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Update AutoXGBoost and AutoEstimator Quickstart (#3327)

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* update autoestimator pytorch quickstart

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Yu Shan 2021-10-29 10:23:15 +08:00 committed by GitHub
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docs/readthedocs/source/doc/Orca/QuickStart/orca-autoestimator-pytorch-quickstart.md
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@ -13,7 +13,7 @@
[Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) is needed to prepare the Python environment for running this example. Please refer to the [install guide](https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/distributed-tuning.html#install) for more details.
```bash
conda create -n bigdl-orca-automl python=3.7 # zoo is conda environment name, you can use any name you like.
conda create -n bigdl-orca-automl python=3.7 # bigdl-orca-automl is conda environment name, you can use any name you like.
conda activate bigdl-orca-automl
pip install bigdl-orca[automl]
pip install torch==1.8.1 torchvision==0.9.1

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docs/readthedocs/source/doc/Orca/QuickStart/orca-autoxgboost-quickstart.md
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---
![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/analytics-zoo/blob/master/docs/docs/colab-notebook/orca/quickstart/autoxgboost_regressor_sklearn_boston.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/analytics-zoo/blob/master/docs/docs/colab-notebook/orca/quickstart/autoxgboost_regressor_sklearn_boston.ipynb)
![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/autoxgboost_regressor_sklearn_boston.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/autoxgboost_regressor_sklearn_boston.ipynb)
---
@ -11,18 +11,12 @@
Orca AutoXGBoost enables distributed automated hyper-parameter tuning for XGBoost, which includes `AutoXGBRegressor` and `AutoXGBClassifier` for sklearn`XGBRegressor` and `XGBClassifier` respectively. See more about [xgboost scikit-learn API](https://xgboost.readthedocs.io/en/latest/python/python_api.html#module-xgboost.sklearn).
### **Step 0: Prepare Environment**
[Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) is needed to prepare the Python environment for running this example. Please refer to the [install guide](../../UserGuide/python.md) for more details.
[Conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) is needed to prepare the Python environment for running this example. Please refer to the [install guide](https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/distributed-tuning.html#install) for more details.
```bash
conda create -n zoo python=3.7 # zoo is conda environment name, you can use any name you like.
conda activate zoo
pip install analytics-zoo[ray]
pip install torch==1.7.1 torchvision==0.8.2
```
### **Step 1: Init Orca Context**
```python
from zoo.orca import init_orca_context, stop_orca_context
from bigdl.orca import init_orca_context, stop_orca_context
if cluster_mode == "local":
init_orca_context(cores=6, memory="2g", init_ray_on_spark=True) # run in local mode
@ -42,10 +36,10 @@ This is the only place where you need to specify local or distributed mode. View
You should define a dictionary as your hyper-parameter search space.
The keys are hyper-parameter names you want to search for `XGBRegressor`, and you can specify how you want to sample each hyper-parameter in the values of the search space. See [automl.hp](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-hp) for more details.
The keys are hyper-parameter names you want to search for `XGBRegressor`, and you can specify how you want to sample each hyper-parameter in the values of the search space. See [automl.hp](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-hp) for more details.
```python
from zoo.orca.automl import hp
from bigdl.orca.automl import hp
search_space = {
"n_estimators": hp.grid_search([50, 100, 200]),
@ -58,7 +52,7 @@ search_space = {
First create an `AutoXGBRegressor`.
```python
from zoo.orca.automl.xgboost import AutoXGBRegressor
from bigdl.orca.automl.xgboost import AutoXGBRegressor
auto_xgb_reg = AutoXGBRegressor(cpus_per_trial=2,
name="auto_xgb_classifier",