Docs: fix typo and quickstart code branch. (#5414)

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Cengguang Zhang 2022-08-16 16:20:46 +08:00 committed by GitHub
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7 changed files with 9 additions and 9 deletions

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---
![](../../../../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)
![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/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/main/python/orca/colab-notebook/quickstart/autoestimator_pytorch_lenet_mnist.ipynb)
---

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---
![](../../../../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)
![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/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/main/python/orca/colab-notebook/quickstart/autoxgboost_regressor_sklearn_boston.ipynb)
---

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---
![](../../../../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/keras_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/keras_lenet_mnist.ipynb)
![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/keras_lenet_mnist.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/keras_lenet_mnist.ipynb)
---

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![](../../../../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/pytorch_distributed_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/pytorch_distributed_lenet_mnist.ipynb)
![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/pytorch_distributed_lenet_mnist.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/pytorch_distributed_lenet_mnist.ipynb)
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---
![](../../../../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/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/pytorch_lenet_mnist.ipynb)
![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/pytorch_lenet_mnist.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/pytorch_lenet_mnist.ipynb)
---
@ -106,7 +106,7 @@ test_loader = torch.utils.data.DataLoader(
batch_size=test_batch_size, shuffle=False)
```
Alternatively, we can also use a [Data Creator Function](https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/colab-notebook/orca/quickstart/pytorch_lenet_mnist_data_creator_func.ipynb) or [Orca XShards](../Overview/data-parallel-processing) as the input data, especially when the data size is very large)
Alternatively, we can also use a [Data Creator Function](https://github.com/intel-analytics/BigDL/blob/main/docs/docs/colab-notebook/orca/quickstart/pytorch_lenet_mnist_data_creator_func.ipynb) or [Orca XShards](../Overview/data-parallel-processing) as the input data, especially when the data size is very large)
### **Step 4: Fit with Orca Estimator**

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---
![](../../../../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/tf_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/tf_lenet_mnist.ipynb)
![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/tf_lenet_mnist.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/tf_lenet_mnist.ipynb)
---

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---
![](../../../../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/tf2_keras_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/tf2_keras_lenet_mnist.ipynb)
![](../../../../image/colab_logo_32px.png)[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/tf2_keras_lenet_mnist.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/tf2_keras_lenet_mnist.ipynb)
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@ -118,7 +118,7 @@ est.shutdown()
print(stats)
```
### **Step5: Save and Load the Model**
### **Step 5: Save and Load the Model**
Orca TF2 Estimator supports two formats to save and load the entire model (**TensorFlow SavedModel and Keras H5 Format**). The recommended format is SavedModel, which is the default format when you use `estimator.save()`.