Question

我从我的机器学习模型中创建了一个泡菜，并将其保存在本地。我想将其推送到蔚蓝的Blob存储中，并希望稍后再检索。如何使用python 3进行操作。请帮忙。

'''

#model
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size = 0.3, random_state = 100)
regressor = LinearRegression()
regressor.fit(X_train, Y_train)

#Creating the pickle file finalized_model.sav on my local

mypickle = 'finalized_model.sav'
pickle.dump(regressor, open(mupickle, 'wb'))

'''

I tried following for .csv importing and pushing from azure, but don't know how to do with pickle files.

'''

with BytesIO() as input_blob:

    block_blob_service = BlockBlobService(account_name='*****', 
    account_key='*********************************************************************')
    block_blob_service.get_blob_to_stream('blobcontainer', 'claims.csv', input_blob)
    input_blob.seek(0) 
    dataframe_blobdata = pd.read_csv(input_blob)

    #transforming the data in between

    output = dataframe_blobdata.to_csv (index_label="idx", encoding = "utf-8")

    block_blob_service.create_blob_from_text('secondforblobcontainer', 'OutFilePy.csv', output)

'''

Answer 1

根据我的理解，您只想将名称为finalized_model.sav的图片文件上传到Azure存储。

然后，我建议您使用azure-storage-blob SDK来上传Blob。这是一个官方示例：Code examples。

详细来说，首先需要从门户网站获取存储帐户的连接字符串，然后使用该连接字符串创建一个BlobServiceClient，然后可以通过blob_service_client.create_container(container_name)创建一个容器客户端。最后，您可以创建一个Blob客户端，然后根据其路径上传本地文件。

从Azure存储中下载回来也很容易。 downloading blobs的示例：

with open(download_file_path, "wb") as download_file:
    download_file.write(blob_client.download_blob().readall())

如何将机器学习模型的泡菜存储到天蓝色斑点并检索它/

1 个答案: