Deep Neural Network Prediction

Step 1. Genernate random parameters and Run them sequentially : $ python3 collect_data.py -gp -ep -pp -pl pooling -num 10 -shuffle -d 1080ti

Step 2. Data Timeline parser : $ python3 preprocess_data.py -pt -pl pooling -d 1080ti

Step 3. Combine all raw data : $ python3 preprocess_data.py -c -pl pooling -d 1080ti

Step 4. Split raw data to train and test data as performance prediction inputs $ python3 preprocess_data.py -sp -pl pooling -d 1080ti

Step 5. Train Model :

python3 train_model.py -ftf ./utils/Feature_Target/conv_pre.json -log2file 1 -e 1000 -st 400 -sg 0.5 -lf maple -n perfnetA -pd 1080ti -pl convolution -psl pre
    
python3 train_model.py -ftf ./utils/Feature_Target/conv_exe.json -log2file 1 -e 1000 -st 400 -sg 0.5 -lf maple -n perfnetA -pd 1080ti -pl convolution -psl exe
        
python3 train_model.py -ftf ./utils/Feature_Target/conv_post.json -log2file 1 -e 1000 -st 400 -sg 0.5 -lf maple -n perfnetA -pd 1080ti -pl convolution -psl post

Step 6. Generate model csv file :

python3 verify_model.py -gmc --model lenet -b 1

Step 7. Predict model csv file :

python3 verify_model.py -pdm -n perfnetA -d 1080ti -l -eva -lf maple --model lenet -b 1

Or you can use script to predict all models with all batches:

python3 predict_script.py -n perfnetA -d 1080ti -lf maple

Predict the latency time of the deep learning models

Related tags

Overview

Deep Neural Network Prediction

Owner

QAQ

Propose a principled and practically effective framework for unsupervised accuracy estimation and error detection tasks with theoretical analysis and state-of-the-art performance.

Database Reasoning Over Text project for ACL paper

CLNTM - Contrastive Learning for Neural Topic Model

using yolox+deepsort for object-tracker

TaCL: Improving BERT Pre-training with Token-aware Contrastive Learning

Official repository of Semantic Image Matting

Implementation of our NeurIPS 2021 paper "A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs".

Degree-Quant: Quantization-Aware Training for Graph Neural Networks.

Pytorch implementation of TailCalibX : Feature Generation for Long-tail Classification

Symbolic Music Generation with Diffusion Models

Bayesian Image Reconstruction using Deep Generative Models

GUI for TOAD-GAN, a PCG-ML algorithm for Token-based Super Mario Bros. Levels.

Compressed Video Action Recognition

3D-Reconstruction 基于深度学习方法的单目多视图三维重建

Build and run Docker containers leveraging NVIDIA GPUs

Code for "R-GCN: The R Could Stand for Random"

Compositional and Parameter-Efficient Representations for Large Knowledge Graphs

Time-Optimal Planning for Quadrotor Waypoint Flight

Parameter Efficient Deep Probabilistic Forecasting

Simulation-based performance analysis of server-less Blockchain-enabled Federated Learning