Add 'inference_config' on ES >=7.8

.ci/test-matrix.yml

@@ -2,6 +2,7 @@
 
 ELASTICSEARCH_VERSION:
   - 8.0.0-SNAPSHOT
+  - 7.x-SNAPSHOT
   - 7.6-SNAPSHOT
 
 TEST_SUITE:

eland/common.py

@@ -12,6 +12,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import re
 import warnings
 from enum import Enum
 from typing import Union, List, Tuple
@@ -266,5 +267,20 @@ def ensure_es_client(
     es_client: Union[str, List[str], Tuple[str, ...], Elasticsearch]
 ) -> Elasticsearch:
     if not isinstance(es_client, Elasticsearch):
-        return Elasticsearch(es_client)
+        es_client = Elasticsearch(es_client)
     return es_client
+
+
+def es_version(es_client: Elasticsearch) -> Tuple[int, int, int]:
+    """Tags the current ES client with a cached '_eland_es_version'
+    property if one doesn't exist yet for the current Elasticsearch version.
+    """
+    if not hasattr(es_client, "_eland_es_version"):
+        major, minor, patch = [
+            int(x)
+            for x in re.match(
+                r"^(\d+)\.(\d+)\.(\d+)", es_client.info()["version"]["number"]
+            ).groups()
+        ]
+        es_client._eland_es_version = (major, minor, patch)
+    return es_client._eland_es_version

eland/ml/_model_serializer.py

@@ -51,7 +51,9 @@ class ModelSerializer(ABC):
         json_string = json.dumps(
             {"trained_model": self.to_dict()}, separators=(",", ":")
         )
-        return base64.b64encode(gzip.compress(bytes(json_string, "utf-8")))
+        return base64.b64encode(gzip.compress(json_string.encode("utf-8"))).decode(
+            "ascii"
+        )
 
 
 class TreeNode:

eland/ml/imported_ml_model.py

@@ -16,6 +16,7 @@ from typing import Union, List
 
 import numpy as np
 
+from eland.common import es_version
 from eland.ml._model_transformers import (
     SKLearnDecisionTreeTransformer,
     SKLearnForestRegressorTransformer,
@@ -157,15 +158,17 @@ class ImportedMLModel(MLModel):
         if overwrite:
             self.delete_model()
 
-        serialized_model = str(serializer.serialize_and_compress_model())[
+        serialized_model = serializer.serialize_and_compress_model()
-            2:-1
+        body = {
-        ]  # remove `b` and str quotes
+            "compressed_definition": serialized_model,
+            "input": {"field_names": feature_names},
+        }
+        # 'inference_config' is required in 7.8+ but isn't available in <=7.7
+        if es_version(self._client) >= (7, 8):
+            body["inference_config"] = {self._model_type: {}}
+
         self._client.ml.put_trained_model(
-            model_id=self._model_id,
+            model_id=self._model_id, body=body,
-            body={
-                "input": {"field_names": feature_names},
-                "compressed_definition": serialized_model,
-            },
         )
 
     def predict(self, X):

eland/ml/ml_model.py

@@ -51,6 +51,6 @@ class MLModel:
         If model doesn't exist, ignore failure.
         """
         try:
-            self._client.ml.delete_trained_model(model_id=self._model_id)
+            self._client.ml.delete_trained_model(model_id=self._model_id, ignore=(404,))
         except elasticsearch.NotFoundError:
             pass