Compute Limits are now easier to use

We improved Compute Limit management for team administrators.  Compute Limits assist teams in monitoring and restricting monthly compute spend by providing fine-grained control over compute usage.  You can now create and edit email alerts and absolute maxes for both teams and team members from the billing page.  Administrators will receive an email when an alert or max amount is reached, and users will be notified in-app if they are blocked from accruing additional compute.  We hope these changes will give administrators more insight and control over their monthly spend.

For more information about Compute Limits, please visit our documentation.

Fixes & Improvements

  • Added the ability to edit team names
  • Improved invoice clarity by adding a section to represent Gradient storage costs
  • Improved visibility into Core machine status by more accurately rendering real-time machine state
  • Fixed a bug where some users could not scroll on streaming Core machines
  • Fixed a bug where non-US countries were not selectable during signup onboarding

Stable Diffusion on Notebooks

We just published a two part blog on getting stable diffusion up and running with Gradient notebooks using Dreambooth. In part 1, we walk through each of the steps for creating a Dreambooth concept from scratch within a Gradient Notebook, generated novel images from inputted prompts, and showed how to export the concept as a model checkpoint. In part 2, we show how to train textual inversion for Stable Diffusion, and use it to generate samples that accurately represent the features of the training images using control over the prompt.

To make it easier for our free users to take advantage of the platform, we have also released the Stable Diffusion models as Public Datasets. These can be mounted in any Gradient Notebook, and removes the need to download the files from HuggingFace each time you restart the notebook. Furthermore, these files will not count toward the storage limits for Free GPU users, so they will no longer be limited by storage space. Be sure to try out the new process!

Deployments Autoscaling

We’ve added the ability to autoscale your Gradient deployments by adding scaling criteria to the spec document. You can autoscale the deployment based on specific metrics including CPU utilization, memory utilization and # of requests. Documentation on how to get started can be found here.

Additionally, the spec has been updated with an enabled flag both for the deployment as a whole and the autoscaling feature. This can be used to turn the deployment and feature on and off. Previously, you had to go into the spec and change the number of replicas to 0 to turn a deployment off.

Activity Log

To track autoscaling events, deployment updates, and deployment start/stops we also added an activity log which can be viewed from the Activity Log tab in the project view.

Fixes and Improvements

  • Added email notifications for when Gradient Deployment do not properly get provisioned
  • Updated the Nvidia RAPIDS container to RAPIDS 22.10
  • Fixed a few documentation links in the console that were taking users to stale urls
  • Upgraded our database to support enhanced metrics to track network and database performance
  • Upgraded the Nvidia templates to support Nvidia 510 drivers
  • Fixed a bug where deployments would sometimes get deleted when team compute limits were hit
  • Fixed a bug that was preventing private S3 buckets from mounting in Gradient notebooks
  • Improved the container caching process allowing more frequent updates to notebook runtimes

Fixes and Improvements

Over the past few weeks, we've shipped a number of improvements to improve your product experience.

  • Improved private network performance and stability in AMS1: A major upgrade has been completed for the network infrastructure in AM1 resulting in increased performance and stability of the network.
  • Improved reliability of machine starts: Starting machines will now encounter fewer issues due to improved automatic remediation of event errors.
  • Improved availability of GPUs: Resolved an issue where specific machine types GPU availability were lower than expected.
  • Prevent users from accidentally creating machines from deleted custom templates: There was an issue that allowed users to create machines from deleted templates resulting in inaccessible machines. Users will now see an error message if they try to create a machine from a deleted template.
  • Improved management of inaccessible machines: Users can now shut down and deactivate machines that are inaccessible from the list views and details view.
  • All new Linux machines have a dynamic public IP by default: A public IP is currently required to access a Linux machine except on a private network. By default, a newly created machine will have a dynamic public IP assigned.
  • Improved summary of billing charges on machine create: We have improved the machine create billing summary to better reflect the total cost for creating and running a machine. Prices are broken down into monthly and active charges to differentiate the costs of running machines vs having machines active.
  • Improved network performance and reliability: We’ve made a change to our QOS policy that now results in greater network availability and reliability for all machines.

Gradient Models are easier to upload

We’ve enhanced the Gradient model upload process through the console and CLI. You can now track progress of the model upload, bring in nested directories and more gracefully handle upload errors and aborts.

Previously, the model upload dialogue would not show you progress and allowed you to close it before the model would complete. This could cause upload failures mid-process. This now lets you know exactly what is being uploaded!

We have also parallelized large uploads by splitting them into chunks, making better use of bandwidth and drastically reducing the amount of time it takes for an upload to finish.

Lastly, the CLI prevents users from uploading with an old version of the CLI which was previously breaking the ability to get data into Gradient.

Fixes & Improvements

  • Fixed a bug causing the workflows status pagination to limit itself to 2 pages
  • Fixed a bug that did not allow you to rename files in the Gradient Notebook file manager
  • Fixed a bug causing readiness errors in notebooks
  • Updated the Gradient Notebooks Rapids runtime to RAPIDS 20.08
  • Fixed a bug that caused the edit button in Gradient Deployment Specs to be stuck in a perpetual load
  • Added the ability to update certain Notebooks to the newest version of our Gradient container
  • Fixed a bug that allowed users to choose an IPU on the Fast.ai runtime
  • Added two shortcuts to Gradient Notebooks: Markdown (m) and code(y)
  • Improvements to the project page to more easily create notebooks and deployments

Autosave, revamped logs, file manager improvements, and more! πŸ’Ύ

We've added a number of new features to Gradient Notebooks, including autosave, improved access to logs, and a host of file management features.

Let's dive in!

Enable autosave in Gradient Notebooks! improvement 

Autosave is now available in Gradient Notebooks. Enable autosave with the button in the bottom right of the IDE.

When autosave is enabled, your notebook will save automatically every thirty seconds.

New panel in the IDE for logs improvement 

Logs have received a refreshed look and feel! We've moved the logs pane from the sidebar to the main window in the IDE. We think this provides a much more comfortable amount of real estate to scroll through system logs. 

Toggle the log pane using the button in the bottom left corner of the IDE above the IDE host metrics.

New file manager CRUD capabilities are now available offline! improvement 

We've added a number of new CRUD capabilities to the IDE -- and not just that but these capabilities are available when your machine is offline! 

The list of upgrades includes uploading and deleting multiple files and/or folders as well as drag-and-drop capabilities for moving multiple files and/or folders.

In addition, we've added the ability to duplicate files and/or folders.

Bugfixes fix 

  • We fixed a bug that sometimes caused the terminal window pane to resize incorrectly
  • We fixed a bug that prevented access to secrets from within a notebook
  • We fixed an issue with font rendering in the terminal


Introducing Gradient Datasets and an all-new Gradient Notebooks IDE! πŸ§‘β€πŸš€

We're excited to announce the arrival of a new and improved Gradient Notebooks experience -- now with Gradient Datasets, native support for interactive widgets, improved cell, file, and kernel management experiences, and much more!

Highlights are below but be sure to read the blogpost for the most detailed explanation of this release.

Introducing Gradient Datasets announcement 

We're pleased to announce the arrival of Gradient Datasets! Datasets make it easy to generate portable datasets to use across Gradient teams and resources.

You can now create and mount datasets for easy use within a notebook and take advantage of a number of public datasets made available by the Paperspace team. 

Check out the blogpost for a full list of public datasets. 

Support for interactive widgets improvement 

Gradient Notebooks now provides first-class support for ipywidgets! This includes sliders, checkboxes, multiselects, TensorFlow and PyTorch dataloaders, and more! 

The full list of supported widgets is available here

Cell management improvements improvement 

We've brought over to notebooks a number of cell management operations from JupyterLab such as insert, join, split, and more! 

We'll be continuing to add cell management capabilities to the new IDE over time. 

File management improvements improvement 

In addition to cell management improvements, we've also made it easier to manage and manipulate files within the notebook file browser. 

The file manager now behaves as expected when dragging and dropping files and folders.

Kernel management controls improvement  

We've improved the controls for starting and stopping individual kernels from within a notebook. 

It's now easy to assign a notebook file to a particular kernel and to restart and stop individual kernels!

Bonus for Pro/Growth users: terminal updates! improvement 

For users on the Pro or Growth plan, we've enabled split-screen terminals! 

Now it's possible to work in a terminal without leaving a notebook file!

More improvements improvement

  • We improved resource allocation and decreased notebook pending timeouts for notebooks which means higher availability of notebook machines and fewer stalled notebook starts
  • We improved the refresh rate of notebook logs and improved notebook metrics to display more useful information
  • We updated the two most popular runtime tiles in Gradient Notebooks: PyTorch and TensorFlow! The latest distribution takes advantage ofPyTorch 1.11 and TensorFlow 2.7.0.

Bugfixes

  • We fixed an issue that sometimes caused users to be signed out of the Paperspace console when swapping between tabs or sessions
  • We fixed an issue that sometimes caused users with multiple teams to view incorrect resource data
  • We fixed an issue that sometimes caused deployment items to expire and be deleted on teams with a large number workflows and deployments read the blogpost

Introducing 100% self-serve private networks, shared drives, and public IPs! πŸ„

We just made a number of improvements to help Core power users self-serve Paperspace resources. 

With this update, you can now create private networks, spin-up shared drives, and assign public IP addresses to any machines that you manage!

Self-serve private networks improvement 

First up, we're pleased to bring private networks to all Core users. When you create a private network, you create a shared resource pool for your team that is isolated from every other machine and customer on Paperspace.

Once you create a private network, you can add machines and drives to the network to share with team members.

Be sure to read the docs for more info!

Self-serve private storage improvement 

Next up, we've made it easy to share a drive among multiple Core machines. After you create a shared network, you can spin-up a shared drive and attach it to the network in a matter of seconds!

For more information on shared drives, check out the docs!

Self-serve public IPs improvement 

Finally, we've made it a lot easier to claim and assign public IP addresses! While previously it was possible to assign a machine to a public IP after the machine was created, we've now streamlined the process to make it more visible at the team level.

To claim a public IP, simply visit the Public IPs tab in the console and claim the address. (Note that Public IPs are region-specific.)

To assign the new public IP to a machine, all we need to do is use the Assign feature to select the machine we want to expose to the public web. That's all there is to it!

If you get stuck please read the docs to learn more or reach out to us with any questions. 

Bugfixes fix 

  • We resolved a troublesome issue that resulted in erroneous invoices being sent to a small number of users
  • We decreased errors related to over-provisioning on the Paperspace public cluster
  • We improved the strategy for guaranteeing hot nodes and faster startup times on the Paperspace public cluster
  • We fixed a number of small issues related to Windows 10 BYOL machines

Autosave, private notebooks, a number of bugfixes, and more! πŸ§‘β€πŸ”§

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


Accelerated Gradient Notebook startup and teardown πŸš…

Gradient Notebooks just received a substantial speed boost during startup and teardown! As a result, you should experience snappier notebook starts and stops.

Notebooks now start and stop much faster improvement 

We've streamlined the architecture behind Gradient Notebooks to enable this improvement.  

What you need to know 

You will now need to install dependencies each time you start a new session. 

We recommend that you import libraries and dependencies at the top of your notebook or within a separate requirements.txt file.

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


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