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

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


Paperspace partners with Graphcore to provide IPU-powered notebooks πŸ”‹

We're excited to launch a partnership to bring new machine learning hardware to Paperspace!

Graphcore IPUs now available in Gradient! announcement 

As of today, Gradient Notebooks users can launch IPUs from Graphcore on Paperspace -- for free up to 6 hours!

Graphcore IPUs are specialty accelerated computing chips designed to maximize machine learning workloads. 

We're pleased to offer Graphcore's IPU-POD16 machine with 10GB of free storage. 

We've made it extremely easy to get started. Just head over to the Gradient console in Paperspace, create a new notebook, and select one of the new Graphcore runtimes.

Once in the notebook it's easy to start running code.

We've created three different runtimes to start -- including Hugging Face, PyTorch, and TensorFlow 2.

TRY NOW


For more information be sure to read the announcement

DALL-E Mini and all-new free Ampere GPUs for Growth plan subscribers! πŸ§‘β€πŸŽ¨

The hype around the internet for DALL-E 2 and DALL-E Mini has been building for weeks -- and now you can train your own DALL-E Mini model on a Gradient Notebook! 

Let's get into the updates.

DALL-E Mini runtime now live on Gradient! announcement 

We're excited to release a new runtime tile for DALL-E Mini. The runtime is based on JAX and makes it easy to create generative art on high-powered Paperspace GPUs.

To get started head over to the console, create a new notebook, select the DALL-E Mini tile and get going!

New ultra-powerful A4000 and A5000 GPUs now FREE on Gradient Growth plan! announcement 

As we continue to offer the best selection of cloud GPUs on the market we also continue to extend our lead in the number of unlimited instances we offer to Gradient subscribers.

We've just added A4000 and A5000 machines to the Gradient Growth plan, which means the list of free GPUs available on Growth is longer than ever.

Check out all the free GPUs available to Gradient subscribers below!


GPU
Price
Architecture
Launch Year
GPU RAM
CPUs
System RAM
Current Street Price (2022)
M4000
Free (Gradient Free-tier)
Maxwell
2015
8 GB
8 vCPU
30 GB
$433
P4000
$8/mo (Gradient Pro)
Pascal
2017
8 GB
8 vCPU
30 GB
$859
P5000
$8/mo (Gradient Pro)
Pascal
2016
16 GB
8 vCPU
30 GB
$1,795
RTX4000
$8/mo (Gradient Pro)
Turing
2018
8 GB
8 vCPU
30 GB
$1,247
RTX5000
$8/mo (Gradient Pro)
Turing
2018
16 GB
8 vCPU
30 GB
$2,649
A4000
$8/mo (Gradient Pro)
Ampere
2021
16 GB
8 vCPU
45 GB
$1,099
A5000
$39/mo (Gradient Growth)
Ampere
2021
24 GB
8 vCPU
45 GB
$2,516
A6000
$39/mo (Gradient Growth)
Ampere
2020
48 GB
8 vCPU
45 GB
$4,599


For more information, be sure to read the docs.


Updated PyTorch, TensorFlow, and RAPIDS runtimes announcement 

We also wanted to let you know that we've rolled out updated Notebook runtimes for PyTorch, TensorFlow, and RAPIDS. 

In the notebook console you'll now find 1-click runtime tiles for PyTorch 1.12, TensorFlow 2.9.1, and RAPIDS 20.6.


Is there another runtime you wish we'd support out of the box? Let us know!

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 a new docs experience for Core and Gradient! πŸ“š

New docs come to Paperspace! announcement 

We're excited to introduce an entirely new unified docs experience for Paperspace! 

After maintaining several different systems for documenting different parts of the product, we're eager to announce that Paperspace docs are now available in a single location with a new unified theme and organizational structure!

You can now find Core documentation, Gradient documentation, and general Account Management documentation all in one place!

If you need a place to start, we recommend starting with the Core overview or the Gradient overview -- you'll be able to launch right into tutorials, guides, and reference materials designed to help you succeed with Paperspace.

Have an idea for how to improve Paperspace documentation further? Please send us a note with any comments or suggestions!

All-new Linux SSH experience and improved machine create experience in Core! πŸ›«

We're excited to announce some brand new Core experiences! Let's jump right into what's new.

All-new Linux SSH experience announcement 

We've reconfigured the Linux machine create experience to optimize for connecting to Linux machines via SSH.

We feel that a direct connection to a Linux machine is a fantastic experience. We'll still support Linux VMs in the browser, but if you get a chance, give SSH a try -- it's so easy to connect!


Managing machines just got a lot better announcement 

We've also released a substantial cleanup of the machines settings page in Core which has made it easier than ever to access and manage machine settings. 

Let's say for example we wanted to create a snapshot of our new machine -- easy!

Or let's say we wanted to update our machine name and adjust the autoshutdown timer? Also easy!

We've also made it easier to do things like assign public IPs, generate templates, and more!

Redesigned account settings improvement

We've also updated the global Paperspace account settings to the latest design system standard. 

You'll now find tabs for Profile, Security, and SSH Keys and in general you should now find it easier to access these important settings.

Dynamic public IP addresses improvement 

  • We added support for dynamic public IP addresses which provide public IP addresses at a bare minimum of cost

Capacity upgrades improvement 

Meanwhile, we've also been busy adding plenty of capacity to Paperspace datacenters.

  • We onboarded a new fleet of RTX4000 machines to the CA1 region
  • We dramatically expanded GPU compute capacity in the NY2 region
  • We added nearly 100TB in shared storage across regions
  • And don't worry, we didn't forget about Europe! New capacity is coming soon!

Bugfixes fix 

  • We fixed a bug that was sometimes causing utilization graphs to display inaccurately



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
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