通过客户端上传学件

在北冥坞系统中，学件既可以从网页端上传，也可以使用 learnware Python 包进行上传，即使用客户端上传。

接下来，我们将介绍如何使用 learnware 包上传学件。首先需要登录：

from learnware.client import LearnwareClient, SemanticSpecificationKey

# Login to Beiming system
client = LearnwareClient()
client.login(email="your email", token="your token")

from learnware.client import LearnwareClient, SemanticSpecificationKey

# Login to Beiming system
client = LearnwareClient()
client.login(email="your email", token="your token")

其中 email 为系统的注册邮箱，token 为访问学件 API 的令牌，可在网页端「个人信息 - 客户端令牌」处生成。

准备语义规约

成功登录后，您需要准备语义规约，此处以「表格数据」的「分类任务」为例：

from learnware.specification import generate_semantic_spec

# Prepare input description when data_type="Table"
input_description = {
    "Dimension": 5,
    "Description": {
        "0": "age",
        "1": "weight",
        "2": "body length",
        "3": "animal type",
        "4": "claw length"
    },
}

# Prepare output description when task_type in ["Classification", "Regression"]
output_description = {
    "Dimension": 3,
    "Description": {
        "0": "cat",
        "1": "dog",
        "2": "bird",
    },
}

# Create semantic specification
semantic_spec = generate_semantic_spec(
    name="learnware_example",
    description="Just a example for uploading a learnware",
    data_type="Table",
    task_type="Classification",
    library_type="Scikit-learn",
    scenarios=["Business", "Financial"],
    license=["Apache-2.0"],
    input_description=input_description,
    output_description=output_description,
)

from learnware.specification import generate_semantic_spec

# Prepare input description when data_type="Table"
input_description = {
    "Dimension": 5,
    "Description": {
        "0": "age",
        "1": "weight",
        "2": "body length",
        "3": "animal type",
        "4": "claw length"
    },
}

# Prepare output description when task_type in ["Classification", "Regression"]
output_description = {
    "Dimension": 3,
    "Description": {
        "0": "cat",
        "1": "dog",
        "2": "bird",
    },
}

# Create semantic specification
semantic_spec = generate_semantic_spec(
    name="learnware_example",
    description="Just a example for uploading a learnware",
    data_type="Table",
    task_type="Classification",
    library_type="Scikit-learn",
    scenarios=["Business", "Financial"],
    license=["Apache-2.0"],
    input_description=input_description,
    output_description=output_description,
)

请确保语义规约的输入在 client.list_semantic_specification_values(key) 给出的范围内：

data_type 必须在 key=SemanticSpecificationKey.DATA_TYPE 对应的结果中；
task_type 必须在 key=SemanticSpecificationKey.TASK_TYPE 对应的结果中；
library_type 必须在 key=SemanticSpecificationKey.LIBRARY_TYPE 对应的结果中；
scenarios 必须为 key=SemanticSpecificationKey.SENARIOES 对应结果的子集。

另外，需要注意：

如果 data_type 为 "Table"，则需要填写模型输入数据的每一维特征语义，使上传的学件可用于特征空间不对齐的任务；
如果 task_type 为 "Classification"，则需要填写模型输出标记的语义（预测标记从 0 开始编号），使上传的学件可用于输出空间不对齐的分类任务；
如果 task_type 为 "Regression"，则需要填写模型输出的每一维语义，使上传的学件可用于输出空间不对齐的回归任务；

学件上传

接下来，填写语义规约和学件 zip 包路径，您即可完成学件上传。

关于学件 zip 包的准备，请参考指南：如何准备一个学件？。在上传前，建议您先对学件进行本地验证，代码示例如下：

# Prepare your learnware zip file
zip_path = "your learnware zip"

# Check your learnware before upload
client.check_learnware(
    learnware_zip_path=zip_path, semantic_specification=semantic_spec
)

# Upload your learnware
learnware_id = client.upload_learnware(
    learnware_zip_path=zip_path, semantic_specification=semantic_spec
)

# Prepare your learnware zip file
zip_path = "your learnware zip"

# Check your learnware before upload
client.check_learnware(
    learnware_zip_path=zip_path, semantic_specification=semantic_spec
)

# Upload your learnware
learnware_id = client.upload_learnware(
    learnware_zip_path=zip_path, semantic_specification=semantic_spec
)

学件上传后，您可以在「个人信息 - 我的学件」页面看到上传的学件。系统后台会自动将该学件加入验证队列，以检验学件是否符合规范。如果学件通过验证，其标签会变为「验证成功」，并出现在系统中。

通过客户端上传学件 ​

准备语义规约 ​

学件上传 ​

通过客户端上传学件

准备语义规约

学件上传