Menu
one of the limitations is that we can get only 12 continuous hours per session. Is there any limitations for the usage for GPU and TPU?
573
In the free version, runtimes are limited to 12 hours and RAM is also limited to 16 GB. In the pro variant, it is possible to select a high-memory option and thus use 32 GB of RAM. The Google Pro+ variant now offers even more options to run Deep Learning relatively inexpensively without a cloud server or local machine.
Notebooks run by connecting to virtual machines that have maximum lifetimes that can be as much as 12 hours. Notebooks will also disconnect from VMs when left idle for too long. Maximum VM lifetime and idle timeout behavior may vary over time, or based on your usage.
To avoid hitting your GPU usage limits, we recommend switching to a standard runtime if you are not utilizing the GPU. Choose Runtime > Change Runtime Type and set Hardware Accelerator to None. For examples of how to utilize GPU and TPU runtimes in Colab, see the Tensorflow With GPU and TPUs In Colab example notebooks.
Colab Pro+ also offers background execution which supports continuous code execution for up to 24 hours. In the version of Colab that is free of charge, notebooks can run for at most 12 hours, and idle timeouts are much stricter than in Colab Pro or Pro+.
From Colab's documentation,
In order to be able to offer computational resources for free, Colab needs to maintain the flexibility to adjust usage limits and hardware availability on the fly. Resources available in Colab vary over time to accommodate fluctuations in demand, as well as to accommodate overall growth and other factors.
In a nutshell, Colab has dynamic resource provisioning. So they can change the hardware, it it is being taxed too much automatically. Google giveth and Google taketh away.
Link
151
If you love us? You can donate to us via Paypal or buy me a coffee so we can maintain and grow! Thank you!
Donate Us With
