Metrics and Visualizations .ipynb

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   "source": [
    "# Copyright 2019 Google Inc. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Install Pipeline SDK - This only needs to be ran once in the enviroment. \n",
    "# you can find the latest package @ https://github.com/kubeflow/pipelines/releases\n",
    "#KFP_PACKAGE = 'https://storage.googleapis.com/ml-pipeline/release/0.1.20/kfp.tar.gz'\n",
    "#!pip3 install $KFP_PACKAGE --upgrade"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": [
     "parameters"
    ]
   },
   "outputs": [],
   "source": [
    "EXPERIMENT_NAME = 'Hello world!'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualize Results in the Pipelines UI\n",
    "\n",
    "Lightweight component documnetation : https://www.kubeflow.org/docs/pipelines/sdk/output-viewer/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kfp.components as comp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Define a Python function\n",
    "def view_metric(a: float, b: float) -> float:\n",
    "    \n",
    "    from tensorflow.python.lib.io import file_io\n",
    "    import json\n",
    "    \n",
    "    # Exports a sample tensorboard:\n",
    "    metadata = {\n",
    "      'outputs' : [{\n",
    "        'type': 'tensorboard',\n",
    "        'source': 'gs://ml-pipeline-dataset/tensorboard-train',\n",
    "      }]\n",
    "    }\n",
    "    with open('/mlpipeline-ui-metadata.json', 'w') as f:\n",
    "      json.dump(metadata, f)\n",
    "\n",
    "    # Exports two sample metrics:\n",
    "    metrics = {\n",
    "      'metrics': [{\n",
    "          'name': 'metrica',\n",
    "          'numberValue':  float(a),\n",
    "        },{\n",
    "          'name': 'metricb',\n",
    "          'numberValue':  float(b),\n",
    "        }]}\n",
    "\n",
    "    with file_io.FileIO('/mlpipeline-metrics.json', 'w') as f:\n",
    "        json.dump(metrics, f)\n",
    "\n",
    "\n",
    "    return a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "view_metric_op = comp.func_to_container_op(view_metric, base_image='tensorflow/tensorflow:1.11.0-py3')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Define the pipeline\n",
    "Pipeline function has to be decorated with the `@dsl.pipeline` decorator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "import kfp.dsl as dsl\n",
    "@dsl.pipeline(\n",
    "   name='Visualize results',\n",
    "   description='A toy pipeline that demonstrates how to visualize results in the Pipelines UI'\n",
    ")\n",
    "def visualize(\n",
    "   a='a',\n",
    "   b='7'\n",
    "):\n",
    "    #building a one step pipeline to Visualize the results \n",
    "    view_metric_task = view_metric_op(a, b)\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Compile the pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline_func = visualize\n",
    "pipeline_filename = pipeline_func.__name__ + '.pipeline.zip'\n",
    "import kfp.compiler as compiler\n",
    "compiler.Compiler().compile(pipeline_func, pipeline_filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Submit the pipeline for execution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Experiment link <a href=\"/pipeline/#/experiments/details/7c060563-13b6-4613-9d5e-e23f33fe0f1d\" target=\"_blank\" >here</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Get or create an experiment and submit a pipeline run\n",
    "import kfp\n",
    "client = kfp.Client()\n",
    "experiment = client.create_experiment(EXPERIMENT_NAME)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "Run link <a href=\"/pipeline/#/runs/details/149015ca-943b-11e9-aeee-42010a80014f\" target=\"_blank\" >here</a>"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Specify pipeline argument values\n",
    "arguments = {'a': '7', 'b': '8'}\n",
    "\n",
    "#Submit a pipeline run\n",
    "run_name = pipeline_func.__name__ + ' run'\n",
    "run_result = client.run_pipeline(experiment.id, run_name, pipeline_filename, arguments)\n",
    "\n",
    "#This link leads to the run information page. \n",
    "#Note: There is a bug in JupyterLab that modifies the URL and makes the link stop working"
   ]
  }
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