two version of R2R are here HEAD master

2 files changed, 90 insertions, 0 deletions

diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/finetuning/__init__.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/finetuning/__init__.py
new file mode 100644
index 00000000..6b9dda88
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/finetuning/__init__.py
@@ -0,0 +1,10 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+"""Contains custom model finetuning classes for AzureML SDK V2.
+"""
+from azure.ai.ml.constants._finetuning import FineTuningTaskType
+from azure.ai.ml.finetuning._create_job import create_finetuning_job
+
+__all__ = ["FineTuningTaskType", "create_finetuning_job"]
diff --git a/.venv/lib/python3.12/site-packages/azure/ai/ml/finetuning/_create_job.py b/.venv/lib/python3.12/site-packages/azure/ai/ml/finetuning/_create_job.py
new file mode 100644
index 00000000..53cffb52
--- /dev/null
+++ b/.venv/lib/python3.12/site-packages/azure/ai/ml/finetuning/_create_job.py
@@ -0,0 +1,80 @@
+# ---------------------------------------------------------
+# Copyright (c) Microsoft Corporation. All rights reserved.
+# ---------------------------------------------------------
+
+from typing import List, Optional, Dict
+from azure.ai.ml.constants._common import AssetTypes
+from azure.ai.ml.entities._inputs_outputs import Input, Output
+from azure.ai.ml.entities._job.finetuning.custom_model_finetuning_job import (
+    CustomModelFineTuningJob,
+)
+from azure.ai.ml.entities._job.job_resources import JobResources
+from azure.ai.ml.entities._job.queue_settings import QueueSettings
+from azure.ai.ml._utils._experimental import experimental
+
+
+@experimental
+def create_finetuning_job(
+    *,
+    model: str,
+    task: str,
+    training_data: str,
+    output_model_name_prefix: str,
+    validation_data: Optional[str] = None,
+    hyperparameters: Optional[Dict[str, str]] = None,
+    compute: Optional[str] = None,
+    instance_types: Optional[List[str]] = None,
+    job_tier: Optional[str] = None,
+    **kwargs,
+) -> CustomModelFineTuningJob:
+
+    if not model:
+        raise ValueError("model is required")
+    if not task:
+        raise ValueError("task is required")
+    if not training_data:
+        raise ValueError("training_data is required")
+    if not output_model_name_prefix:
+        raise ValueError("output_model_name_prefix is required")
+
+    model_input = Input(
+        type=AssetTypes.MLFLOW_MODEL,
+        path=model,
+    )
+
+    outputs = {"registered_model": Output(type="mlflow_model", name=output_model_name_prefix)}
+
+    # For image tasks this would be mltable, check how to handle this
+    training_data_input = Input(
+        type=AssetTypes.URI_FILE,
+        path=training_data,
+    )
+
+    if validation_data:
+        validation_data_input = Input(
+            type=AssetTypes.URI_FILE,
+            path=validation_data,
+        )
+
+    job_resources = None
+    if instance_types:
+        job_resources = JobResources(instance_types=instance_types)
+
+    queue_settings = None
+    if job_tier:
+        queue_settings = QueueSettings(job_tier=job_tier)
+
+    custom_model_finetuning_job = CustomModelFineTuningJob(
+        task=task,
+        model=model_input,
+        training_data=training_data_input,
+        validation_data=validation_data_input,  # pylint: disable=(possibly-used-before-assignment
+        hyperparameters=hyperparameters,
+        compute=compute,
+        resources=job_resources,
+        queue_settings=queue_settings,
+        outputs=outputs,
+        **kwargs,
+    )
+
+    return custom_model_finetuning_job