Comments (5)
I gather that you are using a Linux GPU instance on EC2. Can you check whether PyCUDA works properly, i.e., does the following run without any error?
import numpy as np
import pycuda.autoinit
import pycuda.gpuarray as gpuarray
x_gpu = gpuarray.to_gpu(np.arange(3))
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Yep. That works without errors.
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Are you using the latest revision of the master branch from GitHub?
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Hi, do you have two GPUs accessible on your instance? If not, please try the demo with
python vision_demo.py --lam_dev 0 --med_dev 0
This specifies to run both LPUs on GPU 0.
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@yiyin That worked. Thanks!
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Related Issues (20)
- cross-module fan-in check should only look at connected ports HOT 1
- matching ports in different Interface instances that both have null types should satisfy compatibility check with subset checking enabled HOT 1
- problem running MPI-based branch using SLURM
- Interface.get_common_ports() could be sped up by using explicit set operations
- LPU.utils.visualizer.visualizer should automatically check for ffmpeg/avconv
- handling of emulation with single module in mpi branch is broken HOT 1
- error when module id not in routing table HOT 2
- merge base classes into core module
- GPU-dependent modules should be connectable to non-GPU-dependent modules
- Port Mapper maps empty spike or gpot port data to None
- move compute plane machinery into separate repo HOT 1
- move current Neurokernel demos into separate repos HOT 4
- functions inside utils/ and LPU/tools need to be updated HOT 1
- cannot create a Pattern with selectors containing different numbers of levels
- Neurokernel core configuration mechanism
- support for connecting external input signals/output recorders to ports
- Python 3 support
- srun jupyter notebook cannot run mpi_relaunch
- stuck on self._intercomm.bcast(routing_table, root=MPI.ROOT) HOT 2
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