Merge branch 'main' into cv_strategy

.github/workflows/CD.yml

@@ -20,7 +20,7 @@ jobs:
       - name: Checkout
         uses: actions/checkout@v2
       - name: Cache conda
-        uses: actions/cache@v1
+        uses: actions/cache@v3
         with:
           path: ~/conda_pkgs_dir
           key: conda-${{ matrix.os }}-python-${{ matrix.python-version }}-${{ hashFiles('environment.yml') }}

.github/workflows/deploy-website.yml

@@ -16,7 +16,7 @@ jobs:
         working-directory: website
     steps:
       - uses: actions/checkout@v2
-      - uses: actions/setup-node@v2
+      - uses: actions/setup-node@v3
         with:
           node-version: 14.x
           # cache: yarn
@@ -52,7 +52,7 @@ jobs:
         working-directory: website
     steps:
       - uses: actions/checkout@v2
-      - uses: actions/setup-node@v2
+      - uses: actions/setup-node@v3
         with:
           node-version: 14.x
           # cache: yarn

README.md

@@ -94,7 +94,7 @@ You can find a detailed documentation about FLAML [here](https://microsoft.githu
 
 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).
 

flaml/automl.py

@@ -3107,7 +3107,9 @@ class AutoML(BaseEstimator):
         if mlflow is not None and mlflow.active_run():
             with mlflow.start_run(nested=True):
                 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[
                         "intermediate_results"
                     ]:
@@ -3117,7 +3119,8 @@ class AutoML(BaseEstimator):
                                 "iter_counter", self._iter_per_learner[estimator]
                             )
                     del search_state.metric_for_logging["intermediate_results"]
-                mlflow.log_metrics(search_state.metric_for_logging)
+                if 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("wall_clock_time", self._state.time_from_start)
                 mlflow.log_metric("validation_loss", search_state.val_loss)

flaml/version.py

@@ -1 +1 @@
-__version__ = "1.0.11"
+__version__ = "1.0.12"

test/automl/test_notebook_example.py

@@ -154,6 +154,19 @@ def test_mlflow():
         pass
     # 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__":
     test_automl(600)

website/docs/Getting-Started.md

@@ -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).
 * 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.
 * Refer to [SDK](reference/automl) and [FAQ](FAQ).
 

website/docs/Research.md

@@ -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). 
 * [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.