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test_datascience_model.py
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#!/usr/bin/env python
import copy
# Copyright (c) 2022, 2024 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
import os
import datetime
import hashlib
import json
from copy import deepcopy
from unittest.mock import MagicMock, patch
import pandas
import pytest
from ads.common import utils
from ads.feature_engineering.schema import Schema
from ads.model.artifact_downloader import (
LargeArtifactDownloader,
SmallArtifactDownloader,
)
from ads.model.artifact_uploader import LargeArtifactUploader, SmallArtifactUploader
from ads.model.datascience_model import (
_MAX_ARTIFACT_SIZE_IN_BYTES,
DataScienceModel,
ModelArtifactSizeError,
BucketNotVersionedError,
ModelFileDescriptionError,
InvalidArtifactType,
ModelRetentionSetting,
ModelBackupSetting,
)
from ads.model.model_metadata import (
ModelCustomMetadata,
ModelProvenanceMetadata,
ModelTaxonomyMetadata,
ModelCustomMetadataItem,
MetadataCustomCategory,
)
from ads.model.service.oci_datascience_model import (
ModelProvenanceNotFoundError,
OCIDataScienceModel,
)
from oci.data_science.models import ModelProvenance
from oci.object_storage.models.object_version_summary import ObjectVersionSummary
from ads.config import AQUA_SERVICE_MODELS_BUCKET as SERVICE_MODELS_BUCKET
MODEL_OCID = "ocid1.datasciencemodel.oc1.iad.<unique_ocid>"
OCI_MODEL_PAYLOAD = {
"id": MODEL_OCID,
"compartment_id": "ocid1.compartment.oc1..<unique_ocid>",
"project_id": "ocid1.datascienceproject.oc1.iad.<unique_ocid>",
"display_name": "Generic Model With Small Artifact new",
"description": "The model description",
"lifecycle_state": "ACTIVE",
"created_by": "ocid1.user.oc1..<unique_ocid>",
"freeform_tags": {"key1": "value1"},
"defined_tags": {"key1": {"skey1": "value1"}},
"time_created": "2022-08-24T17:07:39.200000Z",
"custom_metadata_list": [
{
"key": "CondaEnvironment",
"value": "oci://bucket@namespace/service_pack/cpu/Data_Exploration_and_Manipulation_for_CPU_Python_3.7/3.0/dataexpl_p37_cpu_v3",
"description": "The conda environment where the model was trained.",
"category": "Training Environment",
"hasArtifact": False,
},
],
"defined_metadata_list": [
{"key": "Algorithm", "value": "test", "hasArtifact": False},
{"key": "Framework", "hasArtifact": False},
{"key": "FrameworkVersion", "hasArtifact": False},
{
"key": "UseCaseType",
"value": "multinomial_classification",
"hasArtifact": False,
},
{"key": "Hyperparameters", "hasArtifact": False},
{"key": "ArtifactTestResults", "hasArtifact": False},
{"key": "UnexpectedKey", "value": "unexpected_value", "hasArtifact": False},
{"key": "License", "hasArtifact": False},
{"key": "Readme", "hasArtifact": False},
{"key": "FineTuneConfiguration", "hasArtifact": False},
{"key": "DeploymentConfiguration", "hasArtifact": False},
],
"backup_setting": {
"is_backup_enabled": True,
"backup_region": "us-phoenix-1",
"customer_notification_type": "ALL",
},
"retention_setting": {
"archive_after_days": 30,
"delete_after_days": 90,
"customer_notification_type": "ALL",
},
"input_schema": '{"schema": [{"dtype": "int64", "feature_type": "Integer", "name": 0, "domain": {"values": "", "stats": {}, "constraints": []}, "required": true, "description": "0", "order": 0}], "version": "1.1"}',
"output_schema": '{"schema": [{"dtype": "int64", "feature_type": "Integer", "name": 0, "domain": {"values": "", "stats": {}, "constraints": []}, "required": true, "description": "0", "order": 0}], "version": "1.1"}',
}
OCI_MODEL_PROVENANCE_PAYLOAD = {
"git_branch": "master",
"git_commit": "7c8c8502896ba36837f15037b67e05a3cf9722c7",
"repository_url": "file:///home/datascience",
"script_dir": "test_script_dir",
"training_id": None,
"training_script": None,
}
DSC_MODEL_PAYLOAD = {
"compartmentId": "ocid1.compartment.oc1..<unique_ocid>",
"projectId": "ocid1.datascienceproject.oc1.iad.<unique_ocid>",
"displayName": "Generic Model With Small Artifact new",
"description": "The model description",
"freeformTags": {"key1": "value1"},
"definedTags": {"key1": {"skey1": "value1"}},
"inputSchema": {
"schema": [
{
"feature_type": "Integer",
"dtype": "int64",
"name": 0,
"domain": {"values": "", "stats": {}, "constraints": []},
"required": True,
"description": "0",
"order": 0,
}
],
"version": "1.1",
},
"outputSchema": {
"schema": [
{
"dtype": "int64",
"feature_type": "Integer",
"name": 0,
"domain": {"values": "", "stats": {}, "constraints": []},
"required": True,
"description": "0",
"order": 0,
}
],
"version": "1.1",
},
"customMetadataList": {
"data": [
{
"key": "CondaEnvironment",
"value": "oci://bucket@namespace/service_pack/cpu/Data_Exploration_and_Manipulation_for_CPU_Python_3.7/3.0/dataexpl_p37_cpu_v3",
"description": "The conda environment where the model was trained.",
"category": "Training Environment",
"has_artifact": False,
},
]
},
"definedMetadataList": {
"data": [
{"key": "Algorithm", "value": "test", "has_artifact": False},
{"key": "Framework", "value": None, "has_artifact": False},
{"key": "FrameworkVersion", "value": None, "has_artifact": False},
{
"key": "UseCaseType",
"value": "multinomial_classification",
"has_artifact": False,
},
{"key": "Hyperparameters", "value": None, "has_artifact": False},
{"key": "ArtifactTestResults", "value": None, "has_artifact": False},
{
"key": "UnexpectedKey",
"value": "unexpected_value",
"has_artifact": False,
},
{"key": "License", "value": None, "has_artifact": False},
{"key": "Readme", "value": None, "has_artifact": False},
{"key": "FineTuneConfiguration", "value": None, "has_artifact": False},
{"key": "DeploymentConfiguration", "value": None, "has_artifact": False},
]
},
"provenanceMetadata": {
"git_branch": "master",
"git_commit": "7c8c8502896ba36837f15037b67e05a3cf9722c7",
"repository_url": "file:///home/datascience",
"training_script_path": None,
"training_id": None,
"artifact_dir": "test_script_dir",
},
"backupSetting": {
"is_backup_enabled": True,
"backup_region": "us-phoenix-1",
"customer_notification_type": "ALL",
},
"retentionSetting": {
"archive_after_days": 30,
"delete_after_days": 90,
"customer_notification_type": "ALL",
},
"artifact": "ocid1.datasciencemodel.oc1.iad.<unique_ocid>.zip",
}
ARTIFACT_HEADER_INFO = {
"Date": "Sun, 13 Nov 2022 06:01:27 GMT",
"opc-request-id": "E4F7",
"ETag": "77156317-8bb9-4c4a-882b-0d85f8140d93",
"Content-Disposition": "attachment; filename=new_ocid1.datasciencemodel.oc1.iad.<unique_ocid>.zip",
"Last-Modified": "Sun, 09 Oct 2022 16:50:14 GMT",
"Content-Type": "application/json",
"Content-MD5": "orMy3Gs386GZLjYWATJWuA==",
"X-Content-Type-Options": "nosniff",
"Content-Length": _MAX_ARTIFACT_SIZE_IN_BYTES + 100,
}
MODEL_BY_REF_JSON = {
"version": "1.0",
"type": "modelOSSReferenceDescription",
"models": [
{
"namespace": "ociodscdev",
"bucketName": "unzip-multi-model",
"prefix": "model-linear-1",
"objects": [
{
"name": "model-linear-1/linear-1.pkl",
"version": "ee260f4a-920a-4b4e-974a-c13a1032558e",
"sizeInBytes": 565,
}
],
},
{
"namespace": "ociodscdev",
"bucketName": "unzip-multi-model",
"prefix": "model-linear-2",
"objects": [
{
"name": "model-linear-2/linear-2.pkl",
"version": "dc26a7d2-8041-4b37-8ed0-9e8c10869340",
"sizeInBytes": 565,
}
],
},
{
"namespace": "ociodscdev",
"bucketName": "unzip-multi-model",
"prefix": "model-linear-3",
"objects": [
{
"name": "model-linear-3/linear-3.pkl",
"version": "a22c1211-f7d4-4fd4-96d8-4e3a048c5cf7",
"sizeInBytes": 565,
}
],
},
{
"namespace": "ociodscdev",
"bucketName": "unzip-multi-model",
"prefix": "",
"objects": [
{
"name": "runtime.yaml",
"version": "30afb1a6-ab1f-42a3-95e3-09f61a0046fd",
"sizeInBytes": 334,
},
{
"name": "score.py",
"version": "c4ccaf96-05be-4174-ac3b-15dce2f558fe",
"sizeInBytes": 772,
},
],
},
],
}
CONST_MODEL_FILE_DESCRIPTION = "modelDescription"
class TestDataScienceModel:
DEFAULT_PROPERTIES_PAYLOAD = {
"compartmentId": DSC_MODEL_PAYLOAD["compartmentId"],
"projectId": DSC_MODEL_PAYLOAD["projectId"],
"displayName": DSC_MODEL_PAYLOAD["displayName"],
}
def setup_class(cls):
cls.mock_date = datetime.datetime(2022, 7, 1)
cls.curr_dir = os.path.dirname(os.path.abspath(__file__))
def setup_method(self):
self.payload = deepcopy(DSC_MODEL_PAYLOAD)
self.mock_dsc_model = DataScienceModel(**self.payload)
def prepare_dict(self, data):
if "definedMetadataList" in data:
if isinstance(data["definedMetadataList"], dict):
data["definedMetadataList"]["data"] = sorted(
data["definedMetadataList"]["data"], key=lambda x: x["key"]
)
else:
data["definedMetadataList"] = sorted(
data["definedMetadataList"], key=lambda x: x["key"]
)
if "customMetadataList" in data:
if isinstance(data["customMetadataList"], dict):
data["customMetadataList"]["data"] = sorted(
data["customMetadataList"]["data"], key=lambda x: x["key"]
)
else:
data["customMetadataList"] = sorted(
data["customMetadataList"], key=lambda x: x["key"]
)
return data
def hash_dict(self, data):
return hashlib.sha1(
json.dumps(self.prepare_dict(data), sort_keys=True).encode("utf-8")
).hexdigest()
def compare_dict(self, dict1, dict2):
print(
f"dict1_hash: {self.hash_dict(dict1)}; dict2_hash: {self.hash_dict(dict2)}"
)
return self.hash_dict(dict1) == self.hash_dict(dict2)
@patch.object(
DataScienceModel,
"_load_default_properties",
return_value=DEFAULT_PROPERTIES_PAYLOAD,
)
def test__init__default_properties(self, mock_load_default_properties):
dsc_model = DataScienceModel()
assert dsc_model.to_dict()["spec"] == self.DEFAULT_PROPERTIES_PAYLOAD
@patch.object(DataScienceModel, "_load_default_properties", return_value={})
def test__init__(self, mock_load_default_properties):
dsc_model = DataScienceModel(**self.payload)
print(dsc_model.to_dict()["spec"])
assert self.prepare_dict(dsc_model.to_dict()["spec"]) == self.prepare_dict(
self.payload
)
@patch.object(DataScienceModel, "_load_default_properties", return_value={})
def test_with_methods_1(self, mock_load_default_properties):
"""Tests all with methods."""
dsc_model = (
DataScienceModel()
.with_compartment_id(self.payload["compartmentId"])
.with_project_id(self.payload["projectId"])
.with_display_name(self.payload["displayName"])
.with_description(self.payload["description"])
.with_freeform_tags(**(self.payload["freeformTags"] or {}))
.with_defined_tags(**(self.payload["definedTags"] or {}))
.with_input_schema(self.payload["inputSchema"])
.with_output_schema(self.payload["outputSchema"])
.with_custom_metadata_list(self.payload["customMetadataList"])
.with_defined_metadata_list(self.payload["definedMetadataList"])
.with_provenance_metadata(self.payload["provenanceMetadata"])
.with_artifact(self.payload["artifact"])
.with_backup_setting(self.payload["backupSetting"])
.with_retention_setting(self.payload["retentionSetting"])
)
assert self.prepare_dict(dsc_model.to_dict()["spec"]) == self.prepare_dict(
self.payload
)
def test_with_methods_2(self):
"""Tests all with methods."""
dsc_model = (
DataScienceModel()
.with_compartment_id(self.payload["compartmentId"])
.with_project_id(self.payload["projectId"])
.with_display_name(self.payload["displayName"])
.with_description(self.payload["description"])
.with_freeform_tags(**(self.payload["freeformTags"] or {}))
.with_defined_tags(**(self.payload["definedTags"] or {}))
.with_input_schema(Schema.from_dict(self.payload["inputSchema"]))
.with_output_schema(Schema.from_dict(self.payload["outputSchema"]))
.with_custom_metadata_list(
ModelCustomMetadata.from_dict(self.payload["customMetadataList"])
)
.with_defined_metadata_list(
ModelTaxonomyMetadata.from_dict(self.payload["definedMetadataList"])
)
.with_provenance_metadata(
ModelProvenanceMetadata.from_dict(self.payload["provenanceMetadata"])
)
.with_artifact(self.payload["artifact"])
.with_backup_setting(
ModelBackupSetting.from_dict(self.payload["backupSetting"])
)
.with_retention_setting(
ModelRetentionSetting.from_dict(self.payload["retentionSetting"])
)
)
assert self.prepare_dict(dsc_model.to_dict()["spec"]) == self.prepare_dict(
self.payload
)
@patch.object(OCIDataScienceModel, "delete")
@patch.object(DataScienceModel, "sync")
def test_delete(self, mock_sync, mock_delete):
"""Tests deleting model from model catalog."""
self.mock_dsc_model.delete(delete_associated_model_deployment=True)
mock_sync.assert_called()
mock_delete.assert_called_with(True)
@patch.object(DataScienceModel, "_update_from_oci_dsc_model")
@patch.object(OCIDataScienceModel, "list_resource")
def test_list(self, mock_list_resource, mock__update_from_oci_dsc_model):
"""Tests listing datascience models in a given compartment."""
mock_list_resource.return_value = [OCIDataScienceModel(**OCI_MODEL_PAYLOAD)]
mock__update_from_oci_dsc_model.return_value = DataScienceModel(**self.payload)
result = DataScienceModel.list(
compartment_id="test_compartment_id",
project_id="test_project_id",
extra_tag="test_cvalue",
)
mock_list_resource.assert_called_with(
"test_compartment_id",
project_id="test_project_id",
category="USER",
**{"extra_tag": "test_cvalue"},
)
assert len(result) == 1
assert self.prepare_dict(result[0].to_dict()["spec"]) == self.prepare_dict(
self.payload
)
@patch.object(OCIDataScienceModel, "list_resource")
def test_list_df(self, mock_list_resource):
"""Tests listing datascience models in a given compartment."""
model = OCIDataScienceModel(**OCI_MODEL_PAYLOAD)
mock_list_resource.return_value = [model]
records = []
records.append(
{
"id": f"...{model.id[-6:]}",
"display_name": model.display_name,
"description": model.description,
"time_created": model.time_created.strftime(utils.date_format),
"lifecycle_state": model.lifecycle_state,
"created_by": f"...{model.created_by[-6:]}",
"compartment_id": f"...{model.compartment_id[-6:]}",
"project_id": f"...{model.project_id[-6:]}",
}
)
expected_result = pandas.DataFrame.from_records(records)
result = DataScienceModel.list_df(
compartment_id="test_compartment_id",
project_id="test_project_id",
extra_tag="test_cvalue",
)
mock_list_resource.assert_called_with(
"test_compartment_id",
project_id="test_project_id",
category="USER",
**{"extra_tag": "test_cvalue"},
)
assert expected_result.equals(result)
@patch.object(DataScienceModel, "_update_from_oci_dsc_model")
@patch.object(OCIDataScienceModel, "from_id")
def test_from_id(self, mock_oci_from_id, mock__update_from_oci_dsc_model):
"""Tests getting an existing model by OCID."""
mock_oci_model = OCIDataScienceModel(**OCI_MODEL_PAYLOAD)
mock_oci_from_id.return_value = mock_oci_model
mock__update_from_oci_dsc_model.return_value = DataScienceModel(**self.payload)
result = DataScienceModel.from_id(MODEL_OCID)
mock_oci_from_id.assert_called_with(MODEL_OCID)
mock__update_from_oci_dsc_model.assert_called_with(mock_oci_model)
assert self.prepare_dict(result.to_dict()["spec"]) == self.prepare_dict(
self.payload
)
@pytest.mark.parametrize(
"test_args",
[
{"model_by_reference": True},
{"model_by_reference": False},
],
)
@patch.object(OCIDataScienceModel, "create_model_provenance")
@patch.object(OCIDataScienceModel, "create")
@patch.object(DataScienceModel, "sync")
@patch.object(DataScienceModel, "upload_artifact")
@patch.object(DataScienceModel, "_random_display_name", return_value="random_name")
@patch.object(DataScienceModel, "_load_default_properties", return_value={})
def test_create_success(
self,
mock__load_default_properties,
mock__random_display_name,
mock_upload_artifact,
mock_sync,
mock_oci_dsc_model_create,
mock_create_model_provenance,
test_args,
):
"""Tests creating datascience model."""
oci_dsc_model = OCIDataScienceModel(**OCI_MODEL_PAYLOAD)
mock_oci_dsc_model_create.return_value = oci_dsc_model
# to check random display name
self.mock_dsc_model.with_display_name("")
result = self.mock_dsc_model.create(
bucket_uri="test_bucket_uri",
overwrite_existing_artifact=False,
remove_existing_artifact=False,
parallel_process_count=3,
**test_args,
)
mock_oci_dsc_model_create.assert_called()
mock_create_model_provenance.assert_called_with(
self.mock_dsc_model.provenance_metadata._to_oci_metadata()
)
if test_args.get("model_by_reference"):
mock_upload_artifact.assert_called_with(
bucket_uri="test_bucket_uri",
overwrite_existing_artifact=False,
remove_existing_artifact=False,
region=None,
auth=None,
timeout=None,
parallel_process_count=3,
model_by_reference=True,
)
assert result.custom_metadata_list.get("modelDescription").value == "true"
else:
mock_upload_artifact.assert_called_with(
bucket_uri="test_bucket_uri",
overwrite_existing_artifact=False,
remove_existing_artifact=False,
region=None,
auth=None,
timeout=None,
parallel_process_count=3,
model_by_reference=False,
)
assert self.prepare_dict(result.to_dict()["spec"]) == self.prepare_dict(
{**self.payload, "displayName": "random_name"}
)
mock_sync.assert_called()
@patch.object(DataScienceModel, "_load_default_properties", return_value={})
def test_create_fail(self, mock__load_default_properties):
"""Tests creating datascience model."""
dsc_model = DataScienceModel()
with pytest.raises(ValueError, match="Compartment id must be provided."):
dsc_model.create()
dsc_model.with_compartment_id("compartment_id")
with pytest.raises(ValueError, match="Project id must be provided."):
dsc_model.create()
@patch.object(OCIDataScienceModel, "create_model_provenance")
@patch.object(OCIDataScienceModel, "update_model_provenance")
@patch.object(OCIDataScienceModel, "update")
@patch.object(DataScienceModel, "sync")
def test_update_success(
self,
mock_sync,
mock_update,
mock_update_model_provenance,
mock_create_model_provenance,
):
"""Test updating datascience model in model catalog."""
oci_dsc_model = OCIDataScienceModel(**OCI_MODEL_PAYLOAD)
self.mock_dsc_model.dsc_model = oci_dsc_model
mock_update.return_value = oci_dsc_model
# With update Model Provenance
with patch.object(
OCIDataScienceModel, "get_model_provenance"
) as mock_get_model_provenance:
mock_model_provenance = ModelProvenance(**OCI_MODEL_PROVENANCE_PAYLOAD)
mock_get_model_provenance.return_value = mock_model_provenance
self.mock_dsc_model.update(display_name="new_display_name")
mock_update.assert_called()
mock_update_model_provenance.assert_called_with(mock_model_provenance)
mock_sync.assert_called()
# With create Model Provenance
with patch.object(
OCIDataScienceModel,
"get_model_provenance",
side_effect=ModelProvenanceNotFoundError(),
):
self.mock_dsc_model.update(display_name="new_display_name")
mock_model_provenance = ModelProvenance(**OCI_MODEL_PROVENANCE_PAYLOAD)
mock_update.assert_called()
mock_create_model_provenance.assert_called_with(mock_model_provenance)
mock_sync.assert_called()
def test_to_dict(self):
"""Tests serializing model to a dictionary."""
test_dict = self.mock_dsc_model.to_dict()
assert self.prepare_dict(test_dict["spec"]) == self.prepare_dict(self.payload)
assert test_dict["kind"] == self.mock_dsc_model.kind
assert test_dict["type"] == self.mock_dsc_model.type
def test_from_dict(self):
"""Tests loading model instance from a dictionary of configurations."""
assert self.prepare_dict(
self.mock_dsc_model.to_dict()["spec"]
) == self.prepare_dict(
DataScienceModel.from_dict({"spec": self.payload}).to_dict()["spec"]
)
@patch("ads.common.utils.get_random_name_for_resource", return_value="test_name")
def test__random_display_name(self, mock_get_random_name_for_resource):
"""Tests generating a random display name."""
expected_result = f"{self.mock_dsc_model._PREFIX}-test_name"
assert self.mock_dsc_model._random_display_name() == expected_result
def test__to_oci_dsc_model(self):
"""Tests creating an `OCIDataScienceModel` instance from the `DataScienceModel`."""
with patch.object(OCIDataScienceModel, "sync"):
test_oci_dsc_model = OCIDataScienceModel(**OCI_MODEL_PAYLOAD)
test_oci_dsc_model.id = None
test_oci_dsc_model.lifecycle_state = None
test_oci_dsc_model.created_by = None
test_oci_dsc_model.time_created = None
assert self.prepare_dict(test_oci_dsc_model.to_dict()) == self.prepare_dict(
self.mock_dsc_model._to_oci_dsc_model().to_dict()
)
print("test_oci_dsc_model.to_dict(): ", test_oci_dsc_model.to_dict())
print(
"mock_dsc_model._to_oci_dsc_model().to_dict(): ",
self.mock_dsc_model._to_oci_dsc_model().to_dict(),
)
test_oci_dsc_model.display_name = "new_name"
assert self.prepare_dict(test_oci_dsc_model.to_dict()) == self.prepare_dict(
self.mock_dsc_model._to_oci_dsc_model(display_name="new_name").to_dict()
)
@pytest.mark.parametrize(
"test_args",
[
False,
True,
],
)
@patch.object(OCIDataScienceModel, "is_model_created_by_reference")
@patch.object(OCIDataScienceModel, "get_artifact_info")
@patch.object(OCIDataScienceModel, "get_model_provenance")
@patch.object(DataScienceModel, "_download_file_description_artifact")
def test__update_from_oci_dsc_model(
self,
mock__download_file_description_artifact,
mock_get_model_provenance,
mock_get_artifact_info,
mock_is_model_by_reference,
test_args,
):
"""Tests updating the properties from an OCIDataScienceModel object."""
oci_model_payload = {
"compartment_id": "new ocid1.compartment.oc1..<unique_ocid>",
"project_id": "new ocid1.datascienceproject.oc1.iad.<unique_ocid>",
"display_name": "new Generic Model With Small Artifact new",
"description": "new The model description",
"freeform_tags": {"newkey1": "new value1"},
"defined_tags": {"newkey1": {"newskey1": "value1"}},
"custom_metadata_list": [
{
"key": "CondaEnvironment",
"value": "new oci://bucket@namespace/service_pack/cpu/Data_Exploration_and_Manipulation_for_CPU_Python_3.7/3.0/dataexpl_p37_cpu_v3",
"description": "new The conda environment where the model was trained.",
"category": "Training Environment",
},
],
"defined_metadata_list": [
{"key": "Algorithm", "value": "new test"},
{"key": "Framework", "value": "new test"},
{"key": "FrameworkVersion", "value": "new test"},
{"key": "UseCaseType", "value": "multinomial_classification"},
{"key": "Hyperparameters", "value": "new test"},
{"key": "ArtifactTestResults", "value": "new test"},
{"key": "License", "value": None},
{"key": "Readme", "value": None},
{"key": "FineTuneConfiguration", "value": None},
{"key": "DeploymentConfiguration", "value": None},
],
"backup_setting": {
"is_backup_enabled": True,
"backup_region": "us-phoenix-1",
"customer_notification_type": "ALL",
},
"retention_setting": {
"archive_after_days": 30,
"delete_after_days": 90,
"customer_notification_type": "ALL",
},
"input_schema": '{"schema": [{"dtype": "int64", "feature_type": "Integer", "name": 1, "domain": {"values": "", "stats": {}, "constraints": []}, "required": true, "description": "0", "order": 0}], "version": "1.1"}',
"output_schema": '{"schema": [{"dtype": "int64", "feature_type": "Integer", "name": 1, "domain": {"values": "", "stats": {}, "constraints": []}, "required": true, "description": "0", "order": 0}], "version": "1.1"}',
}
artifact_path = (
"oci://my-bucket@my-tenancy/prefix/"
if test_args
else "new_ocid1.datasciencemodel.oc1.iad.<unique_ocid>.zip"
)
dsc_model_payload = {
"compartmentId": "new ocid1.compartment.oc1..<unique_ocid>",
"projectId": "new ocid1.datascienceproject.oc1.iad.<unique_ocid>",
"displayName": "new Generic Model With Small Artifact new",
"description": "new The model description",
"freeformTags": {"newkey1": "new value1"},
"definedTags": {"newkey1": {"newskey1": "value1"}},
"inputSchema": {
"schema": [
{
"feature_type": "Integer",
"dtype": "int64",
"name": 1,
"domain": {"values": "", "stats": {}, "constraints": []},
"required": True,
"description": "0",
"order": 0,
}
],
"version": "1.1",
},
"outputSchema": {
"schema": [
{
"dtype": "int64",
"feature_type": "Integer",
"name": 1,
"domain": {"values": "", "stats": {}, "constraints": []},
"required": True,
"description": "0",
"order": 0,
}
],
"version": "1.1",
},
"customMetadataList": {
"data": [
{
"key": "CondaEnvironment",
"value": "new oci://bucket@namespace/service_pack/cpu/Data_Exploration_and_Manipulation_for_CPU_Python_3.7/3.0/dataexpl_p37_cpu_v3",
"description": "new The conda environment where the model was trained.",
"category": "Training Environment",
"has_artifact": False,
},
]
},
"definedMetadataList": {
"data": [
{"key": "Algorithm", "value": "new test", "has_artifact": False},
{"key": "Framework", "value": "new test", "has_artifact": False},
{
"key": "FrameworkVersion",
"value": "new test",
"has_artifact": False,
},
{
"key": "UseCaseType",
"value": "multinomial_classification",
"has_artifact": False,
},
{
"key": "Hyperparameters",
"value": "new test",
"has_artifact": False,
},
{
"key": "ArtifactTestResults",
"value": "new test",
"has_artifact": False,
},
{"key": "License", "value": None, "has_artifact": False},
{"key": "Readme", "value": None, "has_artifact": False},
{
"key": "FineTuneConfiguration",
"value": None,
"has_artifact": False,
},
{
"key": "DeploymentConfiguration",
"value": None,
"has_artifact": False,
},
]
},
"backupSetting": {
"is_backup_enabled": True,
"backup_region": "us-phoenix-1",
"customer_notification_type": "ALL",
},
"retentionSetting": {
"archive_after_days": 30,
"delete_after_days": 90,
"customer_notification_type": "ALL",
},
"provenanceMetadata": {
"git_branch": "master",
"git_commit": "7c8c8502896ba36837f15037b67e05a3cf9722c7",
"repository_url": "file:///home/datascience",
"training_script_path": None,
"training_id": None,
"artifact_dir": "test_script_dir",
},
"artifact": artifact_path,
}
with patch.object(OCIDataScienceModel, "sync"):
mock_oci_dsc_model = OCIDataScienceModel(**oci_model_payload)
mock_model_provenance = ModelProvenance(**OCI_MODEL_PROVENANCE_PAYLOAD)
mock_get_model_provenance.return_value = mock_model_provenance
mock_get_artifact_info.return_value = ARTIFACT_HEADER_INFO
mock_is_model_by_reference.return_value = test_args
if test_args:
# when model_by_reference is True, update payload
mock_oci_dsc_model.get_artifact_info.return_value = {
"Content-Disposition": "attachment; filename=artifact.json",
}
mock__download_file_description_artifact.return_value = (
artifact_path,
0,
)
self.mock_dsc_model._update_from_oci_dsc_model(mock_oci_dsc_model)
assert self.prepare_dict(
self.mock_dsc_model.to_dict()["spec"]
) == self.prepare_dict(dsc_model_payload)
def test_upload_artifact(self):
"""Tests uploading artifacts to the model catalog."""
self.mock_dsc_model.dsc_model = MagicMock(__class__=MagicMock())
# Artifact size greater than 2GB
with patch.object(
LargeArtifactUploader, "__init__", return_value=None
) as mock_init:
with patch.object(LargeArtifactUploader, "upload") as mock_upload:
with patch(
"ads.common.utils.folder_size",
return_value=_MAX_ARTIFACT_SIZE_IN_BYTES + 100,
):
# If artifact is large and bucket_uri not provided
with pytest.raises(ModelArtifactSizeError):
self.mock_dsc_model.upload_artifact()
self.mock_dsc_model.upload_artifact(
bucket_uri="test_bucket_uri",
auth={"config": {}},
region="test_region",
overwrite_existing_artifact=False,
remove_existing_artifact=False,
timeout=1,
)
assert self.mock_dsc_model.dsc_model.__class__.kwargs == {
"timeout": 1
}
mock_init.assert_called_with(
dsc_model=self.mock_dsc_model.dsc_model,
artifact_path=self.mock_dsc_model.artifact,
auth={"config": {}},
region="test_region",
bucket_uri="test_bucket_uri",
overwrite_existing_artifact=False,
remove_existing_artifact=False,
parallel_process_count=utils.DEFAULT_PARALLEL_PROCESS_COUNT,
)
mock_upload.assert_called()
# Artifact size less than 2GB
with patch.object(
SmallArtifactUploader, "__init__", return_value=None
) as mock_init:
with patch.object(SmallArtifactUploader, "upload") as mock_upload:
with patch(
"ads.common.utils.folder_size",
return_value=_MAX_ARTIFACT_SIZE_IN_BYTES - 100,
):
self.mock_dsc_model.upload_artifact(timeout=2)
assert self.mock_dsc_model.dsc_model.__class__.kwargs == {
"timeout": 2
}
mock_init.assert_called_with(
dsc_model=self.mock_dsc_model.dsc_model,
artifact_path=self.mock_dsc_model.artifact,
)
mock_upload.assert_called()
@patch.object(OCIDataScienceModel, 'register_model_artifact_reference')
def test_register_model_artifact_reference(self, mock_register_model_artifact_reference):
# Sample input for the test
bucket_uri_list = [
"oci://bucket1@namespace1/prefix1/",
"oci://bucket2@namespace2/prefix2/"
]
# Call the function with the test data
self.mock_dsc_model.register_model_artifact_reference(bucket_uri_list=bucket_uri_list)
# Assert that the mocked `register_model_artifact_reference` method was called once
# and with the expected arguments
mock_register_model_artifact_reference.assert_called_once_with(
bucket_uri_list=bucket_uri_list
)
def test_download_artifact(self):
"""Tests downloading artifacts from the model catalog."""
# Artifact size greater than 2GB
mock_get_artifact_info = MagicMock(
return_value={"content-length": _MAX_ARTIFACT_SIZE_IN_BYTES + 100}
)
self.mock_dsc_model.dsc_model = MagicMock(
id="test_id",
get_artifact_info=mock_get_artifact_info,
__class__=MagicMock(),
)
with patch.object(
LargeArtifactDownloader, "__init__", return_value=None
) as mock_init:
with patch.object(LargeArtifactDownloader, "download") as mock_download:
# If artifact is large and bucket_uri not provided
with pytest.raises(ModelArtifactSizeError):
self.mock_dsc_model.download_artifact(target_dir="test_target_dir")
self.mock_dsc_model.download_artifact(
target_dir="test_target_dir",
auth={"config": {}},
force_overwrite=True,
bucket_uri="test_bucket_uri",
region="test_region",
overwrite_existing_artifact=False,
remove_existing_artifact=False,
timeout=1,
)
assert self.mock_dsc_model.dsc_model.__class__.kwargs == {"timeout": 1}
mock_init.assert_called_with(
dsc_model=self.mock_dsc_model.dsc_model,
target_dir="test_target_dir",
auth={"config": {}},
force_overwrite=True,
region="test_region",
bucket_uri="test_bucket_uri",
overwrite_existing_artifact=False,
remove_existing_artifact=False,
model_file_description=None,
)
mock_download.assert_called()
# Artifact size less than 2GB
mock_get_artifact_info = MagicMock(
return_value={"content-length": _MAX_ARTIFACT_SIZE_IN_BYTES - 100}
)
self.mock_dsc_model.dsc_model = MagicMock(
id="test_id",
get_artifact_info=mock_get_artifact_info,
__class__=MagicMock(),
)
with patch.object(
SmallArtifactDownloader, "__init__", return_value=None
) as mock_init:
with patch.object(SmallArtifactDownloader, "download") as mock_download:
self.mock_dsc_model.download_artifact(
target_dir="test_target_dir", force_overwrite=True, timeout=2
)
mock_init.assert_called_with(
dsc_model=self.mock_dsc_model.dsc_model,
target_dir="test_target_dir",
force_overwrite=True,
)
assert self.mock_dsc_model.dsc_model.__class__.kwargs == {"timeout": 2}
mock_download.assert_called()
@patch("ads.common.object_storage_details.ObjectStorageDetails.is_bucket_versioned")
def test_bucket_not_versioned_error(self, mock_is_bucket_versioned):
"""For model passed by reference, check if bucket is not versioned."""
mock_is_bucket_versioned.return_value = False
self.mock_dsc_model.with_artifact(uri="oci://my-bucket@my-tenancy/prefix/")
with pytest.raises(BucketNotVersionedError):
self.mock_dsc_model.upload_artifact(model_by_reference=True)
@patch("ads.common.object_storage_details.ObjectStorageDetails.is_bucket_versioned")
@patch.object(DataScienceModel, "_prepare_file_description_artifact")
@patch.object(SmallArtifactUploader, "upload")
def test_skip_versioning_check_for_service_bucket(
self,
mock_upload,
mock__prepare_file_description_artifact,
mock_is_bucket_versioned,
):
"""For model passed by reference, check if bucket is not versioned."""
mock_is_bucket_versioned.return_value = True
mock_upload.return_value = None
self.mock_artifact_file_path = os.path.join(
self.curr_dir, "test_files/model_description.json"
)
with open(self.mock_artifact_file_path, "r") as _file:
mock__prepare_file_description_artifact.return_value = json.load(_file)