dstack
troposphere
dstack | troposphere | |
---|---|---|
17 | 18 | |
1,131 | 4,908 | |
6.9% | 0.3% | |
9.8 | 9.0 | |
2 days ago | 7 days ago | |
Python | Python | |
Mozilla Public License 2.0 | BSD 2-clause "Simplified" License |
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.
dstack
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Pyinfra: Automate Infrastructure Using Python
We build a similar tool except we focus on AI workloads. Also support on-prem clusters now in addition to GPU clouds. https://github.com/dstackai/dstack
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Show HN: Open-source alternative to HashiCorp/IBM Vault
Not exactly this, but something related. At https://github.com/dstackai/dstack, we build an alternative to K8S for AI infra.
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Ask HN: How does deploying a fine-tuned model work
You can use https://github.com/dstackai/dstack to deploy your model to the most affordable GPU clouds. It supports auto-scaling and other features.
Disclaimer: Iām the creator of dstack.
- FLaNK Stack Weekly 19 Feb 2024
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Show HN: I Built an Open Source API with Insanely Fast Whisper and Fly GPUs
Great job on the project! It looks fantastic. Thanks to your post, I discovered Fly's GPUs. We are currently developing a platform called https://github.com/dstackai/dstack that enables users to run any model on any cloud. I am curious if it would be possible to add support for Fly.io as well. If you are interested in collaborating on this, please let me know!
- Show HN: Dstack ā an open-source engine for running GPU workloads
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[P] I built a tool to compare cloud GPUs. How should I improve it?
I also noticed that the creator of this app, dstack, is affiliated with Tensordock, the top results for most if not all queries. If that's the case, perhaps a direct link to the cheapest machine could be provided? I haven't used Tensordock, so I don't know if this is mechanically possible.
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Running dev environments and ML tasks cost-effectively in any cloud
Here's the repository with all the important links, including documentation, examples, and more: https://github.com/dstackai/dstack
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Dstack Hub
Hey everyone, I'm happy to release dstack Hub, an open-source tool that helps teams manage their ML workflows more effectively without vendor lock-in.
dstack Hub extend dstack [1] with workflow scheduling capabilities and user management. Here's how it works: run dstack Hub via Docker, use its UI to configure projects and cloud credentials, then pass the URL and personal token to the dstack CLI. Now, you can run workflows through the CLI and Hub will orchestrate them in the cloud on your behalf.
This is a beta release and we plan to continuously improve it. We'd love to hear your feedback and answer any questions!
[1] https://github.com/dstackai/dstack
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Running Stable Diffusion Locally & in Cloud with Diffusers & dstack
To help you overcome this challenge, we have written an article to guide you through the simple steps of using both diffusers and dstack to generate images from prompts, both locally and in the cloud, using a simple example.
troposphere
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Pyinfra: Automate Infrastructure Using Python
Seems like an interesting generalized mix of something like https://github.com/cloudtools/troposphere and Ansible from a glance.
The value add would be unifying provisioning and configuration management in a Python-y experience? The lifecycle of each is distinct and that's traditionally where the headaches of using a single tool for both has come in
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AWS Predictions for 2024
Under the IaC category, in July 2023, AWS added loops to CloudFormation, finally ticking a box the community has been asking for since troposphere. I suspect that, in combination with the Terraform licensing changes, it may keep people using CloudFormation for a while longer.
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Journey of creating a new AWS CloudFormation resource
Because ECS Compose-X uses Troposphere, I was able to create a very light and simple python library(https://github.com/JohnPreston/troposphere-awscommunity-applicationautoscaling-scheduledaction) to distribute the resource for other Troposphere users to re-use.
- What are some of your favorite projects to support on GitHub?
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Terraform vs. Cloudformation for an all-AWS Environment in 2023?
Written in house, but the library troposphere is the primary component of how it is built. Example stacks are here.
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How proficient should Solution Architects be at writing code?
I am kind of going off topic here, but isn't the point of being an SA to be created with code services to deliver solutions at scale that are cost-effective? How in the hell can you do that when you can't write a simple Python template that generates code at 50 times the rate you can manually? How can you ever be expected to deploy a serverless solution if you can't write any code yourself? There has to be some level of proficiency there.
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Terraform should have remained stateless
Wouldn't using troposphere[1] be easier?
[1] https://github.com/cloudtools/troposphere
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Hosting your blog on AWS
You might have seen some tutorials on how to set up S3 buckets using the AWS Console. This works fine, but I'm a firm believer of managing your resources with code. I've chosen the native solution of AWS, called AWS CloudFormation. This makes it easier to reproduce the setup if I ever need to tear it down of move it to another account or region. Below is the full CloudFormation template, I've used a framework called Troposphere, a Python library that creates CloudFormation.
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Alert: Cloud Software Startup Hashicorp Files For IPO
For CF for example I no longer write template in yaml or shudders json, and instead I use troposphere.
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AWS pros out here, how can someone get good at CloudFormation ?
refer : https://github.com/cloudtools/troposphere
What are some alternatives?
msdocs-python-django-azure-container-apps - Python web app using Django that can be deployed to Azure Container Apps.
terraform - Terraform enables you to safely and predictably create, change, and improve infrastructure. It is a source-available tool that codifies APIs into declarative configuration files that can be shared amongst team members, treated as code, edited, reviewed, and versioned.
dstack-examples - A collection of examples demonstrating how to use dstack
aws-cloudformation-coverage-roadmap - The AWS CloudFormation Public Coverage Roadmap
zenml - ZenML š: Build portable, production-ready MLOps pipelines. https://zenml.io.
gohugo-theme-ananke - Ananke: A theme for Hugo Sites
flyte - Scalable and flexible workflow orchestration platform that seamlessly unifies data, ML and analytics stacks.
aws-cli - Universal Command Line Interface for Amazon Web Services
lambdapi - Serverless runtime environment tailored for code produced by LLMs. Automatic API generation from your code, support for multiple programming languages, and integrated file and database storage solutions.
dhall-kubernetes - Typecheck, template and modularize your Kubernetes definitions with Dhall
metaflow - :rocket: Build and manage real-life ML, AI, and data science projects with ease!
gitlab-ci-python-library