In partnership with Graphcore, we are excited to share that we are launching additional types of IPUs in Gradient Notebooks. This means that you can now start training on POD4, POD16 and BOW-POD16 IPUs within the notebook experience for longer than 6 hours. Check out these machine specs!

Graphcore’s Intelligence Processing Unit (IPU) are chips designed from the ground up specifically for machine learning user cases and can deliver training speed improvements.

You can try IPU-POD4’s for free by going to your private workspace to test out this new hardware. Check out this blog on how to get started using IPUs on Gradient. If you want to dive into an example of how to get stable diffusion working on IPUs, give this blog a try.

Fixes & Improvements

1. Fixed a bug in the deployments list view which prevented users from seeing all deployments
2. Bug fixes to Core Shared Drive creation
3. Improved error messaging on payment failures
4. Improved the loading speeds for the notebook create and billing pages

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

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

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

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

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! 

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!

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!

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

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!

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.

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! 

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!