPU? Some more info: Hi @ptrblck, I am currently having the GPU memory leakage problem (during evaluation) that (1) the GPU memory usage increased during evaluation, and (2) it is not fully cleared after all variables have been deleted, and i have also cleared the memory using torch. Hello, I have been trying to debug an issue where, when working with a dataset, my RAM is filling up quickly. CPU usage extremely high and Very high CPU utilization with pin_memory=True and num_workers > 0 · Issue #25010 · pytorch/pytorch · GitHub I had tried to set the num_workers in the dataloader to 2 or more, make no difference. eval() return model to run it I do: gc. Familiarize yourself with PyTorch concepts and modules. However, it also blows up the CPU RAM usage and Hello, I am running pytorch and the cpu usage of a single thread is exceeding 100. I don’t know where or what that caused memory leak. At the beginning, GPU memory usage is only 22%. For this you want to use Pytorch Profiler which give you details on both CPU and memory consumption. You have to profile the code to see where tensors are allocated and how they are managed. (I just did the experiment, and there was 16M Hello! I’m working on making a inspector which examines each tensor, or nn. max_memory_allocated(). min-batch size=128. Note however, that this would find real “leaks”, while users often call an increase of memory in PyTorch also a “memory leak”. However, initializing CUDA uses upwards of 2GB of RAM (not GPU memory). Hello, first of all I would like to say that i like PyTorch so far and eager to see what it do in the future. Moreover, it is not true that pytorch only reserves as much GPU memory as it needs. Figure 4: Figure 5: Memory usage over time using the same input file with and without jemalloc. If I then run. to(cuda_device) copies to GPU RAM, but doesn’t release memory of CPU RAM. Hi guys, I trained my model using pytorch lightning. Very high forward/backward pass size . My program’s memory usage is roughly an order of magnitude greater when I specify requires_grad=True on the parameters of my model. Figure 3: CPU Utilization for different number of engine threads. 0+cu111. to(device) does the same My I did not say I expected that CPU usage should be zero or low since the model is trained on GPU. load_state_dict(torch. collect(). May I know where could be the potential issue to cause this memory While training an autoencoder my memory usage will constantly increase over time (using up the full ~64GB available). . but it seems that every step my memory (RAM) usage keep getting bigger and bigger. Anyone faced such an issue in windows with other torchvision models or any other model? Is there a way in pytorch to borrow memory from the CPU when training on GPU. It tells them to behave as in evaluating mode instead of training mode. As you can see on this image, CPU Intel I5 10 th gen 6 core 3. device("cpu") Comparing Trained Models . We also clean model state dict to clean up the memory The CUDA context needs approx. 788s. cuda() The virtual memory used is increased to 15. For each tensor, you have a method element_size() that will give you the size of one element in byte. ## To Reproduce I am quite new to PyTorch, having used TF/Keras extensively in the past, but am now trying to use PyTorch as a replacement. Run PyTorch locally or get started quickly with one of the supported cloud platforms. 1. Running the same script with the --cpu option gives: Line # Mem usage Increment Occurences Line Contents ===== 9 151. 1; CUDA 10. Because I am not familiar with PyTorch so much. The difference between the two machines is one is running PyTorch 1. Categorized Memory Usage. I am using a machine with a Nvidia A10G, 16 CPUs and 64 Gb of RAM. Pytorch model training CPU Memory leak issue. PyTorch memory consumption. in order to compute df/dx you are required to keep x in memory. The same model while testing consumes around ~600 MBs of memory in Ubuntu and it consumes 4 GB+ memory in windows. Expected behavior. tensor([0], device="cuda") and monitoring RAM from any system tool. I’ve read the FAQ about memory increasing and ensured that I’m not unintentionally keeping gradients in memory. Whereas RES is the actual RAM consumed. I’ve Hi I am new to Pytorch (and ML and NN). In case your model suffers indeed from the dispatching mechanism, you could While training on 480x640 RGB images (1200 images) with batch 32. Follow edited Apr 14, 2023 at 22:41. I need some general guidance. memory_usage (device = None) [source] ¶ Return the percent of time over the past sample period during which global (device) memory was being read or written as given by nvidia-smi. Module subclass. while 10 huge linear layers (using the same memory) could execute faster. Process(). Specifications: PyTorch v1. Ask Question Asked 4 years, 9 months ago. 1· Both are run with conda and only on the CPU. Viewed 11k times 5 I am Also, if you're storing tensors on GPU you can move them to cpu using tensor. Specifically, I am facing the following challenges: How can I Hello, i am trying to use pytorchs Dataset and DataLoader to load a large dataset of several 100GB. And we’ll run all exercises on a machine with two Intel(R) Xeon(R) Platinum 8180M CPUs. Tensor. I tried to remove unnecessary tensor and Throughout the blog, we’ll use Intel® VTune™ Profiler to profile and verify optimizations. I’m using about 400,0006464 (about 48G) and I have 32G GPU Memory. It appears to me that calling module. I made sure that loss was detached before logging. Pytorch keeps GPU memory that is not used anymore (e. So I want to add the memory in the CPU as usable memory for the GPU somehow. stack(outs). The Dataloder memory usage continuously increases until it runs of memory. I tried Low GPU utilization Sending random tiny tensor copies to all gpus will still increase ram usage by the same ~600mb. something which disables caching or something like torch. my model: class I used 'memory_profiler' to analyze memory usage, and here's what I found: Before moving to GPU: The model uses a significant amount of CPU memory during the loading and preparation stages. For GPU memory we use a custom caching allocator, which reuses memory if possible without reallocating. Vishal_R (Vishal R) May 18, 2021, 4:50am 3. Initially I thought it was just the loss function, buy I get the same behavior with both BCELoss and MSELoss. After the upgrade i see there is increase in RAM utilization of ~3 GB when i load the model. 2 Python pytorch function consumes memory excessively quickly. device or I’ve been working on tools for memory usage diagnostics and management (ipyexperiments ) to help to get more out of the limited GPU RAM. Thanks but it seems not to make difference. I would now like to experiment with different shapes and how they affect the memory consumption, and I thought the best way to do this is creating a simple random tensor and then measuring the memory consumptions of different shapes. Do you have any idea on why the GPU remains 🐛 Bug Hi guys, I trained my model using pytorch lightning. device('cpu') the memory usage of allocating the LSTM module Encoder increases and never comes back down. cpu() (see torch. The Memory Profiler is an added feature of the PyTorch Profiler that categorizes memory usage over time. The features include tracking real used and peaked used memory (GPU and general RAM). What happens is that small Tensors are actually free'd by PyTorch but glibc default memory allocator decides to not give them back to the OS. max_memory_allocated() to get the peak memory utilization. 13 documentation). What does this mean exactly? Cuda and pytorch memory usage. 1. Dives into OS log files , and I find script was killed by OOM killer because my CPU ran out of memory. join(img_folder, dir1)): image_path = os. To debug CUDA memory use, PyTorch provides a way to generate memory snapshots that record the state of allocated CUDA memory at any point in time, and optionally record the history of allocation events that led up to that snapshot. This is particularly useful when evaluating or testing your model, i. Follow ## 🐛 Bug A possible CPU-side memory leak even when fitting on the GPU using P yTorch 0. Late, but VIRT in htop roughly refers to the amount of RAM your process has access to. listdir(img_folder): for file in os. For this reason, I am using “psutil. Hello, I’m currently experiencing a CPU Memory shortage, so I would like to get help. Currently we are using standard PyTorch load model module. memory_allocated(). The return value of this function is a dictionary of statistics, each of which is a non-negative integer. While using CUDA, I can do torch. This is of course too large to be stored in RAM, so parallel, lazy loading is needed. My code is very simple: for dir1 in os. 17 Kb, while including the child processes 1799765. Hi! I’m training a small transformer using pytorch lightning on 2 GPUs via slurm. Hi, I am noticing a ~3Gb increase in CPU RAM occupancy after the first . After running on 10% of data it ends up using another 30+GB of ram and 40GB + swap Hi, I have trained a scaled-yolov4 object detection model in darknet, which I have converted to a pytorch model via GitHub - Tianxiaomo/pytorch-YOLOv4: PyTorch ,ONNX and TensorRT implementation of YOLOv4. 2 (the machine without the memory leak) and the other machine (the one with the memory leak) is running PyTorch 1. (since nvidia-smi only shows total consumption) Is there any built-in pytorch method to achieve t torch. eval just make differences for specific modules, such as batchnorm or dropout. 04. To my knowledge, model. cpu — PyTorch 1. xpu. 055 MiB 151. 652 In the recent version of PyTorch you can also use torch. Iman Iman Cuda and pytorch memory usage. It seems like some data or buffers might still be retained in CPU memory. Whats new in PyTorch tutorials. 055 MiB 1 @profile 10 Is there a known way to reduce the RAM usage by PyTorch? pytorch; cuda; ram; nvidia-jetson; Share. memory_info(). It’s actually over 1000 and near 2000. py · GitHub I observed that during training, things were fine until 5th epoch when the CPU usage suddenly shot up(see image for RAM usage). I am normally using TensorFlow and the CPU usage is not like in my question. 3. Tutorials. when backpropagation is performed. I have very low GPU utilization. Understanding GPU vs CPU memory usage. Using profiler to analyze memory consumption¶ PyTorch profiler can also show the amount of memory (used by the model’s tensors) that was allocated (or released) during the execution of the model’s operators. 60GHz × Understanding CUDA Memory Usage¶. to load it I do the following: def _load_model(model_path): model = ModelDef(num_classes=35) model. Hot Network Questions Applying l'Hôpital's rule to a limit defining a derivative Here, df/dx = 2x, i. cpu(). 20GHz, 32 Cores Hello everyone, I have been training and fine-tuning large language models using PyTorch recently. The peak memory usage is crucial for being able to fit into the available RAM. I’ve looked through the docs to find a way to reduce my program’s memory consumption, but I can’t seem to figure it out. Commented Feb 18, 2020 at 13:57. However, CPU Mem states 592648. I installed the latest version of pytorch-cpu in windows and I am testing faster-rcnn. Parameters. Load 7 more related questions Show module: cpu CPU specific problem (e. 6 Pytorch model training CPU Memory leak issue. – André Pacheco. The name of any field from memory_stats() can be passed to display() to view the corresponding statistic. Note that we only tested it using one 1. This situation usually leads to a relatively large GPU memory usage, which may lead to memory explosion. There are two scenarios: the operation is expressible with 3d tensors and torch. listdir(os. models and Out-of-memory (OOM) errors are some of the most common errors in PyTorch. Out of Memory by Torch To answer this, let’s visit the Memory Profiler in the next section. But this ends up giving me results that are wildly different from cuda When I trained my pytorch model on GPU device,my python script was killed out of blue. However, after 900 steps, GPU memory usage is around 68%. So when I set it to 4, I have 4 workers at 25%. 100k linear layers with a size of 10x10 would see a large overhead from the dispatching, kernel lanches etc. , perf, algorithm) module: memory usage PyTorch is using more memory than it should, or it is leaking memory module: performance Issues related to performance, either of kernel code or framework glue triaged This issue has been looked at a team member, and triaged and prioritized into an appropriate module Hello everyone, I am thinking that the program is in the memory leak situation and have tried many methods but still not working. collect() with torch. Since now, my way of optimizing training time is None and my reasoning is as simple as: more data = more time, more parameters = more time. I’ve seen several threads (here and elsewhere) discussing similar memory issues on GPUs, but none when running PyTorch on CPUs (no CUDA), so hopefully this isn’t too repetitive. memory_stats¶ torch. g. Hi, my CPU memory consumption gradually increases during training. A very strange behaviour occured (that I could solve) but I thought I would bring it up because I cannot imagine that this is a desired behaviour: So when I just train my model on the CPU on my PC with 24 cores, all 24 cores being used 100% even though my model is rather small (thats why I dont train it on the GPU). This is to know if increasing batch size can improve the results of the model by better training it, especially the batchnorm3d part. Since the dataset is too big to score the model on the whole dataset at once, I'm trying to run it in batches, store the results in a list, and then concatenate those tensors together at the end. Bite-size, ready-to-deploy PyTorch code examples. This gives you all the allocated cuda memory, so you can instrument your code with it. PyTorch Recipes. These processes are containerized for versioning - I can’t share much more, but using a single runtime to load several models isn’t possible. I am trying to train a BERT model on my data using the Trainer class from pytorch-lightning. no_grad(): input_1_torch = in your shell at the same time while training is happening it will show the real memory usage of your model and utilization. torch. The CPU RAM occupancy increase is partially independent from the moved object original CPU size, whether it is a single tensor or a nn. 8 ghz - Ulitization 98-100% I have 16 GB RAM 3200 Mhz - Thanks for replying @ptrblck. The stacked images each goes through a pretrained encoder, and a class token will be When I create the model on my CPU as such, model = Net() Both CPU and GPU memory usage remain unchanged. cuda() # model. 0; JetPack 4. 5GB, and 2GB in RAM. General . However, after sending tensors to all GPUs, sending models to the GPUs still increases CPU RAM but only by ~400mb each. It turns out this is caused by the transformations I am doing to the images, using transforms. I am trying to load one large HDF file with a combination of a custom Dataset and the DataLoader. The code of my custom dataset is below. clear_caches() but for CPU) - as I understand, high memory usage happens because allocations are cached, which makes sense for fixed shapes, but does not work well for I'm trying to do large-scale inference of a pretrained BERT model on a single machine and I'm running into CPU out-of-memory errors. ). However, while attempting this, I noticed anomalies and I I am seeing an unusual memory consumption in Windows. Figure 5: Memory usage over time using the same input file with and without jemalloc. answered Apr 1, 2022 at 19:23. Acknowledgement ¶ We would like to thank Ashok Emani (Intel) and Jiong Gong (Intel) for their immense guidance and support, and thorough feedback and reviews throughout many steps of this blog. empty_cache() and gc. When using torch. To better leverage these tips, we also need to understand how and why they work. After moving to GPU: The memory usage on the 4. My Dataset size is 26GB when initialized, it contains an ndarray from which I return an element based on index value. 6 to v1. The model I’m running causes memory to increase with every iteration. However, I encountered an out-of-memory exception openshift, so I have CPU memory of 350GB (OOM during the training). 2; Whenever I try to use GPU, "torch. py Line # Mem usage Increment Occurences Line Contents 37 2630. I am trying to train a model that requires a lot of memory and I have a training pipeline which offloads various components (model, model ema, optimizer) to CPU at various training step stages, and does so asynchronously (e. hukkelas (Håkon Hukkelås) The virtual memory usage goes up to about 10GB, and 135M in RAM (from almost non-existing). cpu() fail to move the parameters from the GPU memory to the memory of CPU?. 1, there is torch. As previous answers showed you can make your pytorch run on the cpu using: device = torch. E. Everything works fine. From my understanding, RES is something that's based on the parent process – so look at the RES usage of the parent (set yourself to tree view) to get a rough idea of how much RAM you're using, total. join(img_folder, dir1, file) with I am trying to optimize memory consumption of a model and profiled it using memory_profiler. It’s very strange that I trained my model on GPU device but I ran out of my CPU memory. Alternatively, a way to control caching (e. cpu(),torch. Running malloc_trim forces releasing all the cached allocations (probably a bit similar to torch. memory_summary() and third-party libraries like torchsummary to profile and monitor memory usage. Our DevOps team didn't really like it. cpu() is not inplace for a tensor, so assuming loss is a tensor you need to write it this way: loss = loss. getsizeof() will return the size of the python object. RAM isn’t freed after epoch ends. org/blog/introducing-pytorch-profiler-the-new-and-improved-performance-tool/ Import all necessary libraries¶ In this recipe we will use torch, torchvision. The model itself is quite simple: a ViT-inspired architecture. I train a custom Module char-RNN because i want to save the last hidden state. There is a gap between CPU usage of TF and PyTorch in my system. It will the same for all tensors as all tensors are a python object containing a tensor. And I did one for loop check. In fact, this Self CPU time total: 5. When I load the pytorch model onto my CPU, I get a very small increase in CPU memory usage (less than 0. I decided to start small with a seq2seq Skip-Thought model, cobbled together using the PyTorch NLP tutorials. I know that Self CPU refers only to the particular function without the child processes and CPU total includes them. cuda() call. The getitem method of the underlying dataset takes ~2ms, all data comes from the RAM. As a result even though the number of workers are 5 and no other process is running, the cpu load average from ‘htop’ is over 20. Our log shows we need around 900m CPU resources limit. 1 with cuda 10. If I train using the codes below, the memory usage is over 90%. Memory consumption U-Net. Snapshot of OOM killer log file. no_grad() context manager, you will allow PyTorch to not save those values thus saving memory. Python pytorch function consumes memory excessively quickly. So, I am wondering whether I did some mistake or not. I’ve noticed this behavior in my workstation: OS: Ubuntu 20. weights Apparently, it always killed OOM due to high CPU and Memory usage. But the doc didn't mention that it will tell variables not to keep gradients or some other datas. And a function nelement() that returns the number of elements. This is testable like so: import torch a = torch. bmm (backend of matmul) I am facing this issue even with the updated PyTorch nightly version. With memory I think it will be analogous. 600-1000MB of GPU memory depending on the used CUDA version as well as device. What we have tried. Please see attached. for each data buffer, calling buffer. For example: outs = [out. In an ideal case, should CPU RAM usage be increasing with each mini-batch? To give numbers: train_data size is ~6 million. I am training a temporal model, where each data entry is a 2-tuple: (label, a tensor of 15 images stacked). 2GB, which is the size of the . 9. However, when I then move the model to the GPU, model. memory_usage¶ torch. I recently updated the pytorch v1. Summary. Based on the documentation I found, I have 2 main tools available, one is the profiler and the other is torch. The dataset is only 33GB so I'm not sure why the CPU memory usage continues to grow during the training until the crash Hi, sys. device (torch. This helps in identifying After monitoring CPU RAM usage, I find that RAM usage increases for all epoch. I Use PyTorch's built-in tools like torch. by a tensor variable going out of scope) around for future allocations, instead of releasing it to the OS. Chame_call (chame_call) June Hi all, I am training my model on the CPU. Here is my objective function: def fun(x, cons, est, trans, model, data): print(x) for con in cons: valid = Hello, I am doing feature extraction and fine tuning of an efficientnet_b0 model. An explanation of what each column means can be found in the Torch documentation. Note that this is not specific to PyTorch at all (you can try doing small memory allocations in Python, then if you And stay tuned for a follow-up posts on optimized kernels on CPU via Intel® Extension for PyTorch* and advanced launcher configurations such as memory allocator. gfvjo ezfb pglj ohwi imlvev gwrwsx hyg fynlo zjzpd pwcz