Predict the latency time of the deep learning models

Last update: Nov 12, 2021

Overview

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

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Predict the latency time of the deep learning models

Related tags

Overview

Deep Neural Network Prediction

Owner

QAQ

A PyTorch Lightning solution to training OpenAI's CLIP from scratch.

We have implemented shaDow-GNN as a general and powerful pipeline for graph representation learning. For more details, please find our paper titled Deep Graph Neural Networks with Shallow Subgraph Samplers, available on arXiv (https//arxiv.org/abs/2012.01380).

Chinese Mandarin tts text-to-speech 中文 (普通话) 语音 合成 , by fastspeech 2 , implemented in pytorch, using waveglow as vocoder,

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code release for USENIX'22 paper `On the Security Risks of AutoML`

Unsupervised clustering of high content screen samples

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