Autodoc for Flowcept class and format README

README.md

@@ -6,36 +6,23 @@
 
 # FlowCept
 
-FlowCept is a runtime data integration system that empowers any data processing system to capture and query workflow 
-provenance data using data observability, requiring minimal or no changes in the target system code. It seamlessly integrates data from multiple workflows, enabling users to comprehend complex, heterogeneous, and large-scale data from various sources in federated environments.
-
-FlowCept is intended to address scenarios where multiple workflows in a science campaign or in an enterprise run and generate 
-important data to be analyzed in an integrated manner. Since these workflows may use different data manipulation tools (e.g., provenance or lineage capture tools, database systems, performance profiling tools) or can be executed within
-different parallel computing systems (e.g., Dask, Spark, Workflow Management Systems), its key differentiator is the 
-capability to seamless and automatically integrate data from various workflows using data observability.
-It builds an integrated data view at runtime enabling end-to-end exploratory data analysis and monitoring.
-It follows [W3C PROV](https://www.w3.org/TR/prov-overview/) recommendations for its data schema.
-It does not require changes in user codes or systems (i.e., instrumentation). All users need to do is to create adapters for their systems or tools, if one is not available yet. 
-In addition to observability, we provide instrumentation options for convenience. For example, by adding a `@flowcept_task` decorator on functions, FlowCept will observe their executions when they run. Also, we provide special features for PyTorch modules. Adding `@torch_task` to them will enable extra model inspection to be captured and integrated in the database at runtime.    
-
+FlowCept is a runtime data integration system that empowers any data processing system to capture and query workflow provenance data using data observability, requiring minimal or no changes in the target system code. It seamlessly integrates data from multiple workflows, enabling users to comprehend complex, heterogeneous, and large-scale data from various sources in federated environments.
+
+FlowCept is intended to address scenarios where multiple workflows in a science campaign or in an enterprise run and generate important data to be analyzed in an integrated manner. Since these workflows may use different data manipulation tools (e.g., provenance or lineage capture tools, database systems, performance profiling tools) or can be executed within different parallel computing systems (e.g., Dask, Spark, Workflow Management Systems), its key differentiator is the capability to seamless and automatically integrate data from various workflows using data observability. It builds an integrated data view at runtime enabling end-to-end exploratory data analysis and monitoring. It follows [W3C PROV](https://www.w3.org/TR/prov-overview/) recommendations for its data schema. It does not require changes in user codes or systems (i.e., instrumentation). All users need to do is to create adapters for their systems or tools, if one is not available yet. In addition to observability, we provide instrumentation options for convenience. For example, by adding a `@flowcept_task` decorator on functions, FlowCept will observe their executions when they run. Also, we provide special features for PyTorch modules. Adding `@torch_task` to them will enable extra model inspection to be captured and integrated in the database at runtime.
 
 Currently, FlowCept provides adapters for: [Dask](https://www.dask.org/), [MLFlow](https://mlflow.org/), [TensorBoard](https://www.tensorflow.org/tensorboard), and [Zambeze](https://github.com/ORNL/zambeze). 
 
 See the [Jupyter Notebooks](notebooks) for utilization examples.
 
-See the [Contributing](CONTRIBUTING.md) file for guidelines to contribute with new adapters. Note that we may use the
-term 'plugin' in the codebase as a synonym to adapter. Future releases should standardize the terminology to use adapter.
-
+See the [Contributing](CONTRIBUTING.md) file for guidelines to contribute with new adapters. Note that we may use the term 'plugin' in the codebase as a synonym to adapter. Future releases should standardize the terminology to use adapter.
 
 ## Install and Setup:
 
 1. Install FlowCept: 
 
 `pip install .[all]` in this directory (or `pip install flowcept[all]`) if you want to install all dependencies.
 
-For convenience, this will install all dependencies for all adapters. But it can install
-dependencies for adapters you will not use. For this reason, you may want to install 
-like this: `pip install .[adapter_key1,adapter_key2]` for the adapters we have implemented, e.g., `pip install .[dask]`.
+For convenience, this will install all dependencies for all adapters. But it can install dependencies for adapters you will not use. For this reason, you may want to install like this: `pip install .[adapter_key1,adapter_key2]` for the adapters we have implemented, e.g., `pip install .[dask]`.
 Currently, the optional dependencies available are:
 
 ```
@@ -48,23 +35,18 @@ pip install flowcept[analytics]     # For extra analytics features.
 pip install flowcept[dev]           # To install dev dependencies.
 ```
 
-You do not need to install any optional dependency to run Flowcept without any adapter, e.g., if you want to use simple instrumentation (see below).
-In this case, you need to remove the adapter part from the [settings.yaml](resources/settings.yaml) file.
+You do not need to install any optional dependency to run Flowcept without any adapter, e.g., if you want to use simple instrumentation (see below). In this case, you need to remove the adapter part from the [settings.yaml](resources/settings.yaml) file.
 
 2. Start the Database and MQ System:
 
 To use FlowCept, one needs to start a database and a MQ system. Currently, FlowCept supports MongoDB as its database and it supports both Redis and Kafka as the MQ system.
 
-For convenience, the default needed services can be started using a [docker-compose file](deployment/compose.yml) deployment file. 
-You can start them using `$> docker-compose -f deployment/compose.yml up`.
+For convenience, the default needed services can be started using a [docker-compose file](deployment/compose.yml) deployment file. You can start them using `$> docker-compose -f deployment/compose.yml up`.
 
-3. Optionally, define custom settings (e.g., routes and ports) accordingly in a settings.yaml file. There is a sample file [here](resources/sample_settings.yaml), which can be used as basis.
-Then, set an environment var `FLOWCEPT_SETTINGS_PATH` with the absolute path to the yaml file.
-If you do not follow this step, the default values defined [here](resources/sample_settings.yaml) will be used.
+3. Optionally, define custom settings (e.g., routes and ports) accordingly in a settings.yaml file. There is a sample file [here](resources/sample_settings.yaml), which can be used as basis. Then, set an environment var `FLOWCEPT_SETTINGS_PATH` with the absolute path to the yaml file. If you do not follow this step, the default values defined [here](resources/sample_settings.yaml) will be used.
 
 4. See the [Jupyter Notebooks](notebooks) and [Examples directory](examples) for utilization examples.
 
-
 ### Simple Example with Decorators Instrumentation
 
 In addition to existing adapters to Dask, MLFlow, and others (it's extensible for any system that generates data), FlowCept also offers instrumentation via @decorators. 
@@ -104,9 +86,7 @@ plugin:
   enrich_messages: false
 ```
 
-And other variables depending on the Plugin. For instance, in Dask, timestamp creation by workers add interception overhead.
-As we evolve the software, other variables that impact overhead appear and we might not stated them in this README file yet.
-If you are doing extensive performance evaluation experiments using this software, please reach out to us (e.g., create an issue in the repository) for hints on how to reduce the overhead of our software.
+And other variables depending on the Plugin. For instance, in Dask, timestamp creation by workers add interception overhead. As we evolve the software, other variables that impact overhead appear and we might not stated them in this README file yet. If you are doing extensive performance evaluation experiments using this software, please reach out to us (e.g., create an issue in the repository) for hints on how to reduce the overhead of our software.
 
 ## Install AMD GPU Lib
 
@@ -129,8 +109,7 @@ Which was installed using Frontier's /opt/rocm-6.2.0/share/amd_smi
 
 ## Torch Dependencies
 
-Some unit tests utilize `torch==2.2.2`, `torchtext=0.17.2`, and `torchvision==0.17.2`. They are only really needed to run some tests and will be installed if you run `pip install flowcept[ml_dev]` or `pip install flowcept[all]`. 
-If you want to use FlowCept with Torch, please adapt torch dependencies according to your project's dependencies.
+Some unit tests utilize `torch==2.2.2`, `torchtext=0.17.2`, and `torchvision==0.17.2`. They are only really needed to run some tests and will be installed if you run `pip install flowcept[ml_dev]` or `pip install flowcept[all]`. If you want to use FlowCept with Torch, please adapt torch dependencies according to your project's dependencies.
 
 ## Cite us
 
@@ -159,14 +138,8 @@ R. Souza, T. Skluzacek, S. Wilkinson, M. Ziatdinov, and R. da Silva
 
 ## Disclaimer & Get in Touch
 
-Please note that this a research software. We encourage you to give it a try and use it with your own stack. We
-are continuously working on improving documentation and adding more examples and notebooks, but we are still far from
-a good documentation covering the whole system. If you are interested in working with FlowCept in your own scientific
-project, we can give you a jump start if you reach out to us. Feel free to [create an issue](https://github.com/ORNL/flowcept/issues/new), 
-[create a new discussion thread](https://github.com/ORNL/flowcept/discussions/new/choose) or drop us an email (we trust you'll find a way to reach out to us :wink: ).
+Please note that this a research software. We encourage you to give it a try and use it with your own stack. We are continuously working on improving documentation and adding more examples and notebooks, but we are still far from a good documentation covering the whole system. If you are interested in working with FlowCept in your own scientific project, we can give you a jump start if you reach out to us. Feel free to [create an issue](https://github.com/ORNL/flowcept/issues/new), [create a new discussion thread](https://github.com/ORNL/flowcept/discussions/new/choose) or drop us an email (we trust you'll find a way to reach out to us :wink: ).
 
 ## Acknowledgement
 
-This research uses resources of the Oak Ridge Leadership Computing Facility 
-at the Oak Ridge National Laboratory, which is supported by the Office of 
-Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
+This research uses resources of the Oak Ridge Leadership Computing Facility at the Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.

docs/api-reference.rst

@@ -1,13 +1,10 @@
 API Reference
 =============
 
-Here.
+Public API documentation.
 
 Core components
 ---------------
 
-Here.
-
-.. error::
-
-   Can't import source code documentation with ``autodoc`` because flowcept requires a running database just to import the package. This needs be fixed before any API documentation can be generated by Sphinx. See `issue 144 <https://github.com/ORNL/flowcept/issues/144>`_ on GitHub repository for more information.
+.. autoclass:: flowcept.Flowcept
+   :members: