Frequently asked questions -------------------------- Here we try to answer questions that come up often. If you still have questions after reading this, let us know! .. contents:: :local: :depth: 1 Which search algorithm/scheduler should I choose? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Ray Tune offers :ref:`many different search algorithms ` and :ref:`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 :ref:`ASHA for early termination of bad trials `. **If your model is large**, you can try to either use **Bayesian Optimization-based search algorithms** like :ref:`BayesOpt ` or :ref:`Dragonfly ` to get good parameter configurations after few trials. :ref:`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 :ref:`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 :ref:`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 :ref:`ASHA for early stopping ` for smaller problems. Use :ref:`BOHB ` for **larger problems** with a **small number of hyperparameters** and :ref:`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-5** and **1e-1**: ``tune.loguniform(1e-5, 1e-1)``. 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 use 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: .. code-block:: python 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 '''''''''''''''''' :ref:`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: .. code-block:: python 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: .. code-block:: python 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? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ **This is most likely applicable for the Tune function API.** 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. .. code-block:: bash Result for easy_objective_c64c9112: date: 2020-10-07_13-29-18 done: false experiment_id: 6edc31257b564bf8985afeec1df618ee experiment_tag: 7_activation=tanh,height=-53.116,steps=100,width=13.885 hostname: ubuntu iterations: 0 iterations_since_restore: 1 mean_loss: 4.688385317424468 neg_mean_loss: -4.688385317424468 node_ip: 192.168.1.115 pid: 5973 time_since_restore: 7.605552673339844e-05 time_this_iter_s: 7.605552673339844e-05 time_total_s: 7.605552673339844e-05 timestamp: 1602102558 timesteps_since_restore: 0 training_iteration: 1 trial_id: c64c9112 See the :ref:`tune-autofilled-metrics` section for a glossary. 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()``: .. code-block:: python tune.run( train_fn, resources_per_trial={ "cpu": 2, "gpu": 0.5, "extra_cpu": 2, "extra_gpu": 0 }) The example above showcases three things: 1. 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). 2. 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. 3. 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. How can I pass further parameter values to my trainable function? ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ **This is only applicable for the Tune function API.** Ray Tune expects your trainable functions to accept only up to two parameters, ``config`` and ``checkpoint_dir``. But sometimes there are cases where you want to pass constant arguments, like the number of epochs to run, or a dataset to train on. Ray Tune offers a wrapper function to achieve just that, called ``tune.with_parameters()``: .. code-block:: python from ray import tune import numpy as np def train(config, checkpoint_dir=None, num_epochs=10, data=None): for i in range(num_epochs): for sample in data: # ... train on sample # Some huge dataset data = np.random.random(size=100000000) tune.run( tune.with_parameters(train, num_epochs=10, data=data)) This function works similarly to ``functools.partial``, but it stores the parameters directly in the Ray object store. This means that you can pass even huge objects like datasets, and Ray makes sure that these are efficiently stored and retrieved on your cluster machines. How can I reproduce experiments ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Reproducing experiments and experiment results means that you get the exact same results when running an experiment again and again. To achieve this, the conditions have to be exactly the same each time you run the exeriment. In terms of ML training and tuning, this mostly concerns the random number generators that are used for sampling in various places of the training and tuning lifecycle. Random number generators are used to create randomness, for instance to sample a hyperparameter value for a parameter you defined. There is no true randomness in computing, rather there are sophisticated algorithms that generate numbers that *seem* to be random and fulfill all properties of a random distribution. These algorithms can be *seeded* with an initial state, after which the generated random numbers are always the same. .. code-block:: python import random random.seed(1234) print([random.randint(0, 100) for _ in range(10)]) # The output of this will always be # [99, 56, 14, 0, 11, 74, 4, 85, 88, 10] The most commonly used random number generators from Python libraries are those in the native ``random`` submodule and the ``numpy.random`` module. .. code-block:: python # This should suffice to initialize the RNGs for most Python-based libraries import random import numpy as np random.seed(1234) np.random.seed(5678) In your tuning and training run, there are several places where randomness occurrs, and at all these places we will have to introduce seeds to make sure we get the same behavior. * **Search algorithm**: Search algorithms have to be seeded to generate the same hyperparameter configurations in each run. Some search algorithms can be explicitly instantiated with a random seed (look for a ``seed`` parameter in the constructor). For others, try to use the above code block. * **Schedulers**: Schedulers like Population Based Training rely on resampling some of the parameters, requiring randomness. Use the code block above to set the initial seeds. * **Training function**: In addition to initializing the configurations, the training functions themselves have to use seeds. This could concern e.g. the data splitting. You should make sure to set the seed at the start of your training function. PyTorch and TensorFlow use their own RNGs, which have to be initialized, too: .. code-block:: python import torch torch.manual_seed(0) import tensorflow as tf tf.random.set_seed(0) You should thus seed both Ray Tune's schedulers and search algorithms, and the training code. The schedulers and search algorithms should always be seeded with the same seed. This is also true for the training code, but often it is beneficial that the seeds differ *between different training runs*. Here's a blueprint on how to do all this in your training code: .. code-block:: python import random import numpy as np from ray import tune def trainable(config): # config["seed"] is set deterministically, but differs between training runs random.seed(config["seed"]) np.random.seed(config["seed"]) # torch.manual_seed(config["seed"]) # ... training code config = { "seed": tune.randint(0, 10000), # ... } if __name__ == "__main__": # Set seed for the search algorithms/schedulers random.seed(1234) np.random.seed(1234) # Don't forget to check if the search alg has a `seed` parameter tune.run( trainable, config=config ) **Please note** that it is not always possible to control all sources of non-determinism. For instance, if you use schedulers like ASHA or PBT, some trials might finish earlier than other trials, affecting the behavior of the schedulers. Which trials finish first can however depend on the current system load, network communication, or other factors in the envrionment that we cannot control with random seeds. This is also true for search algorithms such as Bayesian Optimization, which take previous results into account when sampling new configurations. This can be tackled by using the **synchronous modes** of PBT and Hyperband, where the schedulers wait for all trials to finish an epoch before deciding which trials to promote. We strongly advise to try reproduction on smaller toy problems first before relying on it for larger experiments. Further Questions or Issues? ---------------------------- .. include:: /_help.rst