DDP-related(?): all CUDA-capable devices are busy or unavailable
**Describe the bug**

"speech_to_text_bpe.py"(ASR example) with ddp (8-gpus) failed to run.
It produced a "RuntimeError: CUDA error: all CUDA-capable devices are busy or unavailable"

**Steps/Code to reproduce bug**

After generating the manifest for LibriSpeech dataset  and tokenization, I ran the following script in a command line
(Below codes are from the comments in examples/asr directory, actually)

python speech_to_text_bpe.py \
    model.train_ds.manifest_filepath=librispeech/train_all.json \
    model.validation_ds.manifest_filepath=librispeech/dev_all.json \
    model.tokenizer.dir=librispeech/tokenizer_spe_bpe_v5000 \
    model.tokenizer.type=bpe \
    trainer.gpus=-1 \
    trainer.accelerator="ddp" \
    trainer.max_epochs=100 \
    model.optim.name="adamw" \
    model.optim.lr=0.001 \
    model.optim.betas=[0.9,0.999] \
    model.optim.weight_decay=0.0001 \
    model.optim.sched.warmup_steps=2000
    exp_manager.create_wandb_logger=True \
    exp_manager.wandb_logger_kwargs.name="exp.tst" \
    exp_manager.wandb_logger_kwargs.project="prj.tst"

**Expected behavior**

It should run without errors

**Environment overview (please complete the following information)**

 - Environment location: NVIDIA V100 x8, docker 
 - Method of NeMo install: from source (git-cloned on 10 July, installed with reinstall.sh)

**Environment details**

If NVIDIA docker image is used you don't need to specify these.
Otherwise, please provide:
- OS version: Ubuntu 18.04.5 LTS
- PyTorch version: 1.7.1+cu110
- Python version: 3.8.5

**Additional context**

Add any other context about the problem here.
Example: GPU model

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No progress after installing Nemo. :( 
Any help would be appreciated.
