All-new high-powered NVIDIA Ampere instances! 🔋

We're pleased to announce a series of new GPU-backed instances available on both Core and Gradient featuring NVIDIA's Ampere microarchitecture!

Introducing all-new Ampere instances! announcement 

Announced in mid-2020, Ampere is the codename for NVIDIA's latest line of GPU accelerator cards. Competition for these cards has been fierce and we're happy to bring you four flavors of Ampere, anchored by the top-of-the-line A100.

Introducing Ampere instances

In addition to the instances listed, we've also introduced 2-way, 4-way, and 8-way configurations for these cards. 

The full table of instances on Paperspace has been updated in the docs. In general, any instance made available on Core will arrive in Gradient shortly thereafter.

Multi-GPU also comes to Windows machines improvement  

One thing you might have noticed already is that multi-GPU instances in Core are no longer exclusive to Linux. You can now spin-up any multi-GPU instance on a Windows machine!

Check out the Paperspace console to get started. 

Model-backed deployments in Gradient Deployments improvement  

We added an important feature to Gradient Deployments: model-backed deployments! 

Gradient Deployments

It's now possible to inject a model at deployment runtime which means Gradient is now able to fetch a model from the Gradient model registry directly. Models can also be referenced from an external S3 bucket.

For more information, read the docs or reach out if you'd like a demo!

State persistence bugs in Gradient Notebooks improvement  

We made substantial improvements to the way that application and cell state is managed in Gradient Notebooks. 

Previously, if you navigated away from a notebook while a cell was running and then returned to the notebook, the cell would sometimes lose its state. We're happy to have implemented a substantial fix to this issue and a number of other issues influencing state management.

If you have feedback for us, please drop us a line!

Autosave, private notebooks, a number of bugfixes, and more! 🧑‍🔧

We've released a number of improvements and bugfixes for Gradient!

Gradient Notebooks now autosave by default improvement

We've improved the autosave functionality of notebooks! Whereas before only .ipynb files would save automatically, we now provide autosave functionality for all filetypes within notebooks.

Notebooks running on free GPU instances can now be private on Pro or Growth subscriptions improvement 

If you're on the Gradient Pro or Growth plan, notebooks that run on Free GPU instances can now be made private.

PyTorch container updated to version 1.10, TensorFlow container updated to 2.6.0 

We've updated both PyTorch and TensorFlow default containers in Gradient Notebooks to their latest stable release versions. The new runtimes are now available in the Gradient console. 

Other improvements

  • Gradient Deployments are now able to pull from models registered in Gradient
  • Overall GPU capacity has increased after addressing an issue related to read-only filesystems used by Gradient Notebooks

Bugfixes

  • We fixed a bug in the notebook create menu that sometimes caused the Workspace URL field not to update when selecting a new runtime
  • We fixed a bug in notebooks that sometimes caused deleted files to linger in the file management pane
  • We fixed a bug in notebooks that caused an empty file to be added to new directories
  • We fixed a bug that sometimes generated duplicate and triplicate notifications when switching teams


Introducing Workflows and Deployments! ⚡️

We're pleased to announce Gradient Workflows and Gradient Deployments! Workflows and Deployments bring production capabilities to all Gradient users.

Make Anything with Gradient. Yes. Anything. announcement 

To celebrate this launch, we've released a brand new commercial about Gradient! 


Introducing Gradient Workflows announcement 

Gradient Workflows is a simple way to automate machine learning tasks. Workflows allows you to build complex, real-world machine learning projects. 

With Workflows you can define arbitrarily complex pipelines for Gradient to orchestrate on your behalf.

To learn more about Workflows, check out the new site


Gradient Deployments announcement 

Gradient Deployments provides effortless model serving. Deployments allow you to host a trained model on an endpoint for consumption by your application.

Deployments are powered by the same high-performance GPU instances that power the rest of Gradient.

To learn more about Deployments, check out the new site!

New instance types across Core 🐣

We've added RTX4000 and RTX5000 instances to CA1 and NY2 regions, as well as multi-GPU instances for Linux, and new low-cost CPU-only instances for Windows!

Introducing RTX4000 and RTX5000 announcement 

We're pleased to announce RTX4000 and RTX5000 instances are now generally available! 

These cards are based on NVIDIA's Turing microarchitecture and are more than 40% faster than their Pascal series counterparts.

Try RTX

Multi-GPU instances now available on Linux! announcement 

You can now access multi-GPU instances across all regions when selecting Linux as your OS!

P5000x2 instances start at $1.56/hr while P6000x2 instances start at $2.20/hr. 

Try multi-GPU


Low-cost Windows instances now available improvement 

We solidified CPU-only offerings for Windows instances and now provide C5 - C10 instances at an affordable hourly rate.

For just $0.08/hr you can run a full Core VM in the cloud!


Other Improvements

  • We improved our backend error monitoring capabilities giving us substantially more insight into performance degradation and remediation
  • We accelerated our equipment purchasing plan to provide new hardware faster to meet demand
  • We re-wrote some business logic around storage capacity to be able to deliver much faster upgrades


Open a terminal in the Gradient IDE 👾

We've added a new terminal interface to the Gradient IDE! 

Gradient Notebooks now offer terminal access announcement 

Previously you needed to open JupyterLab to access a unix terminal -- but now you can do so directly from the Gradient IDE! 

Just look for the terminal icon in the left sidebar of any Gradient Notebook. 

Terminals are great for managing files, installing or uninstalling libraries and packages, and for anything else that you'd like to do quickly without needing to run notebook cells.

Terminals are available today for G1 and higher subscribers. 


Other Improvements

  • We shipped a basic version history viewer to Gradient Notebooks. Look for the History button in the left sidebar of any notebook
  • We shipped a backend improvement that decreased notebook load times across all regions by ~1 second


Bugfixes

  • We fixed a bug that sometimes produced excessive line breaks in cell outputs
  • We now focus files correctly when creating/deleting in the IDE file manager
  • We fixed a bug that sometimes caused stopped notebooks to show empty previews 

File upload from the Gradient IDE ☁️

We've added file uploads as well as a number of useful improvements and bugfixes to Gradient Notebooks.

File uploader arrives in Gradient Notebooks announcement 

You can now upload files directly from the Gradient Notebook IDE file manager. To use the uploader select Upload File from the sidebar. Upload progress is visible at the top of the sidebar and uploaded files are available for immediate use. 


Other Improvements

  • We've added a prominent Start Instance button to stopped notebooks to make it easier to pick up where you left off when returning to a notebook
  • We've increased the visibility of the Stop Instance button
  • We now provide CPU, GPU, and RAM system metrics in the notebook footer
  • We added a link to the Billing  view from the main menu which makes it easier to find account-related information


Bugfixes

  • We fixed a visual bug that caused notebook file delete confirmations to render as a button rather than as a text block
  • We fixed an issue where files within a notebook would sometimes not have syntax highlighting
  • We made it more difficult to run a container that requires a GPU on a CPU instances
  • We fixed a bug that was causing some users to accidentally create multiple notebooks
  • We fixed a bug that occasionally caused some notebooks to get stuck in a saving state 


For more info on this release be sure to check out the blogpost

Notebook enhancements 📚

We've added improved file management functionality to the IDE!

Better file management in Notebooks improvement 

Now you can add files and folders directly from the IDE.

You can also download files using the file manager.

As more file management enhancements come to the IDE we recommend you swap over to JupyterLab if you need something like a terminal with root access.

Other Improvements

  • We now send GPU utilization metrics to the status bar (bottom left) in the notebook IDE
  • When creating a new notebook with a specified workspace, we now load a preview of the files in the IDE while the notebook initializes
  • When you use the Share feature in a notebook, we now include in the link the specific file you are sharing

Bugfixes

  • We fixed a bug that sometimes caused notebooks nested in folders to have an incorrect working directory which caused issues when using relative paths
  • We fixed a bug that sometimes caused long cell outputs to degrade notebook performance
  • We fixed a bug that caused some notebooks to restart after being manually stopped by the user
  • We fixed a bug that occasionally prevented large notebooks from loading offline

Introducing the all-new Gradient Notebooks IDE

This week we released a complete rewrite of Gradient Notebooks!

This is an important release for Gradient. Right away you'll notice that we've brought notebooks up to the new Paperspace design standards while taking big steps to bring simple and scalable ML infrastructure into the notebook itself.

In addition to the new compute instance selection menu, we've also integrated system metrics as part of a status bar and a new file browser which will soon unlock new capabilities around data management.

Last but not least, the notebook experience is now substantially more performant across the board -- from spin-up to teardown to rendering and executing cells. 

There's plenty more to unpack so head over to the blogpost to see what else has changed!

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