OOM is a nightmare for PyTorch users. However, most of them do not know the exact memory footprint during training. This project helps you depict the GPU memory usage changing curve during training. We record the peak GPU memory of an operator at the moment of the operator finished. The operator can be computing of a FWD submodule or a BWD submodule.
python train.py
python visualize.py memstats.pkl
Ego4D Episodic Memory Benchmark EGO4D is the world's largest egocentric (first p
Memory Efficient Attention Pytorch Implementation of a memory efficient multi-head attention as proposed in the paper, Self-attention Does Not Need O(
Self-Supervised Policy Adaptation during Deployment PyTorch implementation of PAD and evaluation benchmarks from Self-Supervised Policy Adaptation dur
MicRank: Learning to Rank Microphones for Distant Speech Recognition Application Scenario Many applications nowadays envision the presence of multiple
Note: This is an alpha (preview) version which is still under refining. nn-Meter is a novel and efficient system to accurately predict the inference l
Deep Learning - EAT Challenge About This project is part of an AI challenge of the DeepLearning course 2021 at the University of Augsburg. The objecti
CUP-DNN CUP-DNN is a deep neural network model used to predict tissues of origin for cancers of unknown of primary. The model was trained on the expre
ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees This repository is the official implementation of the empirica
Energy-Estimator-for-Eyeriss-like-Architecture- An energy estimator for eyeriss-like DNN hardware accelerator This is an energy estimator for eyeriss-
ActNN : Activation Compressed Training This is the official project repository for ActNN: Reducing Training Memory Footprint via 2-Bit Activation Comp
Attention for PyTorch with Linear Memory Footprint Unofficially implements https://arxiv.org/abs/2112.05682 to get Linear Memory Cost on Attention (+
Simple-DMA a simple Dual Memory Architecture for classifications. based on the paper Dual-Memory Deep Learning Architectures for Lifelong Learning of
carbon-footprint-calculator Conda distribution ~/anaconda3/bin/conda install anaconda-client conda-build ~/anaconda3/bin/conda config --set anaconda_u
Memory In Memory Networks It is based on the paper Memory In Memory: A Predictive Neural Network for Learning Higher-Order Non-Stationarity from Spati
mtomo Multiple types of NN model optimization environments. It is possible to directly access the host PC GUI and the camera to verify the operation.
Anakin2.0 Welcome to the Anakin GitHub. Anakin is a cross-platform, high-performance inference engine, which is originally developed by Baidu engineer
GrabGpu_py a scripts for grab gpu when gpu is free. WaitCondition: gpu_memory >
Gradient Cache Gradient Cache is a simple technique for unlimitedly scaling contrastive learning batch far beyond GPU memory constraint. This means tr
Segcache: a memory-efficient and scalable in-memory key-value cache for small objects This repo contains the code of Segcache described in the followi
3 Feb 18, 2022
180 Jan 5, 2023
101 Nov 1, 2022
20 Nov 10, 2022
244 Jan 6, 2023
1 Oct 24, 2021
1 Oct 27, 2021
2 Oct 2, 2022
2 Mar 26, 2022
178 Jan 5, 2023
11 Jan 9, 2022
1 Jan 27, 2022
7 Sep 26, 2022
12 May 30, 2022
24 Mar 2, 2022
514 Dec 28, 2022
3 Jun 18, 2022
198 Dec 29, 2022
78 Jan 7, 2023