mirror of https://github.com/microsoft/autogen.git
Merge branch 'main' into cv_strategy
This commit is contained in:
commit
f06d27aaac
|
@ -20,7 +20,7 @@ jobs:
|
||||||
- name: Checkout
|
- name: Checkout
|
||||||
uses: actions/checkout@v2
|
uses: actions/checkout@v2
|
||||||
- name: Cache conda
|
- name: Cache conda
|
||||||
uses: actions/cache@v1
|
uses: actions/cache@v3
|
||||||
with:
|
with:
|
||||||
path: ~/conda_pkgs_dir
|
path: ~/conda_pkgs_dir
|
||||||
key: conda-${{ matrix.os }}-python-${{ matrix.python-version }}-${{ hashFiles('environment.yml') }}
|
key: conda-${{ matrix.os }}-python-${{ matrix.python-version }}-${{ hashFiles('environment.yml') }}
|
||||||
|
|
|
@ -16,7 +16,7 @@ jobs:
|
||||||
working-directory: website
|
working-directory: website
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v2
|
- uses: actions/checkout@v2
|
||||||
- uses: actions/setup-node@v2
|
- uses: actions/setup-node@v3
|
||||||
with:
|
with:
|
||||||
node-version: 14.x
|
node-version: 14.x
|
||||||
# cache: yarn
|
# cache: yarn
|
||||||
|
@ -52,7 +52,7 @@ jobs:
|
||||||
working-directory: website
|
working-directory: website
|
||||||
steps:
|
steps:
|
||||||
- uses: actions/checkout@v2
|
- uses: actions/checkout@v2
|
||||||
- uses: actions/setup-node@v2
|
- uses: actions/setup-node@v3
|
||||||
with:
|
with:
|
||||||
node-version: 14.x
|
node-version: 14.x
|
||||||
# cache: yarn
|
# cache: yarn
|
||||||
|
|
|
@ -94,7 +94,7 @@ You can find a detailed documentation about FLAML [here](https://microsoft.githu
|
||||||
|
|
||||||
In addition, you can find:
|
In addition, you can find:
|
||||||
|
|
||||||
- Demo and tutorials of FLAML [here](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A).
|
- [Talks](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A) and [tutorials](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) about FLAML.
|
||||||
|
|
||||||
- Research around FLAML [here](https://microsoft.github.io/FLAML/docs/Research).
|
- Research around FLAML [here](https://microsoft.github.io/FLAML/docs/Research).
|
||||||
|
|
||||||
|
|
|
@ -3107,7 +3107,9 @@ class AutoML(BaseEstimator):
|
||||||
if mlflow is not None and mlflow.active_run():
|
if mlflow is not None and mlflow.active_run():
|
||||||
with mlflow.start_run(nested=True):
|
with mlflow.start_run(nested=True):
|
||||||
mlflow.log_metric("iter_counter", self._track_iter)
|
mlflow.log_metric("iter_counter", self._track_iter)
|
||||||
if "intermediate_results" in search_state.metric_for_logging:
|
if (search_state.metric_for_logging is not None) and (
|
||||||
|
"intermediate_results" in search_state.metric_for_logging
|
||||||
|
):
|
||||||
for each_entry in search_state.metric_for_logging[
|
for each_entry in search_state.metric_for_logging[
|
||||||
"intermediate_results"
|
"intermediate_results"
|
||||||
]:
|
]:
|
||||||
|
@ -3117,6 +3119,7 @@ class AutoML(BaseEstimator):
|
||||||
"iter_counter", self._iter_per_learner[estimator]
|
"iter_counter", self._iter_per_learner[estimator]
|
||||||
)
|
)
|
||||||
del search_state.metric_for_logging["intermediate_results"]
|
del search_state.metric_for_logging["intermediate_results"]
|
||||||
|
if search_state.metric_for_logging:
|
||||||
mlflow.log_metrics(search_state.metric_for_logging)
|
mlflow.log_metrics(search_state.metric_for_logging)
|
||||||
mlflow.log_metric("trial_time", search_state.trial_time)
|
mlflow.log_metric("trial_time", search_state.trial_time)
|
||||||
mlflow.log_metric("wall_clock_time", self._state.time_from_start)
|
mlflow.log_metric("wall_clock_time", self._state.time_from_start)
|
||||||
|
|
|
@ -1 +1 @@
|
||||||
__version__ = "1.0.11"
|
__version__ = "1.0.12"
|
||||||
|
|
File diff suppressed because one or more lines are too long
|
@ -154,6 +154,19 @@ def test_mlflow():
|
||||||
pass
|
pass
|
||||||
# subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "mlflow"])
|
# subprocess.check_call([sys.executable, "-m", "pip", "uninstall", "mlflow"])
|
||||||
|
|
||||||
|
from sklearn.datasets import load_iris
|
||||||
|
|
||||||
|
with mlflow.start_run():
|
||||||
|
automl = AutoML()
|
||||||
|
automl_settings = {
|
||||||
|
"time_budget": 2, # in seconds
|
||||||
|
"metric": "accuracy",
|
||||||
|
"task": "classification",
|
||||||
|
"log_file_name": "iris.log",
|
||||||
|
}
|
||||||
|
X_train, y_train = load_iris(return_X_y=True)
|
||||||
|
automl.fit(X_train=X_train, y_train=y_train, **automl_settings)
|
||||||
|
|
||||||
|
|
||||||
if __name__ == "__main__":
|
if __name__ == "__main__":
|
||||||
test_automl(600)
|
test_automl(600)
|
||||||
|
|
|
@ -90,7 +90,7 @@ Then, you can use it just like you use the original `LGMBClassifier`. Your other
|
||||||
|
|
||||||
* Understand the use cases for [Task-oriented AutoML](Use-Cases/task-oriented-automl), [Tune user-defined function](Use-Cases/Tune-User-Defined-Function) and [Zero-shot AutoML](Use-Cases/Zero-Shot-AutoML).
|
* Understand the use cases for [Task-oriented AutoML](Use-Cases/task-oriented-automl), [Tune user-defined function](Use-Cases/Tune-User-Defined-Function) and [Zero-shot AutoML](Use-Cases/Zero-Shot-AutoML).
|
||||||
* Find code examples under "Examples": from [AutoML - Classification](Examples/AutoML-Classification) to [Tune - PyTorch](Examples/Tune-PyTorch).
|
* Find code examples under "Examples": from [AutoML - Classification](Examples/AutoML-Classification) to [Tune - PyTorch](Examples/Tune-PyTorch).
|
||||||
* Watch [video tutorials](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A).
|
* Find [talks](https://www.youtube.com/channel/UCfU0zfFXHXdAd5x-WvFBk5A) and [tutorials](https://github.com/microsoft/FLAML/tree/tutorial/tutorial) about FLAML.
|
||||||
* Learn about [research](Research) around FLAML.
|
* Learn about [research](Research) around FLAML.
|
||||||
* Refer to [SDK](reference/automl) and [FAQ](FAQ).
|
* Refer to [SDK](reference/automl) and [FAQ](FAQ).
|
||||||
|
|
||||||
|
|
|
@ -20,4 +20,4 @@ For technical details, please check our research publications.
|
||||||
* [Fair AutoML](https://arxiv.org/abs/2111.06495). Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021).
|
* [Fair AutoML](https://arxiv.org/abs/2111.06495). Qingyun Wu, Chi Wang. ArXiv preprint arXiv:2111.06495 (2021).
|
||||||
* [Mining Robust Default Configurations for Resource-constrained AutoML](https://arxiv.org/abs/2202.09927). Moe Kayali, Chi Wang. ArXiv preprint arXiv:2202.09927 (2022).
|
* [Mining Robust Default Configurations for Resource-constrained AutoML](https://arxiv.org/abs/2202.09927). Moe Kayali, Chi Wang. ArXiv preprint arXiv:2202.09927 (2022).
|
||||||
|
|
||||||
Many researchers and engineers have contributed to the technology development. In alphabetical order: Vijay Aski, Sebastien Bubeck, Surajit Chaudhuri, Kevin Chen, Yi Wei Chen, Nadiia Chepurko, Ofer Dekel, Alex Deng, Anshuman Dutt, Nicolo Fusi, Jianfeng Gao, Johannes Gehrke, Niklas Gustafsson, Silu Huang, Moe Kayali, Dongwoo Kim, Christian Konig, John Langford, Menghao Li, Mingqin Li, Susan Xueqing Liu, Zhe Liu, Naveen Gaur, Paul Mineiro, Vivek Narasayya, Jake Radzikowski, Marco Rossi, Amin Saied, Neil Tenenholtz, Olga Vrousgou, Chi Wang, Yue Wang, Markus Weimer, Qingyun Wu, Qiufeng Yin, Haozhe Zhang, Minjia Zhang, XiaoYun Zhang, Eric Zhu.
|
Many researchers and engineers have contributed to the technology development. In alphabetical order: Vijay Aski, Sebastien Bubeck, Surajit Chaudhuri, Kevin Chen, Yi Wei Chen, Nadiia Chepurko, Ofer Dekel, Alex Deng, Anshuman Dutt, Nicolo Fusi, Jianfeng Gao, Johannes Gehrke, Niklas Gustafsson, Silu Huang, Moe Kayali, Dongwoo Kim, Christian Konig, John Langford, Menghao Li, Mingqin Li, Susan Xueqing Liu, Zhe Liu, Naveen Gaur, Paul Mineiro, Vivek Narasayya, Jake Radzikowski, Marco Rossi, Amin Saied, Neil Tenenholtz, Olga Vrousgou, Chi Wang, Yue Wang, Markus Weimer, Qingyun Wu, Qiufeng Yin, Haozhe Zhang, Minjia Zhang, XiaoYun Zhang, Eric Zhu, Rui Zhuang.
|
||||||
|
|
Loading…
Reference in New Issue