diff --git a/docs/readthedocs/source/doc/Orca/Overview/distributed-tuning.md b/docs/readthedocs/source/doc/Orca/Overview/distributed-tuning.md
index e3a38070..d7869ab6 100644
--- a/docs/readthedocs/source/doc/Orca/Overview/distributed-tuning.md
+++ b/docs/readthedocs/source/doc/Orca/Overview/distributed-tuning.md
@@ -68,12 +68,12 @@ Note that the `optimizer` argument in Pytorch `AutoEstimator` constructor could
 #### **2.4 Create and Fit Pytorch AutoEstimator**
 User could create a Pytorch `AutoEstimator` as below.
 ```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,
                                     optimizer=optim_creator,
                                     loss=nn.NLLLoss(),
-                                    logs_dir="/tmp/zoo_automl_logs",
+                                    logs_dir="/tmp/orca_automl_logs",
                                     resources_per_trial={"cpu": 2},
                                     name="lenet_mnist")
 ```
@@ -91,7 +91,7 @@ Finally, user can get the best learned model and the best hyper-parameters for f
 best_model = auto_est.get_best_model() # a `torch.nn.Module` object
 best_config = auto_est.get_best_config() # a dictionary of hyper-parameter names and values.
 ```
-View the related [Python API doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details.
+View the related [Python API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details.
 
 ### **3. TensorFlow/Keras AutoEstimator**
 Users can create an `AutoEstimator` for TensorFlow Keras from a `tf.keras` model (using a *Model Creator Function*). For example:
@@ -107,7 +107,7 @@ def model_creator(config):
     return model
 
 auto_est = AutoEstimator.from_keras(model_creator=model_creator,
-                                    logs_dir="/tmp/zoo_automl_logs",
+                                    logs_dir="/tmp/orca_automl_logs",
                                     resources_per_trial={"cpu": 2},
                                     name="auto_keras")
 ```
@@ -128,20 +128,20 @@ Finally, user can get the best learned model and the best hyper-parameters for f
 best_model = auto_est.get_best_model() # a `torch.nn.Module` object
 best_config = auto_est.get_best_config() # a dictionary of hyper-parameter names and values.
 ```
-View the related [Python API doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details.
+View the related [Python API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details.
 
 ### **4. Search Space and Search Algorithms**
 For Hyper-parameter Optimization, user should define the search space of various hyper-parameter values for neural network training, as well as how to search through the chosen hyper-parameter space.
 
 #### **4.1 Basic Search Algorithms**
 
-For basic search algorithms like **Grid Search** and **Random Search**, we provide several sampling functions with `automl.hp`. See [API doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-hp) for more details.
+For basic search algorithms like **Grid Search** and **Random Search**, we provide several sampling functions with `automl.hp`. See [API doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-hp) for more details.
 
 `AutoEstimator` requires a dictionary for the `search_space` argument in `fit`.
 In the dictionary, the keys are the hyper-parameter names, and the values specify how to sample the search spaces for the hyper-parameters.
 
 ```python
-from zoo.orca.automl import hp
+from bigdl.orca.automl import hp
 
 search_space = {
     "fc1_hidden_size": hp.grid_search([500, 600]),
@@ -163,7 +163,7 @@ pip install bayesian-optimization
 
 And pass the search algorithm name to `search_alg` in `AutoEstimator.fit`.
 ```python
-from zoo.orca.automl import hp
+from bigdl.orca.automl import hp
 
 search_space = {
     "width": hp.uniform(0, 20),
@@ -178,7 +178,7 @@ auto_estimator.fit(
     search_alg="bayesopt",
 )
 ```
-See [API Doc](https://analytics-zoo.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details.
+See [API Doc](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/AutoML/automl.html#orca-automl-auto-estimator) for more details.
 
 ### **4. Scheduler**
 *Scheduler* can stop/pause/tweak the hyper-parameters of running trials, making the hyper-parameter tuning process much efficient.