Integration with HuggingFace Hub (#28)

* vaegan

* remove test_vae

* add auto config

* start hf hub integration

* model reloading refacto

* add hf hub to AutoModel

* add nf to AutoConfig

* remove typo

* add wandb tutorial

* add hf hub tutorial

* update gitignore

* Update README

* Update README

* Update README

* Update README

* remove hf hub and wandb from coverage compute

* update demo

* update tests with AutoModel

* remove wandb and hf hub from coverage compute

* update README

* black and isort formatting

* update demo

* add install command to demo

* add finish to wandb callback

* add test AutoModel for NF

* add install comands

* typo correction

* black

* fix typo

* fix logging in load_from_hf_hub

* add ModelOutput to AutoModel

* switch from dill to cloudpickle

* replace pickle by pickle5

* add pickle security to load_from_hf_hub

* fix pickling outside of __main__ scope

* black & isort

* add env config

* update test with env saving

* update setup

* isort & black

* add check to hf_load

* fix typo

* prepare release 0.0.2

* fix typo

.coveragerc

@@ -0,0 +1,3 @@
+[report]
+exclude_lines =
+    # pragma: no cover

.gitignore

@@ -33,7 +33,7 @@ examples/notebooks/dummy_output_dir/
 examples/notebooks/models_training/dummy_output_dir/
 logs/
 downloads_data/
-*wandb*
+*wandb
 *.slurm
 configs
 examples/scripts/generation_jz.py
@@ -48,6 +48,8 @@ results.ipynb
 examples/scripts/artifacts/*
 examples/scripts/plots/*
 examples/notebooks/my_model_with_custom_archi/
+examples/notebooks/my_model/
+examples/net.py
 
 
 

README.md

@@ -31,10 +31,10 @@
 
 # pythae 
 
-This library implements some of the most common (Variational) Autoencoder models. In particular it 
+This library implements some of the most common (Variational) Autoencoder models under a unified implementation. In particular, it 
 provides the possibility to perform benchmark experiments and comparisons by training 
 the models with the same autoencoding neural network architecture. The feature *make your own autoencoder* 
-allows you to train any of these models with your own data and own Encoder and Decoder neural networks.
+allows you to train any of these models with your own data and own Encoder and Decoder neural networks. It integrates an experiment monitoring tool  [wandb](https://wandb.ai/) 🧪 and allows model sharing and loading from the [HuggingFace Hub](https://huggingface.co/models) 🤗 in a few lines of code.
 
 
 # Installation
@@ -244,7 +244,7 @@ The samplers can be used with any model as long as it is suited. For instance, a
 ```
 
 
-## Define you own Autoencoder architecture
+## Define you own Autoencoder architecture 
 
 Pythae provides you the possibility to define your own neural networks within the VAE models. For instance, say you want to train a Wassertstein AE with a specific encoder and decoder, you can do the following:
 
@@ -315,18 +315,93 @@ You can also find predefined neural network architectures for the most common da
 ```
 Replace *mnist* by cifar or celeba to access to other neural nets.
 
-## Getting your hands on the code
+## Sharing your models with the HuggingFace Hub 🤗
+Pythae also allows you to share your models on the [HuggingFace Hub](https://huggingface.co/models). To do so you need:
+- a valid HuggingFace account
+- the package `huggingface_hub` installed in your virtual env. If not you can install it with 
+```
+$ python -m pip install huggingface_hub
+```
+- to be logged in to your HuggingFace account using
+```
+$ huggingface-cli login
+```
+
+### Uploading a model to the Hub
+Any pythae model can be easily uploaded using the method `push_to_hf_hub`
+```python
+>>> my_vae_model.push_to_hf_hub(hf_hub_path="your_hf_username/your_hf_hub_repo")
+```
+**Note:** If `your_hf_hub_repo` already exists and is not empty, files will be overridden. In case, 
+the repo `your_hf_hub_repo` does not exist, a folder having the same name will be created.
+
+### Downloading models from the Hub
+Equivalently, you can download or reload any Pythae's model directly from the Hub using the method `load_from_hf_hub`
+```python
+>>> from pythae.models import AutoModel
+>>> my_downloaded_vae = AutoModel.load_from_hf_hub(hf_hub_path="path_to_hf_repo")
+```
+
+## Monitoring your experiments with **Wandb** 🧪
+Pythae also integrates the experiement tracking tool [wandb](https://wandb.ai/) allowing users to store their configs, monitor their trainings and compare runs through a graphic interface. To be able use this feature you will need:
+- a valid wand account
+- the package `wandb` installed in your virtual env. If not you can install it with 
+```
+$ pip install wandb
+```
+- to be logged in to your wandb account using
+```
+$ wandb login
+```
+
+### Creating a `WandbCallback`
+Launching an experiment monitoring with `wandb` in pythae is pretty simple. The only thing a user needs to do is create a `WandbCallback` instance...
+
+```python
+>>> # Create you callback
+>>> from pythae.trainers.training_callbacks import WandbCallback
+>>> callbacks = [] # the TrainingPipeline expects a list of callbacks
+>>> wandb_cb = WandbCallback() # Build the callback 
+>>> # SetUp the callback 
+>>> wandb_cb.setup(
+...	training_config=your_training_config, # training config
+...	model_config=your_model_config, # model config
+...	project_name="your_wandb_project", # specify your wandb project
+...	entity_name="your_wandb_entity", # specify your wandb entity
+... )
+>>> callbacks.append(wandb_cb) # Add it to the callbacks list
+```
+...and then pass it to the `TrainingPipeline`.
+```python
+>>> pipeline = TrainingPipeline(
+...	training_config=config,
+...	model=model
+... )
+>>> pipeline(
+...	train_data=train_dataset,
+...	eval_data=eval_dataset,
+...	callbacks=callbacks # pass the callbacks to the TrainingPipeline and you are done!
+... )
+>>> # You can log to https://wandb.ai/your_wandb_entity/your_wandb_project to monitor your training
+```
+See a detailes tutorial 
+
+## Getting your hands on the code 
 
 To help you to understand the way pythae works and how you can train your models with this library we also
 provide tutorials:
 
 - [making_your_own_autoencoder.ipynb](https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks) shows you how to pass your own networks to the models implemented in pythae [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/making_your_own_autoencoder.ipynb)
 
+- [hf_hub_models_sharing.ipynb](https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks) shows you how to upload and download models for the HuggingFace Hub [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/hf_hub_models_sharing.ipynb)
+
+- [wandb_experiment_monitoring.ipynb](https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks) shows you how to monitor you experiments using `wandb` [![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/clementchadebec/benchmark_VAE/blob/main/examples/notebooks/wandb_experiment_monitoring.ipynb)
+
 - [models_training](https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/notebooks/models_training) folder provides notebooks showing how to train each implemented model and how to sample from it using `pythae.samplers`.
 
 - [scripts](https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts) folder provides in particular an example of a training script to train the models on benchmark data sets (mnist, cifar10, celeba ...)
 
-## Dealing with issues
+## Dealing with issues 🛠️
 
 If you are experiencing any issues while running the code or request new features/models to be implemented please [open an issue on github](https://github.com/clementchadebec/benchmark_VAE/issues).