Usage:

sh build.sh <NEXUS_USERNAME> <NEXUS_PASSWORD> <BUILD_NUMBER> <BRANCH_NAME> <SEND_TO_NEXUS>

• <NEXUS_USERNAME> and <NEXUS_PASSWORD> is your IPA credentials.
• <BUILD_NUMBER> for development version you can use any value, but if you want to push your package to nexsus please check last version here
• <BRANCH_NAME> your development branch name
• <SEND_TO_NEXUS> by default set this value to 0. If you want to push your package set this to 1.

Dev guide:

1. git clone
2. cd mlp-python-sdk
3. export mlp_sdk_home=$PWD
4. Run pip install -e .
5. Run tests
6. Library is ready to work with

   from mlp_sdk.types import TextsCollection
   
   def TextProcessor():
       my_texts = TextsCollection()
       my_texts.extend(["text1", "text2"])
       ...

6. Run tests
7. In case of using inheritance when defining source (pydantic) classes with replacement of one or more base-fields types you should check that new types are inherited from corresponding old types

Troubleshooting

1. If you have problems like Failed to establish a new connection, try to add --network=host to all docker commands in build.sh

Allowed fields of custom source types:

• Basic python types: int, float, str, bool
• Enumerations:

import enum
from pydantic import BaseModel


class TokenPosTag(enum.Enum):
    UNKNOWN = 'UNKNOWN'


class MyType(BaseModel):
    pos: TokenPosTag

• Another source type:

from pydantic import BaseModel


class TextType(BaseModel):
    text: str


class TextWithExtra(BaseModel):
    text_field: TextType
    extra_field: str

• Any of above with List generic type if you want your field to be repeated:

import enum
from typing import List
from pydantic import BaseModel


class TokenPosTag(enum.Enum):
    UNKNOWN = 'UNKNOWN'


class MyType(BaseModel):
    pos_tags: List[TokenPosTag]

How correctly describe init_config_schema and predict_config_schema.

1. Return only pydantic BaseModel type or your own type inherited from BaseModel.

   • If your schema has no parameters, you can simply return BaseModel

       def init_config_schema(self) -> Type[BaseModel]:
           return BaseModel
       ...
       def predict_config_schema(self) -> Type[BaseModel]:
           return BaseModel

   • For describing your config schema define complex types as classes inherited from BaseModel and fill your config schema by them.

        class DucklingContext(BaseModel):
            locale: str
     
        class ParamsType(BaseModel):
            duckling_context: DucklingContext
     
        class Entity(BaseModel):
            name: str version = "v1"
     
        class SystemEntities(BaseModel):
            entities: List[Entity]
     
        class NerPredictConfigSchema(BaseModel):
            lang: str
            engines: List[str]
            params: ParamsType
            systemEntities: SystemEntities

     and with predict config example above your predict_config_schema will look like this:

       def predict_config_schema(self) -> Type[BaseModel]:
           return NerPredictConfigSchema

How to use storages

• Import one of implemented storage from mlp_sdk.storage (S3Storage or LocalStorage)
• Create config file and pass it in task through Dockerfile variable (also you can put config data in existing container config file)
• Config should contain the storage_type param with s3 or local value
• Also, config should contain all the necessary key-value param pairs for chosen storage (see __init__ params in corresponding files in storage module)
• In the task you can check the given storage type with the following statement:

if container_config['storage_type'] == LocalStorage.name():
    pass

Here's an example of config data for LocalStorage:

storage_type: local
models_dir: my-nlp-models

Attention: the test case for S3 storage is created for Bitbacket CI environment and will fail during local run, it's normal (as you can't provide a necessary config for it).

Examples of usage

See ./examples