distributed training (#74)

* [WIP] distributed * [WIP] distributed training * add script * add smaller script * fix device * fix DDP model setting * reorder model on device * remove model.model... * set print on process 0 * remove duplicate * model folder set on main process * test * model.update fix * exists_ok=True * add exist_ok * jz multinode fix * fix batch size splitting * isort and black * [WIP] work on trainers * [WIP] work on Distributed and trainers * add tets example with adversarial trainer * fix small issue * fix master addr environ * fix typo * fix update with DDP * udpate callback for distributed training * diplay progress per process * enhance display * [WIP] make CoupledAdv distributed * Cealn up trainers and add distributed training * fix piwae tests * fix test piwae * fix some tests * increase coverage * increase coverage * add predict on main process * apply balck and isort * update notebooks with batch_size * update reproducibility scripts * clean up * isort & black * update README * remove assert 0 * update distributed script * add wandb * update script * log only on main process * test batch size * loss dubugging * test with AE * test with adaptive batchsize * test with larger batch size * benchmark * benchmark perf * remove debug prints * redece learning rate * show results * new net * lr * remove sigm * lr * epochs * batch_size * new test * with sigm * test * test * retest * retest * with rank * test in trainer * retest * test * test * test no embedding * test * test distributed * debuggin * debug * not learnable codebook * fix typo * contiguous * fix issue * test inplace * no_grad( * debug * find unused * debug * test with dist_nn * remove find_unused * test with dist.nn * chekc rank * remove all_reduce * test with ddp * second all_reduce * async * add detach * add detach * test * debug * change * with einsum * contiguous * remove parameter * new test * debug * debug * add barrier * remove embeddings * update code * update * update * mass sanity check on all process * revert to good VQVAE * remove prints * add dist backend to script * reduce number of epoch in example * udpate doc * increase batch size in example * add other script * remove find_unused * test without unused * fix ununsed * add num_workers option to Training config * add num_workers to scripts * test with embedding * remove learned codebook * grad accumulation for benchmark * beanchmark * add grad accumulation * remove print * benchmark * remove num_workers * add FFHQ to benchmark * fix predict * fix predict * reduce number of samples in predict * add parser * add sigmoid * update config * add imagenet script * convert img to RGB * add sigmoid to decoder * increase batch size * change nets * change nets * add new script * add convert to RGB * update tests * clean up * prepare release * update doc * fix input_dim * last figures * doc fix
clementchadebec · Feb 6, 2023 · 08f805e · 08f805e
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diff --git a/README.md b/README.md
@@ -36,12 +36,16 @@ provides the possibility to perform benchmark experiments and comparisons by tra
 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. It integrates experiment monitoring tools such [wandb](https://wandb.ai/), [mlflow](https://mlflow.org/) or [comet-ml](https://www.comet.com/signup?utm_source=pythae&utm_medium=partner&utm_campaign=AMS_US_EN_SNUP_Pythae_Comet_Integration) 🧪 and allows model sharing and loading from the [HuggingFace Hub](https://huggingface.co/models) 🤗 in a few lines of code.
 
+**News** 📢
+
+As of v0.1.0, `Pythae` now supports distributed training using PyTorch's [DDP](https://pytorch.org/docs/stable/notes/ddp.html). You can now train your favorite VAE faster and on larger datasets, still with a few lines of code.
+See our speed-up [benchmark](#benchmark).
 
 ## Quick access:
 - [Installation](#installation)
 - [Implemented models](#available-models) / [Implemented samplers](#available-samplers)
 - [Reproducibility statement](#reproducibility) / [Results flavor](#results)
-- [Model training](#launching-a-model-training) / [Data generation](#launching-data-generation) / [Custom network architectures](#define-you-own-autoencoder-architecture)
+- [Model training](#launching-a-model-training) / [Data generation](#launching-data-generation) / [Custom network architectures](#define-you-own-autoencoder-architecture) / [Distributed training](#distributed-training-with-pythae)
 - [Model sharing with 🤗 Hub](#sharing-your-models-with-the-huggingface-hub-) / [Experiment tracking with `wandb`](#monitoring-your-experiments-with-wandb-) / [Experiment tracking with `mlflow`](#monitoring-your-experiments-with-mlflow-) / [Experiment tracking with `comet_ml`](#monitoring-your-experiments-with-comet_ml-)
 - [Tutorials](#getting-your-hands-on-the-code) / [Documentation](https://pythae.readthedocs.io/en/latest/)
 - [Contributing 🚀](#contributing-) / [Issues 🛠️](#dealing-with-issues-%EF%B8%8F)
@@ -141,8 +145,15 @@ To launch a model training, you only need to call a `TrainingPipeline` instance.
 ... output_dir='my_model',
 ... num_epochs=50,
 ... learning_rate=1e-3,
-... batch_size=200,
-... steps_saving=None
+... per_device_train_batch_size=200,
+... per_device_eval_batch_size=200,
+... train_dataloader_num_workers=2,
+... eval_dataloader_num_workers=2,
+... steps_saving=20,
+... optimizer_cls="AdamW",
+... optimizer_params={"weight_decay": 0.05, "betas": (0.91, 0.995)},
+... scheduler_cls="ReduceLROnPlateau",
+... scheduler_params={"patience": 5, "factor": 0.5}
 ... )
 >>> # Set up the model configuration 
 >>> my_vae_config = model_config = VAEConfig(
@@ -334,6 +345,44 @@ 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.
 
+## Distributed Training with `Pythae`
+As of `v0.1.0`, Pythae now supports distributed training using PyTorch's [DDP](https://pytorch.org/docs/stable/notes/ddp.html). It allows you to train your favorite VAE faster and on larger dataset using multi-gpu and/or multi-node training.
+
+To do so, you can build a python script that will then be launched by a launcher (such as `srun` on a cluster). The only thing that is needed in the script is to specify some elements relative to the distributed environment (such as the number of nodes/gpus) directly in the training configuration as follows
+
+```python
+>>> training_config = BaseTrainerConfig(
+... num_epochs=10,
+... learning_rate=1e-3,
+... per_device_train_batch_size=64,
+... per_device_eval_batch_size=64,
+... train_dataloader_num_workers=8,
+... eval_dataloader_num_workers=8,
+... dist_backend="nccl", # distributed backend
+... world_size=8 # number of gpus to use (n_nodes x n_gpus_per_node),
+... rank=5 # process/gpu id,
+... local_rank=1 # node id,
+... master_addr="localhost" # master address,
+... master_port="12345" # master port,
+... )
+```
+
+See this [example script](https://github.com/clementchadebec/benchmark_VAE/blob/main/examples/scripts/distributed_training_imagenet.py) that defines a multi-gpu VQVAE training on ImageNet dataset. Please note that the way the distributed environnement variables (`world_size`, `rank` ...) are recovered may be specific to the cluster and launcher you use. 
+
+### Benchmark
+
+Below are indicated the training times for a Vector Quantized VAE (VQ-VAE) with `Pythae` for 100 epochs on MNIST on V100 16GB GPU(s), for 50 epochs on [FFHQ](https://github.com/NVlabs/ffhq-dataset) (1024x1024 images) and for 20 epochs on [ImageNet-1k](https://huggingface.co/datasets/imagenet-1k) on V100 32GB GPU(s).
+
+| | Train Data | 1 GPU | 4 GPUs | 2x4 GPUs |
+|:---:|:---:|:---:|:---:|---|
+| MNIST (VQ-VAE) | 28x28 images (50k) | 235.18 s | 62.00 s | 35.86 s |
+| FFHQ 1024x1024 (VQVAE) | 1024x1024 RGB images (60k) | 19h 1min | 5h 6min | 2h 37min |
+| ImageNet-1k 128x128 (VQVAE) | 128x128 RGB images ($\approx$ 1.2M) | 6h 25min | 1h 41min | 51min 26s |
+
+
+For each dataset, we provide the benchmarking scripts [here](https://github.com/clementchadebec/benchmark_VAE/tree/main/examples/scripts)
+
+
 ## 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

diff --git a/docs/old/advanced/custom_autoencoder.rst b/docs/old/advanced/custom_autoencoder.rst
diff --git a/docs/old/advanced/setting_configs.rst b/docs/old/advanced/setting_configs.rst