![]() ![]() You may need to create a custom environment to install the package you need. The default environment we provide, based on the Anaconda distribution, contains hundreds of the most common python packages for data science, but it doesn’t include everything. The most common cause of errors is a lack of required package(s) installed in your environment. My notebook is trying to import a package, but I’m getting an error. This limit is reset every day, so full compute access will be restored the next day. ![]() Once an instance hits that limit, it is not shut down, but instead given lower CPU priority and a limit to the amount of compute resources available. Our notebook service accounts have a per-day limit for the maximum number of seconds fully utilizing the CPU. When does the clock on CPU seconds reset? So, in general, CPU time is only used while your program is actively making calculations, not while it is waiting for other systems. When the response comes back from the other end, then it will again use a small amount of CPU to interpret the response and provide your code with the results. Only running python code from within a notebook and running commands from the terminal count against your quota, and even then very few command functions truly tax the CPU.įor example, if your code makes an HTTP request, then it will use a tiny amount of CPU time assembling the request and sending it out over the network, but will then use no CPU at all while it’s waiting for a response. Simply running JupyterLab, writing code, and using the interface don’t really use up quota (though they have a small impact). ![]() We refer to them as “high-compute seconds” on our pricing page to clearly distinguish CPU seconds from “wall clock” seconds. ![]() A CPU second is one second of running code on a single CPU core at 100%. ![]()
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