Unable to train in colad. complaints  Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1` but already have  accelerate Version: 0.25.0
Author: surapuramakhil
Pull request: no

### System Info



2024-01-01 01:45:15.116736: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered
2024-01-01 01:45:15.116796: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered
2024-01-01 01:45:15.118498: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2024-01-01 01:45:16.875070: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT
WARNING:tensorflow:From /usr/local/lib/python3.10/dist-packages/transformers/commands/env.py:100: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.
Instructions for updating:
Use `tf.config.list_physical_devices('GPU')` instead.
2024-01-01 01:45:21.044234: W tensorflow/core/common_runtime/gpu/gpu_bfc_allocator.cc:47] Overriding orig_value setting because the TF_FORCE_GPU_ALLOW_GROWTH environment variable is set. Original config value was 0.
CUDA backend failed to initialize: Found cuBLAS version 120103, but JAX was built against version 120205, which is newer. The copy of cuBLAS that is installed must be at least as new as the version against which JAX was built. (Set TF_CPP_MIN_LOG_LEVEL=0 and rerun for more info.)

Copy-and-paste the text below in your GitHub issue and FILL OUT the two last points.

- `transformers` version: 4.35.2
- Platform: Linux-6.1.58+-x86_64-with-glibc2.35
- Python version: 3.10.12
- Huggingface_hub version: 0.19.4
- Safetensors version: 0.4.1
- Accelerate version: 0.25.0
- Accelerate config: 	not found
- PyTorch version (GPU?): 2.1.0+cu121 (True)
- Tensorflow version (GPU?): 2.15.0 (True)
- Flax version (CPU?/GPU?/TPU?): 0.7.5 (cpu)
- Jax version: 0.4.23
- JaxLib version: 0.4.23
- Using GPU in script?: <fill in>
- Using distributed or parallel set-up in script?: <fill in>



### Who can help?

@muellerzr @pacman100 


!pip show accelerate

Name: accelerate
Version: 0.25.0
Summary: Accelerate
Home-page: https://github.com/huggingface/accelerate
Author: The HuggingFace team
Author-email: [sylvain@huggingface.co](mailto:sylvain@huggingface.co)
License: Apache
Location: /usr/local/lib/python3.10/dist-packages
Requires: huggingface-hub, numpy, packaging, psutil, pyyaml, safetensors, torch
Required-by:




import torch
from transformers import GPT2Tokenizer, GPT2LMHeadModel, GPT2Config
from transformers import TextDataset, DataCollatorForLanguageModeling
from transformers import Trainer, TrainingArguments

# Set the device to GPU if available
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")

# Load pre-trained GPT-2 model and tokenizer
model_name = "gpt2"
model = GPT2LMHeadModel.from_pretrained(model_name)
tokenizer = GPT2Tokenizer.from_pretrained(model_name)

# Load your custom dataset
train_dataset = TextDataset(
    tokenizer=tokenizer,
    file_path="/content/manual.txt",
    block_size=128
)

# Create a data collator for language modeling
data_collator = DataCollatorForLanguageModeling(
    tokenizer=tokenizer,
    mlm=False
)

# Set up training arguments
training_args = TrainingArguments(
    output_dir="./fine-tuned-model",
    overwrite_output_dir=True,
    num_train_epochs=3,
    per_device_train_batch_size=4,
    save_steps=10_000,
    save_total_limit=2,
)

# Create Trainer
trainer = Trainer(
    model=model,
    args=training_args,
    data_collator=data_collator,
    train_dataset=train_dataset,
)

# Fine-tune the model
trainer.train()

# Save the fine-tuned model
model.save_pretrained("./fine-tuned-model")
tokenizer.save_pretrained("./fine-tuned-model")



Error stack trace:
/usr/local/lib/python3.10/dist-packages/transformers/training_args.py in __init__(self, output_dir, overwrite_output_dir, do_train, do_eval, do_predict, evaluation_strategy, prediction_loss_only, per_device_train_batch_size, per_device_eval_batch_size, per_gpu_train_batch_size, per_gpu_eval_batch_size, gradient_accumulation_steps, eval_accumulation_steps, eval_delay, learning_rate, weight_decay, adam_beta1, adam_beta2, adam_epsilon, max_grad_norm, num_train_epochs, max_steps, lr_scheduler_type, warmup_ratio, warmup_steps, log_level, log_level_replica, log_on_each_node, logging_dir, logging_strategy, logging_first_step, logging_steps, logging_nan_inf_filter, save_strategy, save_steps, save_total_limit, save_safetensors, save_on_each_node, no_cuda, use_cpu, use_mps_device, seed, data_seed, jit_mode_eval, use_ipex, bf16, fp16, fp16_opt_level, half_precision_backend, bf16_full_eval, fp16_full_eval, tf32, local_rank, ddp_backend, tpu_num_cores, tpu_metrics_debug, debug, dataloader_drop_last, eval_steps, dataloader_num_workers, past_index, run_name, disable_tqdm, remove_unused_columns, label_names, load_best_model_at_end, metric_for_best_model, greater_is_better, ignore_data_skip, fsdp, fsdp_min_num_params, fsdp_config, fsdp_transformer_layer_cls_to_wrap, deepspeed, label_smoothing_factor, optim, optim_args, adafactor, group_by_length, length_column_name, report_to, ddp_find_unused_parameters, ddp_bucket_cap_mb, ddp_broadcast_buffers, dataloader_pin_memo...

[/usr/local/lib/python3.10/dist-packages/transformers/training_args.py](https://localhost:8080/#) in __post_init__(self)
   1440             self.framework == "pt"
   1441             and is_torch_available()
-> 1442             and (self.device.type != "cuda")
   1443             and (self.device.type != "npu")
   1444             and (self.device.type != "xpu")

[/usr/local/lib/python3.10/dist-packages/transformers/training_args.py](https://localhost:8080/#) in device(self)
   1885         """
   1886         requires_backends(self, ["torch"])
-> 1887         return self._setup_devices
   1888 
   1889     @property

[/usr/local/lib/python3.10/dist-packages/transformers/utils/generic.py](https://localhost:8080/#) in __get__(self, obj, objtype)
     52         cached = getattr(obj, attr, None)
     53         if cached is None:
---> 54             cached = self.fget(obj)
     55             setattr(obj, attr, cached)
     56         return cached

[/usr/local/lib/python3.10/dist-packages/transformers/training_args.py](https://localhost:8080/#) in _setup_devices(self)
   1785         if not is_sagemaker_mp_enabled():
   1786             if not is_accelerate_available(min_version="0.20.1"):
-> 1787                 raise ImportError(
   1788                     "Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`"
   1789                 )

ImportError: Using the `Trainer` with `PyTorch` requires `accelerate>=0.20.1`: Please run `pip install transformers[torch]` or `pip install accelerate -U`


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### Information

- [X] The official example scripts
- [ ] My own modified scripts

### Tasks

- [ ] An officially supported task in the `examples` folder (such as GLUE/SQuAD, ...)
- [ ] My own task or dataset (give details below)

### Reproduction

code , outputs and error already attached

### Expected behavior

it should be run without throwing error

Comments:
After installing Accelerate please try restarting the runtime, this is a common issue :) 

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.

Please note that issues that do not follow the [contributing guidelines](https://github.com/huggingface/transformers/blob/main/CONTRIBUTING.md) are likely to be ignored.