Buckets:
| #### Luôn luôn **sync file bắt đầu từ source code local** (Vì dùng cursor để debug) => tải lên Colab, Kaggle, ThueGPU.vn | |
| **Đang Training data is on MINDsmall** | |
| **LƯU Ý**: | |
| - Running **data_preprocess.py** script and Tensorboard **on Computer** | |
| - Glove: https://nlp.stanford.edu/data/glove.840B.300d.zip | |
| - entity_embedding_dim = 100 instead of 300 | |
| - Training on GPU P100 in Kaggle, A4000 on ThueGPU.vn (~24h) | |
| - Test training on Google Colab | |
| **Using the REMARK Tip:** ~~~~ | |
| The REMARK environment variable lets you customize the run name in TensorBoard for better readability: | |
| - REMARK="num-filters-300-window-size-5" python3 src/train.py | |
| **Ubuntu setup note**: | |
| ```bash | |
| sudo apt-get update | |
| sudo apt-get install python3-pip-whl python3-setuptools-whl python3.10-venv | |
| python3 -m venv venv | |
| source venv/bin/activate | |
| pip install -r requirements.txt | |
| export MODEL_NAME=NAML_LSTUR_DKN | |
| python3 train.py | |
| ``` |
Xet Storage Details
- Size:
- 964 Bytes
- Xet hash:
- acd68ded07b361e8e43d0ec69303b732c473717ad35842d8b9c0f6e3d6b72399
·
Xet efficiently stores files, intelligently splitting them into unique chunks and accelerating uploads and downloads. More info.