parm:logging_dir not works when 
## Environment info
<!-- You can run the command `transformers-cli env` and copy-and-paste its output below.
     Don't forget to fill out the missing fields in that output! -->

- `transformers` version:4.16.2
- Platform:linux
- Python version:3.7.11
- PyTorch version (GPU?):1.10.2 GPU
- Tensorflow version (GPU?):no tensorflow
- Using GPU in script?:yes
- Using distributed or parallel set-up in script?:

## Information

Model I am using (Bert, XLNet ...):unicamp-dl/translation-en-pt-t5

The problem arises when using:
* [ ] the official example scripts: (give details below)
`from transformers import BertForSequenceClassification, Trainer, TrainingArguments

model = BertForSequenceClassification.from_pretrained("bert-large-uncased")

training_args = TrainingArguments(
    output_dir='./results',          # output directory
    num_train_epochs=3,              # total # of training epochs
    per_device_train_batch_size=16,  # batch size per device during training
    per_device_eval_batch_size=64,   # batch size for evaluation
    warmup_steps=500,                # number of warmup steps for learning rate scheduler
    weight_decay=0.01,               # strength of weight decay
    logging_dir='./logs',            # directory for storing logs
)

trainer = Trainer(
    model=model,                         # the instantiated 🤗 Transformers model to be trained
    args=training_args,                  # training arguments, defined above
    train_dataset=train_dataset,         # training dataset
    eval_dataset=test_dataset            # evaluation dataset
)`
* [ ] my own modified scripts: (give details below)
`
model_checkpoint = "unicamp-dl/translation-en-pt-t5"
model = AutoModelForSeq2SeqLM.from_pretrained(model_checkpoint)
args = Seq2SeqTrainingArguments(
    output_dir=f"{model_name}-finetuned-{source_lang}-to-{target_lang}-{time_stamp}",  
    overwrite_output_dir=True,  
    evaluation_strategy="epoch",
    prediction_loss_only=False,  
    learning_rate=2e-5,
    per_device_train_batch_size=batch_size,  # batch size per device during training
    per_device_eval_batch_size=batch_size,   # batch size for evaluation
    weight_decay=0.01,
    save_steps=3000,  
    eval_steps=10000,  
    logging_dir='./logs',
    logging_steps=500,  
    save_total_limit=3,  
    num_train_epochs=10,
    predict_with_generate=True,
    fp16=True,  
    push_to_hub=False
)

trainer = Seq2SeqTrainer(
    model,
    args,
    train_dataset=tokenized_datasets["train"],
    eval_dataset=tokenized_datasets["valid"],
    data_collator=data_collator,
    tokenizer=tokenizer,
    compute_metrics=compute_metrics
)

trainer.train()
`

I could not found the dir logs which should be found in same dir of the script above 

## Expected behavior

<!-- A clear and concise description of what you would expect to happen. -->
![image](https://user-images.githubusercontent.com/44344942/156742529-e062727f-c60c-4d22-8134-7325ee0bd29d.png)


@sgugger thanks
How do you launch the script? In particular if you launch it from another directory, the logging dir will appear there.<|||||>thanks very much，I found it is because I launch the script with Environment without Tensorflow installed（by the way, I get no warning about this during the process, haha）<|||||>I have same problem.
When Run with 1 GPU or no GPU environment, That option works fine. but with multi GPU it not works.<|||||>This issue has been automatically marked as stale because it has not had recent activity. If you think this still needs to be addressed please comment on this thread.

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