I installed pyopencl using apt on ubuntu16.04 but it installs an older
version (which doesn't work when CUDA is installed):
python-pyopencl/xenial 2015.1-2build3 amd64
Python module to access OpenCL parallel computation API
I was wondering if this pkg will be updated to install a new pyopencl.
I have a ppcle64+rhel7.6 setup and I have tried multiple ways to install pyopencl, but nothing works. I have python2.7 and python3.6 installed on the machine. To name a few, I tried installing with:
1. pip3: which gives me Pyhton.h error
SystemError: Cannot compile 'Python.h'. Perhaps you need to install python-dev|python-devel.
2. pip2: which installs it but import doesn't work. Gives me the following error:
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/usr/lib64/python2.7/site-packages/pyopencl/__init__.py", line 39, in <module>
import pyopencl._cl as _cl
ImportError: /lib64/libstdc++.so.6: version `GLIBCXX_3.4.20' not found (required by /usr/lib64/python2.7/site-packages/pyopencl/_cl.so)
3. install with anaconda: installs it but import says module not found
4. building from source: gives Python.h error (python3) or glibc version error at import (python2)
Can someone help me find a straightforward method to install pyopencl on redhat?
I have a pyopencl application passing along pointers for the memory buffers and the context to a C opencl program which is called within the same python script. This worked perfectly in a mockup dummy project, but actually implementing it within my actual library has surfaced a roadblock.
Taking the pointer from pyopencl and passing it along to my opencl code works perfectly fine and doesn't break anything (using copies of buffers initialised in the C opencl code itself). Sharing the buffers to circumvent these extra memory operations breaks the program. The buffers initialised in opencl are initialised as both read and write buffers.
My clFinish() with just my kernel queued returns an error 36: INVALID_COMMAND_QUEUE.
I've also tried sharing the command queue from pyopencl; this doesn't change anything.
I've additionally checked both dimensionality and dtype/nbytes of my buffers. The ones initialised in (and passed on from) pyopencl are in this sense identical to those I made in my C code itself, in the test that does not share the buffers (which works).
I couldn't find a solution to this issue so far, but hope that you may be able to help me.
In any way, thank you for your time!
Yves Surrel <yves(a)surrel.org> writes:
> Modified from https://github.com/inducer/pyopencl/blob/master/pyopencl/__init__.py
> def enqueue_copy(queue, dest, src, **kwargs):
> """Copy from :class:`Image`, :class:`Buffer` or the host to
> :class:`Image`, :class:`Buffer` or the host. (Note: host-to-host
> copies are unsupported.)
> The following keyword arguments are available:
> :arg wait_for: (optional, default empty)
> :arg is_blocking: Wait for completion. Defaults to *True*.
> (Available on any copy involving host memory)
> .. note::
> Mind that when :param wait_for: is set to *False*, the deletion
> of the :class:`NannyEvent` that is returned by the function
> if the transfer involved a host-side buffer will block until
> the transfer is complete, so be sure to keep a live reference
> to this event as long as necessary.
> :return: A :class:`NannyEvent` if the transfer involved a
> host-side buffer, otherwise an :class:`Event`.
> .. note::
> Two types of 'buffer' occur in the arguments to this function,
> :class:`Buffer` and 'host-side buffers'. The latter are
> defined by Python and commonly called `buffer objects
> <https://docs.python.org/3.4/c-api/buffer.html>`_. :mod:`numpy`
> arrays are a very common example.
> Make sure to always be clear on whether a :class:`Buffer` or a
> Python buffer object is needed.
>> Le 23 oct. 2019 à 17:19, Andreas Kloeckner <lists(a)informa.tiker.net> a écrit :
>> Yves Surrel <yves(a)surrel.org> writes:
>>> Got it! Thank you.
>>> Really deserves some info in the doc.
>> I'd be happy to have this spelled out more clearly. Could you write up a
>> patch/a segment of documentation to explain this?
I have been using PyOpenCL for two years now, and I am familiar to using it.
However, I do not understand why the following code is blocking at the last line…
The doc states that is_blocking is « available on any copy involving host memory"…
Any clue will be much appreciated.
import numpy as np
import pyopencl as cl
import pyopencl.array as cla
plat = cl.get_platforms()
GPU = None
for device in plat.get_devices():
if device.type == cl.device_type.GPU:
GPU = device
#Create context for GPU
context = cl.Context((GPU,))
raise Exception('Could not find GPU’)
device_ary = cla.zeros(queue,(10,10), dtype='uint8')
host_ary = np.ones((10,10), 'uint8')
cl.enqueue_copy(queue, host_ary, device_ary.data, wait_for=(ev,), is_blocking=False)
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