December 14, 2023 - The Memory Profiler is an added feature of the PyTorch Profiler that categorizes memory usage over time.

December 24, 2024 - Running this code generates a profile.pkl file that contains a history of GPU memory usage during execution. You can visualize this history at: https://pytorch.org/memory_viz.

July 20, 2022 - This recipe explains how to use PyTorch profiler and measure the time and memory consumption of the model’s operators.

September 3, 2024 - Increasing the num_workersparameter and settingpin_memoryto Trueon my DataLoaders did not work too. Initial instinct when debugging was to see the nvidia-smi logs of the pod. All instances went 0% GPU usage and only spiked for a few iterations at a time. This didn’t make sense for me. All inputs and models were already loaded via CUDA and PyTorch did detect CUDA is available.

May 28, 2019 - In this repo, I'm going to share some useful tools to help debugging OOM, or to inspect the underlying mechanism if anyone is interested in. The memory profiler is a modification of python's line_profiler, it gives the memory usage info for ...

December 9, 2022 - When running out of memory, this function may be called multiple times because as we saw with the spikes earlier convolution might run out of memory and retry with an algorithm that uses less scratch space. The last time the observer is called will hold the information from an uncaught OOM error. torch.profiler can also record memory usage along with additional helpful information such as the location in the module hierarchy, the category of tensor being allocated, the tensor sizes, and the set of operators used to generate the tensor.

Quantization to lower precisions (8-bit, 4-bit, etc) will reduce memory requirements. Estimated GPU VRAM in GB = 40x model parameters (in billions) For example, for LLaMA-3 with 7 billion parameters, we estimate minimum 280GB to train it. This exceeds the VRAM of even a single H100 accelerator, requiring distributed training. See HOWTO: PyTorch Fully Sharded Data Parallel (FSDP) for more details.

1 week ago - By passing profile_memory=True to PyTorch profiler, we enable the memory profiling functionality which records the amount of memory (used by the model’s tensors) that was allocated (or released) during the execution of the model’s operators.

June 14, 2023 - I’m currently using the torch.profiler.profile to analyze memory peak on my GPUs. I fristly use the argument on_trace_ready to generate a tensorboard and read the information by hand, but now I want to read those information directly in my code. So I’ve setup my profiler as : self.prof = torch.profiler.profile( activities=[ torch.profiler.ProfilerActivity.CPU torch.profiler.ProfilerActivity.CUDA ], record_shapes=True, profile_memory=True ) And then I used the f...

April 18, 2025 - PyTorch’s torch.profiler.profile tool offers a deeper view into memory usage, breaking down allocations by operation and layer to pinpoint where your model is hitting bottlenecks.

The PyTorch Profiler (torch.profiler) is the standard tool for answering these questions. The profiler allows you to inspect the time and memory costs associated with different parts of your model's execution, encompassing both Python operations on the CPU and CUDA kernel executions on the GPU.

profile_memory¶ (bool) – Whether to report memory usage, default: True (Introduced in PyTorch 1.6.0)

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Examples of bottlenecks might be related to memory usage and/or identifying functions/libraries that use the majority of the computing time. PyTorch Profiler is a profiling tool for analyzing Deep Learning models, which is based on collecting performance metrics during training and inference.

September 23, 2024 - I followed the tutorial from the PyTorch blog. The main difference in my case is that I profiled the memory usage during the inference step, rather than training. I profiled the model-building process and 4 iterations of inference. Below is a snapshot of the memory usage visualization.

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profile_memory (bool) - track tensor memory allocation/deallocation. record_shapes (bool) - save information about operator’s input shapes. with_stack (bool) - record source information (file and line number) for the ops. ... Holistic Trace Analysis (HTA) is an open source performance analysis and visualization Python library for PyTorch users.

May 29, 2024 - PyTorch Profiler is a performance analysis tool that enables developers to examine various aspects of model training and inference in PyTorch. It allows users to collect and analyze detailed profiling information, including GPU/CPU utilization, ...