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updated docs

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William Falcon
2019-09-16 11:02:04 -04:00
parent b354988255
commit 286625a02f
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docs/Trainer/Distributed training.md
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@@ -108,7 +108,7 @@ trainer = Trainer(gpus=8, distributed_backend='ddp')
Multi-node training is easily done by specifying these flags.
```python
# train on 12*8 GPUs
trainer = Trainer(gpus=8, nb_gpu_nodes=12)
trainer = Trainer(gpus=8, nb_gpu_nodes=12, distributed_backend='ddp')
```
In addition, make sure to set up your SLURM job correctly via the [SlurmClusterObject](https://williamfalcon.github.io/test-tube/hpc/SlurmCluster/). In particular, specify the number of tasks per node correctly.
@@ -140,6 +140,9 @@ cluster.per_experiment_nb_gpus = 8
cluster.add_slurm_cmd(cmd='ntasks-per-node', value=8, comment='1 task per gpu')
```
**NOTE:** When running in DDP mode, any errors in your code will show up as an NCCL issue.
Set the ```NCCL_DEBUG=INFO``` flag to see the ACTUAL error.
Finally, make sure to add a distributed sampler to your dataset. The distributed sampler copies a
portion of your dataset onto each GPU. (World_size = gpus_per_node * nb_nodes).