Paper: “Domain Generalization: A Survey” https://arxiv.org/abs/2103.02503
Dataset yang dipakai untuk Generalization papers:
Teknik untuk generalization
Domain Generalization: A Survey,
Jurnal di Ubuntu Bikin Penuh
Salah satu masalah di Ubuntu adalah fitur jurnal membuat storage cepat penuh.
Solusi:
Cek penggunaan space untuk journal:
journalctl --disk-usageHapus jurnal yang lebih dari sekian hari
sudo journalctl --vacuum-time=2daysHapus jurnal sampai storage yang terpakai di bawah batas tertentu
sudo journalctl --vacuum-size=100MMigrasi Thingsboard ke Server Lain
Prinsip migrasi Thingsboard ke server lain:
- install Thingsboard di server baru, dengan versi sama dengan server asli
- Cek Thingsboard di server baru berjalan normal
- Matikan Thingsboard di server lama dan server baru
- copy database dan file konfigurasi ke server baru
- upload database ke server baru
- timpa konfigurasi di server baru dengan konfigurasi dari server lama
- Nyalakan Thingsboard di server baru
Sumber: https://groups.google.com/g/thingsboard/c/P7TcOnVIRqg
On the first computer, backup the database using postgres dump tool:
pg_dump thingsboard --host=localhost --username postgres -W > thingsboard.dump
Then, backup the /etc/thingsboard/conf directory:
sudo tar cvfz tb.tar.gz /etc/thingsboard/conf/*
Copy thingsboard.dump and tb.tar.gz to the new computer. On the new computer, with a fresh install of Thingsboard as per the posted instructions:
Stop Thingsboard, then:
sudo su postgres
dropdb thingsboard # this removes the database
createdb -T template0 thingsboard
psql thingsboard -U postgres -W < thingsboard.dump
Copy the /etc config files:
cd /
sudo tar xvfz /home/ubuntu/tb.tar.gz
Start Thingsboard.Cara Mengalihkan Redirect di Apache 2
Berikut ini cara mengalihkan akses di Apache 2
RewriteEngine On
RewriteCond %{HTTPS} off
RewriteRule (.*) https://%{HTTP_HOST}%{REQUEST_URI}
RewriteEngine On
RewriteRule (.*) https://iptek.web.id/%{REQUEST_URI}Replikasi Solusi Kompetisi Global Wheat Challenge 2021
Pada artikel ini dibahas usaha untuk mereplikasi solusi dari GWC_solution
Hardware yang dipakai: RTX 3090
Catatan
- RTX3090 tidak kompatibel dengan Cuda 10.2 , minimal perlu CUDA 11.3
- PyTorch 1.9.0 tidak kompatibel dengan CUDA 11.3, sehingga perlu diganti dengan PyTorch 1.10.0
- Script GWC_YOLOv5 tidak kompatibel dengan PyTorch 1.12.0
Prosedur
Create Conda environment
conda create --name gwc8 python=3.7.10 scipy=1.4.
conda activate gwc8Install PyTorch
pip3 install torch==1.10.0 torchvision torchaudio --extra-index-url https://download.pytorch.org/whl/cu113Install prerequisites
pip install -r requirements.txt
pip install numpy==1.19.5
pip uninstall -y PyYAML
pip install PyYAML==5.3.1
pip install ensemble_boxes
pip install setuptools==59.5.0
pip install jupyter
pip install matplotlib
pip install opencv-python
pip install tensorboardClone GWC_solution
cd /home/admin
git clone https://github.com/ksnxr/GWC_solution.gitClone GWC_YOLOv5
cd GWC_solution
git clone https://github.com/ksnxr/GWC_YOLOv5.gitJalankan jupyter notebook
jupyter notebook --allow-root --no-browser --ip=0.0.0.0Setelah itu buka jupyter notebook dari browser
Eksekusi script berikut
- Use data-cleaning.ipynb to generate clean_train.csv.
- Use KFold.ipynb to generate 4-folds train and validation indexes.
- Use basis-0.ipynb, basis-1.ipynb.basis-2.ipynb and basis-3.ipynb to train on the four folds.
- Use pseudo-original-0.ipynb, pseudo-original-1.ipynb, pseudo-original-2.ipynb, pseudo-original-3.ipynb and pseudo-master-3.ipynb to train 5 pseudo models.
- Use get-labels.ipynb to ensemble the 5 weights obtained in step 3 and generate labels for subsequent training.
- Use pseudo-master-final.ipynb to train the final model.
Training
python train.py --name 4fold0 --img 800 --batch 8 --epochs 35 --data custom.yaml --weights yolov5x.pt --cache-images --save_period 1Monitor training
cd /home/admin/GWC_solution/GWC_YOLOv5
tensorboard --logdir runs/train --bind_all
Berikut ini data flow diagram dari proses komputasi
Referensi
Model GPT2 Medium untuk Bahasa Indonesia
Huggingface: https://huggingface.co/indonesian-nlp/gpt2-medium-indonesian
Paper:
Links: https://github.com/cahya-wirawan/indonesian-language-models/tree/master/Transformers
Perilaku Barter pada Reinforcement Learning Dengan Multi-Agent
An example map in Fruit Market
Referensi
7 Layer OSI Model
Akses Proxy SOCKS5 di Ubuntu
Referensi
Digital Ocean Tutorial: https://www.digitalocean.com/community/tutorials/how-to-set-up-dante-proxy-on-ubuntu-20-04
Cryptomining Profitability
- GPU Profitability https://whattomine.com/gpus/
- ETH and ETC mining pool profitability https://miningpoolsprofits.com/
- GPU hashrate & profitability https://hashrate.no/
- Raptoreum (RTM) profitability https://mineraptoreum.com/