Tutorials & FAQ¶

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In this section, you can find material on how to use Tune and its various features. If any of the materials is out of date or broken, or if you’d like to add an example to this page, feel free to raise an issue on our Github repository.

Take a look at any of the below tutorials to get started with Tune.

Colab Exercises¶

Learn how to use Tune in your browser with the following Colab-based exercises.

Exercise Description	Library	Colab Link
Basics of using Tune.	TF/Keras
Using Search algorithms and Trial Schedulers to optimize your model.	Pytorch
Using Population-Based Training (PBT).	Pytorch
Fine-tuning Huggingface Transformers with PBT.	Huggingface Transformers/Pytorch

Tutorial source files can be found here.

What’s Next?¶

Check out:

User Guide & Configuring Tune: A comprehensive overview of Tune’s features.

Examples: End-to-end examples and templates for using Tune with your preferred machine learning library.

Frequently asked questions¶

Here we try to answer questions that come up often. If you still have questions after reading this, let us know!

Which search algorithm/scheduler should I choose?
How do I choose hyperparameter ranges?
How can I used nested/conditional search spaces?
How does early termination (e.g. Hyperband/ASHA) work?
Why are all my trials returning “1” iteration?
What are all these extra outputs?
How do I set resources?

Which search algorithm/scheduler should I choose?¶

Ray Tune offers many different search algorithms and schedulers. Deciding on which to use mostly depends on your problem:

Is it a small or large problem (how long does it take to train? How costly are the resources, like GPUs)? Can you run many trials in parallel?
How many hyperparameters would you like to tune?
What values are valid for hyperparameters?

If your model is small, you can usually try to run many different configurations. A random search can be used to generate configurations. You can also grid search over some values. You should probably still use ASHA for early termination of bad trials.

If your model is large, you can try to either use Bayesian Optimization-based search algorithms like BayesOpt or Dragonfly to get good parameter configurations after few trials. Ax is similar but more robust to noisy data. Please note that these algorithms only work well with a small number of hyperparameters. Alternatively, you can use Population Based Training which works well with few trials, e.g. 8 or even 4. However, this will output a hyperparameter schedule rather than one fixed set of hyperparameters.

If you have a small number of hyperparameters, Bayesian Optimization-methods work well. Take a look at BOHB to combine the benefits of bayesian optimization with early stopping.

If you only have continuous values for hyperparameters this will work well with most Bayesian-Optimization methods. Discrete or categorical variables still work, but less good with an increasing number of categories.

Our go-to solution is usually to use random search with ASHA for early stopping for smaller problems. Use BOHB for larger problems with a small number of hyperparameters and Population Based Training for larger problems with a large number of hyperparameters if a learning schedule is acceptable.

How do I choose hyperparameter ranges?¶

A good start is to look at the papers that introduced the algorithms, and also to see what other people are using.

Most algorithms also have sensible defaults for some of their parameters. For instance, XGBoost’s parameter overview reports to use max_depth=6 for the maximum decision tree depth. Here, anything between 2 and 10 might make sense (though that naturally depends on your problem).

For learning rates, we suggest using a loguniform distribution between 1e-1 and 1e-5: tune.loguniform(1e-1, 1e-5).

For batch sizes, we suggest trying powers of 2, for instance, 2, 4, 8, 16, 32, 64, 128, 256, etc. The magnitude depends on your problem. For easy problems with lots of data, use higher batch sizes, for harder problems with not so much data, use lower batch sizes.

For layer sizes we also suggest trying powers of 2. For small problems (e.g. Cartpole), use smaller layer sizes. For larger problems, try larger ones.

For discount factors in reinforcement learning we suggest sampling uniformly between 0.9 and 1.0. Depending on the problem, a much stricter range above 0.97 or oeven above 0.99 can make sense (e.g. for Atari).

How can I used nested/conditional search spaces?¶

Sometimes you might need to define parameters whose value depend on the value of other parameters. Ray Tune offers some methods to define these.

Nested spaces¶

You can nest hyperparameter definition in sub dictionaries:

config = {
    "a": {
        "x": tune.uniform(0, 10)
    },
    "b": tune.choice([1, 2, 3])
}

The trial config will be nested exactly like the input config.

Conditional spaces¶

Custom and conditional search spaces are explained in detail here. In short, you can pass custom functions to tune.sample_from() that can return values that depend on other values:

config = {
    "a": tune.randint(5, 10)
    "b": tune.sample_from(lambda spec: np.random.randint(0, spec.config.a))
}

Conditional grid search¶

If you would like to grid search over two parameters that depend on each other, this might not work out of the box. For instance say that a should be a value between 5 and 10 and b should be a value between 0 and a. In this case, we cannot use tune.sample_from because it doesn’t support grid searching.

The solution here is to create a list of valid tuples with the help of a helper function, like this:

def _iter():
    for a in range(5, 10):
        for b in range(a):
            yield a, b

config = {
    "ab": tune.grid_search(list(_iter())),
}

Your trainable then can do something like a, b = config["ab"] to split the a and b variables and use them afterwards.

How does early termination (e.g. Hyperband/ASHA) work?¶

Early termination algorithms look at the intermediately reported values, e.g. what is reported to them via tune.report() after each training epoch. After a certain number of steps, they then remove the worst performing trials and keep only the best performing trials. Goodness of a trial is determined by ordering them by the objective metric, for instance accuracy or loss.

In ASHA, you can decide how many trials are early terminated. reduction_factor=4 means that only 25% of all trials are kept each time they are reduced. With grace_period=n you can force ASHA to train each trial at least for n epochs.

Why are all my trials returning “1” iteration?¶

Ray Tune counts iterations internally every time tune.report() is called. If you only call tune.report() once at the end of the training, the counter has only been incremented once. If you’re using the class API, the counter is increased after calling step().

Note that it might make sense to report metrics more often than once. For instance, if you train your algorithm for 1000 timesteps, consider reporting intermediate performance values every 100 steps. That way, schedulers like Hyperband/ASHA can terminate bad performing trials early.

What are all these extra outputs?¶

You’ll notice that Ray Tune not only reports hyperparameters (from the config) or metrics (passed to tune.report()), but also some other outputs. The Trial.last_result dictionary contains the following additional outputs:

config: The hyperparameter configuration
date: String-formatted date and time when the result was processed
done: True if the trial has been finished, False otherwise
episodes_total: Total number of episodes (for RLLib trainables)
experiment_id: Unique experiment ID
experiment_tag: Unique experiment tag (includes parameter values)
hostname: Hostname of the worker
iterations_since_restore: The number of times tune.report() has been called after restoring the run from a checkpoint
node_ip: Host IP of the worker
pid: Process ID (PID) of the worker process
time_since_restore: Time in seconds since restoring from a checkpoint.
time_this_iter_s: Runtime of the current training iteration in seconds (i.e. one call to the trainable function or to _train() in the class API.
time_total_s: Total runtime in seconds.
timestamp: Timestamp when the result was processed
timesteps_since_restore: Number of timesteps since restoring from a checkpoint
timesteps_total: Total number of timesteps
training_iteration: The number of times tune.report() has been called
trial_id: Unique trial ID

How do I set resources?¶

If you want to allocate specific resources to a trial, you can use the resources_per_trial parameter of tune.run():

tune.run(
    train_fn,
    resources_per_trial={
        "cpu": 2,
        "gpu": 0.5,
        "extra_cpu": 2,
        "extra_gpu": 0
    })

The example above showcases three things:

The cpu and gpu options set how many CPUs and GPUs are available for each trial, respectively. Trials cannot request more resources than these (exception: see 3).
It is possible to request fractional GPUs. A value of 0.5 means that half of the memory of the GPU is made available to the trial. You will have to make sure yourself that your model still fits on the fractional memory.
You can request extra resources that are reserved for the trial. This is useful if your trainable starts another process that requires resources. This is for instance the case in some distributed computing settings, including when using RaySGD.

One important thing to keep in mind is that each Ray worker (and thus each Ray Tune Trial) will only be scheduled on one machine. That means if you for instance request 2 GPUs for your trial, but your cluster consists of 4 machines with 1 GPU each, the trial will never be scheduled.

In other words, you will have to make sure that your Ray cluster has machines that can actually fulfill your resource requests.

Further Questions or Issues?¶

Reach out to us if you have any questions or issues or feedback through the following channels:

StackOverflow: For questions about how to use Ray.
GitHub Issues: For bug reports and feature requests.

Ray v1.0.0