add compute block config validation

.gitignore

@@ -6,3 +6,4 @@ __pycache__/
 dist/
 build/
 venv/
+.venv/

README.md

@@ -8,19 +8,37 @@ You can install the package via pip once it's published:
 pip install scystream-sdk
 ```
 
-## Usage
+## Introduction
+
+One of the central concepts of scystream are the so-called **Compute Blocks**.
+
+A Compute Block describes an independent programm, that acts as some kind of worker
+which will be scheduled using the scystream-core application.
+This worker executes a task (e.g. a NLP task, a crawling task).
+
+This SDK aims to provide helper functions and all other requirements you need to implement
+a custom Compute Block on your own.
+
+Each worker can have multiple entrypoints, each aiming to solve one task.
+These entrypoints can be configured from the outside using the **Settings**.
+These are basically ENV-Variables, which will be parsed & validated using pydantic.
+
+You can either set "global" Settings (for the entrypoint), by using the `envs` block.
+Or you can set "input/output-related" Settings by using the `config` block in each input/output.
+
+## Basic Usage of the SDK
 
 ```python3
 from scystream.sdk.core import entrypoint
 from scystream.sdk.scheduler import Scheduler
 
 
-@entrypoint
+@entrypoint()
 def example_task():
     print("Executing example_task...")
 
 
-@entrypoint
+@entrypoint()
 def another_task(task_name):
     print(f"Executing another_task with task name: {task_name}")
 
@@ -35,3 +53,192 @@ if __name__ == "__main__":
     main()
 
 ```
+
+## Defining Settings and Using them.
+
+Earlier, we already wrote about **Settings**.
+Each Input & Output can be configured using these settings.
+There are also Global Settings, refered to as `envs` in the `cbc.yaml`
+
+Below you can find a simple example of how we define & validate these settings.
+Therefore you should use the `EnvSettings` class.
+
+```python3
+from scystream.sdk.core import entrypoint
+from scystream.sdk.env.settings import EnvSettings
+
+class TextDataInputSettings(EnvSettings):
+    TXT_SRC_PATH: str # no default provided, manual setting is a MUST
+
+class DBDataInputSettings(EnvSettings):
+    DATA_TABLE_NAME: str = "nlp_information"
+    DB_HOST: str = "time.rwth-aachen.de"
+    DB_PORT: str = 1234
+
+class TopicModellingEntrypointSettings(EnvSettings):
+    LANGUAGE: str = "de"
+
+    text_data: TextDataInputSettings
+    db_data:  DBDataInputSettings
+
+@entrypoint(TopicModellingEntrypointSettings) # Pass it to the Entrypoint
+def topic_modelling(settings): # The settings param is automatically injected to your function, you can use it
+    print(f"Running topic modelling, using file: {settings.text_data.TXT_SRC_PATH}")
+
+@entrypoint()
+def test_entrypint():
+    print("This entrypoint does not have any configs.")
+```
+
+Of course, you will also be able to use your settings in other files/directories.
+For that, just import your desired setting and use the `get_settings()` function.
+It will load the configurations correctly.
+
+## Compute Block Config
+
+We expect every repository which will be used within the scystream application
+to contain a **Compute Block Config File**, the `cbc.yaml`, within the root directory.
+This `cbc.yaml` will be used to define the entrypoints, the inputs & outputs each
+Compute Block offers, necessary for the scystream-frontend to understand.
+
+This is an example `cbc.yaml`:
+
+```yaml
+name: "NLP toolbox"
+description: "Contains NLP algorithms..."
+author: "John Doe"
+docker_image: "https://ghcr.io/nlp-toolbox"
+
+entrypoints:
+  topic_modelling:
+    description: "Run topic modelling"
+    envs:
+      LANGUAGE: "de"
+    inputs:
+      text_data:
+        description: "Text file. Can be uploaded by the user."
+        type: "file"
+        config:
+          TXT_SRC_PATH: null
+      db_data:
+        description: "Information in a database"
+        type: "db_table"
+        config:
+          DATA_TABLE_NAME: "nlp_information"
+          DB_HOST: "time.rwth-aachen.de"
+          DB_PORT: 1234
+    outputs:
+      topic_model:
+        type: "file"
+        description: "Topic model file"
+        config:
+          OUTPUT_PATH_TOPIC_MODEL: null
+      run_durations:
+        type: "db_table"
+        description: "Table that contains the run durations per day."
+        config:
+          RUN_DURATIONS_TABLE_NAME: "run_durations_nlp"
+
+  analyze_runtime:
+    description: "Analyze the runtimes"
+    inputs:
+      run_durations:
+        description: "Table that contains all runtimes and dates"
+        type: "db_table"
+        config:
+          RUN_DURATIONS_TABLE_NAME: "run_durations_nlp"
+    outputs:
+      csv_output:
+        type: "file"
+        description: "A csv containing statistical information"
+        config:
+          CSV_OUTPUT_PATH: "outputs/statistics.csv"
+```
+
+### Generating a config
+
+After writing the functionality of your ComputeBlock (see more below) you can generate
+the corresponding `cbc.yaml` by using the following function:
+
+```python3
+from scystream.sdk.config import generate_config_from_compute_block, get_compute_block
+from pathlib import Path
+
+@entrypoint()
+def example_entrypoint():
+    print("Example...")
+
+if __name__ == "__main__":
+    compute_block = get_compute_block()
+    generate_config_from_compute_block(cb, Path("cbc.yaml"))
+```
+
+This will take all the entrypoints, their defined settings, and generate a config from them.
+
+> [!NOTE]
+> Make sure to edit the generated config by your user-defined metadata
+> (e.g. author, description, docker_image, ...)
+
+### Validating a config
+
+If you want your `cbc.yaml` to be located in a different directory or have a different name, you
+have to configure that accordingly:
+
+```python3
+from scystream.sdk.config import global_config
+
+if __name__ == "__main__":
+    # Set the config_path
+    global_config.set_config_path("custom_dir/custom_name.yaml")
+```
+
+Of course, you can also write the config completely on your own.
+
+> [!NOTE]
+> When using `Scheduler.execute_function("entrypoint")` the Settings for the
+> entrypoint and the config will be validated.
+> If the Settings do not correspond to the definition in the yaml, execution will not be possible.
+
+To validate the config, you can also use a helper function like this:
+
+```python3
+from scystream.sdk.config import validate_config_with_code
+
+@entrypoint()
+def example_entrypoint():
+    print("Example...")
+
+if __name__ == "__main__":
+    validate_config_with_code()
+```
+
+## Development of the SDK
+
+### Installation
+
+1. Create a venv and use it
+
+```bash
+python3 -m venv .venv
+source .venv/bin/activate
+```
+
+2. Install the package within the venv 
+
+> [!NOTE]
+> This will also install all the install_requirements from the setup.py
+
+```bash
+pip install -e .
+```
+
+3. Develop!
+
+### Tests
+
+To run all the tests run the following command:
+
+```bash
+python3 -m unittest discover -s tests
+```
+

scystream/sdk/config/__init__.py

@@ -0,0 +1,6 @@
+from .config_loader import global_config, \
+    validate_config_with_code, load_config
+from .compute_block_utils import get_compute_block
+
+__all__ = ["global_config", "validate_config_with_code",
+           "load_config", "EnvSettings", "get_compute_block"]

scystream/sdk/config/compute_block_utils.py

@@ -0,0 +1,72 @@
+from typing import Union
+from pydantic_core import PydanticUndefinedType
+from scystream.sdk.config.models import ComputeBlock, Entrypoint, \
+    InputOutputModel
+from scystream.sdk.env.settings import InputSettings, \
+    OutputSettings
+from scystream.sdk.config.entrypoints import get_registered_functions
+
+
+def _get_pydantic_default_value_or_none(value):
+    if type(value.default) is PydanticUndefinedType:
+        return None
+    return value.default
+
+
+def _build_input_output_dict_from_class(
+    subject: Union[InputSettings, OutputSettings]
+):
+    config_dict = {}
+    for key, value in subject.model_fields.items():
+        config_dict[key] = _get_pydantic_default_value_or_none(value)
+    return InputOutputModel(
+        type="TODO: SetType",
+        description="<to-be-set>",
+        config=config_dict
+    )
+
+
+def get_compute_block() -> ComputeBlock:
+    """
+    Converts Entrypoints & Settings to a ComputeBlock
+    """
+    entrypoints = {}
+    for entrypoint, func in get_registered_functions().items():
+        envs = {}
+        inputs = {}
+        outputs = {}
+
+        if func["settings"]:
+            entrypoint_settings_class = func["settings"]
+            for key, value in entrypoint_settings_class.model_fields.items():
+                if (
+                    isinstance(value.default_factory, type) and
+                    issubclass(value.default_factory, InputSettings)
+                ):
+                    inputs[key] = _build_input_output_dict_from_class(
+                        value.default_factory
+                    )
+                elif (
+                    isinstance(value.default_factory, type) and
+                    issubclass(value.default_factory, OutputSettings)
+                ):
+                    outputs[key] = _build_input_output_dict_from_class(
+                        value.default_factory
+                    )
+                else:
+                    envs[key] = _get_pydantic_default_value_or_none(value)
+
+        entrypoints[entrypoint] = Entrypoint(
+            description="<tbd>",
+            envs=envs if envs != {} else None,
+            inputs=inputs if inputs != {} else None,
+            outputs=outputs if outputs != {} else None
+        )
+
+    return ComputeBlock(
+        name="<tbs>",
+        description="<tbs>",
+        author="<tbs>",
+        entrypoints=entrypoints,
+        docker_image="<tbs>"
+    )