covalent
dagster
| covalent | dagster | |
|---|---|---|
| 4 | 46 | |
| 692 | 10,274 | |
| 2.5% | 2.7% | |
| 8.6 | 10.0 | |
| 7 days ago | 2 days ago | |
| Python | Python | |
| Apache License 2.0 | Apache License 2.0 |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
covalent
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Remote execution of code
Pretty interesting request, if SSH is not used, i would try using something like dask which uses tcp to connect and execute assuming your workers are in another machine.I also think something like covalent can be used to extend your own custom plugin in their ecosystem to connect how you want. We have a very custom private plugin written on top of covalent's to have a custom protocol to connect our central on-prem GPU machines to our local laptops that is rpc based, mostly for high performance as well as some mandate security from where the GPU machines are. Once done it is pretty much something like
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Prefect alternatives meant for Slurm (HPC)
Does anyone here have any suggestions of alternatives tailored for Slurm on HPC? I know Covalent is one option, but I'm curious about others as well. Ideally the platform should be Pythonic, have a GUI, and be reasonably active/well-maintained.
- Show HN: Covalent – distributed computing for ML, HPC and Quantum (open source)
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Your strategies for offloading computation
Came across this new tool exactly for this - https://github.com/AgnostiqHQ/covalent
dagster
- Experience with Dagster.io?
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Dagster tutorials
My recommendation is to continue on with the tutorial, then look at one of the larger example projects especially the ones named “project_”, and you should understand most of it. Of what you don't understand and you're curious about, look into the relevant concept page for the functions in the docs.
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The Dagster Master Plan
I found this example that helped me - https://github.com/dagster-io/dagster/tree/master/examples/project_fully_featured/project_fully_featured
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What are some open-source ML pipeline managers that are easy to use?
I would recommend the following: - https://www.mage.ai/ - https://dagster.io/ - https://www.prefect.io/ - https://metaflow.org/ - https://zenml.io/home
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The Why and How of Dagster User Code Deployment Automation
In Helm terms: there are 2 charts, namely the system: dagster/dagster (values.yaml), and the user code: dagster/dagster-user-deployments (values.yaml). Note that you have to set dagster-user-deployments.enabled: true in the dagster/dagster values-yaml to enable this.
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Best Orchestration Tool to run dbt projects?
Dagster seemed really cool when I looked into it as an alternative to airflow. I especially like the software defined assets and built-in lineage which I haven't seen in any other tool. However it seems it does not support RBAC which is a pretty big issue if you want a self-service type of architecture, see https://github.com/dagster-io/dagster/issues/2219. It does seem like it's available in their hosted version, but I wanted to run it myself on k8s.
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dbt Cloud Alternatives?
Dagster? https://dagster.io
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What's the best thing/library you learned this year ?
One that I haven't seen on here yet: dagster
- Anyone have an example of a project where a handful of the more popular Python tools are used? (E.g. airbyte, airflow, dbt, and pandas)
- Can we take a moment to appreciate how much of dataengineering is open source?
What are some alternatives?
SmartSim - SmartSim Infrastructure Library.
Prefect - The easiest way to build, run, and monitor data pipelines at scale.
kestra - Infinitely scalable, event-driven, language-agnostic orchestration and scheduling platform to manage millions of workflows declaratively in code.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
cadence-python - Python framework for Cadence Workflow Service
Mage - 🧙 The modern replacement for Airflow. Mage is an open-source data pipeline tool for transforming and integrating data. https://github.com/mage-ai/mage-ai
mlnotify - 🔔 No need to keep checking your training - just one import line and you'll know the second it's done.
airbyte - The leading data integration platform for ETL / ELT data pipelines from APIs, databases & files to data warehouses, data lakes & data lakehouses. Both self-hosted and Cloud-hosted.
bytehub - ByteHub: making feature stores simple
MLflow - Open source platform for the machine learning lifecycle
streetdensityai - This YoloV5 based model is fit to detect people and different types of land vehicles, and displaying their density on a fitted map, according to their coordinates and detected labels.
meltano