[Fix] Fix #1078, #1077 and add nightly build to readme

README.md

@@ -18,7 +18,7 @@ All model examples can be found [here](https://github.com/dmlc/dgl/tree/master/e
 A summary of part of the model accuracy and training speed with the Pytorch backend (on Amazon EC2 p3.2x instance (w/ V100 GPU)), as compared with the best open-source implementations:
 
 | Model                                                            | Reported <br> Accuracy | DGL <br> Accuracy | Author's training speed (epoch time)                                          | DGL speed (epoch time) | Improvement |
-| -----                                                            | -----------------      | ------------      | ------------------------------------                                          | ---------------------- | ----------- |
+| ---------------------------------------------------------------- | ---------------------- | ----------------- | ----------------------------------------------------------------------------- | ---------------------- | ----------- |
 | [GCN](https://arxiv.org/abs/1609.02907)                          | 81.5%                  | 81.0%             | [0.0051s (TF)](https://github.com/tkipf/gcn)                                  | 0.0031s                | 1.64x       |
 | [GAT](https://arxiv.org/abs/1710.10903)                          | 83.0%                  | 83.9%             | [0.0982s (TF)](https://github.com/PetarV-/GAT)                                | 0.0113s                | 8.69x       |
 | [SGC](https://arxiv.org/abs/1902.07153)                          | 81.0%                  | 81.9%             | n/a                                                                           | 0.0008s                | n/a         |
@@ -28,7 +28,7 @@ A summary of part of the model accuracy and training speed with the Pytorch back
 | [JTNN](https://arxiv.org/abs/1802.04364)                         | 96.44%                 | 96.44%            | [1826s (Pytorch)](https://github.com/wengong-jin/icml18-jtnn)                 | 743s                   | 2.5x        |
 | [LGNN](https://arxiv.org/abs/1705.08415)                         | 94%                    | 94%               | n/a                                                                           | 1.45s                  | n/a         |
 | [DGMG](https://arxiv.org/pdf/1803.03324.pdf)                     | 84%                    | 90%               | n/a                                                                           | 238s                   | n/a         |
-| [GraphWriter](https://www.aclweb.org/anthology/N19-1238.pdf)     | 14.3(BLEU)             | 14.31(BLEU)       | [1970s (PyTorch)](https://github.com/rikdz/GraphWriter)                       | 1192s                  | 1.65x       |                  
+| [GraphWriter](https://www.aclweb.org/anthology/N19-1238.pdf)     | 14.3(BLEU)             | 14.31(BLEU)       | [1970s (PyTorch)](https://github.com/rikdz/GraphWriter)                       | 1192s                  | 1.65x       |
 
 With the MXNet/Gluon backend , we scaled a graph of 50M nodes and 150M edges on a P3.8xlarge instance, 
 with 160s per epoch, on SSE ([Stochastic Steady-state Embedding](https://www.cc.gatech.edu/~hdai8/pdf/equilibrium_embedding.pdf)), 
@@ -75,13 +75,14 @@ conda install -c dglteam dgl-cuda10.1  # CUDA 10.1
 
 ### Using pip
 
-```
-pip install dgl       # cpu version
-pip install dgl-cu90  # CUDA 9.0
-pip install dgl-cu92  # CUDA 9.2
-pip install dgl-cu100 # CUDA 10.0
-pip install dgl-cu101 # CUDA 10.1
-```
+
+|           | Latest Nightly Build Version  | Stable Version          |
+|-----------|-------------------------------|-------------------------|
+| CPU       | `pip install --pre dgl`       | `pip install dgl`       |
+| CUDA 9.0  | `pip install --pre dgl-cu90`  | `pip install dgl-cu90`  |
+| CUDA 9.2  | `pip install --pre dgl-cu92`  | `pip install dgl-cu92`  |
+| CUDA 10.0 | `pip install --pre dgl-cu100` | `pip install dgl-cu100` |
+| CUDA 10.1 | `pip install --pre dgl-cu101` | `pip install dgl-cu101` |
 
 ### From source
 

python/dgl/data/gnn_benckmark.py

@@ -30,7 +30,7 @@ class GNNBenchmarkDataset(object):
 
     @staticmethod
     def load_npz(file_name):
-        with np.load(file_name) as loader:
+        with np.load(file_name, allow_pickle=True) as loader:
             loader = dict(loader)
             num_nodes = loader['adj_shape'][0]
             adj_matrix = sp.csr_matrix((loader['adj_data'], loader['adj_indices'], loader['adj_indptr']),
@@ -116,7 +116,7 @@ class AmazonCoBuy(GNNBenchmarkDataset):
     Parameters
     ---------------
     name: str
-      Name of the dataset, has to be 'computer' or 'photo'
+      Name of the dataset, has to be 'computers' or 'photo'
 
     """
     _url = {