Remove GC from README (#943)

README.md

@@ -19,7 +19,6 @@ If you'd like to use the accelerator-specific features of 🤗 Optimum, you can
 | [ONNX Runtime](https://onnxruntime.ai/docs/) | `python -m pip install optimum[onnxruntime]` |
 | [Intel Neural Compressor](https://www.intel.com/content/www/us/en/developer/tools/oneapi/neural-compressor.html) | `python -m pip install optimum[neural-compressor]`|
 | [OpenVINO](https://docs.openvino.ai/latest/index.html) | `python -m pip install optimum[openvino,nncf]` |
-| [Graphcore IPU](https://www.graphcore.ai/products/ipu) | `python -m pip install optimum[graphcore]` |
 | [Habana Gaudi Processor (HPU)](https://habana.ai/training/) | `python -m pip install optimum[habana]` |
 
 To install from source:
@@ -148,15 +147,10 @@ You can find more examples in the [documentation](https://huggingface.co/docs/op
 We support many providers:
 
 - Habana's Gaudi processors
-- Graphcore's IPUs 
 - ONNX Runtime (optimized for GPUs)
 
 ### Habana
 
-<!--
-To train transformers on Habana's Gaudi processors, 🤗 Optimum provides a `GaudiTrainer` that is very similar to the 🤗 Transformers [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer). Here is a simple example:
-
--->
 ```diff
 - from transformers import Trainer, TrainingArguments
 + from optimum.habana import GaudiTrainer, GaudiTrainingArguments
@@ -189,54 +183,8 @@ To train transformers on Habana's Gaudi processors, 🤗 Optimum provides a `Gau
 
 You can find more examples in the [documentation](https://huggingface.co/docs/optimum/habana/quickstart) and in the [examples](https://github.com/huggingface/optimum-habana/tree/main/examples).
 
-
-### Graphcore
-
-<!--
-To train transformers on Graphcore's IPUs, 🤗 Optimum provides a `IPUTrainer` that is very similar to the 🤗 Transformers [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer). Here is a simple example:
-
--->
-```diff
-- from transformers import Trainer, TrainingArguments
-+ from optimum.graphcore import IPUConfig, IPUTrainer, IPUTrainingArguments
-
- # Download a pretrained model from the Hub
- model = AutoModelForXxx.from_pretrained("bert-base-uncased")
-
- # Define the training arguments
-- training_args = TrainingArguments(
-+ training_args = IPUTrainingArguments(
- output_dir="path/to/save/folder/",
-+ ipu_config_name="Graphcore/bert-base-ipu", # Any IPUConfig on the Hub or stored locally
- ...
- )
-
- # Define the configuration to compile and put the model on the IPU
-+ ipu_config = IPUConfig.from_pretrained(training_args.ipu_config_name)
-
- # Initialize the trainer
-- trainer = Trainer(
-+ trainer = IPUTrainer(
- model=model,
-+ ipu_config=ipu_config
- args=training_args,
- train_dataset=train_dataset
- ...
- )
-
- # Use Graphcore IPU for training!
- trainer.train()
-```
-
-You can find more examples in the [documentation](https://huggingface.co/docs/optimum/graphcore/quickstart) and in the [examples](https://github.com/huggingface/optimum-graphcore/tree/main/examples).
-
-
 ### ONNX Runtime
 
-<!--
-To train transformers with ONNX Runtime's acceleration features, 🤗 Optimum provides a `ORTTrainer` that is very similar to the 🤗 Transformers [Trainer](https://huggingface.co/docs/transformers/main_classes/trainer). Here is a simple example:
--->
-
 ```diff
 - from transformers import Trainer, TrainingArguments
 + from optimum.onnxruntime import ORTTrainer, ORTTrainingArguments