Torch cuda get device id

Torch cuda get device id

Nov 12, 2018 · 94. Therefore, you only need a compatible nvidia driver installed in the host. cuda 会跟踪当前选择的 GPU ，并且将分配您的所有CUDA张量。. get_device_name(device=None) [source] Get the name of a device. DataParallel(model, device_ids =[ 0,2]) But when i run this i get , AssertionError: Invalid device id. cuda as cuda. 1, of a cuda variable? Mar 17, 2021 · In their version they use CUDA but my Mac is not compatible with CUDA and it doesn't have a CUDA enabled GPU so I installed the CPU-only version of PyTorch instead - therefore I changed model = model. cuda command as shown below: # Importing Pytorch import torch # To print Cuda version print(“Pytorch CUDA Version is “, torch. is_available(): print(&quot;Number of Jul 28, 2021 · Go to "https://pytorch. rand(3, 4)). ]) 【Pytorch】デバイス (CPU/CUDA)を指定する方法【GPU】Pytorchでテンソルを扱うデバイスを指定するには、device引数や torch. Oct 25, 2021 · You can set the environment variable CUDA_VISIBLE_DEVICES. memory_allocated(ID of the device) #returns you the current GPU memory usage by tensors in bytes for a given device torch. # this won't copy if they are already on the desired device. devices = torch. Returns printout for the current device, given by current Mar 24, 2019 · PyTorch with CUDA and Nvidia card: RuntimeError: CUDA error: all CUDA-capable devices are busy or unavailable, but torch. In CUDA, the way to select a device to use is via cudaSetDevice(), and this requires an integer argument. cuda. Apr 11, 2024 · torch. data. To automatically assign tensors, you can use the torch. $ pip freeze | grep torch torch==1. シンプルな torch. It is lazily initialized, so you can always import it, and use is_available() to determine if your system supports CUDA. デバイスを明示的に指定することで、コードの可読性と移植性を向上させることができます。. Aug 24, 2019 · Maybe too late, but after reading the correction, I suggest to not use raw string to specify your device number, but instead use a little helper like: auto get_cuda_device (const int number) -> std::string { assert (number >= 0 && number <= torch::cuda::device_count ()); return "cuda:" + std::to_string (number); } just replace: Jun 26, 2018 · Hi guys, I am a PyTorch beginner trying to get my model to train on a specific GPU on my machine. Oct 4, 2022 · print(“Name of the current CUDA Device: ”, torch. to (device_name): Returns new instance of ‘Tensor’ on the device specified by ‘device_name’: ‘cpu’ for CPU and ‘cuda’ for CUDA enabled GPU. Unfortunately, there is no Jul 20, 2020 · To use a different gpu in the system, isn’t when you declare the device. device_count() is not returning the correct number of devices, instead to have the correct number of devices I have to use torch. to (device=“cpu:x”) But how do we get the “cpu:x” device? The setting is, using MPI to spawn multiple threads for computations (not backprop) and then moving tensors from each thread to a central GPU (for backprop) and back to each CPU. Dec 22, 2019 · Torch I'll then automatically get the current device. Users may use this function in dataset code and/or worker_init_fn to individually configure each dataset replica, and to determine whether the code is running May 22, 2023 · From Nvidia documentation I saw that a process sees only 1 MIG instance, so I just want to be able get the UUID for it. What does torch. device (“cuda:2”) or. py", line 312, in get_device_properties raise AssertionError("Invalid device id") AssertionError: Invalid device id Feb 10, 2013 · The well know code for getting properties from CUDA devices (!) is enumerating over all devices, and getting properties from then. And torch. _C. Next Previous. 1 nvidia-smi 代码中加入 device = torch. device_count() # 使用したいCUDAデバイスを選択. cuda模块：. get_device_properties(device) [source] Get the properties of a device. BTW, nvidia-smi basically 🐛 Describe the bug I'm running into some weird problem with our benchmark script, with apparently no change that caused it to stop working overnight on my machine. 1) For single-device modules, device_ids can contain exactly one device id, which represents the only CUDA device where the input module corresponding to this process resides. Mar 31, 2017 · When a computer has multiple CUDA-capable GPUs, each GPU is assigned a device ID. Alternatively, device_ids can also be None. 首先，让我们导入torch和torch. #Change the order so that it is the one used by "nvidia-smi" and not the. I wrote a re-mapper to solve the problem: Jan 6, 2019 · To transfer a "CPU" tensor to "GPU" tensor, simply do: cpuTensor = cpuTensor. 接下来，我们可以使用torch. device('cpu') Since you probably want to store the device for later, you might want something like this instead: device = torch. Im trying to get predictions from my model using multiple GPU’s for which i have, model = retinanet. 但是，一旦张量被分配，您可以对其进行操作，而不考虑您选择的设备，结果将始终放在与张量相同的设备上。. We would like to show you a description here but the site won’t allow us. The solution is simply to make the first line of the following code BEFORE calling functions related to “torch. Using device 0 in your code will use device 1 from global numering. device('cpu') to map your storages to the CPU. Jun 19, 2019 at 13:17. killacan on May 28, 2023. torch. memory_reserved(ID of the device) #returns you the current GPU memory managed by caching allocator in bytes for a given device, in previous PyTorch versions the command was torch. I am giving as an input the following code: torch. saxpy<<<(N+255)/256, 256>>>(N, 2. device. Then I see such piece, that activates given device. If you are running on a CPU-only machine, please use torch. GPU:0 will be the fastest GPU on your host, in your case the K40m. is_available(): torch. is_available() is True 2 torch. load() function to cuda:device_id. deviceを用いて、ブロック内でのデフォルトGPUを指定できる。 使い方は以下の公式ドキュメントのサンプルコードを参照。 torch. Yesterday, I discovered pynvml doesn’t respect CUDA_VISIBLE_DEVICES, so the ids of torch. device("cuda" if torch. set_device(device) [source] Set the current device. 8. cuda(), You can execute test. is_available() else "cpu") # then whenever you get a new Tensor or Module. cuda; Understanding CUDA Memory Usage; Generating a Snapshot; Using the visualizer; Snapshot API Reference; Is the torch. Return the global free and total GPU memory for a given device using cudaMemGetInfo. If it does not resolve the issue then we try other stuff until something works. Aug 21, 2023 · import torch from torchvision. (Optional) if you use Tensorflow as well, go here and install the right version for your CUDA. device 上下文管理器更改所选设备 。. GPUとCPUの性能差を考慮して、適切なデバイスを選択する必要があります。. import torch torch. current_device(). py", line 455, in return [get_device_attr(lambda m: m. cuda and nvml don’t match if you need to change which gpus are used via CUDA_VISIBLE_DEVICES, which breaks the software that uses pynvml. May 11, 2022 · CPUを指定してテンソルを扱う場合は以下のようなコードを書けばよい。. device context manager. After each model finishes their job, DataParallel collects and merges the results before returning it to you. If a given object is not allocated on a GPU, this is a no-op. Sep 23, 2016 · 5. device ( torch. g. # 使用可能なCUDAデバイスのリストを取得. pt", map_location=torch. load with map_location=torch. models import resnet18 # Want to set a 100MB limit on GPU device 0 gpu_id = 0 value = 100 # Get total memory of the device total_memory = torch. This is the first time for me to run Pytorch with GPU on a linux machine. e. if i skip the initialized，it tells RuntimeError: generic type: cannot initialize type "_CudaDeviceProperties": an object with that name is already defined. So, let's say the output is 10. However, UUID is part of the device property structure, so you could retrieve those (e. CUDA语义 中有关于使用CUDA的更多细节。. Example: Jun 16, 2020 · I tried using Cuda in Pytorch in my set up but it can't be detected and I am puzzled as to why. 0, d_x, d_y); cudaError_t errSync class torch. cpu; torch. I’m trying to specify specify which single GPU Aug 7, 2020 · torch. 1 documentation. get_device_name(cuda_id)) The above code will show the following output – # Output CUDA Device ID: 0 Name of the current CUDA Device: NVIDIA GeForce FTX 1650. You could call the internal torch. CUDA semantics. Jan 21, 2023 · When I try to run this code block, I get the following problem: RuntimeError: Attempting to deserialize object on a CUDA device but torch. PyTorch is delivered with its own cuda and cudnn. 4- After all of that, in your Anaconda environment (or any environment you are using), type: import torch. to(device) data = data. Put in your system details and install the right PyTorch for your system. Mar 19, 2020 · When I debug into it, I find the function device_count() in get_device_properties() returns 1 while I have 2 GPU on my machine. Aug 1, 2018 · RegularGuY August 1, 2018, 5:29pm 1. obj ( Tensor or Storage) – object allocated on the selected device. 2 CUDA Capability Major/Minor version number: 8. The problem is that by default the device ordering is FASTEST_FIRST while nvidia-smi uses PCI_BUS_ID. Pytorch version is 2. Yet their examples work. 5. 0 I tried to write a small C program to do the same, e. Set implicitly based on if dla_core is specified. is_available() else 'cpu') if torch. 除了 copy_() 以外 Shadowwalker10 commented on May 20, 2021. In the following code, we iterate over the range of available GPU devices and create a list of them. props = cuda. cuda(device_id) Is there an API that returns the device_id, i. Using version 1. is_available() else 'cpu') May 16, 2023 · At least on my system (Ubuntu 20. cuda) If the installation is successful, the above code will show the following output –. device_of(obj) [source] Context-manager that changes the current device to that of given object. So, the question is with which cuda was your PyTorch built? Check that using torch. memory_cached Nov 5, 2020 · 下面是使用 GPU 的示例代码： ```python import torch # 检查是否有可用的 GPU device = torch. 6 Total amount of global memory: 16101 MBytes (16882663424 bytes) (48) Multiprocessors, (128) CUDA Cores/MP: 6144 CUDA Jun 21, 2023 · Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. get_device_properties(i) Feb 23, 2024 · I update the PyTorch version had the same problem, giid:7 gpu loss. _cuda_getDeviceCount() returns 0. device_count() 7 torch. _Device. device('cuda')) to convert the model’s parameter tensors to CUDA tensors. 複数のGPUを使用できる環境においてはコンテキストマネージャtorch. Torch will read this variable and only use the GPUs specified in there. ), and returns None in main process. Digging deeper, torch. DistributedDataParallel with gpu device ID specified in PyTorch. Be sure to call model. We can send tensors to different devices. device where this Tensor is. Implements data parallelism at the module level. As an example of what to do: model = jit. This loads the model to a given GPU device. So in your case if you always set CUDA_VISIBLE_DEVICES to a single device, in your code, the device id will always be 0, that is expected. When running Pytorch inference on a Resnet model on Jetson Xavier GPU, in my python script I use -. is_available() else "cpu") In my code below, I added this statement: net. 14. See Memory management for more details about GPU memory management. py by giving the list of gpus as a parameter. is_available()) if torch. 4. cuda() This would take this tensor to default GPU device. _cuda_getDeviceCount() returns 2 in Anaconda Prompt. device if self. Before moving forward ensure that you've got an NVIDIA graphics card. 6. get_device_name(0) ‘NVIDIA A100-SXM4-80GB’ Feb 1, 2018 · Hi, The device numbering is consistent accros all applications, except nvidia-smi that ignores the CUDA_DEVICE_ORDER environment variable. Returns. DataParallel (module, device_ids = None, output_device = None, dim = 0) [source] ¶. get_device_properties (gpu_id). Returns statistic for the current device, given by current_device() , if device is None torch. 它是懒惰的初始化，所以你可以随时导入它，并使用 is_available() 来确定系统是否支持CUDA。. if your system has two GPUs and you are using CUDA_VISIBLE_DEVICES=1, you would have to access it inside the script as cuda:0. DataParallel splits your data automatically and sends job orders to multiple models on several GPUs. c = torch. Jan 16, 2019 · model. DeviceType): Target device type (GPU or DLA). get_device() function. Jul 7, 2021 · os. utils. – Steven C. using cudaGetDeviceProperties() similar to the demonstration in the deviceQuery sample code) and then select a device based on the mapping between Nov 6, 2023 · The usual reasons for this are either an improper fabric manager install in a NVLink setup, or MIG mode improperly enabled. Jun 2, 2023 · For interacting Pytorch tensors through CUDA, we can use the following utility functions: Syntax: Tensor. device('cuda' if torch. You can then use this index to direct placement for new tensors. user3901687. It provides useful information about the GPUs installed in your system, helping you identify and select the specific Mar 4, 2023 · Devices are always indexed starting at 0 in pytorch so for example if CUDA_VISIBLE_DEVICES is set to 2,5,7 then you should use pytorch device cuda:0, cuda:1 or cuda:2 to refer to devices 2, 5, or 7 respectively. device_count() returns 2, but torch. device or int) – device index to select. _cuda_getDeviceCount())) that I have 1 device. device_count ()函数来获取可用GPU的数量：. get_device_name(0) The output for the last command is ‘Tesla K40c’, which is the GPU I want to use. It keeps track of the currently selected GPU, and all CUDA tensors you allocate will by default be created on that device. sn710 (srivathsa) July 15, 2022, 11:04am 1. py code at. to(device)) 在运行得时候指定gpu即可（注意DEVICES最后有一个S，被这个 Apr 24, 2019 · model = nn. Jan 21, 2022 · That is, mapping the device should happen during jit. Returns statistic for the current device, given by current_device() , if device is None (default). Parameters. Before running your code, run this shell command to tell torch that there are no GPUs: export CUDA_VISIBLE_DEVICES="". memory_summary. version. Return a human-readable printout of the current memory allocator statistics for a given device. Return type. _raw_device_uuid_nvml() method or re-implement a custom one if needed. ones(1, device='cpu') print(c) # 出力 # tensor([1. Nov 16, 2020 · stas (Stas Bekman) November 16, 2020, 9:19pm 1. Rather than using torch to figure this out, validate your CUDA install using the methods in the CUDA linux install guide. cuda() to model = model. to(device) and added in device = torch. with torch. device(self. Apr 4, 2019 · Current CPU device ID? whoab April 4, 2019, 8:16am 1. Feb 1, 2019 · I received the following error: File "pytorch-1. You can use cudaSetDevice(int device) to select a different device. to (device) to copy the inputs from host to device device¶ class torch. By default, CUDA kernels execute on device ID 0. device('cuda') model = nn. Tensor. But it did not work. py", line 39, in <module>. to(device) copy the data to GPU. cuda. Jun 8, 2023 · Reading some answers to other similar questions, I saw that the function torch. utilization(device=None) [source] Return the percent of time over the past sample period during which one or more kernels was executing on the GPU as given by nvidia-smi. device() は、現在のデフォルトデバイスを設定します。. device ('cuda:0' if torch. Related. device at Tensor Attributes — PyTorch 1. to(device) According to my best knowledge, mode. DataParallel(model. Build innovative and privacy-aware AI experiences for edge devices ExecuTorch End-to-end solution for enabling on-device inference capabilities across mobile and edge devices Nov 19, 2019 · Copy-pasting the example from the blog here: # at beginning of the script. get_device_properties(i)) for i in device_ids] File "C:\Users\Luffy\Anaconda3\lib\site-packages\torch\cuda_init. environ['CUDA_VISIBLE_DEVICES'] = '4, 5, 6, 7' Apr 19, 2020 · self. Next, we print the name of each GPU device to the console. DataParallel¶ class torch. GPUS). The problem is that my the training PyTorchとCUDAに関する情報は、PyTorchの公式ドキュメントやフォーラムで確認することができます。. device('cuda') else: torch. py: import torch def check_cuda(): print("Is CUDA available in PyTorch:", torch. 391. If you set the default tensor device to another device (e. Sep 21, 2019 · Gpu devices: nvidia-smi and cuda. @KurianBenoy setting CUDA_VISIBLE_DEVICE=0 will select GPU 0 to perform any CUDA tasks. mydevice=torch. environ[‘CUDA_LAUNCH_BLOCKING’]= “1” device = torch. device (torch. This can be useful to display periodically during training, or when handling out-of-memory exceptions. According to the documentation for torch. 04 running torch 1. current_blas_handle() 返回 device_ids (list of int or torch. Note that internal methods can easily break and do not guarantee to have a stable interface. device or int or str) – device for which to return the properties of the device. You can either do this directly in your python code like this: import os os. Jul 29, 2020 · @abhigenie92 As @Erricia mentioned, the GPUs are renumbered since CUDA_VISIBLE_DEVICES is [1, 2, 3, 4, 5, 6] . to(device) To use the specific GPU's by setting OS environment variable: Before executing the program, set CUDA_VISIBLE_DEVICES variable as follows: export CUDA_VISIBLE_DEVICES=1,3 (Assuming you want to select 2nd and 4th GPU) Then, within program, you can just use DataParallel() as though you want to use all the GPUs. device or int, optional) – device for which to return the name. 12. info('Using CPU!') return 'cpu'. cuda indexes devices starting from zero based on available devices during initialization, and not on the system device id. device("cuda:0") for GPU 0 and device = torch. I think this is the default behavior, as all my GPU tasks were going to GPU 0 before I set the variable, so it may not be necessary to actually set that, depending on your use case. is_available() else "cpu") # 将模型和数据张量移到 GPU 上 model. While running, you can do nvidia-smi to check the Jul 15, 2022 · Get device id of DLA. to(device) (I don't really need a detailed explanation of what is happening in the backend, just want to know if they are both essentially doing the same thing) . 5, e. We can check for errors in the saxpy kernel used in the first post of this series as follows. Based on that we could use something like. edited Mar 29, 2022 at 7:39. load has already finished. It looks like you need to import more things from torch first before . i have tried your suggestions. // This example is the same as previous example, but explicitly specify device // index and use CUDA stream guard to set current CUDA stream // create a tensor on device 0 torch:: Tensor tensor0 = torch:: ones ({2, 2}, torch:: device (torch:: kCUDA)); // get a new stream from CUDA stream pool on device 0 at:: cuda:: CUDAStream myStream = at Oct 1, 2022 · To make sure whether the installation is successful, use the torch. This function is a no-op if this argument is negative. set_device(0) torch. This container parallelizes the application of the given module by splitting the input across the specified devices by chunking in the batch dimension (other objects will be copied once per device). get_device_name () output appear inconsistent. , cuda) without a device index, tensors will be allocated on whatever the current device for the device type, even after torch. device (device) [source] ¶ Context-manager that changes the selected device. How to check if your GPU/graphics card supports a particular CUDA version. cpu (): Transfers Nov 3, 2023 · RuntimeError: CUDA error: invalid device ordinal CUDA kernel errors might be asynchronously reported at some other API call, so the stacktrace below might be incorrect. If you set CUDA_DEVICE_ORDER='PCI_BUS_ID' then CUDA orders your GPU depending on how you set up your machine meaning that GPU:0 will be the GPU on your first PCI-E lane. device("cuda:1") for GPU 1. DataParallel(model,device_ids=cfg. device( "cuda:" + str (device_id)) # 選択した Aug 30, 2019 · ssh连服务器docker中运行。 可以先查看gpu运行状态，找空的gpu的id，比如是0，2，5，9这四个gpu watch -n 0. This will tell it to use only one GPU (the one with id 0) and so on: export CUDA_VISIBLE_DEVICES="0". cuda” since its initialization is performed only once. device or int, optional) – selected device. 2) For multi-device modules and CPU modules, device_ids must be None. If you have multiple of such GPU devices, then you can also pass device_id like this: cpuTensor = cpuTensor. Could you elaborate a little bit? I think get_device_properties is a device-level function and only returns the information of a device. nn. Defaults to the current CUDA device. load("your_traced_model. Using device 1 in your code will use 2 outside. Mar 18, 2020 · BTW, you mentioned the function device_count() in get_device_properties() returns 1. In most cases it’s better to use CUDA_VISIBLE_DEVICES environmental variable. device(‘cpu’) # our dataset has two classes only - background and person num_classes = 11 # use our dataset and defined transformations Feb 26, 2022 · When I look in Windows Device Manager, and look at the driver, it lists version: 25. See torch. We can also change the default CUDA device by specifying the ID as shown below – # Importing Pytorch import torch # To change The function cudaPeekAtLastError() returns the value of this variable, and the function cudaGetLastError() returns the value of this variable and also resets it to cudaSuccess. import torch. 2 / 12. Returns a copy of this object in CUDA memory. # output: 0 torch. device_count() returns? If it returns 0 that means that no device are available. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. is_available() else torch. is_available() is False. It implements the same function as CPU tensors, but they utilize GPUs for computation. It’s a no-op if this argument is a negative integer or None. Jun 22, 2023 · This tutorial shows how to get available GPU devices using PyTorch. This function is a no-op if this argument is a negative integer. 1917 When I run my code, I get the following error: InternalError: cudaGetDevice() failed. total_memory # Calculate the fraction of memory that is equal to value memory_fraction = min (1, float (value * 1024 * 1024) / total The default device is initially cpu. is_available() return False. device(‘cuda’) if torch. By default, CUDA orders the GPUs by computing power. What is wrong? Qustion: How to solve this problem? How can I manage to use the two GPUs using dataparallel? Thank you guys! This can be done via nvidia-smi (not a PyTorch function), and both your old GPU and the RTX 2070 should show up, as devices 0 and 1. set_device() is called. 接下来，让我们使用torch. get_device_properties. load(model_path, map_location='cuda:1') throws an error Apr 3, 2020 · 1. device (“cuda”, 2) the point is you have to pass the ordinal for the gpu you want to use. get_worker_info() returns various useful information in a worker process (including the worker id, dataset replica, initial seed, etc. to(device) after jit. CUDA语义. (similar to 1st torch. cuda is used to set up and run CUDA operations. set_device(hogehoge)を使う Oct 22, 2021 · I got the same issue here. I have problem in reverse -- let's say the device is already chosen, and I would like to get properties for it (the active one), not for all devices present in the Oct 15, 2018 · 13. And if i leave the parameters empty Oct 12, 2021 · File "C:\Users\Luffy\Anaconda3\lib\site-packages\torch_utils. device("cuda:0" if torch. CUDA semantics has more details about working with CUDA. Then, you check whether your nvidia driver is compatible or not. load, not as a simple call of . _cuda_getDeviceCount(), indeed I got from print(str(torch. The selected device can be changed with a torch. gpu_id (int): Device ID for target GPU dla_core (int): Core ID device¶ class torch. 0+cu121. So basically, 0 is mapped to the physical GPU 1 and 1 Aug 3, 2018 · Hi, The problem is that the current cuda device id is not valid. See this page for more info. May 23, 2023 · ok but if Automatic1111 is running and working, and the GPU is not being used, it means that the wrong device is being used, so selecting the device might resolve the issue. get_device_name() thanks a lot. device("cuda")) This is different from how it works for typical/non-JIT models, where you can Jan 22, 2024 · test_cuda. You can use both tensors and storages as arguments. mem_get_info. device_count() cuda0 = torch. When loading a model on a GPU that was trained and saved on CPU, set the map_location argument in the torch. logger. cuda(device=0) answered Feb 22, 2019 at 1:48. model = torch. to(device) But this seems not right or enough. This function is only supported for GPUs and returns the GPU index. [docs] class Device(object): """ Defines a device that can be used to specify target devices for engines Attributes: device_type (torch_tensorrt. device or int) – selected device. Aug 18, 2021 · 4. Instead of directly changing test. to("dml") works, otherwise it complains about it. get_device_properties ()函数来获取每个GPU的属性信息，包括总内存和可用内存：. # Output Pytorch CUDA Version is 11. Usage of this function is discouraged in favor of device. Mar 6, 2021 · マルチGPU環境におけるコンテキストマネージャtorch. First, identify the model of your graphics card. E. Oct 4, 2018 · Then the device that you will see within python are device 0, 1. 21. org". device) – CUDA devices. device) – The destination GPU device. type == 'cuda' else None): # do a bunch of stuff. This function imposes a slight performance cost on every Python call to the torch API (not Aug 3, 2023 · CUDA Device Query (Runtime API) version (CUDART static linking) Detected 1 CUDA Capable device(s) Device 0: "NVIDIA RTX A4000" CUDA Driver Version / Runtime Version 12. device = torch. 1. cuda¶ This package adds support for CUDA tensor types. device: Returns the device name of ‘Tensor’. Let's say I have two GPUs in my machine: a GTX 480 and a GTX 670. to(device) # 在 GPU 上训练模型 for epoch in range(num_epochs): for batch in data_loader: # 前向传递 Jan 6, 2018 · device_id = 1 v = Variable(torch. ]) # tensor([1. If you are masking devices via CUDA_VISIBLE_DEVICES all visible devices will be mapped to device ids in the range [0, nb_visible_devices]. If this object is already in CUDA memory and on the correct device, then no copy is performed and the original object is returned. Jul 15, 2020 · device = torch. get_device_name. is_available () else 'cpu') so that I can later do something like - inputs = inputs. DataParallel(model) model. 2. is_available() else 'cpu') as you can see Dec 5, 2023 · cuda:4を指定するとlistでいうところのout of rangeのGPUを指定してしまっていることになる(4つのGPUしか指定していないのでインデックスは0~3)ので、invalid device ordinalというエラーが出力される。 torch. device("cuda:0") X = X. Howell. to(torch. 免責事項. 可以使用 torch. 该包增加了对CUDA张量类型的支持，实现了与CPU张量相同的功能，但使用GPU进行计算。. Both those device ids are valid, I’m not sure whats going on here, any suggestions Source code for torch_tensorrt. Compile with `TORCH_USE_CUDA_DSA` to enable device-side assertions. In PyTorch, if you want to pass data to one specific device, you can do device = torch. the properties of the device. Tensor. 1+cu102) it seems that torch. gq zr xe wg gy fg zi gs bf vv