[Seiya Tokui, chainer] As @crcrpar wrote, test refinements and chainerx routines are good for contribution. These are tracked by issues pinned at the top of the issue list (#6423, #6071, and #6628). They have a list of tasks (bullet points or spreadsheet) each of which is a separate task that can be done without interfering with each other. Already done tasks can be used as a reference of how to complete the job.
Documentation is also good. It tends to have a shorter review process, so you can quickly walk through the commit-PR-review-fix-CI-merge cycle.
cat:enhancementissues, which indicates that the fix should not require changes on interface, would also be good to try.
def convert(batch,device): batchData = [X for X,_ in batch] batchLabel = [Y for _,Y in batch] data = xp.array(batchData,dtype=xp.float32).transpose([0,3,1,2]) data = data / 255 label = xp.array(batchLabel,dtype=xp.int32) return (data,label)
env.seed(). I'm curious about the initialization weights of the agent (if there are the same). Does anyone know how to print out chainerRL agent's weights? Thanks!
[Andrew Summers, chainer] I'm having trouble getting the fallback mode to work. Theoretically, I should be able to replace:
import numpy as np
from cupyx.fallback_mode import numpy as np
This should work, right? I have a project that uses
intersect1d, and it fails on that (which requires me to manually convert from cupy > numpy . . . which is the whole point of the fallback mode). Am I doing something wrong?
[Mark Turner, chainer] Hello! Hope I'm not piling on with the questions: Is it possible to use Parallel Updater with Sequential chains that contain functions? Eg/
Sequential(L.Linear(None, 50), F.relu)
Sequential(L.Linear(None, 50), F.relu)I'm getting an error
line 606, in addgrad dst[name].addgrad(src[name]). I think this comes from when it iterates over children and indices, as
self._childrenof the Sequential doesn't contain the ReLU functions, but the index of the Sequential Link still does. This then means it looks for the weight and bias values in
F.relu.__dict__which is just empty. (Is probably a bug somewhere else in my code that actually causes this, but want to make sure)
nvidia-smi). Another parallel process which uses GPU cannot use the memory. Is it intended?
[Arnaud, chainer] Hi everyone, I have some trouble to use my Quadro P1000 gpu with Chainer.
Chainer version installed is 7.7.0 and cupy-cuda80 installed is 7.8.0
Here is the message error I get, while lauching my script:
`\CUDA environment is not correctly set up
(see https://github.com/chainer/chainer#installation).CuPy is not correctly installed.
If you are using wheel distribution (cupy-cudaXX), make sure that the version of CuPy you installed matches with the version of CUDA on your host.
Also, confirm that only one CuPy package is installed:
$ pip freeze
If you are building CuPy from source, please check your environment, uninstall CuPy and reinstall it with:
$ pip install cupy --no-cache-dir -vvvv
Check the Installation Guide for details:
original error: DLL load failed: The specified module could not be found.
I already checked that there is only one package of CuPy installed.
I think the version of CuPy installed matches the P1000.
Does anyone knows how to solve this problem?
Thanks for the support, Arnaud.