Paperspace partners with Graphcore to provide IPU-powered notebooks 🔋

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! 🧑‍🎨

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! 🪣

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! 🧑‍🚀

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! 📚

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! 🛫

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



Updates to the instance selector, a new session countdown, updates to an NVIDIA runtime, and more! 🧘

We're introducing a number of quality-of-life improvements to Gradient Notebooks this month. Let's get into the updates!

Updates to instance selector improvement 

Instance selection, comparison, and education are starting to take on a bigger role within the Gradient product. 

The first improvement we've made is to clarify which instance types are available at your subscription tier as well as which instance types have capacity available.

And now you can also sort by GPU and CPU instance type!

We've heard from a number of users who would like additional instance specs when selecting an instance type. What specs are important to you? Let us know what you look for when selecting an instance!

Kernel health information improvement 

We now pass kernel health information into the Gradient IDE. This means that for each active file in a Gradient Notebook, there is now a little status symbol to represent the kernel state.

The green circles below indicate that there are two kernels are healthy and running:

We're also providing better atomic control for kernels. It's now easier to restart or stop a frozen kernel in the IDE without needing to shut down the entire instance.

Read the docs for more info!

Updated RAPIDS container improvement 

We've updated the NVIDIA RAPIDS runtime to the latest stable release, version 21.12.

This release contains substantial updates to cuDF, cuML, and more so be sure to take it for a spin!

Other improvements improvement 

  • We implemented a session timeout interval for Gradient Notebook sessions on free instance types. This helps us maintain a deeper pool of available free instances for others to use without interrupting active sessions
  • If you've opted for an instance with a fixed session length, we now provide an unobtrusive hourly countdown in the sidebar to let you know when your session is due to end

Bugfixes fix 

  • We fixed a pesky issue that sometimes caused inconsistent logout states
  • We fixed an issue with the Restart Kernel function to make it more reliable

Introducing 100% self-serve private networks, shared drives, and public IPs! 🏄

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

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!

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