Update distributed training documentation
@@ -34,7 +34,7 @@ Note that evaluation and inference will run on a single device.
OpenNMT-tf also supports asynchronous distributed training with *between-graph replication*. In this mode, each graph replica processes a batch independently, compute the gradients, and asynchronously update a shared set of parameters.
-To enable distributed training, the user should set on the command line:
+To enable distributed training, the user should use the `train_and_eval` run type and set on the command line:
* a **chief worker** host that runs a training loop and manages checkpoints, summaries, etc.
* a list of **worker** hosts that run a training loop
@@ -43,7 +43,7 @@ To enable distributed training, the user should set on the command line:
Then a training instance should be started on each host with a selected task, e.g.:
```bash
-CUDA_VISIBLE_DEVICES=0 onmt-main train [...] \
+CUDA_VISIBLE_DEVICES=0 onmt-main train_and_eval [...] \
--ps_hosts localhost:2222 \
--chief_host localhost:2223 \
--worker_hosts localhost:2224,localhost:2225 \
