On Tue, Nov 13, 2012 at 2:27 AM, Austin Bingham wrote:
> OK, if numpy is just subject to Python's behavior then what I'm seeing
> must be due to the vagaries of Python. I've noticed that things like
> removing a particular line of code or reordering seemingly unrelated calls
> (unrelated to the me
On Tue, Nov 13, 2012 at 1:31 PM, Austin Bingham
wrote:
> I've been using psutil, pmap (linux command), and resource in various
> capacities, all on cpython. When I wasn't seeing memory freed when I
> expected, I got to wondering if maybe numpy was maintaining pools of buffers
> for reuse or someth
I've been using psutil, pmap (linux command), and resource in various
capacities, all on cpython. When I wasn't seeing memory freed when I
expected, I got to wondering if maybe numpy was maintaining pools of
buffers for reuse or something like that. It sounds like that's not the
case, though, so I'
How are you monitoring memory usage?
Personally I've been using psutil and it seems to work well, although I've
used it only on Windows and not in applications with large numpy arrays, so
I can't tell whether it would work you.
Also, keep in mind that:
- The "auto-delete object when it goes out of
On 11/13/12 10:27 AM, Austin Bingham wrote:
> OK, if numpy is just subject to Python's behavior then what I'm seeing
> must be due to the vagaries of Python. I've noticed that things like
> removing a particular line of code or reordering seemingly unrelated
> calls (unrelated to the memory issu
OK, if numpy is just subject to Python's behavior then what I'm seeing must
be due to the vagaries of Python. I've noticed that things like removing a
particular line of code or reordering seemingly unrelated calls (unrelated
to the memory issue, that is) can affect when memory is reported as free.
On Tue, Nov 13, 2012 at 8:26 AM, Austin Bingham
wrote:
> I'm trying to understand how numpy decides when to release memory and
> whether it's possible to exert any control over that. The situation is that
> I'm profiling memory usage on a system in which a great deal of the overall
> memory is tie
I'm trying to understand how numpy decides when to release memory and
whether it's possible to exert any control over that. The situation is that
I'm profiling memory usage on a system in which a great deal of the overall
memory is tied up in ndarrays. Since numpy manages ndarray memory on its
own
