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calcoloscientifico:userguide:alphafold [24/01/2025 19:03] – fabio.spatarocalcoloscientifico:userguide:alphafold [06/02/2025 19:46] (versione attuale) – fabio.spataro
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 <code> <code>
 /hpc/share/containers/apptainer/alphafold/3.0.1/alphafold-3.0.1.sif /hpc/share/containers/apptainer/alphafold/3.0.1/alphafold-3.0.1.sif
 +</code>
 +
 +=== Alphafold3 GPU demo ===
 +
 +<code>
 +mkdir -p demo/af_input
 +cp -p /hpc/share/containers/apptainer/alphafold/3/af_input/fold_input.json demo/af_input
 +cp -p /hpc/share/containers/apptainer/alphafold/3.0.1/slurm-alphafold-gpu-a100_40g.sh demo
 +cd demo
 +sbatch slurm-alphafold-gpu-a100_40g.sh
 </code> </code>
  
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 </code> </code>
  
-Script ''slurm-alphafold.sh'' to run ''alphafold'' on 1 node with 1 GPU (8 tasks per node):+Script ''slurm-alphafold-gpu-a100_40g.sh'' to run ''alphafold'' on 1 node with 1 A100 (40 GB) GPU (8 tasks per node):
  
-<code bash slurm-alphafold.sh>+<code bash slurm-alphafold-gpu-a100_40g.sh>
 #!/bin/bash --login #!/bin/bash --login
 #SBATCH --job-name=alphafold #SBATCH --job-name=alphafold
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 #SBATCH --cpus-per-task=8 #SBATCH --cpus-per-task=8
 #SBATCH --time=0-02:00:00 #SBATCH --time=0-02:00:00
-#SBATCH --mem=8G+#SBATCH --mem=10G
 #SBATCH --partition=gpu #SBATCH --partition=gpu
 #SBATCH --qos=gpu #SBATCH --qos=gpu
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 test -n "$ALPHAFOLD_CONTAINER" || exit 1 test -n "$ALPHAFOLD_CONTAINER" || exit 1
  
 +set -x
 +
 +ALPHAFOLD_JSON_INPUT_FILE='fold_input.json'
 ALPHAFOLD_INPUT_DIR="$PWD/af_input" ALPHAFOLD_INPUT_DIR="$PWD/af_input"
 ALPHAFOLD_OUTPUT_DIR="$PWD/af_output/${SLURM_JOB_NAME}.d${SLURM_JOB_ID}" ALPHAFOLD_OUTPUT_DIR="$PWD/af_output/${SLURM_JOB_NAME}.d${SLURM_JOB_ID}"
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 apptainer exec \ apptainer exec \
-    --bind '/opt/hpc/system/nvidia/driver:/usr/local/nvidia/bin' \ +    --nv \
-    --bind '/opt/hpc/system/nvidia/driver:/usr/local/nvidia/lib' \+
     --bind "$ALPHAFOLD_INPUT_DIR:/root/af_input" \     --bind "$ALPHAFOLD_INPUT_DIR:/root/af_input" \
     --bind "$ALPHAFOLD_OUTPUT_DIR:/root/af_output" \     --bind "$ALPHAFOLD_OUTPUT_DIR:/root/af_output" \
-    --bind "$ALPHAFOLD_MODEL_DIR:/root/models" \ 
-    --bind "$ALPHAFOLD_DB_DIR:/root/public_databases" \ 
     "$ALPHAFOLD_CONTAINER" \     "$ALPHAFOLD_CONTAINER" \
     python /app/alphafold/run_alphafold.py \     python /app/alphafold/run_alphafold.py \
-    --json_path=/root/af_input/fold_input.json \+    --json_path="/root/af_input/$ALPHAFOLD_JSON_INPUT_FILE" \
     --model_dir=/root/models \     --model_dir=/root/models \
     --db_dir=/root/public_databases \     --db_dir=/root/public_databases \
 +    --db_dir=/root/public_databases_fallback \
     --output_dir=/root/af_output     --output_dir=/root/af_output
 </code> </code>
  
 The processing result will be saved in the ''af output'' folder. The processing result will be saved in the ''af output'' folder.
 +
 +Scripts for specific NVIDIA GPU models to run ''alphafold'' on 1 node with 1 GPU (8 tasks per node):
 +
 +^ GPU  ^ Path  ^
 +| NVIDIA [[https://github.com/google-deepmind/alphafold3/blob/main/docs/performance.md#nvidia-p100|P100 (12 GB)]]  | ''/hpc/share/containers/apptainer/alphafold/3.0.1/slurm-alphafold-gpu-p100.sh''  |
 +| NVIDIA [[https://github.com/google-deepmind/alphafold3/blob/main/docs/performance.md#nvidia-v100|V100 (32 GB)]]  | ''/hpc/share/containers/apptainer/alphafold/3.0.1/slurm-alphafold-gpu_guest-v100_hylab.sh''  |
 +| NVIDIA [[https://github.com/google-deepmind/alphafold3/blob/main/docs/performance.md#nvidia-a100-40-gb|A100 (40 GB)]]  | ''/hpc/share/containers/apptainer/alphafold/3.0.1/slurm-alphafold-gpu-a100_40g.sh''  |
 +| NVIDIA [[https://github.com/google-deepmind/alphafold3/blob/main/docs/performance.md#accelerator-hardware-requirements|A100 (80 GB)]]  | ''/hpc/share/containers/apptainer/alphafold/3.0.1/slurm-alphafold-gpu-a100_80g.sh''  |
  
 === Documentation ===  === Documentation === 
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   --db_dir: Path to the directory containing the databases. Can be specified multiple times to search multiple directories in order.;   --db_dir: Path to the directory containing the databases. Can be specified multiple times to search multiple directories in order.;
     repeat this option to specify a list of values     repeat this option to specify a list of values
-    (default: "['/home/sti_calcolo/public_databases']")+    (default: "['/hpc/home/sti_calcolo/public_databases']")
   --flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention   --flash_attention_implementation: <triton|cudnn|xla>: Flash attention implementation to use. 'triton' and 'cudnn' uses a Triton and cuDNN flash attention
     implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN kernels require Ampere GPUs or later.     implementation, respectively. The Triton kernel is fastest and has been tested more thoroughly. The Triton and cuDNN kernels require Ampere GPUs or later.
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     (default: '${DB_DIR}/mgy_clusters_2022_05.fa')     (default: '${DB_DIR}/mgy_clusters_2022_05.fa')
   --model_dir: Path to the model to use for inference.   --model_dir: Path to the model to use for inference.
-    (default: '/home/sti_calcolo/models')+    (default: '/hpc/home/sti_calcolo/models')
   --nhmmer_binary_path: Path to the Nhmmer binary.   --nhmmer_binary_path: Path to the Nhmmer binary.
     (default: '/hmmer/bin/nhmmer')     (default: '/hmmer/bin/nhmmer')
calcoloscientifico/userguide/alphafold.1737741838.txt.gz · Ultima modifica: da fabio.spataro
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