Ignite.jl

Documentation for Ignite.jl.

Docstrings

Ignite.Ignite
Ignite.AbstractEvent
Ignite.AbstractFiringEvent
Ignite.AbstractLoopEvent
Ignite.AndEvent
Ignite.Engine
Ignite.EventHandler
Ignite.FilteredEvent
Ignite.OrEvent
Ignite.State
Ignite.add_event_handler!
Ignite.every_filter
Ignite.filter_event
Ignite.fire_event!
Ignite.fire_event_handler!
Ignite.getsomething!
Ignite.once_filter
Ignite.reset!
Ignite.run!
Ignite.terminate!
Ignite.throttle_filter
Ignite.timeout_filter
Ignite.@getsomething!

Ignite.Ignite — Module

Ignite.jl

Welcome to Ignite.jl, a Julia port of the Python library ignite for simplifying neural network training and validation loops using events and handlers.

Ignite.jl provides a simple yet flexible engine and event system, allowing for the easy composition of training pipelines with various events such as artifact saving, metric logging, and model validation. Event-based training abstracts away the training loop, replacing it with:

An engine which wraps a process function that consumes a single batch of data,
An iterable data loader which produces said batches of data, and
Events and corresponding event handlers which are attached to the engine, configured to fire at specific points during training.

Event handlers are much more flexibile compared to other approaches like callbacks: handlers can be any callable; multiple handlers can be attached to a single event; multiple events can trigger the same handler; and custom events can be defined to fire at user-specified points during training. This makes adding functionality to your training pipeline easy, minimizing the need to modify existing code.

Quick Start

The example below demonstrates how to use Ignite.jl to train a simple neural network. Key features to note:

The training step is factored out of the training loop: the train_step process function takes a batch of training data and computes the training loss, gradients, and updates the model parameters.
Data loaders can be any iterable collection. Here, we use a DataLoader from MLUtils.jl

using Ignite
using Flux, Zygote, Optimisers, MLUtils # for training a neural network

# Build simple neural network and initialize Adam optimizer
model = Chain(Dense(1 => 32, tanh), Dense(32 => 1))
optim = Flux.setup(Optimisers.Adam(1.0f-3), model)

# Create mock data and data loaders
f(x) = 2x - x^3
xtrain, xtest = 2 * rand(Float32, 1, 10_000) .- 1, collect(reshape(range(-1.0f0, 1.0f0; length = 100), 1, :))
ytrain, ytest = f.(xtrain), f.(xtest)
train_data_loader = DataLoader((; x = xtrain, y = ytrain); batchsize = 64, shuffle = true, partial = false)
eval_data_loader = DataLoader((; x = xtest, y = ytest); batchsize = 10, shuffle = false)

# Create training engine:
#   - `engine` is a reference to the parent `trainer` engine, created below
#   - `batch` is a batch of training data, retrieved by iterating `train_data_loader`
#   - (optional) return value is stored in `trainer.state.output`
function train_step(engine, batch)
    x, y = batch
    l, gs = Zygote.withgradient(m -> sum(abs2, m(x) .- y), model)
    Optimisers.update!(optim, model, gs[1])
    return Dict("loss" => l)
end
trainer = Engine(train_step)

# Start the training
Ignite.run!(trainer, train_data_loader; max_epochs = 25, epoch_length = 100)

Periodically evaluate model

The real power of Ignite.jl comes when adding event handlers to our training engine.

Let's evaluate our model after every 5th training epoch. This can be easily incorporated without needing to modify any of the above training code:

Create an evaluator engine which consumes batches of evaluation data
Add event handlers to the evaluator engine which accumulate a running average of evaluation metrics over batches of evaluation data; we use OnlineStats.jl to make this easy.
Add an event handler to the trainer which runs the evaluator on the evaluation data loader every 5 training epochs.

using OnlineStats: Mean, fit!, value # for tracking evaluation metrics

# Create an evaluation engine using `do` syntax:
evaluator = Engine() do engine, batch
    x, y = batch
    ypred = model(x) # evaluate model on a single batch of validation data
    return Dict("ytrue" => y, "ypred" => ypred) # result is stored in `evaluator.state.output`
end

# Add event handlers to the evaluation engine to track metrics:
add_event_handler!(evaluator, STARTED()) do engine
    # When `evaluator` starts, initialize the running mean
    engine.state.metrics = Dict("abs_err" => Mean()) # new fields can be added to `engine.state` dynamically
end

add_event_handler!(evaluator, ITERATION_COMPLETED()) do engine
    # Each iteration, compute eval metrics from predictions
    o = engine.state.output
    m = engine.state.metrics["abs_err"]
    fit!(m, abs.(o["ytrue"] .- o["ypred"]) |> vec)
end

# Add an event handler to `trainer` which runs `evaluator` every 5 epochs:
add_event_handler!(trainer, EPOCH_COMPLETED(; every = 5)) do engine
    Ignite.run!(evaluator, eval_data_loader)
    @info "Evaluation metrics: abs_err = $(evaluator.state.metrics["abs_err"])"
end

# Run the trainer with periodic evaluation
Ignite.run!(trainer, train_data_loader; max_epochs = 25, epoch_length = 100)

Terminating a run

There are several ways to stop a training run before it has completed:

Throw an exception as usual. This will immediately stop training. An EXCEPTION_RAISED() event will be subsequently be fired.
Use a keyboard interrupt, i.e. throw an InterruptException via Ctrl+C or Cmd+C. Training will halt, and an INTERRUPT() event will be fired.
Gracefully terminate via Ignite.terminate!(trainer), or equivalently, trainer.should_terminate = true. This will allow the current iteration to finish but no further iterations will begin. Then, a TERMINATE() event will be fired followed by a COMPLETED() event.

Early stopping

To implement early stopping, we can add an event handler to trainer which checks the evaluation metrics and gracefully terminates trainer if the metrics fail to improve. To do so, we first define a training termination trigger using Flux.early_stopping:

# Callback which returns `true` if the eval loss fails to decrease by
# at least `min_dist` for two consecutive evaluations
early_stop_trigger = Flux.early_stopping(2; init_score = Inf32, min_dist = 5.0f-3) do
    return value(evaluator.state.metrics["abs_err"])
end

Then, we add an event handler to trainer which checks the early stopping trigger and terminates training if the trigger returns true:

# This handler must fire every 5th epoch, the same as the evaluation event handler,
# to ensure new evaluation metrics are available
add_event_handler!(trainer, EPOCH_COMPLETED(; every = 5)) do engine
    if early_stop_trigger()
        @info "Stopping early"
        Ignite.terminate!(trainer)
    end
end

# Run the trainer with periodic evaluation and early stopping
Ignite.run!(trainer, train_data_loader; max_epochs = 25, epoch_length = 100)

Note: instead of adding a new event, the evaluation event handler from the previous section could have been modified to check early_stop_trigger() immediately after evaluator is run.

Artifact saving

Logging artifacts can be easily added to the trainer, again without modifying the above code. For example, save the current model and optimizer state to disk every 10 epochs using JLD2.jl:

using JLD2

# Save model and optimizer state every 10 epochs
add_event_handler!(trainer, EPOCH_COMPLETED(; every = 10)) do engine
    model_state = Flux.state(model)
    jldsave("model_and_optim.jld2"; model_state, optim)
    @info "Saved model and optimizer state to disk"
end

Trigger multiple functions per event

Multiple event handlers can be added to the same event:

add_event_handler!(trainer, COMPLETED()) do engine
    # Runs after training has completed
end
add_event_handler!(trainer, COMPLETED()) do engine
    # Also runs after training has completed, after the above function runs
end

Attach the same handler to multiple events

The boolean operators | and & can be used to combine events together:

add_event_handler!(trainer, EPOCH_COMPLETED(; every = 10) | COMPLETED()) do engine
    # Runs at the end of every 10th epoch, or when training is completed
end

throttled_event = EPOCH_COMPLETED(; every = 3) & EPOCH_COMPLETED(; event_filter = throttle_filter(30.0))
add_event_handler!(trainer, throttled_event) do engine
    # Runs at the end of every 3rd epoch if at least 30s has passed since the last firing
end

Define custom events

Custom events can be created and fired at user-defined stages in the training process.

For example, suppose we want to define events that fire at the start and finish of both the backward pass and the optimizer step. All we need to do is define new event types that subtype AbstractLoopEvent, and then fire them at appropriate points in the train_step process function using fire_event!:

struct BACKWARD_STARTED <: AbstractLoopEvent end
struct BACKWARD_COMPLETED <: AbstractLoopEvent end
struct OPTIM_STEP_STARTED <: AbstractLoopEvent end
struct OPTIM_STEP_COMPLETED <: AbstractLoopEvent end

function train_step(engine, batch)
    x, y = batch

    # Compute the gradients of the loss with respect to the model
    fire_event!(engine, BACKWARD_STARTED())
    l, gs = Zygote.withgradient(m -> sum(abs2, m(x) .- y), model)
    engine.state.gradients = gs # the engine state can be accessed by event handlers
    fire_event!(engine, BACKWARD_COMPLETED())

    # Update the model's parameters
    fire_event!(engine, OPTIM_STEP_STARTED())
    Optimisers.update!(optim, model, gs[1])
    fire_event!(engine, OPTIM_STEP_COMPLETED())

    return Dict("loss" => l)
end
trainer = Engine(train_step)

Then, add event handlers for these custom events as usual:

add_event_handler!(trainer, BACKWARD_COMPLETED(; every = 10)) do engine
    # This code runs after every 10th backward pass is completed
end

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