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# Contributor Covenant Code of Conduct
## Our Pledge
In the interest of fostering an open and welcoming environment, we as
contributors and maintainers pledge to making participation in our project and
our community a harassment-free experience for everyone, regardless of age, body
size, disability, ethnicity, sex characteristics, gender identity and expression,
level of experience, education, socio-economic status, nationality, personal
appearance, race, religion, or sexual identity and orientation.
## Our Standards
Examples of behavior that contributes to creating a positive environment
include:
* Using welcoming and inclusive language
* Being respectful of differing viewpoints and experiences
* Gracefully accepting constructive criticism
* Focusing on what is best for the community
* Showing empathy towards other community members
Examples of unacceptable behavior by participants include:
* The use of sexualized language or imagery and unwelcome sexual attention or
advances
* Trolling, insulting/derogatory comments, and personal or political attacks
* Public or private harassment
* Publishing others' private information, such as a physical or electronic
address, without explicit permission
* Other conduct which could reasonably be considered inappropriate in a
professional setting
## Our Responsibilities
Project maintainers are responsible for clarifying the standards of acceptable
behavior and are expected to take appropriate and fair corrective action in
response to any instances of unacceptable behavior.
Project maintainers have the right and responsibility to remove, edit, or
reject comments, commits, code, wiki edits, issues, and other contributions
that are not aligned to this Code of Conduct, or to ban temporarily or
permanently any contributor for other behaviors that they deem inappropriate,
threatening, offensive, or harmful.
## Scope
This Code of Conduct applies both within project spaces and in public spaces
when an individual is representing the project or its community. Examples of
representing a project or community include using an official project e-mail
address, posting via an official social media account, or acting as an appointed
representative at an online or offline event. Representation of a project may be
further defined and clarified by project maintainers.
## Enforcement
Instances of abusive, harassing, or otherwise unacceptable behavior may be
reported by contacting the project team at dongxuanyi888@gmail.com. All
complaints will be reviewed and investigated and will result in a response that
is deemed necessary and appropriate to the circumstances. The project team is
obligated to maintain confidentiality with regard to the reporter of an incident.
Further details of specific enforcement policies may be posted separately.
Project maintainers who do not follow or enforce the Code of Conduct in good
faith may face temporary or permanent repercussions as determined by other
members of the project's leadership.
## Attribution
This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
[homepage]: https://www.contributor-covenant.org
For answers to common questions about this code of conduct, see
https://www.contributor-covenant.org/faq

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# Contributing to AutoDL-Projects
:+1::tada: First off, thanks for taking the time to contribute! :tada::+1:
The following is a set of guidelines for contributing to AutoDL-Projects.
## Table Of Contents
[How Can I Contribute?](#how-can-i-contribute)
* [Reporting Bugs](#reporting-bugs)
* [Suggesting Enhancements](#suggesting-enhancements)
* [Your First Code Contribution](#your-first-code-contribution)
## How Can I Contribute?
### Reporting Bugs
This section guides you through submitting a bug report for AutoDL-Projects.
Following these guidelines helps maintainers and the community understand your report :pencil:, reproduce the behavior :computer: :computer:, and find related reports :mag_right:.
When you are creating a bug report, please include as many details as possible.
Fill out [the required template](https://github.com/D-X-Y/AutoDL-Projects/blob/main/.github/ISSUE_TEMPLATE/bug-report.md). The information it asks for helps us resolve issues faster.
> **Note:** If you find a **Closed** issue that seems like it is the same thing that you're experiencing, open a new issue and include a link to the original issue in the body of your new one.
### Suggesting Enhancements
Please feel free to email me (dongxuanyi888@gmail.com).
### Your First Code Contribution
Please feel free to open an Pull Requests.

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---
name: Bug Report
about: Create a report to help us improve
title: ''
labels: ''
assignees: ''
---
**Describe the bug**
A clear and concise description of what the bug is.
**To Reproduce**
Please provide a small script to reproduce the behavior:
```
codes to reproduce the bug
```
Please let me know your OS, Python version, PyTorch version.
**Expected behavior**
A clear and concise description of what you expected to happen.
**Screenshots**
If applicable, add screenshots to help explain your problem.

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---
name: Questions w.r.t. an AutoDL algorithm
about: Ask questions about or discuss on some algorithms
title: ''
labels: ''
assignees: ''
---
**Which Algorithm**
Please put the title of the AutoDL algorithm that you want to ask here.
**Describe the Question**
A clear and concise description of what the bug is.

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name: Test Spaces
on:
push:
branches:
- main
pull_request:
branches:
- main
jobs:
build:
strategy:
matrix:
os: [ubuntu-18.04, ubuntu-20.04, macos-latest]
python-version: [3.6, 3.7, 3.8, 3.9]
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
- name: Lint with Black
run: |
python -m pip install black
python --version
python -m black --version
echo $PWD ; ls
python -m black ./exps -l 88 --check --diff --verbose
python -m black ./tests -l 88 --check --diff --verbose
python -m black ./xautodl/x* -l 88 --check --diff --verbose
python -m black ./xautodl/spaces -l 88 --check --diff --verbose
python -m black ./xautodl/trade_models -l 88 --check --diff --verbose
python -m black ./xautodl/procedures -l 88 --check --diff --verbose
python -m black ./xautodl/config_utils -l 88 --check --diff --verbose
python -m black ./xautodl/log_utils -l 88 --check --diff --verbose
- name: Install XAutoDL from source
run: |
pip install .
- name: Test Search Space
run: |
python -m pip install pytest
python -m pip install torch torchvision
python -m pip install parameterized
echo $PWD
echo "Show what we have here:"
ls
python --version
python -m pytest ./tests/test_import.py -s
python -m pytest ./tests/test_basic_space.py -s
shell: bash
- name: Test Math
run: |
python -m pytest ./tests/test_math*.py -s
shell: bash
- name: Test Synthetic Data
run: |
python -m pytest ./tests/test_synthetic*.py -s
shell: bash

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name: Test Xmisc
on:
push:
branches:
- main
pull_request:
branches:
- main
jobs:
build:
strategy:
matrix:
os: [ubuntu-18.04, ubuntu-20.04, macos-latest]
python-version: [3.6, 3.7, 3.8, 3.9]
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
- name: Install XAutoDL from source
run: |
pip install .
- name: Test Xmisc
run: |
python -m pip install pytest
python -m pip install torch torchvision
python -m pip install parameterized
echo $PWD
echo "Show what we have here:"
ls
python --version
python -m pytest ./tests/test_misc* -s
shell: bash

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name: Test Super Model
on:
push:
branches:
- main
pull_request:
branches:
- main
jobs:
build:
strategy:
matrix:
os: [ubuntu-18.04, ubuntu-20.04, macos-latest]
python-version: [3.6, 3.7, 3.8, 3.9]
runs-on: ${{ matrix.os }}
steps:
- uses: actions/checkout@v2
- name: Set up Python ${{ matrix.python-version }}
uses: actions/setup-python@v2
with:
python-version: ${{ matrix.python-version }}
- name: Install XAutoDL from source
run: |
pip install .
- name: Test Super Model
run: |
python -m pip install pytest
python -m pip install parameterized
python -m pip install torch torchvision
python -m pytest ./tests/test_super_*.py
shell: bash
- name: Test TAS (NeurIPS 2019)
run: |
python -m pytest ./tests/test_tas.py
shell: bash

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.DS_Store
# Byte-compiled / optimized / DLL files
__pycache__/
*.py[cod]
*$py.class
# C extensions
*.so
# Distribution / packaging
.Python
env/
build/
develop-eggs/
dist/
downloads/
eggs/
.eggs/
lib64/
parts/
sdist/
var/
*.egg-info/
.installed.cfg
*.egg
# PyInstaller
# Usually these files are written by a python script from a template
# before PyInstaller builds the exe, so as to inject date/other infos into it.
*.manifest
*.spec
# Installer logs
pip-log.txt
pip-delete-this-directory.txt
# Unit test / coverage reports
htmlcov/
.tox/
.coverage
.coverage.*
.cache
nosetests.xml
coverage.xml
*,cover
.hypothesis/
# Translations
*.mo
*.pot
# Django stuff:
*.log
local_settings.py
# Flask stuff:
instance/
.webassets-cache
# Scrapy stuff:
.scrapy
# Sphinx documentation
docs/_build/
# PyBuilder
target/
# IPython Notebook
.ipynb_checkpoints
# pyenv
.python-version
# celery beat schedule file
celerybeat-schedule
# dotenv
.env
# virtualenv
venv/
ENV/
# Spyder project settings
.spyderproject
# Rope project settings
.ropeproject
# Pycharm project
.idea
snapshots
*.pytorch
*.tar.bz
data
.*.swp
main_main.py
*.pdf
*/*.pdf
# Device
scripts-nas/.nfs00*
*/.nfs00*
*.DS_Store
# logs and snapshots
output
logs
# snapshot
a.pth
cal-merge*.sh
GPU-*.sh
cal.sh
aaa
cx.sh
NAS-Bench-*-v1_0.pth
lib/NAS-Bench-*-v1_0.pth
others/TF
scripts-search/l2s-algos
TEMP-L.sh
.nfs00*
*.swo
*/*.swo
# Visual Studio Code
.vscode
mlruns*
outputs
pytest_cache
*.pkl
*.pth
*.tgz

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[submodule ".latent-data/qlib"]
path = .latent-data/qlib
url = https://github.com/microsoft/qlib.git
[submodule ".latent-data/NATS-Bench"]
path = .latent-data/NATS-Bench
url = https://github.com/D-X-Y/NATS-Bench.git
[submodule ".latent-data/NAS-Bench-201"]
path = .latent-data/NAS-Bench-201
url = https://github.com/D-X-Y/NAS-Bench-201.git

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# This file shows the major updates of this repo.
- [2020.04.11] [4ef9531](https://github.com/D-X-Y/AutoDL-Projects/tree/4ef9531) Add change log as `CHANGE-LOG.md`.
- [2019.12.20] [69ca086](https://github.com/D-X-Y/AutoDL-Projects/tree/69ca086) Release NAS-Bench-201.
- [2019.09.28] [f8f3f38](https://github.com/D-X-Y/AutoDL-Projects/tree/f8f3f38) TAS and SETN codes were publicly released.
- [2019.01.31] [13e908f](https://github.com/D-X-Y/AutoDL-Projects/tree/13e908f) GDAS codes were publicly released.
- [2020.07.01] [a45808b](https://github.com/D-X-Y/AutoDL-Projects/tree/a45808b) Upgrade NAS-API to the 2.0 version.
- [2020.09.16] [7052265](https://github.com/D-X-Y/AutoDL-Projects/tree/7052265) Create NATS-BENCH.
- [2020.10.15] [446262a](https://github.com/D-X-Y/AutoDL-Projects/tree/446262a) Update NATS-BENCH to version 1.0
- [2020.12.20] [dae387a](https://github.com/D-X-Y/AutoDL-Projects/tree/dae387a) Update NATS-BENCH to version 1.1
- [2021.05.18] [98fadf8](https://github.com/D-X-Y/AutoDL-Projects/tree/98fadf8) Before moving to `xautodl`
- [2021.05.21] [df99173](https://github.com/D-X-Y/AutoDL-Projects/tree/df99173) `xautodl` is close to ready

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MIT License
Copyright (c) since 2019.01.01, author: Xuanyi Dong (GitHub: https://github.com/D-X-Y)
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:
The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.
THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
SOFTWARE.

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<p align="center">
<img src="https://xuanyidong.com/resources/images/AutoDL-log.png" width="400"/>
</p>
---------
[![MIT licensed](https://img.shields.io/badge/license-MIT-brightgreen.svg)](LICENSE.md)
Automated Deep Learning Projects (AutoDL-Projects) is an open source, lightweight, but useful project for everyone.
This project implemented several neural architecture search (NAS) and hyper-parameter optimization (HPO) algorithms.
中文介绍见[README_CN.md](https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/README_CN.md)
**Who should consider using AutoDL-Projects**
- Beginners who want to **try different AutoDL algorithms**
- Engineers who want to **try AutoDL** to investigate whether AutoDL works on your projects
- Researchers who want to **easily** implement and experiement **new** AutoDL algorithms.
**Why should we use AutoDL-Projects**
- Simple library dependencies
- All algorithms are in the same codebase
- Active maintenance
## AutoDL-Projects Capabilities
At this moment, this project provides the following algorithms and scripts to run them. Please see the details in the link provided in the description column.
<table>
<tbody>
<tr align="center" valign="bottom">
<th>Type</th>
<th>ABBRV</th>
<th>Algorithms</th>
<th>Description</th>
</tr>
<tr> <!-- (1-st row) -->
<td rowspan="6" align="center" valign="middle" halign="middle"> NAS </td>
<td align="center" valign="middle"> TAS </td>
<td align="center" valign="middle"> <a href="https://arxiv.org/abs/1905.09717">Network Pruning via Transformable Architecture Search</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NeurIPS-2019-TAS.md">NeurIPS-2019-TAS.md</a> </td>
</tr>
<tr> <!-- (2-nd row) -->
<td align="center" valign="middle"> DARTS </td>
<td align="center" valign="middle"> <a href="https://arxiv.org/abs/1806.09055">DARTS: Differentiable Architecture Search</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/ICLR-2019-DARTS.md">ICLR-2019-DARTS.md</a> </td>
</tr>
<tr> <!-- (3-nd row) -->
<td align="center" valign="middle"> GDAS </td>
<td align="center" valign="middle"> <a href="https://arxiv.org/abs/1910.04465">Searching for A Robust Neural Architecture in Four GPU Hours</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/CVPR-2019-GDAS.md">CVPR-2019-GDAS.md</a> </td>
</tr>
<tr> <!-- (4-rd row) -->
<td align="center" valign="middle"> SETN </td>
<td align="center" valign="middle"> <a href="https://arxiv.org/abs/1910.05733">One-Shot Neural Architecture Search via Self-Evaluated Template Network</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/ICCV-2019-SETN.md">ICCV-2019-SETN.md</a> </td>
</tr>
<tr> <!-- (5-th row) -->
<td align="center" valign="middle"> NAS-Bench-201 </td>
<td align="center" valign="middle"> <a href="https://openreview.net/forum?id=HJxyZkBKDr"> NAS-Bench-201: Extending the Scope of Reproducible Neural Architecture Search</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md">NAS-Bench-201.md</a> </td>
</tr>
<tr> <!-- (6-th row) -->
<td align="center" valign="middle"> NATS-Bench </td>
<td align="center" valign="middle"> <a href="https://xuanyidong.com/assets/projects/NATS-Bench"> NATS-Bench: Benchmarking NAS Algorithms for Architecture Topology and Size</a> </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/NATS-Bench/blob/main/README.md">NATS-Bench.md</a> </td>
</tr>
<tr> <!-- (7-th row) -->
<td align="center" valign="middle"> ... </td>
<td align="center" valign="middle"> ENAS / REA / REINFORCE / BOHB </td>
<td align="center" valign="middle"> Please check the original papers </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/NAS-Bench-201.md">NAS-Bench-201.md</a> <a href="https://github.com/D-X-Y/NATS-Bench/blob/main/README.md">NATS-Bench.md</a> </td>
</tr>
<tr> <!-- (start second block) -->
<td rowspan="1" align="center" valign="middle" halign="middle"> HPO </td>
<td align="center" valign="middle"> HPO-CG </td>
<td align="center" valign="middle"> Hyperparameter optimization with approximate gradient </td>
<td align="center" valign="middle"> coming soon </a> </td>
</tr>
<tr> <!-- (start third block) -->
<td rowspan="1" align="center" valign="middle" halign="middle"> Basic </td>
<td align="center" valign="middle"> ResNet </td>
<td align="center" valign="middle"> Deep Learning-based Image Classification </td>
<td align="center" valign="middle"> <a href="https://github.com/D-X-Y/AutoDL-Projects/tree/main/docs/BASELINE.md">BASELINE.md</a> </a> </td>
</tr>
</tbody>
</table>
## Requirements and Preparation
**First of all**, please use `pip install .` to install `xautodl` library.
Please install `Python>=3.6` and `PyTorch>=1.5.0`. (You could use lower versions of Python and PyTorch, but may have bugs).
Some visualization codes may require `opencv`.
CIFAR and ImageNet should be downloaded and extracted into `$TORCH_HOME`.
Some methods use knowledge distillation (KD), which require pre-trained models. Please download these models from [Google Drive](https://drive.google.com/open?id=1ANmiYEGX-IQZTfH8w0aSpj-Wypg-0DR-) (or train by yourself) and save into `.latent-data`.
Please use
```
git clone --recurse-submodules https://github.com/D-X-Y/AutoDL-Projects.git XAutoDL
```
to download this repo with submodules.
## Citation
If you find that this project helps your research, please consider citing the related paper:
```
@inproceedings{dong2021autohas,
title = {{AutoHAS}: Efficient Hyperparameter and Architecture Search},
author = {Dong, Xuanyi and Tan, Mingxing and Yu, Adams Wei and Peng, Daiyi and Gabrys, Bogdan and Le, Quoc V},
booktitle = {2nd Workshop on Neural Architecture Search at International Conference on Learning Representations (ICLR)},
year = {2021}
}
@article{dong2021nats,
title = {{NATS-Bench}: Benchmarking NAS Algorithms for Architecture Topology and Size},
author = {Dong, Xuanyi and Liu, Lu and Musial, Katarzyna and Gabrys, Bogdan},
doi = {10.1109/TPAMI.2021.3054824},
journal = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
year = {2021},
note = {\mbox{doi}:\url{10.1109/TPAMI.2021.3054824}}
}
@inproceedings{dong2020nasbench201,
title = {{NAS-Bench-201}: Extending the Scope of Reproducible Neural Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {International Conference on Learning Representations (ICLR)},
url = {https://openreview.net/forum?id=HJxyZkBKDr},
year = {2020}
}
@inproceedings{dong2019tas,
title = {Network Pruning via Transformable Architecture Search},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Neural Information Processing Systems (NeurIPS)},
pages = {760--771},
year = {2019}
}
@inproceedings{dong2019one,
title = {One-Shot Neural Architecture Search via Self-Evaluated Template Network},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Proceedings of the IEEE International Conference on Computer Vision (ICCV)},
pages = {3681--3690},
year = {2019}
}
@inproceedings{dong2019search,
title = {Searching for A Robust Neural Architecture in Four GPU Hours},
author = {Dong, Xuanyi and Yang, Yi},
booktitle = {Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
pages = {1761--1770},
year = {2019}
}
```
# Others
If you want to contribute to this repo, please see [CONTRIBUTING.md](.github/CONTRIBUTING.md).
Besides, please follow [CODE-OF-CONDUCT.md](.github/CODE-OF-CONDUCT.md).
We use [`black`](https://github.com/psf/black) for Python code formatter.
Please use `black . -l 88`.
# License
The entire codebase is under the [MIT license](LICENSE.md).

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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "110"],
"module" : ["str" , "ResNetBasicblock"],
"super_type" : ["str" , "infer-shape"],
"zero_init_residual" : ["bool" , "0"],
"class_num" : ["int" , "10"],
"search_mode" : ["str" , "shape"],
"xchannels" : ["int" , ["3", "16", "14", "16", "14", "16", "16", "16", "16", "16", "14", "16", "16", "16", "12", "16", "16", "16", "9", "16", "8", "16", "4", "16", "4", "4", "4", "16", "4", "4", "4", "4", "6", "6", "4", "6", "11", "4", "32", "32", "32", "32", "32", "32", "32", "32", "28", "32", "32", "28", "22", "22", "22", "32", "32", "25", "28", "9", "9", "28", "12", "9", "12", "32", "9", "9", "22", "12", "16", "9", "12", "9", "9", "9", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "64", "38", "64", "25", "19", "19", "19", "19", "19", "25", "32", "19", "19", "25", "25", "19", "19", "38", "38", "19", "19", "51"]],
"xblocks" : ["int" , ["11", "11", "9"]],
"estimated_FLOP" : ["float" , "117.498238"]
}

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{
"dataset" : ["str" , "cifar"],
"arch" : ["str" , "resnet"],
"depth" : ["int" , "164"],
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{
"dataset" : ["str" , "cifar"],
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{
"dataset" : ["str" , "cifar"],
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{
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{
"dataset" : ["str" , "cifar"],
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}

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{
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}

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{
"dataset" : ["str" , "cifar"],
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}

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{
"dataset" : ["str" , "cifar"],
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}

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{
"dataset" : ["str" , "cifar"],
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}

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{
"dataset" : ["str" , "imagenet"],
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}

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{
"dataset" : ["str" , "imagenet"],
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}

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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
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"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
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"module" : ["str", "ResNetBottleneck"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
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"module" : ["str", "ResNetBasicblock"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
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"module" : ["str", "ResNetBottleneck"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
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"module" : ["str", "ResNetBasicblock"],
"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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{
"dataset" : ["str", "cifar"],
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"zero_init_residual" : ["bool", "0"]
}

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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "resnet"],
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"zero_init_residual" : ["bool", "0"]
}

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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "simres"],
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"super_type": ["str" , "basic"],
"zero_init_residual" : ["bool", "0"]
}

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{
"dataset" : ["str", "cifar"],
"arch" : ["str", "wideresnet"],
"depth" : ["int", 28],
"wide_factor":["int", 10],
"dropout" : ["bool", 0],
"super_type": ["str" , "basic"]
}

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{
"dataset" : ["str", "imagenet"],
"arch" : ["str", "resnet"],
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"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
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"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
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"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
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"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
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"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
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"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
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"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
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"norm_layer" : ["none", "None"]
}

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{
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"norm_layer" : ["none", "None"]
}

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{
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"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
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"norm_layer" : ["none", "None"]
}

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{
"dataset" : ["str", "imagenet"],
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"width_per_group" : ["int", 64],
"norm_layer" : ["none", "None"]
}

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{
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"groups" : ["int", 32],
"width_per_group" : ["int", 4],
"norm_layer" : ["none", "None"]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "DARTS"],
"dataset" : ["str", "cifar"],
"ichannel" : ["int", 36],
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"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0.2]
}

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{
"arch" : ["str", "dxys"],
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"drop_path_prob": ["float", 0.2]
}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "NASNet"],
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"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0.2]
}

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{
"arch" : ["str", "dxys"],
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"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0.2]
}

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{
"super_type": ["str", "infer-nasnet.cifar"],
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}

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{
"arch" : ["str", "dxys"],
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"auxiliary" : ["bool", 1],
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}

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{
"arch" : ["str", "dxys"],
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}

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{
"arch" : ["str", "dxys"],
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}

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{
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}

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{
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}

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{
"arch" : ["str", "dxys"],
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}

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{
"arch" : ["str", "dxys"],
"genotype" : ["str", "SETN"],
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"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0]
}

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{
"super_type": ["str", "infer-nasnet.imagenet"],
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"auxiliary" : ["bool", 1],
"drop_path_prob": ["float", 0]
}

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{
"dataset" : ["str" , "cifar"],
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}

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{
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{
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}

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{
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}

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{
"dataset" : ["str" , "cifar"],
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}

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{
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}

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{
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{
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"batch_size": ["int", "256"]
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{
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}

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{
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"batch_size": ["int", "256"]
}

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{
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"batch_size": ["int", "64"]
}

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{
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"controller_betas": ["float", [0, 0.999]],
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}

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{
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"batch_size": ["int", "64"]
}

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{
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}

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{
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"test_batch_size": ["int", "512"]
}

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{
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}

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{
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{
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"batch_size": ["int", "256"]
}

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{
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"batch_size": ["int", "256"]
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{
"scheduler": ["str", "cos"],
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{
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{
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"auxiliary": ["float", "-1"]
}

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{
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"eta_min" : ["float", "0.0"],
"epochs" : ["int", "300"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.4"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"]
}

12
AutoDL-Projects/configs/opts/CIFAR-E600-W5-L1-COS.config Normal file
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{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "600"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"]
}

13
AutoDL-Projects/configs/opts/CIFAR-Fast-Random.config Normal file
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@ -0,0 +1,13 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "100"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

13
AutoDL-Projects/configs/opts/CIFAR-Slow-Random.config Normal file
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@ -0,0 +1,13 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "300"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

13
AutoDL-Projects/configs/opts/Com-Paddle-NAS.config Normal file
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@ -0,0 +1,13 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "595"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.025"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "0.4"]
}

13
AutoDL-Projects/configs/opts/Com-Paddle-RES.config Normal file
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@ -0,0 +1,13 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "300"],
"warmup" : ["int", "5"] ,
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool" , "1"] ,
"criterion": ["str" , "Softmax"],
"auxiliary": ["float", "-1"]
}

14
AutoDL-Projects/configs/opts/ImageNet-E120-Cos-Smooth.config Normal file
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@ -0,0 +1,14 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "120"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0001"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "SmoothSoftmax"],
"label_smooth": ["float", 0.1],
"auxiliary": ["float", "-1"]
}

13
AutoDL-Projects/configs/opts/ImageNet-E120-Cos-Soft.config Normal file
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@ -0,0 +1,13 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "120"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.0001"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

14
AutoDL-Projects/configs/opts/ImageNet-E120-Step-Soft.config Normal file
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@ -0,0 +1,14 @@
{
"scheduler": ["str", "multistep"],
"epochs" : ["int", "120"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"milestones":["int", [30, 60, 90]],
"gammas" : ["float", [0.1, 0.1, 0.1]],
"decay" : ["float", "0.0001"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "-1"]
}

13
AutoDL-Projects/configs/opts/NAS-CIFAR-V2.config Normal file
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@ -0,0 +1,13 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "595"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.025"],
"decay" : ["float", "0.0003"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "0.4"]
}

13
AutoDL-Projects/configs/opts/NAS-CIFAR.config Normal file
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@ -0,0 +1,13 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "295"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.025"],
"decay" : ["float", "0.0005"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "Softmax"],
"auxiliary": ["float", "0.4"]
}

14
AutoDL-Projects/configs/opts/NAS-IMAGENET.config Normal file
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@ -0,0 +1,14 @@
{
"scheduler": ["str", "cos"],
"eta_min" : ["float", "0.0"],
"epochs" : ["int", "245"],
"warmup" : ["int", "5"],
"optim" : ["str", "SGD"],
"LR" : ["float", "0.1"],
"decay" : ["float", "0.00003"],
"momentum" : ["float", "0.9"],
"nesterov" : ["bool", "1"],
"criterion": ["str", "SmoothSoftmax"],
"label_smooth": ["float", 0.1],
"auxiliary" : ["float", "0.4"]
}

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AutoDL-Projects/configs/qlib/workflow_config_TabNet_Alpha360.yaml Normal file
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qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: TabnetModel
module_path: qlib.contrib.model.pytorch_tabnet
kwargs:
d_feat: 360
pretrain: True
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
pretrain: [2008-01-01, 2014-12-31]
pretrain_validation: [2015-01-01, 2016-12-31]
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

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AutoDL-Projects/configs/qlib/workflow_config_alstm_Alpha360.yaml Normal file
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qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors:
- class: RobustZScoreNorm
kwargs:
fields_group: feature
clip_outlier: true
- class: Fillna
kwargs:
fields_group: feature
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: ALSTM
module_path: qlib.contrib.model.pytorch_alstm
kwargs:
d_feat: 6
hidden_size: 64
num_layers: 2
dropout: 0.0
n_epochs: 200
lr: 1e-3
early_stop: 20
batch_size: 800
metric: loss
loss: mse
GPU: 0
rnn_type: GRU
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

100
AutoDL-Projects/configs/qlib/workflow_config_doubleensemble_Alpha360.yaml Normal file
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qlib_init:
provider_uri: "~/.qlib/qlib_data/cn_data"
region: cn
market: &market all
benchmark: &benchmark SH000300
data_handler_config: &data_handler_config
start_time: 2008-01-01
end_time: 2020-08-01
fit_start_time: 2008-01-01
fit_end_time: 2014-12-31
instruments: *market
infer_processors: []
learn_processors:
- class: DropnaLabel
- class: CSRankNorm
kwargs:
fields_group: label
label: ["Ref($close, -2) / Ref($close, -1) - 1"]
port_analysis_config: &port_analysis_config
strategy:
class: TopkDropoutStrategy
module_path: qlib.contrib.strategy.strategy
kwargs:
topk: 50
n_drop: 5
backtest:
verbose: False
limit_threshold: 0.095
account: 100000000
benchmark: *benchmark
deal_price: close
open_cost: 0.0005
close_cost: 0.0015
min_cost: 5
task:
model:
class: DEnsembleModel
module_path: qlib.contrib.model.double_ensemble
kwargs:
base_model: "gbm"
loss: mse
num_models: 6
enable_sr: True
enable_fs: True
alpha1: 1
alpha2: 1
bins_sr: 10
bins_fs: 5
decay: 0.5
sample_ratios:
- 0.8
- 0.7
- 0.6
- 0.5
- 0.4
sub_weights:
- 1
- 0.2
- 0.2
- 0.2
- 0.2
- 0.2
epochs: 136
colsample_bytree: 0.8879
learning_rate: 0.0421
subsample: 0.8789
lambda_l1: 205.6999
lambda_l2: 580.9768
max_depth: 8
num_leaves: 210
num_threads: 20
verbosity: -1
dataset:
class: DatasetH
module_path: qlib.data.dataset
kwargs:
handler:
class: Alpha360
module_path: qlib.contrib.data.handler
kwargs: *data_handler_config
segments:
train: [2008-01-01, 2014-12-31]
valid: [2015-01-01, 2016-12-31]
test: [2017-01-01, 2020-08-01]
record:
- class: SignalRecord
module_path: qlib.workflow.record_temp
kwargs: {}
- class: SignalMseRecord
module_path: qlib.contrib.workflow.record_temp
kwargs: {}
- class: SigAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
ana_long_short: False
ann_scaler: 252
- class: PortAnaRecord
module_path: qlib.workflow.record_temp
kwargs:
config: *port_analysis_config

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