diff --git a/README.md b/README.md
index 68402281..e0009776 100644
--- a/README.md
+++ b/README.md
@@ -77,7 +77,7 @@ Most AI projects start with a Python notebook running on a single laptop; howeve
 First, initialize [Orca Context](https://bigdl.readthedocs.io/en/latest/doc/Orca/Overview/orca-context.html):
 
 ```python
-from bigdl.orca import init_orca_context
+from bigdl.orca import init_orca_context, OrcaContext
 
 # cluster_mode can be "local", "k8s" or "yarn"
 sc = init_orca_context(cluster_mode="yarn", cores=4, memory="10g", num_nodes=2) 
@@ -88,6 +88,7 @@ Next, perform [data-parallel processing in Orca](https://bigdl.readthedocs.io/en
 ```python
 from pyspark.sql.functions import array
 
+spark = OrcaContext.get_spark_session()
 df = spark.read.parquet(file_path)
 df = df.withColumn('user', array('user')) \  
        .withColumn('item', array('item'))
diff --git a/docs/readthedocs/source/doc/Orca/Overview/data-parallel-processing.md b/docs/readthedocs/source/doc/Orca/Overview/data-parallel-processing.md
index 6667ec6c..cdc8c6c4 100644
--- a/docs/readthedocs/source/doc/Orca/Overview/data-parallel-processing.md
+++ b/docs/readthedocs/source/doc/Orca/Overview/data-parallel-processing.md
@@ -12,7 +12,7 @@ TensorFlow Dataset:
 ```python
 import tensorflow as tf
 import tensorflow_datasets as tfds
-from zoo.orca.learn.tf.estimator import Estimator
+from bigdl.orca.learn.tf.estimator import Estimator
 
 def preprocess(data):
     data['image'] = tf.cast(data["image"], tf.float32) / 255.
@@ -30,7 +30,7 @@ Pytorch DataLoader:
 ```python
 import torch
 from torchvision import datasets, transforms
-from zoo.orca.learn.pytorch import Estimator
+from bigdl.orca.learn.pytorch import Estimator
 
 train_loader = torch.utils.data.DataLoader(
         datasets.MNIST("/tmp/mnist", train=True, download=True,
@@ -41,7 +41,7 @@ train_loader = torch.utils.data.DataLoader(
         batch_size=batch_size, shuffle=True)
 
 est = Estimator.from_torch(model=torch_model, optimizer=torch_optim, loss=torch_criterion)
-zoo_estimator.fit(data=train_loader)
+est.fit(data=train_loader)
 ```
 
 Under the hood, Orca will automatically replicate the _TensorFlow Dataset_ or _PyTorch DataLoader_ pipeline on each node in the cluster, shard the input data, and execute the data pipelines using Apache Spark and/or Ray distributedly. 
@@ -101,7 +101,7 @@ est.fit(data=df,
 
 ```python
 import numpy as np
-from zoo.orca.data import XShards
+from bigdl.orca.data import XShards
 
 train_images = np.random.random((20, 3, 224, 224))
 train_label_images = np.zeros(20)
@@ -122,7 +122,7 @@ The user may use `XShards` to efficiently process large-size Pandas Dataframes i
 
 First, the user can read CVS, JSON or Parquet files (stored on local disk, HDFS, AWS S3, etc.) to obtain an `XShards` of Pandas Dataframe, as shown below:
 ```python
-from zoo.orca.data.pandas import read_csv
+from bigdl.orca.data.pandas import read_csv
 csv_path = "/path/to/csv_file_or_folder"
 shard = read_csv(csv_path)
 ```
diff --git a/docs/readthedocs/source/doc/Orca/Overview/distributed-training-inference.md b/docs/readthedocs/source/doc/Orca/Overview/distributed-training-inference.md
index 983748ef..c8b53084 100644
--- a/docs/readthedocs/source/doc/Orca/Overview/distributed-training-inference.md
+++ b/docs/readthedocs/source/doc/Orca/Overview/distributed-training-inference.md
@@ -163,7 +163,7 @@ View the related [Python API doc]() for more details.
 
 The user may create a MXNet `Estimator` as follows:
 ```python
-from zoo.orca.learn.mxnet import Estimator, create_config
+from bigdl.orca.learn.mxnet import Estimator, create_config
 
 def get_model(config):
     net = LeNet() # a mxnet.gluon.Block
@@ -200,10 +200,10 @@ View the related [Python API doc]() for more details.
 
 The user may create a BigDL `Estimator` as follows:
 ```python
-from bigdl.nn.criterion import *
-from bigdl.nn.layer import *
-from bigdl.optim.optimizer import *
-from zoo.orca.learn.bigdl import Estimator
+from bigdl.dllib.nn.criterion import *
+from bigdl.dllib.nn.layer import *
+from bigdl.dllib.optim.optimizer import *
+from bigdl.orca.learn.bigdl import Estimator
 
 linear_model = Sequential().add(Linear(2, 2))
 mse_criterion = MSECriterion()
@@ -230,7 +230,7 @@ View the related [Python API doc]() for more details.
 
 The user may create a OpenVINO `Estimator` as follows:
 ```python
-from zoo.orca.learn.openvino import Estimator
+from bigdl.orca.learn.openvino import Estimator
 
 model_path = "The/file_path/to/the/OpenVINO_IR_xml_file"
 est = Estimator.from_openvino(model_path=model_path)
diff --git a/docs/readthedocs/source/doc/Orca/Overview/orca-context.md b/docs/readthedocs/source/doc/Orca/Overview/orca-context.md
index 737dfb9e..597746b8 100644
--- a/docs/readthedocs/source/doc/Orca/Overview/orca-context.md
+++ b/docs/readthedocs/source/doc/Orca/Overview/orca-context.md
@@ -10,7 +10,7 @@
 An Orca program usually starts with the initialization of `OrcaContext` as follows:
 
 ```python
-from zoo.orca import init_orca_context
+from bigdl.orca import init_orca_context
 
 init_orca_context(...)
 ```
@@ -47,7 +47,7 @@ Under the hood, `OrcaContext` will automatically provision Apache Spark and/or R
 Users can easily retrieve `SparkContext` and `RayContext`, the main entry point for Spark and Ray respectively, via `OrcaContext`:
 
 ```python
-from zoo.orca import OrcaContext
+from bigdl.orca import OrcaContext
 
 sc = OrcaContext.get_spark_context()
 ray_ctx = OrcaContext.get_ray_context()
@@ -75,7 +75,7 @@ Users can make extra configurations when using the functionalities of Project Or
 After the Orca program finishes, the user can call `stop_orca_context` to release resources and shut down the underlying Spark and/or Ray execution engine.
 
 ```python
-from zoo.orca import stop_orca_context
+from bigdl.orca import stop_orca_context
 
 stop_orca_context()
 ```
diff --git a/docs/readthedocs/source/doc/Orca/Overview/orca.md b/docs/readthedocs/source/doc/Orca/Overview/orca.md
index 7d138fee..69857d24 100644
--- a/docs/readthedocs/source/doc/Orca/Overview/orca.md
+++ b/docs/readthedocs/source/doc/Orca/Overview/orca.md
@@ -1,14 +1,43 @@
 # The Orca Library
 
+## 1. Overview
+
 Most AI projects start with a Python notebook running on a single laptop; however, one usually needs to go through a mountain of pains to scale it to handle larger data set in a distributed fashion. The  _**Orca**_ library seamlessly scales out your single node Python notebook across large clusters (so as to process distributed Big Data).
 
+## 2. 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 py37 python=3.7  # "py37" is conda environment name, you can use any name you like.
+conda activate py37
+pip install bigdl-orca
+```
+
+When installing bigdl-orca with pip, you can specify the extras key `[ray]` to additionally install the additional dependencies
+essential for running [RayOnSpark](../../Ray/Overview/ray.md)
+```bash
+pip install bigdl-orca[ray]
+```
+
+You can install bigdl-orca nightly release version using
+```bash
+pip install --pre --upgrade bigdl-orca
+pip install --pre --upgrade bigdl-orca[ray]
+```
+
+## 3. Run
+
+This section uses TensorFlow 1.15, and you should install TensorFlow before running this example:
+```bash
+pip install tensorflow==1.15
+```
+
 First, initialize [Orca Context](orca-context.md):
 
 ```python
-from zoo.orca import init_orca_context
+from bigdl.orca import init_orca_context, OrcaContext
 
 # cluster_mode can be "local", "k8s" or "yarn"
-sc = init_orca_context(cluster_mode="yarn", cores=4, memory="10g", num_nodes=2) 
+sc = init_orca_context(cluster_mode="local", cores=4, memory="10g", num_nodes=1) 
 ```
 
 Next, perform [data-parallel processing in Orca](data-parallel-processing.md) (supporting standard Spark Dataframes, TensorFlow Dataset, PyTorch DataLoader, Pandas, etc.):
@@ -16,6 +45,7 @@ Next, perform [data-parallel processing in Orca](data-parallel-processing.md) (s
 ```python
 from pyspark.sql.functions import array
 
+spark = OrcaContext.get_spark_session()
 df = spark.read.parquet(file_path)
 df = df.withColumn('user', array('user')) \  
        .withColumn('item', array('item'))
@@ -25,7 +55,7 @@ Finally, use [sklearn-style Estimator APIs in Orca](distributed-training-inferen
 
 ```python
 from tensorflow import keras
-from zoo.orca.learn.tf.estimator import Estimator
+from bigdl.orca.learn.tf.estimator import Estimator
 
 user = keras.layers.Input(shape=[1])  
 item = keras.layers.Input(shape=[1])  
@@ -44,5 +74,7 @@ est.fit(data=df,
         label_cols=['label'])
 ```
 
+## Get Started
+
 See [TensorFlow](../QuickStart/orca-tf-quickstart.md) and [PyTorch](../QuickStart/orca-pytorch-quickstart.md) quickstart for more details.
 
diff --git a/docs/readthedocs/source/doc/Orca/QuickStart/orca-keras-quickstart.md b/docs/readthedocs/source/doc/Orca/QuickStart/orca-keras-quickstart.md
index 623111f5..afb946a3 100644
--- a/docs/readthedocs/source/doc/Orca/QuickStart/orca-keras-quickstart.md
+++ b/docs/readthedocs/source/doc/Orca/QuickStart/orca-keras-quickstart.md
@@ -2,7 +2,7 @@
 
 ---
 
-![](../../../../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/keras_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/keras_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/docs/docs/colab-notebook/orca/quickstart/keras_lenet_mnist.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/colab-notebook/orca/quickstart/keras_lenet_mnist.ipynb)
 
 ---
 
@@ -14,9 +14,9 @@
 We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the environment. Please refer to the [install guide](../../UserGuide/python.md) for more details.
 
 ```bash
-conda create -n zoo python=3.7 # "zoo" is conda environment name, you can use any name you like.
-conda activate zoo
-pip install analytics_zoo-${VERSION} # install either version 0.9 or latest nightly build
+conda create -n py37 python=3.7  # "py37" is conda environment name, you can use any name you like.
+conda activate py37
+pip install bigdl-orca
 pip install tensorflow==1.15.0
 pip install tensorflow-datasets==2.1.0
 pip install psutil
@@ -26,7 +26,7 @@ pip install scikit-learn
 
 ### **Step 1: Init Orca Context**
 ```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":  # For local machine
     init_orca_context(cluster_mode="local", cores=4, memory="10g")
@@ -60,7 +60,7 @@ model = keras.Sequential(
      ]
 )
 
-model.compile(optimizer=tf.keras.optimizers.RMSprop(),
+model.compile(optimizer=keras.optimizers.RMSprop(),
               loss='sparse_categorical_crossentropy',
               metrics=['accuracy'])
 ```
@@ -77,6 +77,7 @@ def preprocess(data):
     return data['image'], data['label']
 
 # get DataSet
+dataset_dir = "./mnist_data"
 mnist_train = tfds.load(name="mnist", split="train", data_dir=dataset_dir)
 mnist_test = tfds.load(name="mnist", split="test", data_dir=dataset_dir)
 
@@ -89,7 +90,7 @@ mnist_test = mnist_test.map(preprocess)
 First, create an Estimator.
 
 ```python
-from zoo.orca.learn.tf.estimator import Estimator
+from bigdl.orca.learn.tf.estimator import Estimator
 
 est = Estimator.from_keras(keras_model=model)
 ```
diff --git a/docs/readthedocs/source/doc/Orca/QuickStart/orca-pytorch-distributed-quickstart.md b/docs/readthedocs/source/doc/Orca/QuickStart/orca-pytorch-distributed-quickstart.md
index cb988042..9ce4e5c1 100644
--- a/docs/readthedocs/source/doc/Orca/QuickStart/orca-pytorch-distributed-quickstart.md
+++ b/docs/readthedocs/source/doc/Orca/QuickStart/orca-pytorch-distributed-quickstart.md
@@ -2,7 +2,7 @@
 
 ---
 
-![](../../../../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/pytorch_distributed_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/pytorch_distributed_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/docs/docs/colab-notebook/orca/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/docs/docs/colab-notebook/orca/quickstart/pytorch_distributed_lenet_mnist.ipynb)
 
 ---
 
@@ -13,15 +13,15 @@
 [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.
 
 ```bash
-conda create -n zoo python=3.7 # zoo is conda environment name, you can use any name you like.
-conda activate zoo
-pip install analytics-zoo[ray]
+conda create -n py37 python=3.7  # "py37" is conda environment name, you can use any name you like.
+conda activate py37
+pip install bigdl-orca[ray]
 pip install torch==1.7.1 torchvision==0.8.2
 ```
 
 ### **Step 1: Init Orca Context**
 ```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":  # For local machine
     init_orca_context(cores=4, memory="10g")
@@ -114,8 +114,8 @@ def test_loader_creator(config, batch_size):
 First, Create an Estimator
 
 ```python
-from zoo.orca.learn.pytorch import Estimator 
-from zoo.orca.learn.metrics import Accuracy
+from bigdl.orca.learn.pytorch import Estimator 
+from bigdl.orca.learn.metrics import Accuracy
 
 est = Estimator.from_torch(model=model_creator, optimizer=optim_creator, loss=criterion, metrics=[Accuracy()],
                            backend="torch_distributed")
diff --git a/docs/readthedocs/source/doc/Orca/QuickStart/orca-pytorch-quickstart.md b/docs/readthedocs/source/doc/Orca/QuickStart/orca-pytorch-quickstart.md
index d7d9ccd2..439e9043 100644
--- a/docs/readthedocs/source/doc/Orca/QuickStart/orca-pytorch-quickstart.md
+++ b/docs/readthedocs/source/doc/Orca/QuickStart/orca-pytorch-quickstart.md
@@ -2,7 +2,7 @@
 
 ---
 
-![](../../../../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/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/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/docs/docs/colab-notebook/orca/quickstart/pytorch_lenet_mnist.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/colab-notebook/orca/quickstart/pytorch_lenet_mnist.ipynb)
 
 ---
 
@@ -14,9 +14,9 @@
 
 
 ```bash
-conda create -n zoo python=3.7 # zoo is conda environment name, you can use any name you like.
-conda activate zoo
-pip install analytics-zoo # install either version 0.9 or latest nightly build
+conda create -n py37 python=3.7  # "py37" is conda environment name, you can use any name you like.
+conda activate py37
+pip install bigdl-orca
 pip install torch==1.7.1 torchvision==0.8.2
 pip install six cloudpickle
 pip install jep==3.9.0
@@ -24,7 +24,7 @@ pip install jep==3.9.0
 
 ### **Step 1: Init Orca Context**
 ```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":  # For local machine
     init_orca_context(cores=4, memory="10g")
@@ -105,15 +105,15 @@ 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/analytics-zoo/blob/master/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/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)
 
 ### **Step 4: Fit with Orca Estimator**
 
 First, Create an Estimator
 
 ```python
-from zoo.orca.learn.pytorch import Estimator 
-from zoo.orca.learn.metrics import Accuracy
+from bigdl.orca.learn.pytorch import Estimator 
+from bigdl.orca.learn.metrics import Accuracy
 
 est = Estimator.from_torch(model=model, optimizer=adam, loss=criterion, metrics=[Accuracy()])
 ```
@@ -121,7 +121,7 @@ est = Estimator.from_torch(model=model, optimizer=adam, loss=criterion, metrics=
 Next, fit and evaluate using the Estimator
 
 ```python
-from zoo.orca.learn.trigger import EveryEpoch 
+from bigdl.orca.learn.trigger import EveryEpoch 
 
 est.fit(data=train_loader, epochs=10, validation_data=test_loader,
         checkpoint_trigger=EveryEpoch())
diff --git a/docs/readthedocs/source/doc/Orca/QuickStart/orca-tf-quickstart.md b/docs/readthedocs/source/doc/Orca/QuickStart/orca-tf-quickstart.md
index e1104901..001a2eb2 100644
--- a/docs/readthedocs/source/doc/Orca/QuickStart/orca-tf-quickstart.md
+++ b/docs/readthedocs/source/doc/Orca/QuickStart/orca-tf-quickstart.md
@@ -2,7 +2,7 @@
 
 ---
 
-![](../../../../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/tf_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/tf_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/docs/docs/colab-notebook/orca/quickstart/tf_lenet_mnist.ipynb)  ![](../../../../image/GitHub-Mark-32px.png)[View source on GitHub](https://github.com/intel-analytics/BigDL/blob/branch-2.0/docs/docs/colab-notebook/orca/quickstart/tf_lenet_mnist.ipynb)
 
 ---
 
@@ -14,17 +14,17 @@ We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-g
 
 
 ```bash
-conda create -n zoo python=3.7 # "zoo" is conda environment name, you can use any name you like.
-conda activate zoo
-pip install analytics-zoo # install either version 0.9 or latest nightly build
-pip install tensorflow==1.15.0
+conda create -n py37 python=3.7  # "py37" is conda environment name, you can use any name you like.
+conda activate py37
+pip install bigdl-orca
+pip install tensorflow==1.15
 pip install tensorflow-datasets==2.0
 pip install psutil
 ```
 
 ### **Step 1: Init Orca Context**
 ```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":  # For local machine
     init_orca_context(cluster_mode="local", cores=4, memory="10g")
@@ -83,6 +83,7 @@ def preprocess(data):
     return data['image'], data['label']
 
 # get DataSet
+dataset_dir = "./mnist_data"
 mnist_train = tfds.load(name="mnist", split="train", data_dir=dataset_dir)
 mnist_test = tfds.load(name="mnist", split="test", data_dir=dataset_dir)
 
@@ -95,7 +96,7 @@ mnist_test = mnist_test.map(preprocess)
 First, create an Estimator.
 
 ```python
-from zoo.orca.learn.tf.estimator import Estimator
+from bigdl.orca.learn.tf.estimator import Estimator
 
 est = Estimator.from_graph(inputs=images,
                            outputs=logits,
@@ -107,7 +108,7 @@ est = Estimator.from_graph(inputs=images,
 
 Next, fit and evaluate using the Estimator.
 ```python
-est.fit(data=train_dataset,
+est.fit(data=mnist_train,
         batch_size=320,
         epochs=5,
         validation_data=mnist_test)
diff --git a/docs/readthedocs/source/doc/Orca/QuickStart/orca-tf2keras-quickstart.md b/docs/readthedocs/source/doc/Orca/QuickStart/orca-tf2keras-quickstart.md
index 614303c4..68092dcc 100644
--- a/docs/readthedocs/source/doc/Orca/QuickStart/orca-tf2keras-quickstart.md
+++ b/docs/readthedocs/source/doc/Orca/QuickStart/orca-tf2keras-quickstart.md
@@ -2,7 +2,7 @@
 
 ---
 
-![](../../../../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/tf2_keras_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/tf2_keras_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/docs/docs/colab-notebook/orca/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/docs/docs/colab-notebook/orca/quickstart/tf2_keras_lenet_mnist.ipynb)
 
 ---
 
@@ -13,15 +13,15 @@
 We recommend using [conda](https://docs.conda.io/projects/conda/en/latest/user-guide/install/) to prepare the environment. Please refer to the [install guide](../../UserGuide/python.md) for more details.
 
 ```bash
-conda create -n zoo python=3.7 # "zoo" is conda environment name, you can use any name you like.
-conda activate zoo
-pip install analytics-zoo[ray] # install either version 0.9 or latest nightly build
+conda create -n py37 python=3.7  # "py37" is conda environment name, you can use any name you like.
+conda activate py37
+pip install bigdl-orca[ray]
 pip install tensorflow==2.3.0
 ```
 
 ### **Step 1: Init Orca Context**
 ```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":  # For local machine
     init_orca_context(cluster_mode="local", cores=4, memory="10g")
@@ -96,7 +96,7 @@ def val_data_creator(config, batch_size):
 First, create an Estimator.
 
 ```python
-from zoo.orca.learn.tf2 import Estimator
+from bigdl.orca.learn.tf2 import Estimator
 
 est = Estimator.from_keras(model_creator=model_creator, workers_per_node=2)
 ```
@@ -118,6 +118,6 @@ est.shutdown()
 print(stats)
 ```
 
-That's it, the same code can run seamlessly in your local laptop and the distribute K8s or Hadoop cluster.
+That's it, the same code can run seamlessly in your local laptop and to distribute K8s or Hadoop cluster.
 
 **Note:** You should call `stop_orca_context()` when your program finishes.