soopervisor VS pipelines

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!

Kubeflow Pipelines comes with a pre-defined KFServing component which can be imported from the GitHub repo and reused across the pipelines without the need to define it every time. KFServing is Kubeflow's solution for "productionizing" your ML models and works with a lot of frameworks like Tensorflow, sci-kit, and PyTorch among others.

You can run the notebook from the dashboard and create the pipeline. Please note, in Kubeflow v1.2, there is an issue causing RBAC: permission denied error while connecting to the pipeline. This will be fixed in v1.3 and you can read more about the issue here. As a workaround, you need to create Istio ServiceRoleBinding and EnvoyFilter to add an identity in the header. Refer this gist for the patch.