Docs: fix typo and quickstart code branch. (#5414)
This commit is contained in:
parent
6af23499e7
commit
241b0f1712
7 changed files with 9 additions and 9 deletions
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
---
|
||||
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/autoestimator_pytorch_lenet_mnist.ipynb)
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/autoestimator_pytorch_lenet_mnist.ipynb)
|
||||
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
---
|
||||
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/autoxgboost_regressor_sklearn_boston.ipynb)
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/autoxgboost_regressor_sklearn_boston.ipynb)
|
||||
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
---
|
||||
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/keras_lenet_mnist.ipynb)
|
||||
[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/keras_lenet_mnist.ipynb) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/keras_lenet_mnist.ipynb)
|
||||
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
---
|
||||
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/pytorch_distributed_lenet_mnist.ipynb)
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/pytorch_distributed_lenet_mnist.ipynb)
|
||||
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
---
|
||||
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/pytorch_lenet_mnist.ipynb)
|
||||
[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/pytorch_lenet_mnist.ipynb) [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**
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
---
|
||||
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/tf_lenet_mnist.ipynb)
|
||||
[Run in Google Colab](https://colab.research.google.com/github/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/tf_lenet_mnist.ipynb) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/tf_lenet_mnist.ipynb)
|
||||
|
||||
---
|
||||
|
||||
|
|
|
|||
|
|
@ -2,7 +2,7 @@
|
|||
|
||||
---
|
||||
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/python/orca/colab-notebook/quickstart/tf2_keras_lenet_mnist.ipynb)
|
||||
[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) [View source on GitHub](https://github.com/intel-analytics/BigDL/blob/main/python/orca/colab-notebook/quickstart/tf2_keras_lenet_mnist.ipynb)
|
||||
|
||||
---
|
||||
|
||||
|
|
@ -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()`.
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue