Output is below. ‘io’ is the particular node that gives the error, so we get the error when running in lane ‘io’ or ‘all’, if the job is assigned to io. I don’t have ConstrainDevices set in slurm.
[{'cache_path': '/scratch/cryosparc_cache', 'cache_quota_mb': None, 'cache_reserve_mb': 10000, 'custom_var_names': [], 'custom_vars': {}, 'desc': None, 'hostname': 'all', 'lane': 'all', 'name': 'all', 'qdel_cmd_tpl': 'scancel {{ cluster_job_id }}', 'qinfo_cmd_tpl': 'sinfo', 'qstat_cmd_tpl': 'squeue -j {{ cluster_job_id }}', 'qstat_code_cmd_tpl': None, 'qsub_cmd_tpl': 'sbatch {{ script_path_abs }}', 'script_tpl': '#!/usr/bin/env bash\n#### cryoSPARC cluster submission script template for SLURM\n## Available variables:\n## {{ run_cmd }} - the complete command string to run the job\n## {{ num_cpu }} - the number of CPUs needed\n## {{ num_gpu }} - the number of GPUs needed. \n## Note: the code will use this many GPUs starting from dev id 0\n## the cluster scheduler or this script have the responsibility\n## of setting CUDA_VISIBLE_DEVICES so that the job code ends up\n## using the correct cluster-allocated GPUs.\n## {{ ram_gb }} - the amount of RAM needed in GB\n## {{ job_dir_abs }} - absolute path to the job directory\n## {{ project_dir_abs }} - absolute path to the project dir\n## {{ job_log_path_abs }} - absolute path to the log file for the job\n## {{ worker_bin_path }} - absolute path to the cryosparc worker command\n## {{ run_args }} - arguments to be passed to cryosparcw run\n## {{ project_uid }} - uid of the project\n## {{ job_uid }} - uid of the job\n## {{ job_creator }} - name of the user that created the job (may contain spaces)\n## {{ cryosparc_username }} - cryosparc username of the user that created the job (usually an email)\n##\n## What follows is a simple SLURM script:\n\n#SBATCH --job-name cryosparc_{{ project_uid }}_{{ job_uid }}\n#SBATCH -p all\n#SBATCH -n {{ num_cpu }}\n#SBATCH --gres=gpu:{{ num_gpu }}\n#SBATCH --mem={{ (ram_gb)|int }}GB \n#SBATCH -o {{ job_dir_abs }}\n#SBATCH -e {{ job_dir_abs }}\n#SBATCH --error=/home/exx//Slurmlogs/%j.err\n#SBATCH --output=/home/exx//Slurmlogs/%j.out\n\navailable_devs=""\nfor devidx in $(seq 0 15);\ndo\n if [[ -z $(nvidia-smi -i $devidx --query-compute-apps=pid --format=csv,noheader) ]] ; then\n if [[ -z "$available_devs" ]] ; then\n available_devs=$devidx\n else\n available_devs=$available_devs,$devidx\n fi\n fi\ndone\nexport CUDA_VISIBLE_DEVICES=$available_devs\n\n{{ run_cmd }}\n\n', 'send_cmd_tpl': '{{ command }}', 'title': 'garcialab', 'tpl_vars': ['worker_bin_path', 'num_cpu', 'ram_gb', 'run_args', 'job_log_path_abs', 'command', 'cluster_job_id', 'num_gpu', 'project_uid', 'job_uid', 'job_creator', 'run_cmd', 'project_dir_abs', 'job_dir_abs', 'cryosparc_username'], 'type': 'cluster', 'worker_bin_path': '/home/exx/software/cryosparc/cryosparc_worker/bin/cryosparcw'}, {'cache_path': '/scratch/cryosparc_cache', 'cache_quota_mb': None, 'cache_reserve_mb': 10000, 'custom_var_names': [], 'custom_vars': {}, 'desc': None, 'hostname': 'notio', 'lane': 'notio', 'name': 'notio', 'qdel_cmd_tpl': 'scancel {{ cluster_job_id }}', 'qinfo_cmd_tpl': 'sinfo', 'qstat_cmd_tpl': 'squeue -j {{ cluster_job_id }}', 'qstat_code_cmd_tpl': None, 'qsub_cmd_tpl': 'sbatch {{ script_path_abs }}', 'script_tpl': '#!/usr/bin/env bash\n#### cryoSPARC cluster submission script template for SLURM\n## Available variables:\n## {{ run_cmd }} - the complete command string to run the job\n## {{ num_cpu }} - the number of CPUs needed\n## {{ num_gpu }} - the number of GPUs needed. \n## Note: the code will use this many GPUs starting from dev id 0\n## the cluster scheduler or this script have the responsibility\n## of setting CUDA_VISIBLE_DEVICES so that the job code ends up\n## using the correct cluster-allocated GPUs.\n## {{ ram_gb }} - the amount of RAM needed in GB\n## {{ job_dir_abs }} - absolute path to the job directory\n## {{ project_dir_abs }} - absolute path to the project dir\n## {{ job_log_path_abs }} - absolute path to the log file for the job\n## {{ worker_bin_path }} - absolute path to the cryosparc worker command\n## {{ run_args }} - arguments to be passed to cryosparcw run\n## {{ project_uid }} - uid of the project\n## {{ job_uid }} - uid of the job\n## {{ job_creator }} - name of the user that created the job (may contain spaces)\n## {{ cryosparc_username }} - cryosparc username of the user that created the job (usually an email)\n##\n## What follows is a simple SLURM script:\n\n#SBATCH --job-name cryosparc_{{ project_uid }}_{{ job_uid }}\n#SBATCH -p notio\n#SBATCH -n {{ num_cpu }}\n#SBATCH --gres=gpu:{{ num_gpu }}\n#SBATCH --mem={{ (ram_gb)|int }}GB \n#SBATCH -o {{ job_dir_abs }}\n#SBATCH -e {{ job_dir_abs }}\n#SBATCH --error=/home/exx//Slurmlogs/%j.err\n#SBATCH --output=/home/exx//Slurmlogs/%j.out\n\navailable_devs=""\nfor devidx in $(seq 0 15);\ndo\n if [[ -z $(nvidia-smi -i $devidx --query-compute-apps=pid --format=csv,noheader) ]] ; then\n if [[ -z "$available_devs" ]] ; then\n available_devs=$devidx\n else\n available_devs=$available_devs,$devidx\n fi\n fi\ndone\nexport CUDA_VISIBLE_DEVICES=$available_devs\n\n{{ run_cmd }}\n\n', 'send_cmd_tpl': '{{ command }}', 'title': 'garcialab', 'tpl_vars': ['worker_bin_path', 'num_cpu', 'ram_gb', 'run_args', 'job_log_path_abs', 'command', 'cluster_job_id', 'num_gpu', 'project_uid', 'job_uid', 'job_creator', 'run_cmd', 'project_dir_abs', 'job_dir_abs', 'cryosparc_username'], 'type': 'cluster', 'worker_bin_path': '/home/exx/software/cryosparc/cryosparc_worker/bin/cryosparcw'}, {'cache_path': '/scratch/cryosparc_cache', 'cache_quota_mb': None, 'cache_reserve_mb': 10000, 'custom_var_names': [], 'custom_vars': {}, 'desc': None, 'hostname': 'jupiter', 'lane': 'jupiter', 'name': 'jupiter', 'qdel_cmd_tpl': 'scancel {{ cluster_job_id }}', 'qinfo_cmd_tpl': 'sinfo', 'qstat_cmd_tpl': 'squeue -j {{ cluster_job_id }}', 'qstat_code_cmd_tpl': None, 'qsub_cmd_tpl': 'sbatch {{ script_path_abs }}', 'script_tpl': '#!/usr/bin/env bash\n#### cryoSPARC cluster submission script template for SLURM\n## Available variables:\n## {{ run_cmd }} - the complete command string to run the job\n## {{ num_cpu }} - the number of CPUs needed\n## {{ num_gpu }} - the number of GPUs needed. \n## Note: the code will use this many GPUs starting from dev id 0\n## the cluster scheduler or this script have the responsibility\n## of setting CUDA_VISIBLE_DEVICES so that the job code ends up\n## using the correct cluster-allocated GPUs.\n## {{ ram_gb }} - the amount of RAM needed in GB\n## {{ job_dir_abs }} - absolute path to the job directory\n## {{ project_dir_abs }} - absolute path to the project dir\n## {{ job_log_path_abs }} - absolute path to the log file for the job\n## {{ worker_bin_path }} - absolute path to the cryosparc worker command\n## {{ run_args }} - arguments to be passed to cryosparcw run\n## {{ project_uid }} - uid of the project\n## {{ job_uid }} - uid of the job\n## {{ job_creator }} - name of the user that created the job (may contain spaces)\n## {{ cryosparc_username }} - cryosparc username of the user that created the job (usually an email)\n##\n## What follows is a simple SLURM script:\n\n#SBATCH --job-name cryosparc_{{ project_uid }}_{{ job_uid }}\n#SBATCH -p jupiter\n#SBATCH -n {all{ num_cpu }}\n#SBATCH --gres=gpu:{{ num_gpu }}\n#SBATCH --mem={{ (ram_gb)|int }}GB \n#SBATCH -o {{ job_dir_abs }}\n#SBATCH -e {{ job_dir_abs }}\n#SBATCH --error=/home/exx//Slurmlogs/%j.err\n#SBATCH --output=/home/exx//Slurmlogs/%j.out\n\navailable_devs=""\nfor devidx in $(seq 0 15);\ndo\n if [[ -z $(nvidia-smi -i $devidx --query-compute-apps=pid --format=csv,noheader) ]] ; then\n if [[ -z "$available_devs" ]] ; then\n available_devs=$devidx\n else\n available_devs=$available_devs,$devidx\n fi\n fi\ndone\nexport CUDA_VISIBLE_DEVICES=$available_devs\n\n{{ run_cmd }}\n\n', 'send_cmd_tpl': '{{ command }}', 'title': 'garcialab', 'tpl_vars': ['worker_bin_path', 'num_cpu', 'ram_gb', 'run_args', 'job_log_path_abs', 'command', 'cluster_job_id', 'num_gpu', 'project_uid', 'job_uid', 'job_creator', 'run_cmd', 'project_dir_abs', 'job_dir_abs', 'cryosparc_username'], 'type': 'cluster', 'worker_bin_path': '/home/exx/software/cryosparc/cryosparc_worker/bin/cryosparcw'}, {'cache_path': '/scratch/cryosparc_cache', 'cache_quota_mb': None, 'cache_reserve_mb': 10000, 'custom_var_names': [], 'custom_vars': {}, 'desc': None, 'hostname': 'io', 'lane': 'io', 'name': 'io', 'qdel_cmd_tpl': 'scancel {{ cluster_job_id }}', 'qinfo_cmd_tpl': 'sinfo', 'qstat_cmd_tpl': 'squeue -j {{ cluster_job_id }}', 'qstat_code_cmd_tpl': None, 'qsub_cmd_tpl': 'sbatch {{ script_path_abs }}', 'script_tpl': '#!/usr/bin/env bash\n#### cryoSPARC cluster submission script template for SLURM\n## Available variables:\n## {{ run_cmd }} - the complete command string to run the job\n## {{ num_cpu }} - the number of CPUs needed\n## {{ num_gpu }} - the number of GPUs needed. \n## Note: the code will use this many GPUs starting from dev id 0\n## the cluster scheduler or this script have the responsibility\n## of setting CUDA_VISIBLE_DEVICES so that the job code ends up\n## using the correct cluster-allocated GPUs.\n## {{ ram_gb }} - the amount of RAM needed in GB\n## {{ job_dir_abs }} - absolute path to the job directory\n## {{ project_dir_abs }} - absolute path to the project dir\n## {{ job_log_path_abs }} - absolute path to the log file for the job\n## {{ worker_bin_path }} - absolute path to the cryosparc worker command\n## {{ run_args }} - arguments to be passed to cryosparcw run\n## {{ project_uid }} - uid of the project\n## {{ job_uid }} - uid of the job\n## {{ job_creator }} - name of the user that created the job (may contain spaces)\n## {{ cryosparc_username }} - cryosparc username of the user that created the job (usually an email)\n##\n## What follows is a simple SLURM script:\n\n#SBATCH --job-name cryosparc_{{ project_uid }}_{{ job_uid }}\n#SBATCH -p io\n#SBATCH -n {{ num_cpu }}\n#SBATCH --gres=gpu:{{ num_gpu }}\n#SBATCH --mem={{ (ram_gb)|int }}GB \n#SBATCH -o {{ job_dir_abs }}\n#SBATCH -e {{ job_dir_abs }}\n#SBATCH --error=/home/exx//Slurmlogs/%j.err\n#SBATCH --output=/home/exx//Slurmlogs/%j.out\n\navailable_devs=""\nfor devidx in $(seq 0 15);\ndo\n if [[ -z $(nvidia-smi -i $devidx --query-compute-apps=pid --format=csv,noheader) ]] ; then\n if [[ -z "$available_devs" ]] ; then\n available_devs=$devidx\n else\n available_devs=$available_devs,$devidx\n fi\n fi\ndone\nexport CUDA_VISIBLE_DEVICES=$available_devs\n\n{{ run_cmd }}\n\n', 'send_cmd_tpl': '{{ command }}', 'title': 'garcialab', 'tpl_vars': ['worker_bin_path', 'num_cpu', 'ram_gb', 'run_args', 'job_log_path_abs', 'command', 'cluster_job_id', 'num_gpu', 'project_uid', 'job_uid', 'job_creator', 'run_cmd', 'project_dir_abs', 'job_dir_abs', 'cryosparc_username'], 'type': 'cluster', 'worker_bin_path': '/home/exx/software/cryosparc/cryosparc_worker/bin/cryosparcw'}, {'cache_path': '/scratch/cryosparc_cache', 'cache_quota_mb': None, 'cache_reserve_mb': 10000, 'custom_var_names': [], 'custom_vars': {}, 'desc': None, 'hostname': 'europa', 'lane': 'europa', 'name': 'europa', 'qdel_cmd_tpl': 'scancel {{ cluster_job_id }}', 'qinfo_cmd_tpl': 'sinfo', 'qstat_cmd_tpl': 'squeue -j {{ cluster_job_id }}', 'qstat_code_cmd_tpl': None, 'qsub_cmd_tpl': 'sbatch {{ script_path_abs }}', 'script_tpl': '#!/usr/bin/env bash\n#### cryoSPARC cluster submission script template for SLURM\n## Available variables:\n## {{ run_cmd }} - the complete command string to run the job\n## {{ num_cpu }} - the number of CPUs needed\n## {{ num_gpu }} - the number of GPUs needed. \n## Note: the code will use this many GPUs starting from dev id 0\n## the cluster scheduler or this script have the responsibility\n## of setting CUDA_VISIBLE_DEVICES so that the job code ends up\n## using the correct cluster-allocated GPUs.\n## {{ ram_gb }} - the amount of RAM needed in GB\n## {{ job_dir_abs }} - absolute path to the job directory\n## {{ project_dir_abs }} - absolute path to the project dir\n## {{ job_log_path_abs }} - absolute path to the log file for the job\n## {{ worker_bin_path }} - absolute path to the cryosparc worker command\n## {{ run_args }} - arguments to be passed to cryosparcw run\n## {{ project_uid }} - uid of the project\n## {{ job_uid }} - uid of the job\n## {{ job_creator }} - name of the user that created the job (may contain spaces)\n## {{ cryosparc_username }} - cryosparc username of the user that created the job (usually an email)\n##\n## What follows is a simple SLURM script:\n\n#SBATCH --job-name cryosparc_{{ project_uid }}_{{ job_uid }}\n#SBATCH -p europa\n#SBATCH -n {{ num_cpu }}\n#SBATCH --gres=gpu:{{ num_gpu }}\n#SBATCH --mem={{ (ram_gb)|int }}GB \n#SBATCH -o {{ job_dir_abs }}\n#SBATCH -e {{ job_dir_abs }}\n#SBATCH --error=/home/exx//Slurmlogs/%j.err\n#SBATCH --output=/home/exx//Slurmlogs/%j.out\n\navailable_devs=""\nfor devidx in $(seq 0 15);\ndo\n if [[ -z $(nvidia-smi -i $devidx --query-compute-apps=pid --format=csv,noheader) ]] ; then\n if [[ -z "$available_devs" ]] ; then\n available_devs=$devidx\n else\n available_devs=$available_devs,$devidx\n fi\n fi\ndone\nexport CUDA_VISIBLE_DEVICES=$available_devs\n\n{{ run_cmd }}\n\n', 'send_cmd_tpl': '{{ command }}', 'title': 'garcialab', 'tpl_vars': ['worker_bin_path', 'num_cpu', 'ram_gb', 'run_args', 'job_log_path_abs', 'command', 'cluster_job_id', 'num_gpu', 'project_uid', 'job_uid', 'job_creator', 'run_cmd', 'project_dir_abs', 'job_dir_abs', 'cryosparc_username'], 'type': 'cluster', 'worker_bin_path': '/home/exx/software/cryosparc/cryosparc_worker/bin/cryosparcw'}, {'cache_path': '/scratch/cryosparc_cache', 'cache_quota_mb': None, 'cache_reserve_mb': 10000, 'custom_var_names': [], 'custom_vars': {}, 'desc': None, 'hostname': 'ganymede', 'lane': 'ganymede', 'name': 'ganymede', 'qdel_cmd_tpl': 'scancel {{ cluster_job_id }}', 'qinfo_cmd_tpl': 'sinfo', 'qstat_cmd_tpl': 'squeue -j {{ cluster_job_id }}', 'qstat_code_cmd_tpl': None, 'qsub_cmd_tpl': 'sbatch {{ script_path_abs }}', 'script_tpl': '#!/usr/bin/env bash\n#### cryoSPARC cluster submission script template for SLURM\n## Available variables:\n## {{ run_cmd }} - the complete command string to run the job\n## {{ num_cpu }} - the number of CPUs needed\n## {{ num_gpu }} - the number of GPUs needed. \n## Note: the code will use this many GPUs starting from dev id 0\n## the cluster scheduler or this script have the responsibility\n## of setting CUDA_VISIBLE_DEVICES so that the job code ends up\n## using the correct cluster-allocated GPUs.\n## {{ ram_gb }} - the amount of RAM needed in GB\n## {{ job_dir_abs }} - absolute path to the job directory\n## {{ project_dir_abs }} - absolute path to the project dir\n## {{ job_log_path_abs }} - absolute path to the log file for the job\n## {{ worker_bin_path }} - absolute path to the cryosparc worker command\n## {{ run_args }} - arguments to be passed to cryosparcw run\n## {{ project_uid }} - uid of the project\n## {{ job_uid }} - uid of the job\n## {{ job_creator }} - name of the user that created the job (may contain spaces)\n## {{ cryosparc_username }} - cryosparc username of the user that created the job (usually an email)\n##\n## What follows is a simple SLURM script:\n\n#SBATCH --job-name cryosparc_{{ project_uid }}_{{ job_uid }}\n#SBATCH -p ganymede\n#SBATCH -n {{ num_cpu }}\n#SBATCH --gres=gpu:{{ num_gpu }}\n#SBATCH --mem={{ (ram_gb)|int }}GB \n#SBATCH -o {{ job_dir_abs }}\n#SBATCH -e {{ job_dir_abs }}\n#SBATCH --error=/home/exx//Slurmlogs/%j.err\n#SBATCH --output=/home/exx//Slurmlogs/%j.out\n\navailable_devs=""\nfor devidx in $(seq 0 15);\ndo\n if [[ -z $(nvidia-smi -i $devidx --query-compute-apps=pid --format=csv,noheader) ]] ; then\n if [[ -z "$available_devs" ]] ; then\n available_devs=$devidx\n else\n available_devs=$available_devs,$devidx\n fi\n fi\ndone\nexport CUDA_VISIBLE_DEVICES=$available_devs\n\n{{ run_cmd }}\n\n', 'send_cmd_tpl': '{{ command }}', 'title': 'garcialab', 'tpl_vars': ['worker_bin_path', 'num_cpu', 'ram_gb', 'run_args', 'job_log_path_abs', 'command', 'cluster_job_id', 'num_gpu', 'project_uid', 'job_uid', 'job_creator', 'run_cmd', 'project_dir_abs', 'job_dir_abs', 'cryosparc_username'], 'type': 'cluster', 'worker_bin_path': '/home/exx/software/cryosparc/cryosparc_worker/bin/cryosparcw'}]
I thought Docker was the “approved” way of running local AlphaFold, which is why I asked. From a search, seems since I set up my testing local copy others have come up with non-Docker ways of doing it.