Merge pull request #129 from aviaIguazio/item_to_yaml

Item to yaml

Author: aviaIguazio

cli/item_to_function.py

@@ -1,22 +1,48 @@
 from pathlib import Path
 from typing import Optional
-
+import os
 import click
 from mlrun import code_to_function
 from yaml import full_load
+from cli.path_iterator import PathIterator
+from cli.helpers import (
+    is_item_dir
+)
 
 
 @click.command()
 @click.option(
     "-i", "--item-path", help="Path to item.yaml file or a directory containing one"
 )
 @click.option("-o", "--output-path", help="Path to code_to_function output")
-def item_to_function(item_path: str, output_path: Optional[str] = None):
-    item_path = Path(item_path)
+@click.option("-r", "--root-directory", help="Path to root directory containing multiple functions")
+def item_to_function(item_path: str, output_path: Optional[str] = None, root_directory: Optional[str] = None):
+    if root_directory is None:
+        item_to_function_from_path(item_path, output_path)
+    else:
+        item_iterator = PathIterator(root=root_directory, rule=is_item_dir, as_path=True)
+
+        for inner_dir in item_iterator:
+            # Iterate individual files in each directory
+            for inner_file in inner_dir.iterdir():
+                if inner_file.is_file() and inner_file.name == 'item.yaml':
+                    inner_file_dir = os.path.dirname(str(inner_file))
+                    output_file_path = inner_file_dir + "/" + "gen_function.yaml"
+                    print(inner_file_dir)
+                    try:
+                        item_to_function_from_path(str(inner_file_dir), output_file_path)
+                    except Exception :
+                        print("failed to generate yaml for {}".format(str(inner_dir)))
+
 
+def item_to_function_from_path(item_path: str, output_path: Optional[str] = None):
+    item_path = Path(item_path)
+    base_path = ""
     if item_path.is_dir():
         if (item_path / "item.yaml").exists():
+            base_path = str(item_path)
             item_path = item_path / "item.yaml"
+
         else:
             raise FileNotFoundError(f"{item_path} does not contain a item.yaml file")
     elif not item_path.exists():
@@ -30,10 +56,11 @@ def item_to_function(item_path: str, output_path: Optional[str] = None):
     if filename.endswith(".ipynb"):
         code_output = Path(filename)
         code_output = code_output.parent / f"{code_output.stem}.py"
+    code_file_name = get_filename(base_path, item_yaml)
 
     function_object = code_to_function(
         name=item_yaml["name"],
-        filename=item_yaml.get("spec", {}).get("filename"),
+        filename=code_file_name,
         handler=item_yaml.get("spec", {}).get("handler"),
         kind=item_yaml.get("spec", {}).get("kind"),
         code_output=code_output,
@@ -58,5 +85,14 @@ def item_to_function(item_path: str, output_path: Optional[str] = None):
     function_object.export(target=str(output_path.resolve()))
 
 
+def get_filename(base_path, item_yaml):
+    filename = item_yaml.get("spec", {}).get("filename")
+    if filename is '':
+        filename = base_path + "/" + item_yaml.get("example")
+    else:
+        filename = base_path + "/" + item_yaml.get("spec", {}).get("filename")
+    return filename
+
+
 if __name__ == "__main__":
     item_to_function()

concept_drift/item.yaml

@@ -16,7 +16,7 @@ mlrunVersion: ''
 name: concept-drift
 platformVersion: ''
 spec:
-  filename: ''
+  filename: concept_drift.ipynb
   handler: concept_drift_deployer
   image: mlrun/ml-models
   kind: job

coxph_test/coxph_test.py

@@ -0,0 +1,55 @@
+from mlrun.datastore import DataItem
+from mlrun.artifacts import get_model
+from cloudpickle import load
+from mlrun.mlutils.models import eval_class_model
+
+
+def cox_test(
+        context,
+        models_path: DataItem,
+        test_set: DataItem,
+        label_column: str,
+        plots_dest: str = "plots",
+        model_evaluator=None
+) -> None:
+    """Test one or more classifier models against held-out dataset
+
+    Using held-out test features, evaluates the peformance of the estimated model
+
+    Can be part of a kubeflow pipeline as a test step that is run post EDA and
+    training/validation cycles
+
+    :param context:         the function context
+    :param model_file:      model artifact to be tested
+    :param test_set:        test features and labels
+    :param label_column:    column name for ground truth labels
+    :param score_method:    for multiclass classification
+    :param plots_dest:      dir for test plots
+    :param model_evaluator: WIP: specific method to generate eval, passed in as string
+                            or available in this folder
+    """
+    xtest = test_set.as_df()
+    ytest = xtest.pop(label_column)
+
+    model_file, model_obj, _ = get_model(models_path.url, suffix='.pkl')
+    model_obj = load(open(str(model_file), "rb"))
+
+    try:
+        # there could be different eval_models, type of model (xgboost, tfv1, tfv2...)
+        if not model_evaluator:
+            # binary and multiclass
+            eval_metrics = eval_class_model(context, xtest, ytest, model_obj)
+
+        # just do this inside log_model?
+        model_plots = eval_metrics.pop("plots")
+        model_tables = eval_metrics.pop("tables")
+        for plot in model_plots:
+            context.log_artifact(plot, local_path=f"{plots_dest}/{plot.key}.html")
+        for tbl in model_tables:
+            context.log_artifact(tbl, local_path=f"{plots_dest}/{plot.key}.csv")
+
+        context.log_results(eval_metrics)
+    except:
+        # dummy log:
+        context.log_dataset("cox-test-summary", df=model_obj.summary, index=True, format="csv")
+        context.logger.info("cox tester not implemented")

coxph_test/item.yaml

@@ -16,7 +16,7 @@ mlrunVersion: ''
 name: coxph-test
 platformVersion: ''
 spec:
-  filename: ''
+  filename: coxph_test.py
   handler: cox_test
   image: mlrun/ml-models
   kind: job

describe_dask/item.yaml

@@ -18,9 +18,6 @@ spec:
   handler: summarize
   image: mlrun/ml-models
   kind: job
-  requirements:
-  - mlrun
-  - dask
-  - yellowbrick
+  requirements: []
 url: ''
 version: 0.0.1

describe_dask/requirements.txt

@@ -0,0 +1,3 @@
+mlrun
+dask
+yellowbrick

load_dask/item.yaml

@@ -15,7 +15,7 @@ mlrunVersion: ''
 name: load-dask
 platformVersion: ''
 spec:
-  filename: ''
+  filename: load_dask.py
   handler: load_dask
   image: mlrun/ml-models
   kind: dask

load_dask/load_dask.py

@@ -0,0 +1,54 @@
+from mlrun.execution import MLClientCtx
+from mlrun.datastore import DataItem
+
+from typing import List, Optional
+
+
+def load_dask(
+        context: MLClientCtx,
+        src_data: DataItem,
+        dask_key: str = "dask_key",
+        inc_cols: Optional[List[str]] = None,
+        index_cols: Optional[List[str]] = None,
+        dask_persist: bool = True,
+        refresh_data: bool = True,
+        scheduler_key: str = "scheduler"
+) -> None:
+    """Load dataset into an existing dask cluster
+
+    dask jobs define the dask client parameters at the job level, this method will raise an error if no client is detected.
+
+    :param context:         the function context
+    :param src_data:        url of the data file or partitioned dataset as either
+                            artifact DataItem, string, or path object (similar to
+                            pandas read_csv)
+    :param dask_key:        destination key of data on dask cluster and artifact store
+    :param inc_cols:        include only these columns (very fast)
+    :param index_cols:      list of index column names (can be a long-running process)
+    :param dask_persist:    (True) should the data be persisted (through the `client.persist` op)
+    :param refresh_data:    (False) if the dask_key already exists in the dask cluster, this will
+                            raise an Exception.  Set to True to replace the existing cluster data.
+    :param scheduler_key:   (scheduler) the dask scheduler configuration, json also logged as an artifact
+    """
+    if hasattr(context, "dask_client"):
+        dask_client = context.dask_client
+    else:
+        raise Exception("a dask client was not found in the execution context")
+
+    df = src_data.as_df(df_module=dd)
+
+    if dask_persist:
+        df = dask_client.persist(df)
+        if dask_client.datasets and dask_key in dask_client.datasets:
+            dask_client.unpublish_dataset(dask_key)
+        dask_client.publish_dataset(df, name=dask_key)
+
+    if context:
+        context.dask_client = dask_client
+
+    # share the scheduler, whether data is persisted or not
+    dask_client.write_scheduler_file(scheduler_key + ".json")
+
+    # we don't use log_dataset here until it can take into account
+    # dask origin and apply dask describe.
+    context.log_artifact(scheduler_key, local_path=scheduler_key + ".json")

rnn_serving/item.yaml

@@ -18,11 +18,7 @@ spec:
   handler: handler
   image: mlrun/ml-model
   kind: serving
-  requirements:
-  - mlrun==0.6.2
-  - keras
-  - tensorflow
-  - wget
-  - pygit2
+  requirements: []
+
 url: ''
 version: 0.0.1

rnn_serving/requirements.txt

@@ -0,0 +1,5 @@
+mlrun==0.6.2
+keras
+tensorflow
+wget
+pygit2

spark_submit/function.yaml

@@ -74,8 +74,6 @@ spec:
     spark_submit:
       name: spark_submit
       doc: 'spark_submit function
-
-
         submiting spark via shell'
       parameters:
       - name: context
@@ -115,4 +113,4 @@ spec:
   build:
     functionSourceCode: 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     commands: []
-    code_origin: https://github.com/marcelonyc/functions#500364cb773cf4d2f23df1449ad32b6a1e370b8c:submit.ipynb
+    code_origin: https://github.com/marcelonyc/functions#500364cb773cf4d2f23df1449ad32b6a1e370b8c:submit.ipynb