The product is different than other Google-backed notebook options such as Kaggle Notebooks or Colab in that these notebooks are backed by specific (and potentially more powerful than the P100 you get on Kaggle or the K80 from Colab) GCP instances. Deploy – Prediction, AutoML Vision Edge, TensorFlow EnterpriseĪI Project Notebooks are in Google's parlance part of the "Build" step.Validate – AI Explanations, What-If Tool, Vizier.Prepare – BigQuery, Cloud Storage, Data Labeling Service.Google's effort to provide a full lifecycle of software tools for machine learning is called AI Platform.ĪI platform is billed as an "end-to-end machine learning life cycle" and contains the following components: In-depth look at Cloud AI Notebooks on GCP On the downside, AI Platform Notebooks requires a lot of setup time, requires GCP instances to fund notebooks, and has some confusing interface quirks that make it difficult to get up and running quickly – and even to accomplish some basic tasks. Google AI Platform Notebooks are enterprise-grade notebooks best suited for those with compliance requirements, those with a need to ingest data from GCP sources like BigQuery, and those who are already in the GCP ecosystem and can take advantage of existing compute instances. Console view of a Paperspace Gradient notebook running the latest version of Fast.ai's Practical Deep Learning for Coders Paperspace Gradient notebooks offer some of the professional appeal of Google AI Platform notebooks (like powerful GPU instances, team collaboration, and building from your own container) but with many of the usability features that Kaggle Kernels and Google Colab users enjoy – like being able to startup a notebook in a few seconds and invite a collaborator with the press of a button. Paperspace Gradient notebooks, which were introduced in early 2018, are already among the most popular cloud notebooks, with the product officially recommended by Fast.ai as a cloud notebook provider. Paperspace is a young company compared to Google but boasts nearly 500,000 cloud GPU users across three data center regions. Some of the other differences among Google notebook products are as follows: Kaggle Kernels meanwhile is the Kaggle community's data-science centric version of a "light" JupyterLab-style IDE that also supports R.Īlthough Colab and Kaggle Kernels have their tradeoffs, AI Notebooks stands alone as the only "full" version of JupyterLab that Google offers in the cloud. Google Colab, meanwhile is a "light" version of JupyterLab commonly used as a scratchpad for ML engineers doing exploratory work and for sharing the latest libraries and tools with collaborators and the public. GCP AI Notebooks (today's comparison) are geared toward enterprise clients who need a full JupyterLab instance hosted in the cloud (on GCP) with enterprise features like role-based access control and compliance guarantees. AI Notebooks are part of Google GCP's AI Project – and are a different product entirely than Google Colab or Kaggle Notebooks To be clear, the target of our comparison today is Google Cloud Platform's AI Notebooks product – not Kaggle Kernels or Google Colab – although we will dive deeper into those products at a later date. Google owns and operates a large number of companies in the machine learning space. Today we'll be looking at AI Platform Notebooks – a product that competes directly with enterprise notebooks from other public clouds such as Azure's Machine Learning Notebooks and AWS's SageMaker notebooks – and we'll be comparing it to Paperspace Gradient, a product that competes on both usability and power. Hardware support: Nvidia GeForce 400 series and newer, AMD Radeon HD 5000 Series and newer (FP64 shaders implemented by emulation on some TeraScale GPUs), Intel HD Graphics in Intel Broadwell processors and newer (Linux Mesa: Haswell and newer), Tegra K1, and Tegra X1.Google Cloud Platform offers a set of machine learning tools called AI Platform, which includes tools for labeling data, creating pipelines (via Kubeflow), running jobs, deploying models, and creating shareable cloud-based notebooks. So i gues requirement of OpenGl 4.5, and according to wikipedia: GTX 4xx< and HD5xxx< It’s not limited to DX11 either: DX12 and OpenGL 4.5 are also supported. So VXGI in general and VXAO in particular can work on all DX11 class GPUs, including ones made by NVIDIA competitors, but Maxwell GPUs deliver the best performance. Maxwell does have some useful hardware features, but the only one relevant to VXAO is pass-through geometry shaders, which improve voxelization performance by approximately 30%, and they can be safely replaced with regular geometry shaders. Isn’t VGI as well as VXAO only aviable in 900 nVidia series and up?įinally, if you saw the original announcement of VXGI at Maxwell launch, you may think it works only on Maxwell.
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