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Unverified Commit c23a61bd authored by Jinjing Zhou's avatar Jinjing Zhou Committed by GitHub
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fix s3 link (#1310)

parent 349a48bd
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tutorials/models/3_generative_model/5_dgmg.py
View file @ c23a61bd
...@@ -715,7 +715,7 @@ class DGMG(DGMGSkeleton): ...@@ -715,7 +715,7 @@ class DGMG(DGMGSkeleton):
import torch.utils.model_zoo as model_zoo import torch.utils.model_zoo as model_zoo
# Download a pre-trained model state dict for generating cycles with 10-20 nodes. # Download a pre-trained model state dict for generating cycles with 10-20 nodes.
state_dict = model_zoo.load_url('https://s3.us-east-2.amazonaws.com/dgl.ai/model/dgmg_cycles-5a0c40be.pth') state_dict = model_zoo.load_url('https://data.dgl.ai/model/dgmg_cycles-5a0c40be.pth')
model = DGMG(v_max=20, node_hidden_size=16, num_prop_rounds=2) model = DGMG(v_max=20, node_hidden_size=16, num_prop_rounds=2)
model.load_state_dict(state_dict) model.load_state_dict(state_dict)
model.eval() model.eval()
......
tutorials/models/4_old_wines/7_transformer.py
View file @ c23a61bd
...@@ -875,6 +875,6 @@ Transformer tutorial ...@@ -875,6 +875,6 @@ Transformer tutorial
# #
# .. note:: # .. note::
# The notebook itself is not executable due to many dependencies. # The notebook itself is not executable due to many dependencies.
# Download `7_transformer.py <https://s3.us-east-2.amazonaws.com/dgl.ai/tutorial/7_transformer.py>`__, # Download `7_transformer.py <https://data.dgl.ai/tutorial/7_transformer.py>`__,
# and copy the python script to directory ``examples/pytorch/transformer`` # and copy the python script to directory ``examples/pytorch/transformer``
# then run ``python 7_transformer.py`` to see how it works. # then run ``python 7_transformer.py`` to see how it works.
tutorials/models/5_giant_graph/1_sampling_mx.py
View file @ c23a61bd
...@@ -419,6 +419,6 @@ for nf in dgl.contrib.sampling.NeighborSampler(g, batch_size, num_neighbors, ...@@ -419,6 +419,6 @@ for nf in dgl.contrib.sampling.NeighborSampler(g, batch_size, num_neighbors,
# advantages of this API are 1) simplicity, 2) allowing more system-level # advantages of this API are 1) simplicity, 2) allowing more system-level
# optimization in the future. # optimization in the future.
# #
# .. |image0| image:: https://s3.us-east-2.amazonaws.com/dgl.ai/tutorial/sampling/NodeFlow.png # .. |image0| image:: https://data.dgl.ai/tutorial/sampling/NodeFlow.png
# .. |image1| image:: https://s3.us-east-2.amazonaws.com/dgl.ai/tutorial/sampling/sampling_result.png # .. |image1| image:: https://data.dgl.ai/tutorial/sampling/sampling_result.png
# #
tutorials/models/5_giant_graph/2_giant.py
View file @ c23a61bd
...@@ -356,7 +356,7 @@ Large-Scale Training of Graph Neural Networks ...@@ -356,7 +356,7 @@ Large-Scale Training of Graph Neural Networks
# We can see that DGL can scale to graphs with up to 500M nodes and 25B # We can see that DGL can scale to graphs with up to 500M nodes and 25B
# edges. # edges.
# #
# .. |image0| image:: https://s3.us-east-2.amazonaws.com/dgl.ai/tutorial/sampling/arch.png # .. |image0| image:: https://data.dgl.ai/tutorial/sampling/arch.png
# .. |image1| image:: https://s3.us-east-2.amazonaws.com/dgl.ai/tutorial/sampling/NUMA_speedup.png # .. |image1| image:: https://data.dgl.ai/tutorial/sampling/NUMA_speedup.png
# .. |image2| image:: https://s3.us-east-2.amazonaws.com/dgl.ai/tutorial/sampling/whole_speedup.png # .. |image2| image:: https://data.dgl.ai/tutorial/sampling/whole_speedup.png
# #
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