Adding a split utility to support data splitting -- enables key AIX36…

codeflare.egg-info/PKG-INFO

@@ -0,0 +1,22 @@
+Metadata-Version: 1.2
+Name: codeflare
+Version: 0.1.2.dev0
+Summary: Codeflare pipelines
+Home-page: https://github.com/project-codeflare/codeflare
+Author: CodeFlare team
+Author-email: [email protected]
+License: Apache v2.0
+Project-URL: Bug Reports, https://github.com/project-codeflare/codeflare/issues
+Project-URL: Source, https://github.com/project-codeflare/codeflare
+Description: UNKNOWN
+Keywords: ray pipelines
+Platform: UNKNOWN
+Classifier: Development Status :: 4 - Beta
+Classifier: Intended Audience :: Developers
+Classifier: License :: OSI Approved :: Apache Software License
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 3 :: Only
+Classifier: Operating System :: OS Independent
+Classifier: Topic :: System :: Distributed Computing
+Requires-Python: >=3.7

codeflare.egg-info/SOURCES.txt

@@ -0,0 +1,26 @@
+README.md
+setup.cfg
+setup.py
+codeflare/__init__.py
+codeflare/_version.py
+codeflare.egg-info/PKG-INFO
+codeflare.egg-info/SOURCES.txt
+codeflare.egg-info/dependency_links.txt
+codeflare.egg-info/requires.txt
+codeflare.egg-info/top_level.txt
+codeflare/pipelines/Datamodel.py
+codeflare/pipelines/Exceptions.py
+codeflare/pipelines/Runtime.py
+codeflare/pipelines/__init__.py
+codeflare/pipelines/utils.py
+codeflare/pipelines/tests/__init__.py
+codeflare/pipelines/tests/test_Datamodel.py
+codeflare/pipelines/tests/test_and.py
+codeflare/pipelines/tests/test_helper.py
+codeflare/pipelines/tests/test_multibranch.py
+codeflare/pipelines/tests/test_or.py
+codeflare/pipelines/tests/test_pipeline_predict.py
+codeflare/pipelines/tests/test_runtime.py
+codeflare/pipelines/tests/test_save_load.py
+codeflare/pipelines/tests/test_singleton.py
+codeflare/pipelines/tests/test_utils.py

codeflare.egg-info/dependency_links.txt

@@ -0,0 +1 @@
+

codeflare.egg-info/requires.txt

@@ -0,0 +1,8 @@
+ray[default,k8s,serve]>=1.3.0
+setuptools>=52.0.0
+sklearn>=0.0
+scikit-learn>=0.24.1
+pandas>=1.2.4
+numpy>=1.18.5
+pickle5>=0.0.11
+graphviz>=0.16

codeflare.egg-info/top_level.txt

@@ -0,0 +1 @@
+codeflare

codeflare/pipelines/Runtime.py

@@ -146,6 +146,7 @@ def execute_or_node_remote(node: dm.EstimatorNode, mode: ExecutionType, xy_ref:
         result = dm.XYRef(res_Xref, xy_ref.get_yref(), prev_node_ptr, prev_node_ptr, [xy_ref])
         return result
 
+
 def execute_or_node(node, pre_edges, edge_args, post_edges, mode: ExecutionType, is_outputNode):
     """
     Inner method that executes the estimator node parallelizing at the level of input objects. This defines the

codeflare/pipelines/tests/test_Datamodel.py

@@ -13,15 +13,20 @@
 from codeflare.pipelines.Datamodel import Xy
 from codeflare.pipelines.Runtime import ExecutionType
 
+
 class FeatureUnion(dm.AndEstimator):
     def __init__(self):
         pass
+
     def get_estimator_type(self):
         return 'transform'
+
     def clone(self):
         return base.clone(self)
+
     def fit_transform(self, xy_list):
         return self.transform(xy_list)
+
     def transform(self, xy_list):
         X_list = []
         y_vec = None
@@ -31,6 +36,7 @@ def transform(self, xy_list):
         X_concat = np.concatenate(X_list, axis=1)
         return Xy(X_concat, y_vec)
 
+
 class MultibranchTestCase(unittest.TestCase):
 
     def test_multibranch(self):

codeflare/pipelines/tests/test_utils.py

@@ -0,0 +1,22 @@
+import ray
+import pandas as pd
+import codeflare.pipelines.Datamodel as dm
+import codeflare.pipelines.utils as cfutils
+
+
+def test_utils_split():
+    ray.shutdown()
+    ray.init()
+    d = {'col1': [1, 2, 3, 4, 5, 6, 7, 8], 'col2': [3, 4, 5, 6, 7, 8, 9, 10]}
+    df = pd.DataFrame(d)
+    x_test_ref = ray.put(df)
+    y_test_ref = ray.put(None)
+    xy_ref_test = dm.XYRef(x_test_ref, y_test_ref)
+
+    split_ref = cfutils.split.remote(xy_ref_test, 4)
+    xy_ref_splits = ray.get(split_ref)
+    assert len(xy_ref_splits) == 4
+
+    # get the output and assert again
+    X_in_ray = ray.get(xy_ref_splits[0].get_Xref())
+    assert len(X_in_ray) == 2

codeflare/pipelines/utils.py

@@ -27,6 +27,8 @@
 
 import graphviz
 import codeflare.pipelines.Datamodel as dm
+import ray
+import numpy as np
 
 
 def pipeline_to_graph(pipeline: dm.Pipeline) -> graphviz.Digraph:
@@ -45,3 +47,36 @@ def pipeline_to_graph(pipeline: dm.Pipeline) -> graphviz.Digraph:
             graph.node(post_node.get_node_name())
             graph.edge(pre_node.get_node_name(), post_node.get_node_name())
     return graph
+
+
+@ray.remote
+def split(xy_ref: dm.XYRef, num_splits):
+    """
+    Takes input as XYRef, splits the X and sends back the data as chunks. This is quite
+    useful when we have to break a raw array into smaller pieces. This current implementation
+    requires the input X of XYRef to be a pandas dataframe.
+    """
+    x = ray.get(xy_ref.get_Xref())
+    y = ray.get(xy_ref.get_yref())
+
+    xy_split_refs = []
+
+    # TODO: How do we split y if it is needed, lets revisit it later, will these be aligned?
+    x_split = np.array_split(x, num_splits)
+    if y is not None:
+        y_split = np.array_split(y, num_splits)
+
+    # iterate over each and then insert into Plasma
+    for i in range(0, len(x_split)):
+        x_part = x_split[i]
+        y_part = None
+
+        if y is not None:
+            y_part = y_split[i]
+
+        x_part_ref = ray.put(x_part)
+        y_part_ref = ray.put(y_part)
+        xy_ref_part = dm.XYRef(x_part_ref, y_part_ref)
+        xy_split_refs.append(xy_ref_part)
+
+    return xy_split_refs