Show HN: Ploomber Cloud (YC W22) – run notebooks at scale without infrastructure

[idomi]

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!

You may also reach out to me at ido@ploomber.io.

[indoorskier]

Very nice, and well done on building an active community around it. I like the integration with the cached data, makes it feel like a build system :)

We were unaware of ploomber until now, but have been building meadowrun https://news.ycombinator.com/item?id=31694827 and share many goals with you. So obviously I think this is solving a real problem.

All the best with the cloud launch!

[idomi → indoorskier]

Thanks for sharing! Yeah definitely a problem, we've been trying to build it mainly on the feedbacks we're getting from the open-source solution.

[edublancas]

Hi all! This is Eduardo, Ploomber co-founder. I'm excited to show the HN community what we've been working! We'd love to get your feedback so please give it a try and let us know what you think!

[etaioinshrdlu]

This seems somewhat similar to what Floydhub was doing: https://news.ycombinator.com/item?id=13659914

[idomi → etaioinshrdlu]

You're right to some extent - at least on some of the concepts. We don't focus on a specific ML use case, like computer vision. In addition, we're oriented towards notebooks and this notion allows us to break it into smaller tasks, cache the results and execute in parallel (locally or via this new cloud service). BTW, we tried talking to the founders but couldn't get a hold of them, if you or anyone know them - we'd love to chat!

[mvoodarla]

This looks really cool. Are there ways to orchestrate jobs? Like having one notebooks output trigger another based on some logic? I'm imagining running a bunch of different deep learning models on separate notebooks, or running the same model on different chunks of a piece of data in parallel.

[idomi → mvoodarla]

Yes, you should follow best practices and isolate each job to the smallest task possible (and then reuse components). We have this functionality in 2 flavors, you can define hooks as part of your pipelines(https://docs.ploomber.io/en/latest/api/spec.html#id2), in addition you can define this dependency as part of your jobs DAG (https://docs.ploomber.io/en/latest/get-started/basic-concept...), e.g get the data, clean it, train the model and test it.