new_task -> NewTask in examples (#456)

README.md

@@ -167,7 +167,7 @@ You run the task on a "job" function, and print the result output (in this case,
 For more information and examples, see the [**examples/mlrun_basics.ipynb**](examples/mlrun_basics.ipynb) notebook.
 ```python
 # Create a task and set its attributes
-task = new_task(handler=handler, name='demo', params={'p1': 5})
+task = NewTask(handler=handler, name='demo', params={'p1': 5})
 task.with_secrets('file', 'secrets.txt').set_label('type', 'demo')
 
 run = new_function(command='myfile.py', kind='job').run(task)
@@ -361,7 +361,7 @@ For example, the following code demonstrates how to use hyperparameters to run t
          "gamma":     [0.0, 0.1, 0.2, 0.3],
          }
 
-    task = new_task(handler=xgb_train, out_path='/User/mlrun/data').with_hyper_params(parameters, 'max.accuracy')
+    task = NewTask(handler=xgb_train, out_path='/User/mlrun/data').with_hyper_params(parameters, 'max.accuracy')
     run = run_local(task)
 ```
 
@@ -377,7 +377,7 @@ mlrun run --name train_hyper -x p1="[3,7,5]" -x p2="[5,2,9]" --out-path '/User/m
 You can also use a parameters file if you want to control the parameter combinations or if the parameters are more complex.
 The following code from the example [**mlrun_basics.ipynb**](examples/mlrun_basics.ipynb) notebook demonstrates how to run a task that uses a CSV parameters file (**params.csv** in the current directory):
 ```python
-    task = new_task(handler=xgb_train).with_param_file('params.csv', 'max.accuracy')
+    task = NewTask(handler=xgb_train).with_param_file('params.csv', 'max.accuracy')
     run = run_local(task)
 ```
 

docs/job-submission-and-tracking.md

@@ -72,7 +72,7 @@ You run the task on a "job" function, and print the result output (in this case,
 For more information and examples, see the [**Examples section**](examples.html).
 ```python
 # Create a task and set its attributes
-task = new_task(handler=handler, name='demo', params={'p1': 5})
+task = NewTask(handler=handler, name='demo', params={'p1': 5})
 task.with_secrets('file', 'secrets.txt').set_label('type', 'demo')
 
 run = new_function(command='myfile.py', kind='job').run(task)
@@ -280,7 +280,7 @@ For example, the following code demonstrates how to use hyperparameters to run t
          "gamma":     [0.0, 0.1, 0.2, 0.3],
          }
 
-    task = new_task(handler=xgb_train, out_path='/User/mlrun/data').with_hyper_params(parameters, 'max.accuracy')
+    task = NewTask(handler=xgb_train, out_path='/User/mlrun/data').with_hyper_params(parameters, 'max.accuracy')
     run = run_local(task)
 ```
 
@@ -296,7 +296,7 @@ mlrun run --name train_hyper -x p1="[3,7,5]" -x p2="[5,2,9]" --out-path '/User/m
 You can also use a parameters file if you want to control the parameter combinations or if the parameters are more complex.
 The following code from the [**Examples section**](examples.html) demonstrates how to run a task that uses a CSV parameters file (**params.csv** in the current directory):
 ```python
-    task = new_task(handler=xgb_train).with_param_file('params.csv', 'max.accuracy')
+    task = NewTask(handler=xgb_train).with_param_file('params.csv', 'max.accuracy')
     run = run_local(task)
 ```
 

examples/load-project.ipynb

@@ -518,8 +518,8 @@
     }
    ],
    "source": [
-    "from mlrun import run_local, new_task\n",
-    "run_local(new_task(handler='iris_generator'), proj.func('xgb'), workdir='./')"
+    "from mlrun import run_local, NewTask\n",
+    "run_local(NewTask(handler='iris_generator'), proj.func('xgb'), workdir='./')"
    ]
   },
   {

examples/mlrun_basics.ipynb

@@ -97,7 +97,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mlrun import run_local, RunTemplate, new_task, mlconf\n",
+    "from mlrun import run_local, RunTemplate, NewTask, mlconf\n",
     "from os import path\n",
     "mlconf.dbpath = mlconf.dbpath or './'"
    ]
@@ -177,7 +177,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "task = new_task(name='demo', params={'p1': 5}, artifact_path=artifact_path).with_secrets('file', 'secrets.txt').set_label('type', 'demo')"
+    "task = NewTask(name='demo', params={'p1': 5}, artifact_path=artifact_path).with_secrets('file', 'secrets.txt').set_label('type', 'demo')"
    ]
   },
   {
@@ -1723,7 +1723,7 @@
     }
    ],
    "source": [
-    "task = new_task(name='demo2', handler=handler, artifact_path=artifact_path).with_params(p1=7)\n",
+    "task = NewTask(name='demo2', handler=handler, artifact_path=artifact_path).with_params(p1=7)\n",
     "run = run_local(task)"
    ]
   },
@@ -2025,7 +2025,7 @@
     }
    ],
    "source": [
-    "task = new_task(name='demo2', handler=handler, artifact_path=artifact_path).with_param_file('params.csv', 'max.accuracy')\n",
+    "task = NewTask(name='demo2', handler=handler, artifact_path=artifact_path).with_param_file('params.csv', 'max.accuracy')\n",
     "run = run_local(task)"
    ]
   },

examples/mlrun_dask.ipynb

@@ -85,7 +85,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mlrun import new_function, mlconf, code_to_function, mount_v3io, new_task\n",
+    "from mlrun import new_function, mlconf, code_to_function, mount_v3io, NewTask\n",
     "#mlconf.dbpath = 'http://mlrun-api:8080'"
    ]
   },
@@ -562,7 +562,7 @@
     "@dsl.pipeline(name=\"dask_pipeline\")\n",
     "def dask_pipe(x=1,y=10):\n",
     "    # use_db option will use a function (DB) pointer instead of adding the function spec to the YAML\n",
-    "    myrun = dsf.as_step(new_task(handler=hndlr, name=\"dask_pipeline\", params={'x': x, 'y': y}), use_db=True)\n",
+    "    myrun = dsf.as_step(NewTask(handler=hndlr, name=\"dask_pipeline\", params={'x': x, 'y': y}), use_db=True)\n",
     "    \n",
     "    # if the step (dask client) need v3io access u should add: .apply(mount_v3io())\n",
     "    \n",

examples/mlrun_export_import.ipynb

@@ -234,7 +234,7 @@
     "\n",
     "# create and run the task\n",
     "images_path = path.abspath('images')\n",
-    "open_archive_task = mlrun.new_task('download',\n",
+    "open_archive_task = mlrun.NewTask('download',\n",
     "    params={'target_dir': images_path},\n",
     "    inputs={'archive_url': 'http://iguazio-sample-data.s3.amazonaws.com/catsndogs.zip'})"
    ]

examples/mlrun_jobs.ipynb

@@ -261,7 +261,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from mlrun import run_local, code_to_function, mlconf, new_task\n",
+    "from mlrun import run_local, code_to_function, mlconf, NewTask\n",
     "from mlrun.platforms.other import auto_mount\n",
     "mlconf.dbpath = mlconf.dbpath or 'http://mlrun-api:8080'"
    ]
@@ -539,7 +539,7 @@
     }
    ],
    "source": [
-    "train_run = run_local(new_task(handler=training, params={'p1': 5}, artifact_path=out))"
+    "train_run = run_local(NewTask(handler=training, params={'p1': 5}, artifact_path=out))"
    ]
   },
   {
@@ -813,7 +813,7 @@
    "source": [
     "model = train_run.outputs['mymodel']\n",
     "\n",
-    "validation_run = run_local(new_task(handler=validation, inputs={'model': model}, artifact_path=out))"
+    "validation_run = run_local(NewTask(handler=validation, inputs={'model': model}, artifact_path=out))"
    ]
   },
   {
@@ -1057,7 +1057,7 @@
    "outputs": [],
    "source": [
     "# create the base task (common to both steps), and set the output path and experiment label\n",
-    "base_task = new_task(artifact_path=out).set_label('stage', 'dev')"
+    "base_task = NewTask(artifact_path=out).set_label('stage', 'dev')"
    ]
   },
   {
@@ -1296,7 +1296,7 @@
    ],
    "source": [
     "# run our training task, with hyper params, and select the one with max accuracy\n",
-    "train_task = new_task(name='my-training', handler='training', params={'p1': 9}, base=base_task)\n",
+    "train_task = NewTask(name='my-training', handler='training', params={'p1': 9}, base=base_task)\n",
     "train_run = trainer.run(train_task)"
    ]
   },

examples/mlrun_sparkk8s.ipynb

@@ -29,7 +29,7 @@
    "source": [
     "import os\n",
     "from os.path import isfile, join\n",
-    "from mlrun import new_function, new_task, mlconf\n",
+    "from mlrun import new_function, NewTask, mlconf\n",
     "\n",
     "#Set the mlrun database/api\n",
     "mlconf.dbpath = 'http://mlrun-api:8080'\n",
@@ -83,7 +83,7 @@
     "         'query': QUERY,\n",
     "         'write_options': WRITE_OPTIONS}\n",
     "\n",
-    "SPARK_TASK = new_task(params=PARAMS)"
+    "SPARK_TASK = NewTask(params=PARAMS)"
    ]
   },
   {

mlrun/model.py

@@ -463,7 +463,7 @@ def NewTask(
     """
     warnings.warn(
         "NewTask will be deprecated in 0.7.0, and will be removed in 0.9.0, use new_task instead",
-        # TODO: change to FutureWarning in 0.7.0
+        # TODO: In 0.7.0 and replace NewTask to new_task in examples & demos
         PendingDeprecationWarning,
     )
     return new_task(