[](https://github.com/ORNL/flowcept/actions/workflows/create-release-n-publish.yml)
[](https://pypi.org/project/flowcept)
[](https://github.com/ORNL/flowcept/actions/workflows/run-tests.yml)
[](https://github.com/ORNL/flowcept/actions/workflows/run-checks.yml)
[](LICENSE)
# FlowCept
FlowCept is a runtime data integration system that captures and queries workflow provenance with minimal or no code changes. It unifies data across diverse workflows and tools, enabling integrated analysis and insights, especially in federated environments. Designed for scenarios involving critical data from multiple workflows, FlowCept seamlessly integrates data at runtime, providing a unified view for end-to-end monitoring and analysis and enhanced support for Machine Learning (ML) workflows.
Other capabilities include:
- Automatic multi-workflow provenance data capture;
- Data observability, enabling minimal intrusion to user workflows;
- Explicit user workflow instrumentation, if this is preferred over implicit data observability;
- ML data capture in various levels of details: workflow, model fitting or evaluation task, epoch iteration, layer forwarding;
- ML model management;
- Adapter-based, loosely-coupled system architecture, making it easy to plug and play with different data processing systems and backend database (e.g., MongoDB) or MQ services (e.g., Redis, Kafka);
- Low-overhead focused system architecture, to avoid adding performance overhead particularly to workloads that run on HPC machines;
- Telemetry data capture (e.g., CPU, GPU, Memory consumption) linked to the application dataflow;
- Highly customizable to multiple use cases, enabling easy toggle between settings (e.g., with/without provenance capture; with/without telemetry and which telemetry type to capture; which adapters or backend services to run with);
- [W3C PROV](https://www.w3.org/TR/prov-overview/) adherence;
Notes:
- Currently implemented data observability adapters:
- MLFlow
- Dask
- TensorBoard
- Python scripts can be easily instrumented via `@decorators` using `@flowcept_task` (for generic Python method) or `@torch_task` (for methods that encapsulate PyTorch model manipulation, such as training or evaluation).
- Currently supported MQ systems:
- Kafka
- Redis
- Currently supported database systems:
- MongoDB
- Lightning Memory-Mapped Database (lightweight file-only database system)
Explore [Jupyter Notebooks](notebooks) and [Examples](examples) for usage.
Refer to [Contributing](CONTRIBUTING.md) for adding new adapters. Note: The term "plugin" in the codebase is synonymous with "adapter," and future updates will standardize terminology.
## 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 .[extra_dep1,extra_dep2]` for the adapters we have implemented, e.g., `pip install .[dask]`.
Currently, the optional dependencies available are:
```
pip install flowcept[mongo] # To install FlowCept with MongoDB
pip install flowcept[mlflow] # To install mlflow's adapter.
pip install flowcept[dask] # To install dask's adapter.
pip install flowcept[tensorboard] # To install tensorboaard's adapter.
pip install flowcept[kafka] # To utilize Kafka as the MQ, instead of Redis.
pip install flowcept[nvidia] # To capture NVIDIA GPU runtime information.
pip install flowcept[analytics] # For extra analytics features.
pip install flowcept[dev] # To install FlowCept's developer 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.
2. Start the MQ System:
To use FlowCept, one needs to start a MQ system `$> make services`. This will start up Redis but see other options in the [deployment](deployment) directory and see [Data Persistence](#data-persistence) notes below.
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 variable `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.
## Installing and Running with Docker
To use containers instead of installing FlowCept's dependencies on your host system, we provide a [Dockerfile](deployment/Dockerfile) alongside a [docker-compose.yml](deployment/compose.yml) for dependent services (e.g., Redis, MongoDB).
#### Notes:
- As seen in the steps below, there are [Makefile](Makefile) commands to build and run the image. Please use them instead of running the Docker commands to build and run the image.
- The Dockerfile builds from a local `miniconda` image, which will be built first using the [build-image.sh](deployment/build-image.sh) script.
- All dependencies for all adapters are installed, increasing build time. Edit the Dockerfile to customize dependencies based on our [pyproject.toml](pyproject.toml) to reduce build time if needed.
#### Steps:
1. Build the Docker image:
```bash
make build
```
2. Start dependent services:
```bash
make services
```
3. Run the image interactively:
```bash
make run
```
4. Optionally, run Unit tests in the container:
```bash
make tests-in-container
```
### 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.
```python
from flowcept import Flowcept, flowcept_task
@flowcept_task
def sum_one(n):
return n + 1
@flowcept_task
def mult_two(n):
return n * 2
with Flowcept(workflow_name='test_workflow'):
n = 3
o1 = sum_one(n)
o2 = mult_two(o1)
print(o2)
print(Flowcept.db.query(filter={"workflow_id": Flowcept.current_workflow_id}))
```
## Data Persistence
FlowCept uses an ephemeral message queue (MQ) with a pub/sub system to flush observed data. For optional data persistence, you can choose between:
- [LMDB](https://lmdb.readthedocs.io/) (default): A lightweight, file-based database requiring no external services (but note it might require `gcc`). Ideal for simple tests or cases needing basic data persistence without query capabilities. Data stored in LMDB can be loaded into tools like Pandas for complex analysis. FlowCept's database API provides methods to export data in LMDB into Pandas DataFrames.
- [MongoDB](https://www.mongodb.com/): A robust, service-based database with advanced query capabilities. Required to use FlowCept's Query API (i.e., `flowcept.Flowcept.db`) to run more complex queries and other features like ML model management or runtime queries (i.e., query while writing). To use MongoDB, initialize the service with `make services-mongo`.
You can use both of them, meaning that the data pruducers will write data into both, none of them, or each of them. All is customizable in the settings file.
If data persistence is disabled, captured data is sent to the MQ without any default consumer subscribing to persist it. In this case, querying the data requires creating a custom consumer to subscribe to the MQ.
However, for querying, FlowCept Database API uses only one at a time. If both are enabled in the settings file, MongoDB will be used. If none is enable, an error is thrown.
Data stored in MongoDB and LMDB are interchangeable. You can switch between them by transferring data from one to the other as needed.
## Performance Tuning for Performance Evaluation
In the settings.yaml file, the following variables might impact interception performance:
```yaml
main_redis:
buffer_size: 50
insertion_buffer_time_secs: 5
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.
## Install AMD GPU Lib
This section is only important if you want to enable GPU runtime data capture and the GPU is from AMD. NVIDIA GPUs don't need this step.
For AMD GPUs, we rely on the official AMD ROCM library to capture GPU data.
Unfortunately, this library is not available as a pypi/conda package, so you must manually install it. See instructions in the link: https://rocm.docs.amd.com/projects/amdsmi/en/latest/
Here is a summary:
1. Install the AMD drivers on the machine (check if they are available already under `/opt/rocm-*`).
2. Suppose it is /opt/rocm-6.2.0. Then, make sure it has a share/amd_smi subdirectory and pyproject.toml or setup.py in it.
3. Copy the amd_smi to your home directory: `cp -r /opt/rocm-6.2.0/share/amd_smi ~`
4. cd ~/amd_smi
5. In your python environment, do a pip install .
Current code is compatible with this version: amdsmi==24.6.2+2b02a07
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.
## Cite us
If you used FlowCept in your research, consider citing our paper.
```
Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability
R. Souza, T. Skluzacek, S. Wilkinson, M. Ziatdinov, and R. da Silva
19th IEEE International Conference on e-Science, 2023.
```
**Bibtex:**
```latex
@inproceedings{souza2023towards,
author = {Souza, Renan and Skluzacek, Tyler J and Wilkinson, Sean R and Ziatdinov, Maxim and da Silva, Rafael Ferreira},
booktitle = {IEEE International Conference on e-Science},
doi = {10.1109/e-Science58273.2023.10254822},
link = {https://doi.org/10.1109/e-Science58273.2023.10254822},
pdf = {https://arxiv.org/pdf/2308.09004.pdf},
title = {Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability},
year = {2023}
}
```
## 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:).
## 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.
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"download_url": "https://files.pythonhosted.org/packages/ed/f6/a9b2752ca76694e530d8f2052e57ec32afbe7bc05be80d8327ca585a8595/flowcept-0.7.5.tar.gz",
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"description": "[](https://github.com/ORNL/flowcept/actions/workflows/create-release-n-publish.yml)\n[](https://pypi.org/project/flowcept)\n[](https://github.com/ORNL/flowcept/actions/workflows/run-tests.yml)\n[](https://github.com/ORNL/flowcept/actions/workflows/run-checks.yml)\n[](LICENSE)\n\n# FlowCept\n\nFlowCept is a runtime data integration system that captures and queries workflow provenance with minimal or no code changes. It unifies data across diverse workflows and tools, enabling integrated analysis and insights, especially in federated environments. Designed for scenarios involving critical data from multiple workflows, FlowCept seamlessly integrates data at runtime, providing a unified view for end-to-end monitoring and analysis and enhanced support for Machine Learning (ML) workflows.\n\nOther capabilities include:\n\n- Automatic multi-workflow provenance data capture;\n- Data observability, enabling minimal intrusion to user workflows;\n- Explicit user workflow instrumentation, if this is preferred over implicit data observability;\n- ML data capture in various levels of details: workflow, model fitting or evaluation task, epoch iteration, layer forwarding;\n- ML model management;\n- Adapter-based, loosely-coupled system architecture, making it easy to plug and play with different data processing systems and backend database (e.g., MongoDB) or MQ services (e.g., Redis, Kafka);\n- Low-overhead focused system architecture, to avoid adding performance overhead particularly to workloads that run on HPC machines;\n- Telemetry data capture (e.g., CPU, GPU, Memory consumption) linked to the application dataflow;\n- Highly customizable to multiple use cases, enabling easy toggle between settings (e.g., with/without provenance capture; with/without telemetry and which telemetry type to capture; which adapters or backend services to run with); \n- [W3C PROV](https://www.w3.org/TR/prov-overview/) adherence;\n \nNotes:\n\n- Currently implemented data observability adapters:\n - MLFlow\n - Dask\n - TensorBoard\n- Python scripts can be easily instrumented via `@decorators` using `@flowcept_task` (for generic Python method) or `@torch_task` (for methods that encapsulate PyTorch model manipulation, such as training or evaluation). \n- Currently supported MQ systems:\n - Kafka\n - Redis\n- Currently supported database systems:\n - MongoDB\n - Lightning Memory-Mapped Database (lightweight file-only database system)\n\nExplore [Jupyter Notebooks](notebooks) and [Examples](examples) for usage.\n\nRefer to [Contributing](CONTRIBUTING.md) for adding new adapters. Note: The term \"plugin\" in the codebase is synonymous with \"adapter,\" and future updates will standardize terminology.\n\n## Install and Setup:\n\n1. Install FlowCept: \n\n`pip install .[all]` in this directory (or `pip install flowcept[all]`) if you want to install all dependencies.\n\nFor 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 .[extra_dep1,extra_dep2]` for the adapters we have implemented, e.g., `pip install .[dask]`.\nCurrently, the optional dependencies available are:\n\n```\npip install flowcept[mongo] # To install FlowCept with MongoDB\npip install flowcept[mlflow] # To install mlflow's adapter.\npip install flowcept[dask] # To install dask's adapter.\npip install flowcept[tensorboard] # To install tensorboaard's adapter.\npip install flowcept[kafka] # To utilize Kafka as the MQ, instead of Redis.\npip install flowcept[nvidia] # To capture NVIDIA GPU runtime information.\npip install flowcept[analytics] # For extra analytics features.\npip install flowcept[dev] # To install FlowCept's developer dependencies.\n```\n\nYou 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.\n \n2. Start the MQ System:\n\nTo use FlowCept, one needs to start a MQ system `$> make services`. This will start up Redis but see other options in the [deployment](deployment) directory and see [Data Persistence](#data-persistence) notes below.\n\n3. 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 variable `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.\n\n4. See the [Jupyter Notebooks](notebooks) and [Examples directory](examples) for utilization examples.\n\n## Installing and Running with Docker\n\nTo use containers instead of installing FlowCept's dependencies on your host system, we provide a [Dockerfile](deployment/Dockerfile) alongside a [docker-compose.yml](deployment/compose.yml) for dependent services (e.g., Redis, MongoDB). \n\n#### Notes: \n- As seen in the steps below, there are [Makefile](Makefile) commands to build and run the image. Please use them instead of running the Docker commands to build and run the image.\n- The Dockerfile builds from a local `miniconda` image, which will be built first using the [build-image.sh](deployment/build-image.sh) script. \n- All dependencies for all adapters are installed, increasing build time. Edit the Dockerfile to customize dependencies based on our [pyproject.toml](pyproject.toml) to reduce build time if needed. \n\n#### Steps:\n\n1. Build the Docker image: \n ```bash\n make build\n ```\n\n2. Start dependent services:\n ```bash\n make services\n ```\n\n3. Run the image interactively:\n ```bash\n make run\n ```\n\n4. Optionally, run Unit tests in the container:\n ```bash\n make tests-in-container\n ```\n\n### Simple Example with Decorators Instrumentation\n\nIn addition to existing adapters to Dask, MLFlow, and others (it's extensible for any system that generates data), FlowCept also offers instrumentation via @decorators. \n\n```python \nfrom flowcept import Flowcept, flowcept_task\n\n@flowcept_task\ndef sum_one(n):\n return n + 1\n\n\n@flowcept_task\ndef mult_two(n):\n return n * 2\n\n\nwith Flowcept(workflow_name='test_workflow'):\n n = 3\n o1 = sum_one(n)\n o2 = mult_two(o1)\n print(o2)\n\nprint(Flowcept.db.query(filter={\"workflow_id\": Flowcept.current_workflow_id}))\n```\n\n## Data Persistence\n\nFlowCept uses an ephemeral message queue (MQ) with a pub/sub system to flush observed data. For optional data persistence, you can choose between:\n\n- [LMDB](https://lmdb.readthedocs.io/) (default): A lightweight, file-based database requiring no external services (but note it might require `gcc`). Ideal for simple tests or cases needing basic data persistence without query capabilities. Data stored in LMDB can be loaded into tools like Pandas for complex analysis. FlowCept's database API provides methods to export data in LMDB into Pandas DataFrames.\n- [MongoDB](https://www.mongodb.com/): A robust, service-based database with advanced query capabilities. Required to use FlowCept's Query API (i.e., `flowcept.Flowcept.db`) to run more complex queries and other features like ML model management or runtime queries (i.e., query while writing). To use MongoDB, initialize the service with `make services-mongo`.\n\nYou can use both of them, meaning that the data pruducers will write data into both, none of them, or each of them. All is customizable in the settings file.\n\nIf data persistence is disabled, captured data is sent to the MQ without any default consumer subscribing to persist it. In this case, querying the data requires creating a custom consumer to subscribe to the MQ.\n\nHowever, for querying, FlowCept Database API uses only one at a time. If both are enabled in the settings file, MongoDB will be used. If none is enable, an error is thrown. \n\nData stored in MongoDB and LMDB are interchangeable. You can switch between them by transferring data from one to the other as needed.\n\n## Performance Tuning for Performance Evaluation\n\nIn the settings.yaml file, the following variables might impact interception performance:\n\n```yaml\nmain_redis:\n buffer_size: 50\n insertion_buffer_time_secs: 5\n\nplugin:\n enrich_messages: false\n```\n\nAnd 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.\n\n## Install AMD GPU Lib\n\nThis section is only important if you want to enable GPU runtime data capture and the GPU is from AMD. NVIDIA GPUs don't need this step.\n\nFor AMD GPUs, we rely on the official AMD ROCM library to capture GPU data.\n\nUnfortunately, this library is not available as a pypi/conda package, so you must manually install it. See instructions in the link: https://rocm.docs.amd.com/projects/amdsmi/en/latest/\n\nHere is a summary:\n\n1. Install the AMD drivers on the machine (check if they are available already under `/opt/rocm-*`).\n2. Suppose it is /opt/rocm-6.2.0. Then, make sure it has a share/amd_smi subdirectory and pyproject.toml or setup.py in it.\n3. Copy the amd_smi to your home directory: `cp -r /opt/rocm-6.2.0/share/amd_smi ~`\n4. cd ~/amd_smi\n5. In your python environment, do a pip install .\n\nCurrent code is compatible with this version: amdsmi==24.6.2+2b02a07\nWhich was installed using Frontier's /opt/rocm-6.2.0/share/amd_smi\n\n## Torch Dependencies\n\nSome 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.\n\n## Cite us\n\nIf you used FlowCept in your research, consider citing our paper.\n\n```\nTowards Lightweight Data Integration using Multi-workflow Provenance and Data Observability\nR. Souza, T. Skluzacek, S. Wilkinson, M. Ziatdinov, and R. da Silva\n19th IEEE International Conference on e-Science, 2023.\n```\n\n**Bibtex:**\n\n```latex\n@inproceedings{souza2023towards, \n author = {Souza, Renan and Skluzacek, Tyler J and Wilkinson, Sean R and Ziatdinov, Maxim and da Silva, Rafael Ferreira},\n booktitle = {IEEE International Conference on e-Science},\n doi = {10.1109/e-Science58273.2023.10254822},\n link = {https://doi.org/10.1109/e-Science58273.2023.10254822},\n pdf = {https://arxiv.org/pdf/2308.09004.pdf},\n title = {Towards Lightweight Data Integration using Multi-workflow Provenance and Data Observability},\n year = {2023}\n}\n\n```\n\n## Disclaimer & Get in Touch\n\nPlease 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:).\n\n## Acknowledgement\n\nThis 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.\n",
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