torch.cuda.DeferredCudaCallError: [address=0.0.0.0:45545, pid=2324] CUDA call failed lazily at initialization with error: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "../aten/src/ATen/cuda/CUDAContext.cpp":50, please report a bug to PyTorch. device=, num_gpus= CUDA ca...
append(calls) try: for queued_call, orig_traceback in _queued_calls: try: queued_call() except Exception as e: msg = ( f"CUDA call failed lazily at initialization with error: {str(e)}\n\n" f"CUDA call was originally invoked at:\n\n{''.join(orig_traceback)}" ) raise Deferred...
Figure 1 depicts the scheduling and execution of a number of GPU activities. With the traditional stream model (left), each GPU activity is scheduled separately by a CPU API call. Using CUDA Graphs (right), a single API call can schedule the full set of GPU activities. Figure 1. An illu...
_tls.is_initializing = True try: for queued_call, orig_traceback in _queued_calls: try: queued_call() except Exception as e: msg = (f"CUDA call failed lazily at initialization with error: {str(e)}\n\n" f"CUDA call was originally invoked at:\n\n{orig_traceback}") raise Deferred...
It was a known limitation until now. Device Assertions Support The R285 driver released with the 4.1 version of the toolkit supports device assertions. CUDA_GDB supports the assertion call and stops the execution of the application when the assertion is hit. Then the variables and memory can ...
It is possible for numDependencies to be 0, in which case the node will be placed at the root of the graph. pDependencies may not have any duplicate entries. A handle to the new node will be returned in pGraphNode. If hGraph contains allocation or free nodes, this call will return...
_initialized, since some queued calls # may themselves call _lazy_init() for queued_call, orig_traceback in _queued_calls: try: queued_call() except Exception as e: msg = ("CUDA call failed lazily at initialization with error: {}\n\n" "CUDA call was originally invoked at:\n\n{}"...
Originally published at: https://developer.nvidia.com/blog/gpu-pro-tip-cuda-7-streams-simplify-concurrency/ Heterogeneous computing is about efficiently using all processors in the system, including CPUs and GPUs. To…
CudaCallError(msg) from e torch.cuda.DeferredCudaCallError: CUDA call failed lazily at initialization with error: device >= 0 && device < num_gpus INTERNAL ASSERT FAILED at "../aten/src/ATen/cuda/CUDAContext.cpp":50, please report a bug to PyTorch. CUDA call was originally invoked at:...
This was needed to add support for older GPUs and based on the testing we did at the time, didn't seem to have a major performance impact for newer GPUs. For Jetson support Compute Capability 5.0 support isn't relevant as far as I know, so this flag can be omitted. Contributor Author...