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kubeflow_gpu_validation_notebook.html
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kubeflow_gpu_validation_notebook.html
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">from</span> <span class="nn">platform</span> <span class="kn">import</span> <span class="n">python_version</span>
<span class="nb">print</span><span class="p">(</span><span class="n">python_version</span><span class="p">())</span>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>3.7.11
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="n">tf</span><span class="o">.</span><span class="n">__version__</span>
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<div class="output traceback highlight-ipythontb notranslate"><div class="highlight"><pre><span></span><span class="gt">---------------------------------------------------------------------------</span>
<span class="ne">ModuleNotFoundError</span><span class="g g-Whitespace"> </span>Traceback (most recent call last)
<span class="o">/</span><span class="n">var</span><span class="o">/</span><span class="n">folders</span><span class="o">/</span><span class="n">wd</span><span class="o">/</span><span class="mi">5</span><span class="n">mwvr_hx3p5436myvcwcvk6w0000gn</span><span class="o">/</span><span class="n">T</span><span class="o">/</span><span class="n">ipykernel_19501</span><span class="o">/</span><span class="mf">1143249662.</span><span class="n">py</span> <span class="ow">in</span> <span class="o"><</span><span class="n">module</span><span class="o">></span>
<span class="ne">----> </span><span class="mi">1</span> <span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
<span class="g g-Whitespace"> </span><span class="mi">2</span> <span class="n">tf</span><span class="o">.</span><span class="n">__version__</span>
<span class="g g-Whitespace"> </span><span class="mi">3</span>
<span class="ne">ModuleNotFoundError</span>: No module named 'tensorflow'
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tf</span><span class="o">.</span><span class="n">debugging</span><span class="o">.</span><span class="n">set_log_device_placement</span><span class="p">(</span><span class="kc">True</span><span class="p">)</span>
<span class="n">tf</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">list_physical_devices</span><span class="p">(</span><span class="s1">'GPU'</span><span class="p">)</span>
<span class="c1"># "/GPU:0": Short-hand notation for the first GPU of your machine that is visible to TensorFlow.</span>
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<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="nb">print</span><span class="p">(</span><span class="s2">"Num GPUs Available: "</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">experimental</span><span class="o">.</span><span class="n">list_physical_devices</span><span class="p">(</span><span class="s1">'GPU'</span><span class="p">)))</span>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Num GPUs Available: 1
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># per https://www.tensorflow.org/guide/gpu</span>
<span class="c1"># "/job:localhost/replica:0/task:0/device:GPU:1": Fully qualified name of the second GPU of your machine that is visible to TensorFlow.</span>
<span class="c1"># Create some tensors</span>
<span class="n">a</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">,</span> <span class="mf">3.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">4.0</span><span class="p">,</span> <span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">]])</span>
<span class="n">b</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([[</span><span class="mf">1.0</span><span class="p">,</span> <span class="mf">2.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">3.0</span><span class="p">,</span> <span class="mf">4.0</span><span class="p">],</span> <span class="p">[</span><span class="mf">5.0</span><span class="p">,</span> <span class="mf">6.0</span><span class="p">]])</span>
<span class="n">c</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">matmul</span><span class="p">(</span><span class="n">a</span><span class="p">,</span> <span class="n">b</span><span class="p">)</span>
<span class="c1"># below when we debug the output of the MatMul Op, it should confirm: "Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0"</span>
<span class="nb">print</span><span class="p">(</span><span class="n">c</span><span class="p">)</span>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Executing op MatMul in device /job:localhost/replica:0/task:0/device:GPU:0
tf.Tensor(
[[22. 28.]
[49. 64.]], shape=(2, 2), dtype=float32)
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="c1"># now let's build the canonical mnist model w keras on tf2</span>
<span class="n">mnist</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">datasets</span><span class="o">.</span><span class="n">mnist</span>
<span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">),</span> <span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">)</span> <span class="o">=</span> <span class="n">mnist</span><span class="o">.</span><span class="n">load_data</span><span class="p">()</span>
<span class="n">x_train</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">=</span> <span class="n">x_train</span> <span class="o">/</span> <span class="mf">255.0</span><span class="p">,</span> <span class="n">x_test</span> <span class="o">/</span> <span class="mf">255.0</span>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">models</span><span class="o">.</span><span class="n">Sequential</span><span class="p">([</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Flatten</span><span class="p">(</span><span class="n">input_shape</span><span class="o">=</span><span class="p">(</span><span class="mi">28</span><span class="p">,</span> <span class="mi">28</span><span class="p">)),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">128</span><span class="p">,</span> <span class="n">activation</span><span class="o">=</span><span class="s1">'relu'</span><span class="p">),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dropout</span><span class="p">(</span><span class="mf">0.2</span><span class="p">),</span>
<span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">layers</span><span class="o">.</span><span class="n">Dense</span><span class="p">(</span><span class="mi">10</span><span class="p">)</span>
<span class="p">])</span>
</pre></div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Executing op RandomUniform in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Sub in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Mul in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Add in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarIsInitializedOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op LogicalNot in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Assert in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Fill in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
</pre></div>
</div>
</div>
</div>
<div class="cell docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">predictions</span> <span class="o">=</span> <span class="n">model</span><span class="p">(</span><span class="n">x_train</span><span class="p">[:</span><span class="mi">1</span><span class="p">])</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="n">predictions</span>
</pre></div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>WARNING:tensorflow:Layer flatten is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because it's dtype defaults to floatx.
If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2.
To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor.
Executing op Cast in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Reshape in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op ReadVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op BiasAdd in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Relu in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Identity in device /job:localhost/replica:0/task:0/device:GPU:0
</pre></div>
</div>
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>array([[-0.02667147, -0.2259462 , 0.5034827 , 0.08966304, -0.07757394,
-0.28460842, 0.05604312, 0.17487651, 0.30380425, 0.05608713]],
dtype=float32)
</pre></div>
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</div>
<div class="cell docutils container">
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">tf</span><span class="o">.</span><span class="n">nn</span><span class="o">.</span><span class="n">softmax</span><span class="p">(</span><span class="n">predictions</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
<span class="n">loss_fn</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">keras</span><span class="o">.</span><span class="n">losses</span><span class="o">.</span><span class="n">SparseCategoricalCrossentropy</span><span class="p">(</span><span class="n">from_logits</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
<span class="n">model</span><span class="o">.</span><span class="n">compile</span><span class="p">(</span><span class="n">optimizer</span><span class="o">=</span><span class="s1">'adam'</span><span class="p">,</span>
<span class="n">loss</span><span class="o">=</span><span class="n">loss_fn</span><span class="p">,</span>
<span class="n">metrics</span><span class="o">=</span><span class="p">[</span><span class="s1">'accuracy'</span><span class="p">])</span>
</pre></div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Executing op Softmax in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
</pre></div>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">fit</span><span class="p">(</span><span class="n">x_train</span><span class="p">,</span> <span class="n">y_train</span><span class="p">,</span> <span class="n">epochs</span><span class="o">=</span><span class="mi">5</span><span class="p">)</span>
</pre></div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Executing op RangeDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op RepeatDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op MapDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op PrefetchDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op FlatMapDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op TensorDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op RepeatDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op ZipDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op ParallelMapDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op DatasetCardinality in device /job:localhost/replica:0/task:0/device:CPU:0
Train on 60000 samples
Epoch 1/5
Executing op ModelDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op AnonymousIteratorV2 in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op MakeIterator in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op AssignVariableOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op LogicalNot in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op Assert in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op VarHandleOp in device /job:localhost/replica:0/task:0/device:GPU:0
Executing op __inference_distributed_function_665 in device /job:localhost/replica:0/task:0/device:GPU:0
60000/60000 [==============================] - 5s 79us/sample - loss: 0.2919 - accuracy: 0.9142
Epoch 2/5
60000/60000 [==============================] - 4s 74us/sample - loss: 0.1401 - accuracy: 0.9587
Epoch 3/5
60000/60000 [==============================] - 4s 75us/sample - loss: 0.1062 - accuracy: 0.9671
Epoch 4/5
60000/60000 [==============================] - 4s 75us/sample - loss: 0.0863 - accuracy: 0.9734
Epoch 5/5
60000/60000 [==============================] - 4s 74us/sample - loss: 0.0744 - accuracy: 0.9764
Executing op DeleteIterator in device /job:localhost/replica:0/task:0/device:CPU:0
</pre></div>
</div>
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span><tensorflow.python.keras.callbacks.History at 0x7f6fe05042e8>
</pre></div>
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<div class="highlight-ipython3 notranslate"><div class="highlight"><pre><span></span><span class="n">model</span><span class="o">.</span><span class="n">evaluate</span><span class="p">(</span><span class="n">x_test</span><span class="p">,</span> <span class="n">y_test</span><span class="p">,</span> <span class="n">verbose</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
</pre></div>
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<div class="output stream highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>Executing op RangeDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op RepeatDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op MapDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op PrefetchDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op FlatMapDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op TensorDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op RepeatDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op ZipDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op ParallelMapDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op ModelDataset in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op AnonymousIteratorV2 in device /job:localhost/replica:0/task:0/device:CPU:0
Executing op __inference_distributed_function_28941 in device /job:localhost/replica:0/task:0/device:GPU:0
10000/10000 - 1s - loss: 0.0740 - accuracy: 0.9774
</pre></div>
</div>
<div class="output text_plain highlight-myst-ansi notranslate"><div class="highlight"><pre><span></span>[0.07395664743543602, 0.9774]
</pre></div>
</div>
</div>
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