Strumenti Utente

Strumenti Sito

Traccia:
calcoloscientifico:userguide:alphafold

Differenze

Queste sono le differenze tra la revisione selezionata e la versione attuale della pagina.

Link a questa pagina di confronto

Entrambe le parti precedenti la revisioneRevisione precedente
Prossima revisione		Revisione precedente
calcoloscientifico:userguide:alphafold [24/01/2025 19:03] – fabio.spatarocalcoloscientifico:userguide:alphafold [06/02/2025 19:46] (versione attuale) – fabio.spataro
Linea 13: Linea 13:
 <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>
  
Linea 36: Linea 46:
 </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
Linea 47: Linea 57:
 #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
Linea 62: Linea 72:
 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}"
Linea 68: Linea 81:
  
 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 === 
Linea 119: Linea 138:
   --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.
Linea 147: Linea 166:
     (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: 24/01/2025 19:03 da fabio.spataro
Donate Powered by PHP Valid HTML5 Valid CSS Driven by DokuWiki