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PyTorch is an optimised tensor library for deep learning using GPUs and CPUs. |
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$ docker pull quay.io/pawsey/pytorch:2.2.0-rocm5.7.3
The container can be also pulled using singularity:
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| Code Block |
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| language | bash |
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| theme | DJango |
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| title | Terminal 2. Using PyTorch on a compute node in an interactive Slurm session. |
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| setonix-05$ salloc -p gpu -A yourProjectCode-gpu --gres=gpu:1 --time=00:20:00
salloc: Pending job allocation 12386179
salloc: job 12386179 queued and waiting for resources
salloc: job 12386179 has been allocated resources
salloc: Granted job allocation 12386179
salloc: Waiting for resource configuration
salloc: Nodes nid002096 are ready for job
nid002096$ module load pytorch/2.2.0-rocm5.7.3
nid002096$ python3 main.py
NeuralNetwork(
(flatten): Flatten(start_dim=1, end_dim=-1)
(linear_relu_stack): Sequential(
(0): Linear(in_features=784, out_features=512, bias=True)
(1): ReLU()
(2): Linear(in_features=512, out_features=512, bias=True)
(3): ReLU()
(4): Linear(in_features=512, out_features=10, bias=True)
)
)
### Epoch 0/10 ###
loss: 2.289750 [ 64/60000]
loss: 2.287861 [ 6464/60000]
loss: 2.263056 [12864/60000]
loss: 2.261112 [19264/60000]
loss: 2.240377 [25664/60000]
loss: 2.208018 [32064/60000]
loss: 2.225265 [38464/60000]
loss: 2.183236 [44864/60000]
|
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Note that when requesting the interactive allocation, users should use their correct project name instead of the "yourProjectCode" place holder used in the example. Also notice the use of the "-gpu" postfix to the project name in order to be able to access any partition with GPU-nodes. Please refer to the page Example Slurm Batch Scripts for Setonix on GPU Compute Nodes for a detailed explanation of resource allocation on GPU nodes.
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