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Add installation guide in Orca AutoML Doc (#3304)

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* add install guide in orca.automl doc

* typo

* update

* add sklearn and tensorboard
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Yu Shan 2021-10-28 14:45:16 +08:00 committed by GitHub
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docs/readthedocs/source/doc/Orca/Overview/distributed-tuning.md
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@ -4,6 +4,25 @@
**Orca `AutoEstimator` provides similar APIs as Orca `Estimator` for distributed hyper-parameter tuning.**
### **Install**
We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the Python environment.
```bash
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]
````
You can install the latest release version of BigDL Orca as follows:
```bash
pip install --pre --upgrade bigdl-orca[automl]
```
_Note that with extra key of [automl], `pip` will automatically install the additional dependencies for distributed hyper-parameter tuning,
including `ray[tune]==1.2.0`, `psutil`, `aiohttp==3.7.0`, `aioredis==1.1.0`, `setproctitle`, `hiredis==1.1.0`, `async-timeout==3.0.1`, `scikit-learn`, `tensorboard`, `xgboost`._
To use [Pytorch Estimator](#pytorch-autoestimator), you need to install Pytorch with `pip install torch==1.8.1`.
To use [TensorFlow/Keras AutoEstimator](#tensorflow-keras-autoestimator), you need to install Tensorflow with `pip install tensorflow==1.15.0`.
### **1. AutoEstimator**
To perform distributed hyper-parameter tuning, user can first create an Orca `AutoEstimator` from standard TensorFlow Keras or PyTorch model, and then call `AutoEstimator.fit`.
@ -180,7 +199,7 @@ auto_estimator.fit(
```
See [API Doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details.
### **4. Scheduler**
### **5. Scheduler**
*Scheduler* can stop/pause/tweak the hyper-parameters of running trials, making the hyper-parameter tuning process much efficient.
We support all *Schedulers* in [Ray Tune](https://docs.ray.io/en/master/index.html). See [Ray Tune Schedulers](https://docs.ray.io/en/master/tune/api_docs/schedulers.html#schedulers-ref) for more details.