D2L - Local Environment Setting
2023. 6. 26. 04:52 |
반응형
* Python and pip upgrade
python --version
pip install pip --upgrade
* D2L install
pip install d2l==1.0.0a0
* Pytorch install
pip install torch==1.12.0 torchvision==0.13.0
* Jupyter lab install
pip install jupyterlab
https://jupyterlab.readthedocs.io/en/latest/getting_started/installation.html
Installation — JupyterLab 4.0.2 documentation
Installation JupyterLab can be installed using conda, mamba, pip, pipenv or docker. Warning New versions of JupyterLab may break backwards compatibility with extensions and other Jupyter customizations. As noted in Backwards Compatibility, Versions and Bre
jupyterlab.readthedocs.io
* Jupyter Lab 실행
python -m jupyterlab
==> Python 3 (ipykernel)
반응형
'Dive into Deep Learning > D2L Linear Neural Networks' 카테고리의 다른 글
D2L - 4.4. Softmax Regression Implementation from Scratch (0) | 2023.06.26 |
---|---|
D2L - 4.3. The Base Classification Model (0) | 2023.06.26 |
D2L - 4.2. The Image Classification Dataset (0) | 2023.06.26 |
D2L 4.1. Softmax Regression (0) | 2023.06.26 |
D2L - 4. Linear Neural Networks for Classification (0) | 2023.06.26 |
D2L - 3.5. Concise Implementation of Linear Regression, 이미지 분류 데이터 (Fashion-MNIST) (0) | 2023.06.22 |
D2L - 3.4. Linear Regression Implementation from Scratch , Softmax 회귀(regression) (0) | 2023.06.22 |
D2L - 3.3. Synthetic Regression Data (0) | 2023.06.22 |
D2L 3.2. Object-Oriented Design for Implementation (0) | 2023.06.22 |
D2L 3.2. 선형 회귀를 처음부터 구현하기 (0) | 2023.06.22 |