Open spoonbender76 opened 2 months ago
I'm encountering similar when training a model with HybridModel.py using Apptainer v1.1.8 (rebranded Singularity) with the latest Docker container (helixer-docker:helixer_v0.3.3_cuda_11.8.0-cudnn8). Now worrying that my model training is running sub-optimally (i.e. slow), so would appreciate a response.
I met the same error,.
I'm encountering similar when training a model with HybridModel.py using Apptainer v1.1.8 (rebranded Singularity) with the latest Docker container (helixer-docker:helixer_v0.3.3_cuda_11.8.0-cudnn8). Now worrying that my model training is running sub-optimally (i.e. slow), so would appreciate a response.
i met the same error , but i didnot have the root ,just to use the singularity,
Hi, thanks for raising, will check out these errors more closely for the next release. I strongly suspect you can ignore them.
Helixer should run on the order of magnitude of 100mbp of genome/30min (or faster, hardware, batch size and gene density dependent). If it's much slower than that, then please let us know, that would be unexpectedly slow and might be running on the CPU instead of GPU.
Hi, I'm running Helixer v0.3.3 via Singularity v4.0.3
singularity pull docker://gglyptodon/helixer-docker:helixer_v0.3.3_cuda_11.8.0-cudnn8
singularity run --nv helixer-docker_helixer_v0.3.3_cuda_11.8.0-cudnn8.sif Helixer.py --fasta-path Nm.softmasked.fa --lineage invertebrate --gff-output-path Nm_helixer.gff3 --batch-size 8
Can I safely ignore these TensorFlow warnings/error messages, or might they affect performance/results?