freeze d2l lib for v1.0.0 release (d2l-ai#2545)

* d2l library 1.0.0 release freeze

* CI: disable caching for scratch tests

* Use numpy==1.24.4 for py3.8 support

* Use numpy==1.23.5

* PyTorch: Fix numpy object to float conversion

* MXNet: Fix mxnet arange issue

* CI: enable caching switchback

chapter_multilayer-perceptrons/kaggle-house-price.md

@@ -304,7 +304,7 @@ def get_dataloader(self, train):
     label = 'SalePrice'
     data = self.train if train else self.val
     if label not in data: return
-    get_tensor = lambda x: d2l.tensor(x.values, dtype=d2l.float32)
+    get_tensor = lambda x: d2l.tensor(x.values.astype(float), dtype=d2l.float32)
     # Logarithm of prices 
     tensors = (get_tensor(data.drop(columns=[label])),  # X
                d2l.reshape(d2l.log(get_tensor(data[label])), (-1, 1)))  # Y
@@ -403,11 +403,11 @@ The following code will generate a file called `submission.csv`.
 ```{.python .input}
 %%tab all
 if tab.selected('pytorch', 'mxnet', 'tensorflow'):
-    preds = [model(d2l.tensor(data.val.values, dtype=d2l.float32))
+    preds = [model(d2l.tensor(data.val.values.astype(float), dtype=d2l.float32))
              for model in models]
 if tab.selected('jax'):
     preds = [model.apply({'params': trainer.state.params},
-             d2l.tensor(data.val.values, dtype=d2l.float32))
+             d2l.tensor(data.val.values.astype(float), dtype=d2l.float32))
              for model in models]
 # Taking exponentiation of predictions in the logarithm scale
 ensemble_preds = d2l.reduce_mean(d2l.exp(d2l.concat(preds, 1)), 1)

chapter_optimization/gd.md

@@ -165,7 +165,21 @@ def train_2d(trainer, steps=20, f_grad=None):  #@save
 ```
 
 ```{.python .input}
-#@tab mxnet, tensorflow
+#@tab mxnet
+def show_trace_2d(f, results):  #@save
+    """Show the trace of 2D variables during optimization."""
+    d2l.set_figsize()
+    d2l.plt.plot(*zip(*results), '-o', color='#ff7f0e')
+    x1, x2 = d2l.meshgrid(d2l.arange(-55, 1, 1),
+                          d2l.arange(-30, 1, 1))
+    x1, x2 = x1.asnumpy()*0.1, x2.asnumpy()*0.1
+    d2l.plt.contour(x1, x2, f(x1, x2), colors='#1f77b4')
+    d2l.plt.xlabel('x1')
+    d2l.plt.ylabel('x2')
+```
+
+```{.python .input}
+#@tab tensorflow
 def show_trace_2d(f, results):  #@save
     """Show the trace of 2D variables during optimization."""
     d2l.set_figsize()

d2l/mxnet.py

@@ -1321,8 +1321,9 @@ def show_trace_2d(f, results):
     Defined in :numref:`subsec_gd-learningrate`"""
     d2l.set_figsize()
     d2l.plt.plot(*zip(*results), '-o', color='#ff7f0e')
-    x1, x2 = d2l.meshgrid(d2l.arange(-5.5, 1.0, 0.1),
-                          d2l.arange(-3.0, 1.0, 0.1))
+    x1, x2 = d2l.meshgrid(d2l.arange(-55, 1, 1),
+                          d2l.arange(-30, 1, 1))
+    x1, x2 = x1.asnumpy()*0.1, x2.asnumpy()*0.1
     d2l.plt.contour(x1, x2, f(x1, x2), colors='#1f77b4')
     d2l.plt.xlabel('x1')
     d2l.plt.ylabel('x2')

setup.py

@@ -2,18 +2,18 @@
 import d2l
 
 requirements = [
-    'jupyter',
-    'numpy<=1.23.5',
-    'matplotlib',
-    'matplotlib-inline',
-    'requests',
-    'pandas'
+    'jupyter==1.0.0',
+    'numpy==1.23.5',
+    'matplotlib==3.7.2',
+    'matplotlib-inline==0.1.6',
+    'requests==2.31.0',
+    'pandas==2.0.3'
 ]
 
 setup(
     name='d2l',
     version=d2l.__version__,
-    python_requires='>=3.5',
+    python_requires='>=3.8',
     author='D2L Developers',
     author_email='[email protected]',
     url='https://d2l.ai',