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


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!

S3 added as a data source, a new unified storage dashboard, and more! πŸͺ£

We're excited to roll out a new data source in Gradient Notebooks: S3 buckets! We're also announcing a new unified storage utilization dashboard for Gradient and a couple of useful quality of life improvements.

S3 integration in Gradient Notebooks improvement  

We're pleased to offer a new data source for mounting S3 buckets within Gradient Notebooks! The S3 integration, which is available to Pro or Growth subscribers, allows you to mount an S3 volume directly within a notebook.

S3-compatible storage systems are also supported so be sure to read the docs for more information!

Storage utilization view upgrades improvement 

Meanwhile, we've also made it 10x easier to understand how much storage you're using in Gradient. Whereas before it was sometimes difficult to understand which resources were consuming storage bandwidth, there is now a single-source-of-truth dashboard within Team Settings. 

The dashboard quantifies storage use across resources while providing a precise breakdown of storage used and available storage remaining.

Other improvements improvement 

  • We improved the Members page within Team Settings to make it easier to define and manage your team
  • We improved the visual language related to save states and kernel status in the notebook IDE
  • We added directory size information to the datasets list view

New NVIDIA Ampere capacity, network upgrades, and more! 🐎

We've added a large number of high-end NVIDIA Ampere machines to pools across all three datacenter regions. We've also released a number of improvements related to network connectivity and uplink speed. Additionally, we're rolling out new peering capabilities for select customers -- more on that below. 

Let's dive in!

A100s in eight configurations improvement 

According to NVIDIA, the A100 has succeeded the V100 as the world's best GPU for deep learning. To answer the surge in demand from Paperspace deep learning users, we've been busy racking and stacking A100 machines as fast as we can – starting with the NY2 region.

We're pleased to offer A100 machines with maximum flexibility when it comes to configuration and price point. You should now notice A100s in both 40 GB of graphics memory and 80 GB of graphics memory flavors with 1-way configurations as well as 2-way, 4-way, and 8-way multi-GPU configurations available.

Here's the full table of A100 machines now available:

GPU

Graphics Memory

CPUs

System Memory

Price

A100 x 1

40 GB

12

90 GB

$3.09 / hr

A100 x 2 

40 GB

24

180 GB

$6.18 / hr

A100 x 4

40 GB

48

360 GB

$12.36 / hr

A100 x 8

40 GB 

96

720 GB

$24.72 / hr

A100 x 1

80 GB

12

90 GB

$3.18 / hr

A100 x 2

80 GB

24

180 GB

$6.36 / hr

A100 x 4

80 GB

48

360 GB

$12.72 / hr

A100 x 8

80 GB

96

720 GB

$25.44 / hr

We’re working to bring A100s to other regions soon.

Ampere is also for CA1 and AMS1! improvement  

We've also loaded-up the CA1 and AMS1 datacenter regions with plenty of Ampere machines. We now offer A4000 machines in 1-way, 2-way, and 4-way configurations in the CA1 and AMS1 regions and have added A6000 machines in a variety of configurations to AMS1.

Watch this space for more Ampere availability across all datacenter regions!

Peering and network upgrades IMPROVEMENT 

We're pleased to be rolling out peering arrangements with customers whose data transfer requirements to/from Paperspace machines are exceptional. If you'd like to talk more about your use case, please reach out to schedule a conversation.

On a related note, we've now enabled a second 10 Gbps uplink in NY2! We are multiplying efforts to increase speeds in every region to help customers access improved bandwidth and traffic paths no matter where their machines are located.

Finally, we've also improved private network performance among machines on a single private network and among multiple connected private networks hosted in the Paperspace cloud. As part of these improvements, we’ve enabled machine swapping across private networks.

As always drop us a line if you have any questions!

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
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