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[examples/flax] use Repository API for push_to_hub (#13672)

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* use Repository for push_to_hub

* update readme

* update other flax scripts

* update readme

* update qa example

* fix push_to_hub call

* fix typo

* fix more typos

* update readme

* use abosolute path to get repo name

* fix glue script
This commit is contained in:
Suraj Patil
2021-09-30 16:38:07 +05:30
committed by GitHub
parent b90096fe14
commit 7db2a79b38
15 changed files with 183 additions and 292 deletions
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examples/flax/vision/README.md
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@@ -25,37 +25,6 @@ way which enables simple and efficient model parallelism.
In this example we will train/fine-tune the model on the [imagenette](https://github.com/fastai/imagenette) dataset.
Let's start by creating a model repository to save the trained model and logs.
Here we call the model `"vit-base-patch16-imagenette"`, but you can change the model name as you like.
You can do this either directly on [huggingface.co](https://huggingface.co/new) (assuming that
you are logged in) or via the command line:
```
huggingface-cli repo create vit-base-patch16-imagenette
```
Next we clone the model repository to add the tokenizer and model files.
```
git clone https://huggingface.co/<your-username>/vit-base-patch16-imagenette
```
To ensure that all tensorboard traces will be uploaded correctly, we need to
track them. You can run the following command inside your model repo to do so.
```
cd vit-base-patch16-imagenette
git lfs track "*tfevents*"
```
Great, we have set up our model repository. During training, we will automatically
push the training logs and model weights to the repo.
Next, let's add a symbolic link to the `run_image_classification_flax.py`.
```bash
export MODEL_DIR="./vit-base-patch16-imagenette
ln -s ~/transformers/examples/flax/summarization/run_image_classification_flax.py run_image_classification_flax.py
```
## Prepare the dataset
We will use the [imagenette](https://github.com/fastai/imagenette) dataset to train/fine-tune our model. Imagenette is a subset of 10 easily classified classes from Imagenet (tench, English springer, cassette player, chain saw, church, French horn, garbage truck, gas pump, golf ball, parachute).
@@ -86,7 +55,7 @@ Next we can run the example script to fine-tune the model:
```bash
python run_image_classification.py \
--output_dir ${MODEL_DIR} \
--output_dir ./vit-base-patch16-imagenette \
--model_name_or_path google/vit-base-patch16-224-in21k \
--train_dir="imagenette2/train" \
--validation_dir="imagenette2/val" \