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40 lines
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1.6 KiB
Markdown
40 lines
No EOL
1.6 KiB
Markdown
## Visualizing training with TensorBoard
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With the summary info generated, we can then use [TensorBoard](https://pypi.python.org/pypi/tensorboard) to visualize the behaviors of the BigDL program.
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* **Installing TensorBoard**
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Prerequisites:
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1. Python verison: 2.7, 3.4, 3.5, or 3.6
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2. Pip version >= 9.0.1
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To install TensorBoard using Python 2, you may run the command:
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```bash
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pip install tensorboard==1.0.0a4
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```
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To install TensorBoard using Python 3, you may run the command:
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```bash
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pip3 install tensorboard==1.0.0a4
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```
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Please refer to [this page](https://github.com/intel-analytics/BigDL/tree/master/spark/dl/src/main/scala/com/intel/analytics/bigdl/visualization#known-issues) for possible issues when installing TensorBoard.
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* **Launching TensorBoard**
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You can launch TensorBoard using the command below:
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```bash
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tensorboard --logdir=/tmp/bigdl_summaries
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```
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After that, navigate to the TensorBoard dashboard using a browser. You can find the URL in the console output after TensorBoard is successfully launched; by default the URL is http://your_node:6006
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* **Visualizations in TensorBoard**
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Within the TensorBoard dashboard, you will be able to read the visualizations of each run, including the “Loss” and “Throughput” curves under the SCALARS tab (as illustrated below):
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And “weights”, “bias”, “gradientWeights” and “gradientBias” under the DISTRIBUTIONS and HISTOGRAMS tabs (as illustrated below):
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