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Merge pull request #494 from Kaggle/tensorboard · RTDataScience/docker-python@a6ba32e · GitHub

/ docker-python Public
forked from Kaggle/docker-python

Commit a6ba32e

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Aurelio Agundez
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Merge pull request Kaggle#494 from Kaggle/tensorboard
Add TensorBoard to Kaggle Kernels
2 parents 6d5f6a1 + 33ddc9d commit a6ba32e

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‎Dockerfile‎

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@@ -514,5 +514,13 @@ ADD patches/kaggle_gcp.py /root/.local/lib/python3.6/site-packages/kaggle_gcp.py
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ADD patches/kaggle_secrets.py /root/.local/lib/python3.6/site-packages/kaggle_secrets.py
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ADD patches/sitecustomize.py /root/.local/lib/python3.6/site-packages/sitecustomize.py
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# TensorBoard Jupyter extension. Should be replaced with TensorBoard's provided magic once we have
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# worker tunneling support in place.
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ENV JUPYTER_CONFIG_DIR "/root/.jupyter/"
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RUN pip install jupyter_tensorboard && \
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jupyter serverextension enable jupyter_tensorboard && \
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jupyter tensorboard enable
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ADD patches/tensorboard/notebook.py /opt/conda/lib/python3.6/site-packages/tensorboard/notebook.py
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# Set backend for matplotlib
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ENV MPLBACKEND "agg"

‎gpu.Dockerfile‎

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@@ -62,3 +62,6 @@ RUN pip install pycuda && \
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pip install cupy-cuda92 && \
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pip install pynvrtc && \
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/tmp/clean-layer.sh
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# Re-add TensorBoard Jupyter extension patch
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ADD patches/tensorboard/notebook.py /opt/conda/lib/python3.6/site-packages/tensorboard/notebook.py

‎patches/tensorboard/notebook.py‎

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"""%tensorboard line magic that patches TensorBoard's implementation to make use of Jupyter
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TensorBoard server extension providing built-in proxying.
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Use:
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%load_ext tensorboard.notebook
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%tensorboard --logdir /logs
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"""
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import argparse
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import uuid
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from IPython.display import display, HTML, Javascript
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def _tensorboard_magic(line):
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"""Line magic function.
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Makes an AJAX call to the Jupyter TensorBoard server extension and outputs
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an IFrame displaying the TensorBoard instance.
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"""
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parser = argparse.ArgumentParser()
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parser.add_argument('--logdir', default='/kaggle/working')
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args = parser.parse_args(line.split())
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iframe_id = 'tensorboard-' + str(uuid.uuid4())
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html = """
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<!-- JUPYTER_TENSORBOARD_TEST_MARKER -->
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<script>
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const req = {
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method: 'POST',
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contentType: 'application/json',
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body: JSON.stringify({ 'logdir': '%s' }),
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headers: { 'Content-Type': 'application/json' }
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};
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const baseUrl = Jupyter.notebook.base_url;
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fetch(baseUrl + 'api/tensorboard', req)
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.then(res => res.json())
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.then(res => {
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const iframe = document.getElementById('%s');
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iframe.src = baseUrl + 'tensorboard/' + res.name;
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iframe.style.display = 'block';
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});
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</script>
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<iframe
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id="%s"
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style="width: 100%%; height: 620px; display: none;"
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frameBorder="0">
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</iframe>
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""" % (args.logdir, iframe_id, iframe_id)
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display(HTML(html))
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def load_ipython_extension(ipython):
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"""IPython extension entry point."""
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ipython.register_magic_function(
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_tensorboard_magic,
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magic_kind='line',
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magic_name='tensorboard',
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)

‎tests/data/jupyter_tensorboard.ipynb‎

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{
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"cells": [
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{
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"metadata": {
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"_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5",
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"_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
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"trusted": true
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},
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"cell_type": "code",
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"source": "%load_ext tensorboard.notebook\n%tensorboard --logdir /logs",
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"execution_count": null,
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"outputs": []
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},
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{
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"metadata": {
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"_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
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"collapsed": true,
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"_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a",
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"trusted": false
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},
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"cell_type": "code",
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"source": "",
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"execution_count": null,
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"outputs": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"name": "python",
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"version": "3.6.4",
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"mimetype": "text/x-python",
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"codemirror_mode": { "name": "ipython", "version": 3 },
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"pygments_lexer": "ipython3",
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"nbconvert_exporter": "python",
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"file_extension": ".py"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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}

‎tests/test_jupytertensorboard.py‎

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import os
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import subprocess
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import unittest
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class TestJupyterTensorBoard(unittest.TestCase):
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def test_jupytertensorboard(self):
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file_path = os.path.realpath(__file__)
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ipynb_path = os.path.join(
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os.path.dirname(file_path),
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'data',
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'jupyter_tensorboard.ipynb',
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)
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result = subprocess.run([
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'jupyter',
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'nbconvert',
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'--execute',
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'--stdout',
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ipynb_path,
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],
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stdout=subprocess.PIPE
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)
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self.assertTrue(result.returncode == 0)
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self.assertTrue(b'JUPYTER_TENSORBOARD_TEST_MARKER' in result.stdout)

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