[doc] use tqdm from tqdm.auto (#7191)

notebooks/stochastic_training/link_prediction.ipynb

@@ -249,11 +249,11 @@
       },
       "outputs": [],
       "source": [
-        "import tqdm\n",
+        "from tqdm.auto import tqdm\n",
         "for epoch in range(3):\n",
         "    model.train()\n",
         "    total_loss = 0\n",
-        "    for step, data in tqdm.tqdm(enumerate(create_train_dataloader())):\n",
+        "    for step, data in tqdm(enumerate(create_train_dataloader())):\n",
         "        # Get node pairs with labels for loss calculation.\n",
         "        compacted_pairs, labels = data.node_pairs_with_labels\n",
         "        node_feature = data.node_features[\"feat\"]\n",
@@ -306,7 +306,7 @@
         "\n",
         "logits = []\n",
         "labels = []\n",
-        "for step, data in tqdm.tqdm(enumerate(eval_dataloader)):\n",
+        "for step, data in tqdm(enumerate(eval_dataloader)):\n",
         "    # Get node pairs with labels for loss calculation.\n",
         "    compacted_pairs, label = data.node_pairs_with_labels\n",
         "\n",
@@ -370,4 +370,4 @@
   },
   "nbformat": 4,
   "nbformat_minor": 0
-}
\ No newline at end of file
+}

notebooks/stochastic_training/node_classification.ipynb

@@ -297,12 +297,12 @@
       },
       "outputs": [],
       "source": [
-        "import tqdm\n",
+        "from tqdm.auto import tqdm\n",
         "\n",
         "for epoch in range(10):\n",
         "    model.train()\n",
         "\n",
-        "    with tqdm.tqdm(train_dataloader) as tq:\n",
+        "    with tqdm(train_dataloader) as tq:\n",
         "        for step, data in enumerate(tq):\n",
         "            x = data.node_features[\"feat\"]\n",
         "            labels = data.labels\n",
@@ -328,7 +328,7 @@
         "\n",
         "    predictions = []\n",
         "    labels = []\n",
-        "    with tqdm.tqdm(valid_dataloader) as tq, torch.no_grad():\n",
+        "    with tqdm(valid_dataloader) as tq, torch.no_grad():\n",
         "        for data in tq:\n",
         "            x = data.node_features[\"feat\"]\n",
         "            labels.append(data.labels.cpu().numpy())\n",

python/dgl/data/lrgb.py

@@ -4,7 +4,7 @@ import pickle
 
 import pandas as pd
 from ogb.utils import smiles2graph as smiles2graph_OGB
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from .. import backend as F
 

python/dgl/data/superpixel.py

@@ -3,7 +3,7 @@ import pickle
 
 import numpy as np
 from scipy.spatial.distance import cdist
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from .. import backend as F
 from ..convert import graph as dgl_graph

python/dgl/data/utils.py

@@ -12,7 +12,7 @@ import networkx.algorithms as A
 
 import numpy as np
 import requests
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from .. import backend as F
 from .graph_serialize import load_graphs, load_labels, save_graphs

python/dgl/nn/pytorch/explain/gnnexplainer.py

@@ -5,7 +5,7 @@ from math import sqrt
 import torch
 
 from torch import nn
-from tqdm import tqdm
+from tqdm.auto import tqdm
 
 from ....base import EID, NID
 from ....subgraph import khop_in_subgraph

python/dgl/nn/pytorch/network_emb.py

 """Network Embedding NN Modules"""
+
 # pylint: disable= invalid-name
 
 import random
 
 import torch
 import torch.nn.functional as F
-import tqdm
 from torch import nn
 from torch.nn import init
+from tqdm.auto import trange
 
 from ...base import NID
 from ...convert import to_heterogeneous, to_homogeneous
@@ -340,7 +341,7 @@ class MetaPath2Vec(nn.Module):
         num_nodes_total = hg.num_nodes()
         node_frequency = torch.zeros(num_nodes_total)
         # random walk
-        for idx in tqdm.trange(hg.num_nodes(node_metapath[0])):
+        for idx in trange(hg.num_nodes(node_metapath[0])):
             traces, _ = random_walk(g=hg, nodes=[idx], metapath=metapath)
             for tr in traces.cpu().numpy():
                 tr_nids = [

script/dgl_dev.yml.template

@@ -47,5 +47,7 @@ dependencies:
     - clang-format
     - pylint
     - lintrunner
+    - jupyterlab
+    - ipywidgets
 variables:
   DGL_HOME: __DGL_HOME__

tutorials/models/4_old_wines/7_transformer.py

@@ -589,7 +589,7 @@ Transformer as a Graph Neural Network
 #
 # .. code:: python
 #
-#    from tqdm import tqdm
+#    from tqdm.auto import tqdm
 #    import torch as th
 #    import numpy as np
 #

tutorials/multi/2_node_classification.py

@@ -20,7 +20,6 @@ models with multi-GPU with ``DistributedDataParallel``.
 
 """
 
-
 ######################################################################
 # Importing Packages
 # ---------------
@@ -42,9 +41,9 @@ import torch.multiprocessing as mp
 import torch.nn as nn
 import torch.nn.functional as F
 import torchmetrics.functional as MF
-import tqdm
 from torch.distributed.algorithms.join import Join
 from torch.nn.parallel import DistributedDataParallel as DDP
+from tqdm.auto import tqdm
 
 
 ######################################################################
@@ -155,7 +154,7 @@ def evaluate(rank, model, graph, features, itemset, num_classes, device):
         is_train=False,
     )
 
-    for data in tqdm.tqdm(dataloader) if rank == 0 else dataloader:
+    for data in tqdm(dataloader) if rank == 0 else dataloader:
         blocks = data.blocks
         x = data.node_features["feat"]
         y.append(data.labels)
@@ -212,7 +211,7 @@ def train(
         total_loss = torch.tensor(0, dtype=torch.float, device=device)
         num_train_items = 0
         with Join([model]):
-            for data in tqdm.tqdm(dataloader) if rank == 0 else dataloader:
+            for data in tqdm(dataloader) if rank == 0 else dataloader:
                 # The input features are from the source nodes in the first
                 # layer's computation graph.
                 x = data.node_features["feat"]