OtherSelf-paced documentation and example notebooks·Documentation free; GPU compute billed by the hour, with limited free notebook tiers

Paperspace Documentation and Tutorials (DigitalOcean)

3.0

Paperspace was once a go-to place for students to get a GPU and a Jupyter notebook, and its blog was a real source of ML tutorials. Under DigitalOcean the docs are competent but thin, a lot of the ecosystem has been deprecated, and it is no longer an obvious place to learn.

What We Liked

Gradient Notebooks remain one of the quicker ways to get a GPU-backed Jupyter environment
Example notebooks, including fast.ai material, run without setup
The docs are clear about deprecations rather than letting old pages rot silently
Access to DigitalOcean's large community tutorial library alongside

What Could Be Better

No course, learning path or structured ML education, only product documentation
Legacy Gradient and Core APIs are deprecated, so older tutorials and code break
The GRID machine types are retired and Windows templates deprecated, which strands older guides
The Paperspace blog, once a genuinely useful ML tutorial source, has been absorbed and is harder to find
Search interest in Paperspace is down around two thirds in a year

Detailed review

For a few years Paperspace occupied a useful spot in the ML learning world. Gradient Notebooks gave students a GPU-backed Jupyter environment with less friction than AWS, fast.ai recommended it as one of its supported platforms, and the Paperspace blog published a steady stream of practical tutorials on everything from object detection to diffusion models. If you learned deep learning between 2019 and 2022, there is a decent chance you touched it. DigitalOcean bought Paperspace in 2023, and the documentation now lives inside DigitalOcean's docs. The products are still there: Notebooks, Machines, Deployments for serving containers, and Workflows for pipelines.

The docs are clear, reasonably current, and honest about what has been retired. Windows templates are deprecated for new users, the NVIDIA GRID machine types are gone, and the old Gradient and Core APIs are deprecated in favour of a new Paperspace API. We give DigitalOcean credit for stating this plainly. Many vendors leave dead pages up and let you find out the hard way. But the effect on learners is real.

A big share of the Paperspace tutorials still floating around the internet reference APIs or machine types that no longer exist, and there is no curated path for someone who arrives wanting to learn ML on the platform. The documentation explains how to use the product. It does not teach you anything about machine learning, which the old blog did. The decline in interest is visible in search data: Paperspace searches are down by roughly two thirds year on year. That is consistent with a product that is being maintained inside a larger company's portfolio rather than pushed as a destination. Our take: the docs are fine for existing users.

As a learning environment, Paperspace has been overtaken. Kaggle Notebooks and Colab give beginners free GPUs with far less friction, and anyone needing serious compute is better served by a dedicated GPU cloud with active documentation. We rate it low not because the docs are bad but because there is little here for someone setting out to learn.

[ final ]

The verdict.

Use it if you already have a Paperspace notebook and need to know how something works. If you are looking for a place to learn ML with a free GPU, Kaggle Notebooks and Google Colab are simpler and better supported today.