Collaboratory (Colab) is a very useful tool for Deep Learning learners launched by Google two years ago, that gave access to GPUs and TPUs, with a free service, so far. Although Colab can’t be used for full-length projects, it does the job for beginners and mid-level practitioners. I’m using it in my courses, in such a way that the students can write and execute Python on the web with zero configuration required and use a GPU or TPU.
However, because this environment has become very popular lately the service was very saturated. In this context Google introduced “Colab Pro” (link) , an upgrade that provides three primary benefits for $9.99/month:
- Faster GPUs: “Priority access to faster GPUs and TPUs means you spend less time waiting while code is running.” Instead of free K80s, subscribers get access to the Nvidia Tesla T4 and P100, as well as being prioritized for Tensor Processing Units. Google notes that there are still usage limits with Colab Pro.
- Longer runtimes: “Longer running notebooks and fewer idle timeouts mean you disconnect less often.” Instead of 12 hours, Colab Pro lets notebooks “stay connected for up to 24 hours, and idle timeouts are relatively lenient.”
- More memory: “More RAM means better performance, and less running out of memory.” Notebooks will feature a “high-memory VM” preference that “generally have double the memory of standard Colab VMs, and twice as many CPUs.”
This paid “Colab Pro” tier is currently available only in the US. I hope that soon the service will also be extended to Europe because I think that for the price and the ease of use it can be useful for many people who start in this world of Deep Learning.