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Update Automl pytorch quickstart (#3306)

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* update quick start doc

* change code and link

* Update image

* colab link

* update link and name

* Update install guide link
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Yu Shan 2021-10-28 15:25:42 +08:00 committed by GitHub
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docs/readthedocs/source/doc/Orca/QuickStart/orca-autoestimator-pytorch-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/autoestimator_pytorch_lenet_mnist.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/autoestimator_pytorch_lenet_mnist.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/autoestimator_pytorch_lenet_mnist.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/autoestimator_pytorch_lenet_mnist.ipynb)
--- ---
@ -10,18 +10,18 @@
### **Step 0: Prepare Environment** ### **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 ```bash
conda create -n zoo python=3.7 # zoo is conda environment name, you can use any name you like. conda create -n bigdl-orca-automl python=3.7 # zoo is conda environment name, you can use any name you like.
conda activate zoo conda activate bigdl-orca-automl
pip install analytics-zoo[ray] pip install bigdl-orca[automl]
pip install torch==1.7.1 torchvision==0.8.2 pip install torch==1.8.1 torchvision==0.9.1
``` ```
### **Step 1: Init Orca Context** ### **Step 1: Init Orca Context**
```python ```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": if cluster_mode == "local":
init_orca_context(cores=4, memory="2g", init_ray_on_spark=True) # run in local mode init_orca_context(cores=4, memory="2g", init_ray_on_spark=True) # run in local mode
@ -113,10 +113,10 @@ def test_loader_creator(config):
### **Step 4: Define Search Space** ### **Step 4: Define Search Space**
You should define a dictionary as your hyper-parameter search space. You should define a dictionary as your hyper-parameter search space.
The keys are hyper-parameter names which should be the same with those in your creators, 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 which should be the same with those in your creators, 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 ```python
from zoo.orca.automl import hp from bigdl.orca.automl import hp
search_space = { search_space = {
"fc1_hidden_size": hp.choice([500, 600]), "fc1_hidden_size": hp.choice([500, 600]),
@ -127,15 +127,15 @@ search_space = {
### **Step 5: Automatically Fit and Search with Orca AutoEstimator** ### **Step 5: Automatically Fit and Search with Orca AutoEstimator**
First, create an `AutoEstimator`. You can refer to [AutoEstimator API doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details. First, create an `AutoEstimator`. You can refer to [AutoEstimator API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details.
```python ```python
from zoo.orca.automl.auto_estimator import AutoEstimator from bigdl.orca.automl.auto_estimator import AutoEstimator
auto_est = AutoEstimator.from_torch(model_creator=model_creator, auto_est = AutoEstimator.from_torch(model_creator=model_creator,
optimizer=optim_creator, optimizer=optim_creator,
loss=criterion, loss=criterion,
logs_dir="/tmp/zoo_automl_logs", logs_dir="/tmp/orca_automl_logs",
resources_per_trial={"cpu": 2}, resources_per_trial={"cpu": 2},
name="lenet_mnist") name="lenet_mnist")
``` ```