Ignite Your Networks!#
ignite
is a high-level library to help with training neural networks in PyTorch.
ignite helps you write compact but full-featured training loops in a few lines of code
you get a training loop with metrics, early-stopping, model checkpointing and other features without the boilerplate
Below we show a side-by-side comparison of using pure pytorch and using ignite to create a training loop to train and validate your model with occasional checkpointing:
As you can see, the code is more concise and readable with ignite. Furthermore, adding additional metrics, or things like early stopping is a breeze in ignite, but can start to rapidly increase the complexity of your code when “rolling your own” training loop.
Installation#
From pip:
pip install pytorch-ignite
From conda:
conda install ignite -c pytorch
From source:
pip install git+https://github.com/pytorch/ignite
Nightly releases#
From pip:
pip install --pre pytorch-ignite
From conda (this suggests to install pytorch nightly release instead of stable version as dependency):
conda install ignite -c pytorch-nightly
- ignite.engine
- ignite.handlers
- ignite.metrics
- ignite.distributed
has_xla_support
all_gather()
all_reduce()
available_backends()
backend()
barrier()
device()
finalize()
get_local_rank()
get_node_rank()
get_ntasks_per_node()
get_num_nodes()
get_rank()
get_world_size()
hostname()
initialize()
model_name()
one_rank_only()
set_local_rank()
show_config()
spawn()
sync()
- ignite.exceptions
- ignite.utils