I have tried this with #!/bin/bash
#SBATCH --job-name=easybuild_gpu #SBATCH --ntasks=4 #SBATCH --time=12:00:00 #SBATCH --mem-per-cpu=1G #SBATCH --partition=gpu #SBATCH --qos=medium srun eb Keras-2.2.4-fosscuda-2019a-Python-3.7.2.eb --robot but get the error == FAILED: Installation ended unsuccessfully (build directory: /trinity/shared/easybuild/build/TensorFlow/1.13.1/fosscuda-2019a-Python-3.7.2): build failed (first 300 chars): Failed to chmod/chown several paths: ['/trinity/shared/easybuild/build/TensorFlow/1.13.1/fosscuda-2019a-Python-3.7.2', '/trinity/shared/easybuild/build/TensorFlow/1.13.1/fosscuda-2019a-Python-3.7.2/protobufpython', '/trinity/shared/easybuild/build/TensorFlow/1.13.1/fosscuda-2019a-Python-3.7.2/abslpy (took 4 sec) I'm running the Slurm job as the same user I use always to run Easybuild, so all the above directories are already owned by that user. Any ideas about what I might be doing wrong? Cheers, Loris Åke Sandgren <[email protected]> writes: > If you're building a single easyconfig you could also just make a small > submit file that runs eb the same way you would do manually, and send it > to the right node. > > On 12/4/19 10:23 AM, Loris Bennett wrote: >> Hi, >> >> With Kenneth's help I have realised/remembered that I need to compile >> Keras / TensorFlow on a machine with the appropriate CUDA drivers. This >> seemed like a good scenario in which to use the >> >> --job >> >> option. However, if I write >> >> ... --job --job-backend=Slurm --job-cores=4 >> >> I would still need to specify the partition with the GPU nodes, so that >> the job is indeed scheduled to a machine on which the drivers are >> installed. >> >> How would I do that? >> >> Cheers, >> >> Loris >> -- Dr. Loris Bennett (Mr.) ZEDAT, Freie Universität Berlin Email [email protected]
