Sarah Segel: Merge pull request #98 from automl/enhancement/dependenc…

…y_versions

development/.buildinfo

@@ -1,4 +1,4 @@
 # Sphinx build info version 1
 # This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 5eac73f91509e54461eda47c063a78e4
+config: 1a3e2d12d88382c2eaa1d36d686cb541
 tags: 645f666f9bcd5a90fca523b33c5a78b7

development/_downloads/2769ed1bb81a7c36ce249476914fbf92/mnist_pytorch.py

@@ -63,15 +63,14 @@ def __init__(self, activation="relu", learning_rate=1e-4, dropout_rate=0.1, batc
             ]
         )
 
-        self.accuracy = Accuracy()
+        self.accuracy = Accuracy(task="multiclass", num_classes=self.num_classes)
 
     def prepare_data(self):
         # download
         MNIST(self.data_dir, train=True, download=True)
         MNIST(self.data_dir, train=False, download=True)
 
     def setup(self, stage=None):
-
         # Assign train/val datasets for use in dataloaders
         if stage == "fit" or stage is None:
             mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
@@ -257,10 +256,9 @@ def get_configspace(seed):
 
                     # The model weights are trained
                     trainer = pl.Trainer(
-                        accelerator="gpu",
+                        accelerator="cpu",
                         devices=1,
                         num_sanity_val_steps=0,  # No validation sanity
-                        auto_scale_batch_size="power",
                         deterministic=True,
                         min_epochs=epochs,
                         max_epochs=epochs,

development/_sources/examples/record/mnist_pytorch.rst.txt

@@ -24,7 +24,7 @@ Multi-Layer Perceptron via PyTorch
 This more advanced example incorporates multiple objectives, budgets and statusses to
 show the strenghts of DeepCAVE's recorder.
 
-.. GENERATED FROM PYTHON SOURCE LINES 8-288
+.. GENERATED FROM PYTHON SOURCE LINES 8-286
 
 .. code-block:: Python
 
@@ -86,15 +86,14 @@ show the strenghts of DeepCAVE's recorder.
                 ]
             )
 
-            self.accuracy = Accuracy()
+            self.accuracy = Accuracy(task="multiclass", num_classes=self.num_classes)
 
         def prepare_data(self):
             # download
             MNIST(self.data_dir, train=True, download=True)
             MNIST(self.data_dir, train=False, download=True)
 
         def setup(self, stage=None):
-
             # Assign train/val datasets for use in dataloaders
             if stage == "fit" or stage is None:
                 mnist_full = MNIST(self.data_dir, train=True, transform=self.transform)
@@ -280,10 +279,9 @@ show the strenghts of DeepCAVE's recorder.
 
                         # The model weights are trained
                         trainer = pl.Trainer(
-                            accelerator="gpu",
+                            accelerator="cpu",
                             devices=1,
                             num_sanity_val_steps=0,  # No validation sanity
-                            auto_scale_batch_size="power",
                             deterministic=True,
                             min_epochs=epochs,
                             max_epochs=epochs,

development/examples/record/mnist_pytorch.html

@@ -758,15 +758,14 @@
             <span class="p">]</span>
         <span class="p">)</span>
 
-        <span class="bp">self</span><span class="o">.</span><span class="n">accuracy</span> <span class="o">=</span> <span class="n">Accuracy</span><span class="p">()</span>
+        <span class="bp">self</span><span class="o">.</span><span class="n">accuracy</span> <span class="o">=</span> <span class="n">Accuracy</span><span class="p">(</span><span class="n">task</span><span class="o">=</span><span class="s2">&quot;multiclass&quot;</span><span class="p">,</span> <span class="n">num_classes</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">num_classes</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">prepare_data</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
         <span class="c1"># download</span>
         <span class="n">MNIST</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
         <span class="n">MNIST</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span> <span class="n">download</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
 
     <span class="k">def</span> <span class="nf">setup</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">stage</span><span class="o">=</span><span class="kc">None</span><span class="p">):</span>
-
         <span class="c1"># Assign train/val datasets for use in dataloaders</span>
         <span class="k">if</span> <span class="n">stage</span> <span class="o">==</span> <span class="s2">&quot;fit&quot;</span> <span class="ow">or</span> <span class="n">stage</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
             <span class="n">mnist_full</span> <span class="o">=</span> <span class="n">MNIST</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data_dir</span><span class="p">,</span> <span class="n">train</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span> <span class="n">transform</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">transform</span><span class="p">)</span>
@@ -952,10 +951,9 @@
 
                     <span class="c1"># The model weights are trained</span>
                     <span class="n">trainer</span> <span class="o">=</span> <span class="n">pl</span><span class="o">.</span><span class="n">Trainer</span><span class="p">(</span>
-                        <span class="n">accelerator</span><span class="o">=</span><span class="s2">&quot;gpu&quot;</span><span class="p">,</span>
+                        <span class="n">accelerator</span><span class="o">=</span><span class="s2">&quot;cpu&quot;</span><span class="p">,</span>
                         <span class="n">devices</span><span class="o">=</span><span class="mi">1</span><span class="p">,</span>
                         <span class="n">num_sanity_val_steps</span><span class="o">=</span><span class="mi">0</span><span class="p">,</span>  <span class="c1"># No validation sanity</span>
-                        <span class="n">auto_scale_batch_size</span><span class="o">=</span><span class="s2">&quot;power&quot;</span><span class="p">,</span>
                         <span class="n">deterministic</span><span class="o">=</span><span class="kc">True</span><span class="p">,</span>
                         <span class="n">min_epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span>
                         <span class="n">max_epochs</span><span class="o">=</span><span class="n">epochs</span><span class="p">,</span>