File size: 3,855 Bytes
7ef0503 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 | description: angcb
# amlt cred storage set angcb4wjp --subscription e033d461-1923-44a7-872b-78f1d35a86dd --resource-group Shun --allow-local-storage False
target:
service: sing
name: msrresrchlab
workspace_name: epeastus
environment:
registry: singularitybase.azurecr.io
image: base/job/pytorch/acpt-2.2.1-py3.10-cuda12.1:20240312T225111416
storage:
data:
# storage_account_name: epeastus
# container_name: cov19
# mount_dir: /home/xiaofangui/Blob_EastUS
storage_account_name: angcb4wjp
container_name: angcb
mount_dir: /data/angcb
code:
# local directory of the code. this will be uploaded to the server.
local_dir: $CONFIG_DIR/..
jobs:
- name: ANGCB_land
sku: 32G4-V100
# process_count_per_node: 1
# sla_tier: Standard
sla_tier: premium
priority: high
identity: managed
submit_args:
env:
SHARED_MEMORY_PERCENT: 1.0
_AZUREML_SINGULARITY_JOB_UAI: "/subscriptions/e033d461-1923-44a7-872b-78f1d35a86dd/resourcegroups/Shun/providers/Microsoft.ManagedIdentity/userAssignedIdentities/epeastus_mi"
command:
- bash install_dep_pkgs.sh
- pip list
- free -h
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target ACCESS --exp_id 101 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target CESM_ETHZ --exp_id 102 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target CNRM --exp_id 103 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target FESOM_REcoM --exp_id 104 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target IPSL --exp_id 105 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target MPIOM-HAMOCC --exp_id 106 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target MRI --exp_id 107 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target NEMO_PlankTOM --exp_id 108 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target NorESM --exp_id 109 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target Princeton --exp_id 110 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target CMEMS-LSCE-FFNN --exp_id 111 --model_type dataproduct
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target CSIR-ML6 --exp_id 112 --model_type dataproduct
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target JENA-MLS --exp_id 113 --model_type dataproduct
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target JMA-MLR --exp_id 114 --model_type dataproduct
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target LDEO-HPD --exp_id 115 --model_type dataproduct
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target NIES-ML3 --exp_id 116 --model_type dataproduct
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target OceanSODA-ETHZv2 --exp_id 117 --model_type dataproduct
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target UoEX-UEPFFNU --exp_id 118 --model_type dataproduct
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target VLIZ-SOMFFN --exp_id 119 --model_type dataproduct
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target CNRM --exp_id 203 --model_type gobm
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target UoEX-UEPFFNU --exp_id 218 --model_type dataproduct
# - torchrun --nproc-per-node=4 multi_prediction_new.py --target average_gobm --exp_id 120 --model_type gobm
# - CUDA_VISIBLE_DEVICES=4,5,6,7 torchrun --nproc-per-node=4 --master_port=12345 multi_prediction_new.py --target average_datapro --exp_id 121 --model_type dataproduct |