Replaced torch dist launch by torchru (#164)

Author: vfdev-5

src/blog/2020-09-10-pytorch-ignite.md

@@ -698,7 +698,7 @@ PyTorch-Ignite provides an ensemble of metrics dedicated to many Deep Learning t
 
 - For classification : `Precision`, `Recall`, `Accuracy`, `ConfusionMatrix` and more!
 - For segmentation : `DiceCoefficient`, `IoU`, `mIOU` and more!
-- ~20 regression metrics, e.g. MSE, MAE, MedianAbsoluteError, etc 
+- ~20 regression metrics, e.g. MSE, MAE, MedianAbsoluteError, etc
 - Metrics that store the entire output history per epoch
   - Possible to use with `scikit-learn` metrics, e.g. `EpochMetric`, `AveragePrecision`, `ROC_AUC`, etc
 - Easily composable to assemble a custom metric
@@ -942,7 +942,7 @@ with idist.Parallel(backend=backend, **dist_configs) as parallel:
 ```
 
     2020-08-31 11:27:07,128 ignite.distributed.launcher.Parallel INFO: Initialized distributed launcher with backend: 'gloo'
-    2020-08-31 11:27:07,128 ignite.distributed.launcher.Parallel INFO: - Parameters to spawn processes: 
+    2020-08-31 11:27:07,128 ignite.distributed.launcher.Parallel INFO: - Parameters to spawn processes:
     	nproc_per_node: 2
     	nnodes: 1
     	node_rank: 0
@@ -951,7 +951,7 @@ with idist.Parallel(backend=backend, **dist_configs) as parallel:
     1 : run with config: {'c': 12345} - backend= gloo
     2020-08-31 11:27:09,959 ignite.distributed.launcher.Parallel INFO: End of run
 
-The above code with a single modification can run on a GPU, single-node multiple GPUs, single or multiple TPUs etc. It can be executed with the `torch.distributed.launch` tool or by Python and spawning the required number of processes. For more details, see [the documentation](https://pytorch.org/ignite/distributed.html).
+The above code with a single modification can run on a GPU, single-node multiple GPUs, single or multiple TPUs etc. It can be executed with the `torchrun` or by Python and spawning the required number of processes. For more details, see [the documentation](https://pytorch.org/ignite/distributed.html).
 
 In addition, methods like `auto_model()`, `auto_optim()` and `auto_dataloader()` help to adapt in a transparent way the provided model, optimizer and data loaders to an existing configuration:
 

src/blog/2021-06-28-pytorch-ignite-distributed.md

@@ -53,7 +53,7 @@ Then we will also cover several ways of spawning processes via torch native `tor
 
 - [`torch.multiprocessing.spawn`](https://pytorch.org/docs/stable/multiprocessing.html#torch.multiprocessing.spawn)
 
-- [`torch.distributed.launch`](https://pytorch.org/docs/stable/distributed.html#launch-utility)
+- [`torchrun`](https://pytorch.org/docs/stable/elastic/run.html#launcher-api)
 
 - [`horovodrun`](https://horovod.readthedocs.io/en/stable/running_include.html)
 
@@ -183,7 +183,7 @@ dist.destroy_process_group()
 PyTorch-Ignite's `idist` also unifies the distributed codes launching method and makes the distributed configuration setup easier with the
 [ignite.distributed.launcher.Parallel (idist Parallel)](https://pytorch.org/ignite/distributed.html#ignite.distributed.launcher.Parallel) context manager.
 
-This context manager has the capability to either spawn `nproc_per_node` (passed as a script argument) child processes and initialize a processing group according to the provided backend or use tools like `torch.distributed.launch`, `slurm`, `horovodrun` by initializing the processing group given the `backend` argument only
+This context manager has the capability to either spawn `nproc_per_node` (passed as a script argument) child processes and initialize a processing group according to the provided backend or use tools like `torchrun`, `slurm`, `horovodrun` by initializing the processing group given the `backend` argument only
 in a general way.
 
 ### With `torch.multiprocessing.spawn`
@@ -212,13 +212,13 @@ python -u ignite_idist.py --backend xla-tpu --nproc_per_node 8 --batch_size 32
 PyTorch-Ignite's `idist Parallel` context manager is also compatible
 with multiple distributed launchers.
 
-#### With torch.distributed.launch
+#### With `torchrun`
 
-Here we are using the `torch.distributed.launch` script in order to
+Here we are using the `torchrun` script in order to
 spawn the processes:
 
 ```bash
-python -m torch.distributed.launch --nproc_per_node 2 --use_env ignite_idist.py --backend gloo
+torchrun --nproc_per_node 2 ignite_idist.py --backend gloo
 ```
 
 #### With horovodrun