soopervisor
elyra
Our great sponsors
soopervisor | elyra | |
---|---|---|
3 | 2 | |
42 | 1,773 | |
- | 1.7% | |
5.0 | 6.4 | |
3 months ago | 7 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.
soopervisor
- Ploomber Cloud - Parametrizing and running notebooks in the cloud in parallel
-
Show HN: Ploomber Cloud (YC W22) – run notebooks at scale without infrastructure
Hi, we’re Ido & Eduardo, the founders of Ploomber. We’re launching Ploomber Cloud today, a service that allows data scientists to scale their work from their laptops to the cloud.
Our open-source users (https://github.com/ploomber/ploomber) usually start their work on their laptops; however, often, their local environment falls short, and they need more resources. Typical use cases run out of memory or optimize models to squeeze out the best performance. Ploomber Cloud eases this transition by allowing users to quickly move their existing projects into the cloud without extra configurations. Furthermore, users can request custom resources for specific tasks (vCPUs, GPUs, RAM).
Both of us experienced this challenge firsthand. Analysis usually starts in a local notebook or script, and whenever we wanted to run our code on a larger infrastructure we had to refactor the code (i.e. rewrite our notebooks using Kubeflow’s SDK) and add a bunch of cloud configurations. Ploomber Cloud is a lot simpler, if your notebook or script runs locally, you can run it in the cloud with no code changes and no extra configuration. Furthermore, you can go back and forth between your local/interactive environment and the cloud.
We built Ploomber Cloud on top of AWS. Users only need to declare their dependencies via a requirements.txt file, and Ploomber Cloud will take care of making the Docker image and storing it on ECR. Part of this implementation is open-source and available at: https://github.com/ploomber/soopervisor
Once the Docker image is ready, we spin up EC2 instances to run the user’s pipeline distributively (for example, to run hundreds of ML experiments in parallel) and store the results in S3. Users can monitor execution through the logs and download artifacts. If source code hasn’t changed for a given pipeline task, we use cached artifacts and skip redundant computations, severely cutting each run's cost, especially for pipelines that require GPUs.
Users can sign up to Ploomber Cloud for free and get started quickly. We made a significant effort to simplify the experience (https://docs.ploomber.io/en/latest/cloud/index.html). There are three plans (https://ploomber.io/pricing/): the first is the Community plan, which is free with limited computing. The Teams plan has a flat $50 monthly and usage-based billing, and the Enterprise plan includes SLAs and custom pricing.
We’re thrilled to share Ploomber Cloud with you! So if you’re a data scientist who has experienced these endless cycles of getting a machine and going through an ops team, an ML engineer who helps data scientists scale their work, or you have any feedback, please share your thoughts! We love discussing these problems since exchanging ideas sparks exciting discussions and brings our attention to issues we haven’t considered before!
- Export Ploomber pipelines to Kubernetes (Argo), Airflow, AWS Batch, SLURM, and Kubeflow
elyra
-
Introducing Elyra pipelines with custom component support
The Elyra open source project for JupyterLab aims to simplify common data science tasks. Its most popular feature is the Visual Pipeline Editor, which is used to create pipelines without the need for coding. You can run these pipelines in JupyterLab or on Kubeflow Pipelines or Apache Airflow.
-
What's new in Elyra 2.0
During the past year we have received many suggestions and feedback from users like you. We appreciate your input and encourage you to stay involved. Open GitHub issues, reach out on gitter, post in our new forum, or join our weekly community meeting.
What are some alternatives?
couler - Unified Interface for Constructing and Managing Workflows on different workflow engines, such as Argo Workflows, Tekton Pipelines, and Apache Airflow.
Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows
kfserving - Standardized Serverless ML Inference Platform on Kubernetes [Moved to: https://github.com/kserve/kserve]
sagemaker-run-notebook - Tools to run Jupyter notebooks as jobs in Amazon SageMaker - ad hoc, on a schedule, or in response to events
pipelines - Machine Learning Pipelines for Kubeflow
argo - Workflow Engine for Kubernetes
jupyterlab-lsp - Coding assistance for JupyterLab (code navigation + hover suggestions + linters + autocompletion + rename) using Language Server Protocol
kserve - Standardized Serverless ML Inference Platform on Kubernetes
ipyflow - A reactive Python kernel for Jupyter notebooks.
ploomber - The fastest ⚡️ way to build data pipelines. Develop iteratively, deploy anywhere. ☁️
airflow-docker - Source code of the Apache Airflow Tutorial for Beginners on YouTube Channel Coder2j (https://www.youtube.com/c/coder2j)