apebench
[Neurips 2024] A benchmark suite for autoregressive neural emulation of PDEs. (≥46 PDEs in 1D, 2D, 3D; Differentiable Physics; Unrolled Training; Rollout Metrics)
파일 탐색기
- build_docs.yml
- pre_commit.yml
- publish.yml
- test.yml
- validate.yml
- __init__.py
- _activation_function.py
- _architectures.py
- _initial_condition.py
- _metrics.py
- _optimization.py
- __init__.py
- _convection.py
- _linear.py
- _nonlinear.py
- __init__.py
- _convection.py
- _linear.py
- _nonlinear.py
- __init__.py
- _convection.py
- _gray_scott.py
- _linear.py
- _navier_stokes.py
- _nonlinear.py
- _poisson.py
- _polynomial.py
- _special_linear.py
- _swift_hohenberg.py
- __init__.py
- __init__.py
- __main__.py
- _base_scenario.py
- _cli.py
- _corrected_stepper.py
- _run.py
- _scraper.py
- _utils.py
- dataframe_manipulation.md
- high_level.md
- low_level.md
- one_experiment.md
- study.md
- generic.md
- linear.md
- nonlinear.md
- react.md
- generic.md
- linear.md
- nonlinear.md
- react.md
- generic.md
- linear.md
- linear_special.md
- navier_stokes.md
- nonlinear.md
- poisson.md
- react.md
- base.md
- overview.md
- curations.md
- utilities.md
- utils.md
- animate_emulator_learning_burgers.ipynb
- benchmark_flax_models_with_linen.ipynb
- benchmark_flax_models_with_nnx.ipynb
- benchmark_pytorch_models.ipynb
- component_extensions.ipynb
- difficulty_and_receptive_field_advection_1d.ipynb
- scrape_datasets.ipynb
- simple_advection_1d_learning.ipynb
- sources_of_randomness_and_reproducibility.ipynb
- apebench_favicon.png
- apebench_logo.png
- apebench_teaser_burgers.png
- mathjax.js
- faq.md
- gotchas.md
- tips.md
- extending_apebench.md
- index.md
- requirements.txt
- typical_workflow.md
- use_cases.md
- using_cli.md
- test_activation_fn_components.py
- test_architecture_components.py
- test_initial_condition_components.py
- test_metric_components.py
- test_optimization_components.py
- requirements.txt
- test_builtin_scenarios.py
- test_data_generation.py
- test_external_metric_computation.py
- test_trainer_parsing.py
- test_check_nans.py
- .gitignore
- .pre-commit-config.yaml
- CITATION.cff
- explore_sample_data_streamlit.py
- LICENSE.txt
- mkdocs.yml
- pyproject.toml
- README.md
- setup.cfg
# CDN으로 사용하기
jsDelivrjsDelivr는 공개 GitHub 리포지토리를 별도 설정 없이 CDN으로 즉시 서빙합니다. 버전과 파일을 고르면 웹페이지에 바로 붙일 수 있는 링크와 예시 코드가 만들어집니다.
명령어 용어집
이 문서에서 사용된 명령어를 모아봤습니다. 낯선 명령어가 있다면 펼쳐서 확인해보세요.
conda activate
설명 보기 ▼
conda activate
Activate a conda environment.
See also: `conda deactivate`.
conda activate myenv
Activate an existing environment named `myenv`:
conda activate {{path/to/myenv}}
Activate an existing environment located at custom path:
conda activate --stack myenv
Stack `myenv` environment on top of a previous environment making libraries/commands/variables from both accessible:
conda create
설명 보기 ▼
conda create
Create new conda environments.
conda create {{[-y|--yes]}} {{[-n|--name]}} py39 python=3.9 "numpy>=1.11" scipy
Create a new environment named `py39`, install Python 3.9, NumPy v1.11 or above in it, and the latest stable version of SciPy. Say yes to all confirmations:
conda create {{[-n|--name]}} myenv --file {{file1.yml}} --file {{file2.yml}}
Create a new environment named `myenv` and install packages listed in files:
conda create {{[-p|--prefix]}} {{path/to/myenv}}
Create a new environment at a custom path (i.e. prefix):
pip install
설명 보기 ▼
pip install
Install Python packages.
pip install {{package1 package2 ...}}
Install one or more packages:
pip install {{package1 package2 ...}} {{[-U|--upgrade]}}
Upgrade all specified packages to the latest version, installing any that are not already present:
pip install {{package}}=={{version}}
Install a specific version of a package:
streamlit
설명 보기 ▼
streamlit
Python에서 상호작용하는 데이터 기반 웹 앱을 만드는 프레임워크.
더 많은 정보: <https://docs.streamlit.io/develop/api-reference/cli>.
streamlit hello
Streamlit 설치 여부 확인:
streamlit run {{project_name}}
Streamlit 애플리케이션 실행:
streamlit --help
도움말 표시: