SDK - Client - Added a way to set experiment name using environment variables

samples/README.md

@@ -75,11 +75,11 @@ For better readability and integrations with the sample test infrastructure, sam
 * The sample file should be either `*.py` or `*.ipynb`, and its file name is consistent with its directory name.
 * For `*.py` sample, it's recommended to have a main invoking `kfp.compiler.Compiler().compile()` to compile the 
 pipeline function into pipeline yaml spec.
-* For `*.ipynb` sample, parameters (e.g., `experiment_name` and `project_name`)
+* For `*.ipynb` sample, parameters (e.g., `project_name`)
 should be defined in a dedicated cell and tagged as parameter. 
 (If the author would like the sample test infra to run it by setting the `run_pipeline` flag to True in 
-the associated `config.yaml` file, the sample test infra will expect a parameter `experiment_name`
-to inject so that it can run in the sample test experiment.)
+the associated `config.yaml` file, the sample test infra will expect the sample to use the
+`kfp.Client().create_run_from_pipeline_func` method for starting the run so that the sample test can watch the run.)
 Detailed guideline is 
 [here](https://github.com/nteract/papermill). Also, all the environment setup and 
 preparation should be within the notebook, such as by `!pip install packages` 

samples/contrib/arena-samples/standalonejob/standalone_pipeline.ipynb

@@ -48,7 +48,6 @@
     "\n",
     "**Please fill in the below environment variables with you own settings.**\n",
     "\n",
-    "- **EXPERIMENT_NAME**: A unique experiment name that will be created for this notebook demo.\n",
     "- **KFP_PACKAGE**: The latest release of kubeflow pipeline platform library.\n",
     "- **KUBEFLOW_PIPELINE_LINK**: The link to access the KubeFlow pipeline API.\n",
     "- **MOUNT**: The mount configuration to map data above into the training job. The format is 'data:/directory'\n",
@@ -61,8 +60,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "EXPERIMENT_NAME = 'myjob'\n",
-    "RUN_ID=\"run\"\n",
     "KFP_SERVICE=\"ml-pipeline.kubeflow.svc.cluster.local:8888\"\n",
     "KFP_PACKAGE = 'http://kubeflow.oss-cn-beijing.aliyuncs.com/kfp/0.1.14/kfp.tar.gz'\n",
     "KFP_ARENA_PACKAGE = 'http://kubeflow.oss-cn-beijing.aliyuncs.com/kfp-arena/kfp-arena-0.3.tar.gz'\n",

samples/contrib/ibm-samples/ffdl-seldon/ffdl_pipeline.ipynb

@@ -90,10 +90,7 @@
     "# KUBEFLOW_PIPELINE_LINK = ''\n",
     "# client = kfp.Client(KUBEFLOW_PIPELINE_LINK)\n",
     "\n",
-    "client = kfp.Client()\n",
-    "\n",
-    "\n",
-    "EXPERIMENT_NAME = 'FfDL-Seldon Experiments'"
+    "client = kfp.Client()\n"
    ]
   },
   {
@@ -179,7 +176,7 @@
     "            'model-class-file': 'gender_classification.py'}\n",
     "\n",
     "\n",
-    "run = client.create_run_from_pipeline_func(ffdlPipeline, arguments=parameters, experiment_name=EXPERIMENT_NAME).run_info\n",
+    "run = client.create_run_from_pipeline_func(ffdlPipeline, arguments=parameters).run_info\n",
     "\n",
     "import IPython\n",
     "html = ('<p id=\"link\"> </p> <script> document.getElementById(\"link\").innerHTML = \"Actual Run link <a href=//\" + location.hostname + \"%s/#/runs/details/%s target=_blank >here</a>\"; </script>'\n",

samples/contrib/image-captioning-gcp/Image Captioning TF 2.0.ipynb

@@ -135,7 +135,6 @@
    "outputs": [],
    "source": [
     "# Kubeflow project settings\n",
-    "EXPERIMENT_NAME = 'Image Captioning'\n",
     "PROJECT_NAME = '[YOUR-PROJECT-NAME]' \n",
     "PIPELINE_STORAGE_PATH = GCS_BUCKET + '/ms-coco/components' # path to save pipeline component images\n",
     "BASE_IMAGE = 'gcr.io/%s/img-cap:latest' % PROJECT_NAME # using image created in README instructions\n",
@@ -913,7 +912,7 @@
     "    'training_batch_size': 16, # has to be smaller since only training on 80/100 examples \n",
     "}\n",
     "\n",
-    "kfp.Client().create_run_from_pipeline_func(pipeline, arguments=arguments, experiment_name=EXPERIMENT_NAME)"
+    "kfp.Client().create_run_from_pipeline_func(pipeline, arguments=arguments)"
    ]
   },
   {

samples/core/ai_platform/ai_platform.ipynb

@@ -31,7 +31,7 @@
     "%%capture\n",
     "\n",
     "# Install the SDK (Uncomment the code if the SDK is not installed before)\n",
-    "!python3 -m pip install kfp --upgrade -q\n",
+    "!python3 -m pip install 'kfp>=0.1.31' --quiet\n",
     "!python3 -m pip install pandas --upgrade -q"
    ]
   },
@@ -79,8 +79,7 @@
    "source": [
     "# Required Parameters\n",
     "project_id = '<ADD GCP PROJECT HERE>'\n",
-    "output = 'gs://<ADD STORAGE LOCATION HERE>' # No ending slash\n",
-    "experiment_name = 'Chicago Crime Prediction'"
+    "output = 'gs://<ADD STORAGE LOCATION HERE>' # No ending slash\n"
    ]
   },
   {
@@ -280,7 +279,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "pipeline = kfp.Client().create_run_from_pipeline_func(pipeline, arguments={}, experiment_name=experiment_name)"
+    "pipeline = kfp.Client().create_run_from_pipeline_func(pipeline, arguments={})"
    ]
   },
   {

samples/core/component_build/component_build.ipynb

@@ -30,7 +30,7 @@
    "outputs": [],
    "source": [
     "# Install Pipeline SDK - This only needs to be ran once in the enviroment. \n",
-    "!pip3 install kfp --upgrade --quiet"
+    "!python3 -m pip install 'kfp>=0.1.31' --quiet\n"
    ]
   },
   {
@@ -65,7 +65,6 @@
    },
    "outputs": [],
    "source": [
-    "experiment_name = 'container_building'"
    ]
   },
   {
@@ -202,7 +201,7 @@
    "outputs": [],
    "source": [
     "arguments = {'a': '7', 'b': '8'}\n",
-    "kfp.Client().create_run_from_pipeline_func(pipeline_func=calc_pipeline, arguments=arguments, experiment_name=experiment_name)\n",
+    "kfp.Client().create_run_from_pipeline_func(pipeline_func=calc_pipeline, arguments=arguments)\n",
     "\n",
     "# This should output link that leads to the run information page. \n",
     "# Note: There is a bug in JupyterLab that modifies the URL and makes the link stop working"

samples/core/dataflow/dataflow.ipynb

@@ -74,8 +74,7 @@
    "outputs": [],
    "source": [
     "project = 'Input your PROJECT ID'\n",
-    "output = 'Input your GCS bucket name' # No ending slash\n",
-    "experiment_name = 'Dataflow - Launch Python'"
+    "output = 'Input your GCS bucket name' # No ending slash\n"
    ]
   },
   {
@@ -95,8 +94,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "%%capture --no-stderr\n",
-    "!pip3 install kfp --upgrade"
+    "!python3 -m pip install 'kfp>=0.1.31' --quiet\n"
    ]
   },
   {
@@ -368,7 +366,7 @@
     }
    ],
    "source": [
-    "kfp.Client().create_run_from_pipeline_func(pipeline, arguments={}, experiment_name=experiment_name)"
+    "kfp.Client().create_run_from_pipeline_func(pipeline, arguments={})"
    ]
   },
   {

samples/core/dsl_static_type_checking/dsl_static_type_checking.ipynb

@@ -162,7 +162,7 @@
     }
    ],
    "source": [
-    "!pip3 install kfp --upgrade"
+    "!python3 -m pip install 'kfp>=0.1.31' --quiet\n"
    ]
   },
   {

samples/core/kubeflow_tf_serving/kubeflow_tf_serving.ipynb

@@ -132,7 +132,7 @@
    ],
    "source": [
     "# Install Pipeline SDK - This only needs to be ran once in the enviroment. \n",
-    "!pip3 install kfp --upgrade\n",
+    "!python3 -m pip install 'kfp>=0.1.31' --quiet\n",
     "!pip3 install tensorflow==1.14 --upgrade"
    ]
   },
@@ -172,7 +172,6 @@
     "# Set your output and project. !!!Must Do before you can proceed!!!\n",
     "project =  'Your-Gcp-Project-ID'                          #'Your-GCP-Project-ID'\n",
     "model_name = 'model-name'                                      # Model name matching TF_serve naming requirements       \n",
-    "experiment_name = 'serving_component'\n",
     "import time\n",
     "ts = int(time.time())\n",
     "model_version = str(ts)                                            # Here we use timestamp as version to avoid conflict \n",
@@ -323,7 +322,7 @@
     }
    ],
    "source": [
-    "kfp.Client().create_run_from_pipeline_func(model_server, arguments={}, experiment_name=experiment_name)\n",
+    "kfp.Client().create_run_from_pipeline_func(model_server, arguments={})\n",
     "\n",
     "#vvvvvvvvv This link leads to the run information page. (Note: There is a bug in JupyterLab that modifies the URL and makes the link stop working)"
    ]

samples/core/lightweight_component/lightweight_component.ipynb

@@ -29,7 +29,6 @@
    },
    "outputs": [],
    "source": [
-    "experiment_name = 'lightweight python components'"
    ]
   },
   {
@@ -39,7 +38,7 @@
    "outputs": [],
    "source": [
     "# Install the SDK\n",
-    "!pip3 install kfp --upgrade"
+    "#!pip3 install 'kfp>=0.1.31.2' --quiet"
    ]
   },
   {
@@ -243,7 +242,7 @@
     "arguments = {'a': '7', 'b': '8'}\n",
     "\n",
     "#Submit a pipeline run\n",
-    "kfp.Client().create_run_from_pipeline_func(calc_pipeline, arguments=arguments, experiment_name=experiment_name)\n",
+    "kfp.Client().create_run_from_pipeline_func(calc_pipeline, arguments=arguments)\n",
     "\n",
     "#vvvvvvvvv This link leads to the run information page. (Note: There is a bug in JupyterLab that modifies the URL and makes the link stop working)"
    ]

samples/core/multiple_outputs/multiple_outputs.ipynb

@@ -30,7 +30,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install kfp --upgrade"
+    "!python3 -m pip install 'kfp>=0.1.31' --quiet\n"
    ]
   },
   {
@@ -51,8 +51,7 @@
    "outputs": [],
    "source": [
     "output = 'gs://[BUCKET-NAME]' # GCS bucket name\n",
-    "project_id = '[PROJECT-NAME]'   # GCP project name\n",
-    "experiment_name = 'Multiple Outputs Sample'"
+    "project_id = '[PROJECT-NAME]'   # GCP project name\n"
    ]
   },
   {
@@ -161,7 +160,7 @@
     "    'b': 2.5,\n",
     "    'c': 3.0,\n",
     "}\n",
-    "run_result = kfp.Client().create_run_from_pipeline_func(pipeline, arguments=arguments, experiment_name=experiment_name)"
+    "run_result = kfp.Client().create_run_from_pipeline_func(pipeline, arguments=arguments)"
    ]
   }
  ],

samples/core/tfx-oss/TFX Example.ipynb

@@ -18,7 +18,7 @@
    "outputs": [],
    "source": [
     "!pip3 install tfx==0.13.0 --upgrade\n",
-    "!pip3 install kfp --upgrade"
+    "!python3 -m pip install 'kfp>=0.1.31' --quiet\n"
    ]
   },
   {

sdk/python/kfp/_client.py

@@ -64,6 +64,8 @@ def camel_case_to_snake_case(name):
 
 KF_PIPELINES_ENDPOINT_ENV = 'KF_PIPELINES_ENDPOINT'
 KF_PIPELINES_UI_ENDPOINT_ENV = 'KF_PIPELINES_UI_ENDPOINT'
+KF_PIPELINES_DEFAULT_EXPERIMENT_NAME = 'KF_PIPELINES_DEFAULT_EXPERIMENT_NAME'
+KF_PIPELINES_OVERRIDE_EXPERIMENT_NAME = 'KF_PIPELINES_OVERRIDE_EXPERIMENT_NAME'
 
 class Client(object):
   """ API Client for KubeFlow Pipeline.
@@ -365,7 +367,12 @@ def __str__(self):
 
     #TODO: Check arguments against the pipeline function
     pipeline_name = os.path.basename(pipeline_file)
-    experiment_name = experiment_name or 'Default'
+    experiment_name = experiment_name or os.environ.get(KF_PIPELINES_DEFAULT_EXPERIMENT_NAME, None)
+    overridden_experiment_name = os.environ.get(KF_PIPELINES_OVERRIDE_EXPERIMENT_NAME, experiment_name)
+    if overridden_experiment_name != experiment_name:
+      import warnings
+      warnings.warn('Changing experiment name from "{}" to "{}".'.format(experiment_name, overridden_experiment_name))
+    experiment_name = overridden_experiment_name or 'Default'
     run_name = run_name or pipeline_name + ' ' + datetime.now().strftime('%Y-%m-%d %H-%M-%S')
     experiment = self.create_experiment(name=experiment_name)
     run_info = self.run_pipeline(experiment.id, run_name, pipeline_file, arguments)

test/sample-test/check_notebook_results.py

@@ -21,26 +21,27 @@
 
 
 class NoteBookChecker(object):
-    def __init__(self, testname, result, run_pipeline, namespace='kubeflow'):
+    def __init__(self, testname, result, run_pipeline, experiment_name, namespace='kubeflow'):
         """ Util class for checking notebook sample test running results.
 
         :param testname: test name in the json xml.
         :param result: name of the file that stores the test result
         :param run_pipeline: whether to submit for a pipeline run.
         :param namespace: where the pipeline system is deployed.
+        :param experiment_name: Name of the experiment to monitor
         """
         self._testname = testname
         self._result = result
         self._exit_code = None
         self._run_pipeline = run_pipeline
         self._namespace = namespace
+        self._experiment_name = experiment_name
 
     def run(self):
         """ Run the notebook sample as a python script. """
         self._exit_code = str(
             subprocess.call(['ipython', '%s.py' % self._testname]))
 
-
     def check(self):
         """ Check the pipeline running results of the notebook sample. """
         test_cases = []
@@ -63,7 +64,7 @@ def check(self):
             test_timeout = raw_args['test_timeout']
 
         if self._run_pipeline:
-            experiment = self._testname + '-test'
+            experiment = self._experiment_name
             ###### Initialization ######
             host = 'ml-pipeline.%s.svc.cluster.local:8888' % self._namespace
             client = Client(host=host)

test/sample-test/configs/ai_platform.config.yaml

@@ -16,4 +16,3 @@ test_name: ai_platform
 notebook_params:
   output:
   project_id: ml-pipeline-test
-  experiment_name: ai_platform-test

test/sample-test/configs/component_build.config.yaml

@@ -14,5 +14,4 @@
 
 test_name: component_build
 notebook_params:
-  experiment_name: component_build-test
   PROJECT_NAME: ml-pipeline-test

test/sample-test/configs/dataflow.config.yaml

@@ -16,5 +16,4 @@ test_name: dataflow
 notebook_params:
   output:
   project: ml-pipeline-test
-  experiment_name: dataflow-test
 run_pipeline: False

test/sample-test/configs/kubeflow_tf_serving.config.yaml

@@ -16,4 +16,3 @@ test_name: kubeflow_tf_serving
 notebook_params:
   output:
   project: ml-pipeline-test
-  experiment_name: kubeflow_tf_serving-test

test/sample-test/configs/multiple_outputs.config.yaml

@@ -16,4 +16,3 @@ test_name: multiple_outputs
 notebook_params:
   output:
   project_id: ml-pipeline-test
-  experiment_name: multiple_outputs-test

test/sample-test/run_sample_test.py

@@ -25,16 +25,18 @@
 
 
 class PySampleChecker(object):
-  def __init__(self, testname, input, output, result, namespace='kubeflow'):
+  def __init__(self, testname, input, output, result, experiment_name, namespace='kubeflow'):
     """Util class for checking python sample test running results.
 
     :param testname: test name.
     :param input: The path of a pipeline file that will be submitted.
     :param output: The path of the test output.
     :param result: The path of the test result that will be exported.
     :param namespace: namespace of the deployed pipeline system. Default: kubeflow
+    :param experiment_name: Name of the experiment to monitor
     """
     self._testname = testname
+    self._experiment_name = experiment_name
     self._input = input
     self._output = output
     self._result = result
@@ -68,8 +70,7 @@ def run(self):
       exit(1)
 
     ###### Create Experiment ######
-    experiment_name = self._testname + ' sample experiment'
-    response = self._client.create_experiment(experiment_name)
+    response = self._client.create_experiment(self._experiment_name)
     self._experiment_id = response.id
     utils.add_junit_test(self._test_cases, 'create experiment', True)