Foundations Atlas

What it does

Atlas allows you to run, track & evaluate machine learning experiments on your infrastructure. Currently used by 700+ data scientists, Atlas helps run experiments concurrently & supports the usage of preemptible/spot instances, saving you up to 8x in GPU cost

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  TensorFlow 1.02017 · tensorflow.org · ▲823

  Open-source machine learning library by Google

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  TensorPool2025 · tensorpool.dev · ▲192

  The easiest way to use cloud GPUs

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  Atlas – A deployment pipeline platform built on Argo CD2022 · greenops.io · ▲66

  Atlas is an open-source deployment pipeline platform built for cloud-native applications. Atlas allows users to: - Create continuous pipelines across all their environments and clusters - Add custom tasks/tests plugins (Python scripts, K8S manifests, Argo Workflows, environment setup, etc.) - Automatically rollback applications in case of failure or degradation (Atlas watches the application past the scope of a pipeline run to ensure and enforce stability) - Use all existing Argo features Would love to hear all of your feedback and thoughts on this!

• AI
  Atlas – Interactively Visualizing the Loss Landscape of Neural Nets2019 · atls.ml · ▲61
• TC
  TensorDock Core GPU Cloud – GPU servers from $0.29/hr2022 · tensordock.com · ▲147

  Hello HN! I’m Jonathan from TensorDock. After 7 months in beta, we’re finally launching Core Cloud, our platform to deploy GPU virtual machines in as little as 45 seconds! https://www.tensordock.com/product-core Why? Training machine learning workloads at large clouds can be extremely expensive. This left us wondering, “how did cloud ever become more expensive than on-prem?” I’ve seen too many ML startups buy their own hardware. Cheaper dedicated servers with NVIDIA GPUs are not too hard to find, but they lack the functionality and scalability of the big clouds. We thought to…

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  GPU.LAND2021 · vedge.cloud · ▲126

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