.. _sec_custom_advancedhpo: Getting started with Advanced HPO Algorithms ============================================ This tutorial provides a complete example of how to use AutoGluon's state-of-the-art hyperparameter optimization (HPO) algorithms to tune a basic Multi-Layer Perceptron (MLP) model, which is the most basic type of neural network. Loading libraries ----------------- .. code:: python # Basic utils for folder manipulations etc import time import multiprocessing # to count the number of CPUs available # External tools to load and process data import numpy as np import pandas as pd # MXNet (NeuralNets) import mxnet as mx from mxnet import gluon, autograd from mxnet.gluon import nn # AutoGluon and HPO tools import autogluon.core as ag from autogluon.mxnet.utils import load_and_split_openml_data Check the version of MxNet, you should be fine with version >= 1.5 .. code:: python mx.__version__ .. parsed-literal:: :class: output '1.7.0' You can also check the version of AutoGluon and the specific commit and check that it matches what you want. .. code:: python import autogluon.core.version ag.version.__version__ .. parsed-literal:: :class: output '0.1.0b20210224' Hyperparameter Optimization of a 2-layer MLP -------------------------------------------- Setting up the context ~~~~~~~~~~~~~~~~~~~~~~ Here we declare a few "environment variables" setting the context for what we're doing .. code:: python OPENML_TASK_ID = 6 # describes the problem we will tackle RATIO_TRAIN_VALID = 0.33 # split of the training data used for validation RESOURCE_ATTR_NAME = 'epoch' # how do we measure resources (will become clearer further) REWARD_ATTR_NAME = 'objective' # how do we measure performance (will become clearer further) NUM_CPUS = multiprocessing.cpu_count() Preparing the data ~~~~~~~~~~~~~~~~~~ We will use a multi-way classification task from OpenML. Data preparation includes: - Missing values are imputed, using the 'mean' strategy of ``sklearn.impute.SimpleImputer`` - Split training set into training and validation - Standardize inputs to mean 0, variance 1 .. code:: python X_train, X_valid, y_train, y_valid, n_classes = load_and_split_openml_data( OPENML_TASK_ID, RATIO_TRAIN_VALID, download_from_openml=False) n_classes .. parsed-literal:: :class: output 100%|██████████| 704/704 [00:00<00:00, 34971.16KB/s] 100%|██████████| 2521/2521 [00:00<00:00, 11286.95KB/s] 3KB [00:00, 4398.08KB/s] 8KB [00:00, 12056.93KB/s] 15KB [00:00, 12370.15KB/s] 2998KB [00:00, 23933.10KB/s] 881KB [00:00, 48962.26KB/s] 3KB [00:00, 4228.13KB/s] .. parsed-literal:: :class: output 26 The problem has 26 classes. Declaring a model specifying a hyperparameter space with AutoGluon ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Two layer MLP where we optimize over: - the number of units on the first layer - the number of units on the second layer - the dropout rate after each layer - the learning rate - the scaling - the ``@ag.args`` decorator allows us to specify the space we will optimize over, this matches the `ConfigSpace `__ syntax The body of the function ``run_mlp_openml`` is pretty simple: - it reads the hyperparameters given via the decorator - it defines a 2 layer MLP with dropout - it declares a trainer with the 'adam' loss function and a provided learning rate - it trains the NN with a number of epochs (most of that is boilerplate code from ``mxnet``) - the ``reporter`` at the end is used to keep track of training history in the hyperparameter optimization **Note**: The number of epochs and the hyperparameter space are reduced to make for a shorter experiment .. code:: python @ag.args(n_units_1=ag.space.Int(lower=16, upper=128), n_units_2=ag.space.Int(lower=16, upper=128), dropout_1=ag.space.Real(lower=0, upper=.75), dropout_2=ag.space.Real(lower=0, upper=.75), learning_rate=ag.space.Real(lower=1e-6, upper=1, log=True), batch_size=ag.space.Int(lower=8, upper=128), scale_1=ag.space.Real(lower=0.001, upper=10, log=True), scale_2=ag.space.Real(lower=0.001, upper=10, log=True), epochs=9) def run_mlp_openml(args, reporter, **kwargs): # Time stamp for elapsed_time ts_start = time.time() # Unwrap hyperparameters n_units_1 = args.n_units_1 n_units_2 = args.n_units_2 dropout_1 = args.dropout_1 dropout_2 = args.dropout_2 scale_1 = args.scale_1 scale_2 = args.scale_2 batch_size = args.batch_size learning_rate = args.learning_rate ctx = mx.cpu() net = nn.Sequential() with net.name_scope(): # Layer 1 net.add(nn.Dense(n_units_1, activation='relu', weight_initializer=mx.initializer.Uniform(scale=scale_1))) # Dropout net.add(gluon.nn.Dropout(dropout_1)) # Layer 2 net.add(nn.Dense(n_units_2, activation='relu', weight_initializer=mx.initializer.Uniform(scale=scale_2))) # Dropout net.add(gluon.nn.Dropout(dropout_2)) # Output net.add(nn.Dense(n_classes)) net.initialize(ctx=ctx) trainer = gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': learning_rate}) for epoch in range(args.epochs): ts_epoch = time.time() train_iter = mx.io.NDArrayIter( data={'data': X_train}, label={'label': y_train}, batch_size=batch_size, shuffle=True) valid_iter = mx.io.NDArrayIter( data={'data': X_valid}, label={'label': y_valid}, batch_size=batch_size, shuffle=False) metric = mx.metric.Accuracy() loss = gluon.loss.SoftmaxCrossEntropyLoss() for batch in train_iter: data = batch.data[0].as_in_context(ctx) label = batch.label[0].as_in_context(ctx) with autograd.record(): output = net(data) L = loss(output, label) L.backward() trainer.step(data.shape[0]) metric.update([label], [output]) name, train_acc = metric.get() metric = mx.metric.Accuracy() for batch in valid_iter: data = batch.data[0].as_in_context(ctx) label = batch.label[0].as_in_context(ctx) output = net(data) metric.update([label], [output]) name, val_acc = metric.get() print('Epoch %d ; Time: %f ; Training: %s=%f ; Validation: %s=%f' % ( epoch + 1, time.time() - ts_start, name, train_acc, name, val_acc)) ts_now = time.time() eval_time = ts_now - ts_epoch elapsed_time = ts_now - ts_start # The resource reported back (as 'epoch') is the number of epochs # done, starting at 1 reporter( epoch=epoch + 1, objective=float(val_acc), eval_time=eval_time, time_step=ts_now, elapsed_time=elapsed_time) **Note**: The annotation ``epochs=9`` specifies the maximum number of epochs for training. It becomes available as ``args.epochs``. Importantly, it is also processed by ``HyperbandScheduler`` below in order to set its ``max_t`` attribute. **Recommendation**: Whenever writing training code to be passed as ``train_fn`` to a scheduler, if this training code reports a resource (or time) attribute, the corresponding maximum resource value should be included in ``train_fn.args``: - If the resource attribute (``time_attr`` of scheduler) in ``train_fn`` is ``epoch``, make sure to include ``epochs=XYZ`` in the annotation. This allows the scheduler to read ``max_t`` from ``train_fn.args.epochs``. This case corresponds to our example here. - If the resource attribute is something else than ``epoch``, you can also include the annotation ``max_t=XYZ``, which allows the scheduler to read ``max_t`` from ``train_fn.args.max_t``. Annotating the training function by the correct value for ``max_t`` simplifies scheduler creation (since ``max_t`` does not have to be passed), and avoids inconsistencies between ``train_fn`` and the scheduler. Running the Hyperparameter Optimization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ You can use the following schedulers: - FIFO (``fifo``) - Hyperband (either the stopping (``hbs``) or promotion (``hbp``) variant) And the following searchers: - Random search (``random``) - Gaussian process based Bayesian optimization (``bayesopt``) - SkOpt Bayesian optimization (``skopt``; only with FIFO scheduler) Note that the method known as (asynchronous) Hyperband is using random search. Combining Hyperband scheduling with the ``bayesopt`` searcher uses a novel method called asynchronous BOHB. Pick the combination you're interested in (doing the full experiment takes around 120 seconds, see the ``time_out`` parameter), running everything with multiple runs can take a fair bit of time. In real life, you will want to choose a larger ``time_out`` in order to obtain good performance. .. code:: python SCHEDULER = "hbs" SEARCHER = "bayesopt" .. code:: python def compute_error(df): return 1.0 - df["objective"] def compute_runtime(df, start_timestamp): return df["time_step"] - start_timestamp def process_training_history(task_dicts, start_timestamp, runtime_fn=compute_runtime, error_fn=compute_error): task_dfs = [] for task_id in task_dicts: task_df = pd.DataFrame(task_dicts[task_id]) task_df = task_df.assign(task_id=task_id, runtime=runtime_fn(task_df, start_timestamp), error=error_fn(task_df), target_epoch=task_df["epoch"].iloc[-1]) task_dfs.append(task_df) result = pd.concat(task_dfs, axis="index", ignore_index=True, sort=True) # re-order by runtime result = result.sort_values(by="runtime") # calculate incumbent best -- the cumulative minimum of the error. result = result.assign(best=result["error"].cummin()) return result resources = dict(num_cpus=NUM_CPUS, num_gpus=0) .. code:: python search_options = { 'num_init_random': 2, 'debug_log': True} if SCHEDULER == 'fifo': myscheduler = ag.scheduler.FIFOScheduler( run_mlp_openml, resource=resources, searcher=SEARCHER, search_options=search_options, time_out=120, time_attr=RESOURCE_ATTR_NAME, reward_attr=REWARD_ATTR_NAME) else: # This setup uses rung levels at 1, 3, 9 epochs. We just use a single # bracket, so this is in fact successive halving (Hyperband would use # more than 1 bracket). # Also note that since we do not use the max_t argument of # HyperbandScheduler, this value is obtained from train_fn.args.epochs. sch_type = 'stopping' if SCHEDULER == 'hbs' else 'promotion' myscheduler = ag.scheduler.HyperbandScheduler( run_mlp_openml, resource=resources, searcher=SEARCHER, search_options=search_options, time_out=120, time_attr=RESOURCE_ATTR_NAME, reward_attr=REWARD_ATTR_NAME, type=sch_type, grace_period=1, reduction_factor=3, brackets=1) # run tasks myscheduler.run() myscheduler.join_jobs() results_df = process_training_history( myscheduler.training_history.copy(), start_timestamp=myscheduler._start_time) .. parsed-literal:: :class: output /var/lib/jenkins/workspace/workspace/autogluon-tutorial-course-v3/venv/lib/python3.8/site-packages/distributed/worker.py:3451: UserWarning: Large object of size 1.30 MB detected in task graph: (0, , { ... sReporter}, []) Consider scattering large objects ahead of time with client.scatter to reduce scheduler burden and keep data on workers future = client.submit(func, big_data) # bad big_future = client.scatter(big_data) # good future = client.submit(func, big_future) # good warnings.warn( .. parsed-literal:: :class: output Epoch 1 ; Time: 0.501428 ; Training: accuracy=0.260079 ; Validation: accuracy=0.531250 Epoch 2 ; Time: 0.927829 ; Training: accuracy=0.496365 ; Validation: accuracy=0.655247 Epoch 3 ; Time: 1.340136 ; Training: accuracy=0.559650 ; Validation: accuracy=0.694686 Epoch 4 ; Time: 1.753211 ; Training: accuracy=0.588896 ; Validation: accuracy=0.711063 Epoch 5 ; Time: 2.168135 ; Training: accuracy=0.609385 ; Validation: accuracy=0.726939 Epoch 6 ; Time: 2.633001 ; Training: accuracy=0.628139 ; Validation: accuracy=0.745321 Epoch 7 ; Time: 3.104744 ; Training: accuracy=0.641193 ; Validation: accuracy=0.750501 Epoch 8 ; Time: 3.582661 ; Training: accuracy=0.653751 ; Validation: accuracy=0.763202 Epoch 9 ; Time: 4.002226 ; Training: accuracy=0.665482 ; Validation: accuracy=0.766043 Epoch 1 ; Time: 0.920221 ; Training: accuracy=0.271154 ; Validation: accuracy=0.443771 Epoch 1 ; Time: 0.354071 ; Training: accuracy=0.045066 ; Validation: accuracy=0.045047 Epoch 1 ; Time: 0.612909 ; Training: accuracy=0.038997 ; Validation: accuracy=0.046671 Epoch 1 ; Time: 1.098973 ; Training: accuracy=0.045658 ; Validation: accuracy=0.035450 Epoch 1 ; Time: 0.378468 ; Training: accuracy=0.040652 ; Validation: accuracy=0.036634 Epoch 1 ; Time: 2.718110 ; Training: accuracy=0.282589 ; Validation: accuracy=0.425758 Epoch 2 ; Time: 5.402659 ; Training: accuracy=0.345902 ; Validation: accuracy=0.548485 Epoch 3 ; Time: 8.251918 ; Training: accuracy=0.355929 ; Validation: accuracy=0.511279 Epoch 1 ; Time: 0.352776 ; Training: accuracy=0.142408 ; Validation: accuracy=0.183016 Epoch 1 ; Time: 0.664635 ; Training: accuracy=0.444048 ; Validation: accuracy=0.599331 Epoch 2 ; Time: 1.269985 ; Training: accuracy=0.567225 ; Validation: accuracy=0.672575 Epoch 3 ; Time: 1.825110 ; Training: accuracy=0.588694 ; Validation: accuracy=0.694147 Epoch 4 ; Time: 2.358906 ; Training: accuracy=0.618700 ; Validation: accuracy=0.707525 Epoch 5 ; Time: 2.880280 ; Training: accuracy=0.624503 ; Validation: accuracy=0.706689 Epoch 6 ; Time: 3.423817 ; Training: accuracy=0.636356 ; Validation: accuracy=0.725920 Epoch 7 ; Time: 3.971068 ; Training: accuracy=0.650365 ; Validation: accuracy=0.734950 Epoch 8 ; Time: 4.567538 ; Training: accuracy=0.665948 ; Validation: accuracy=0.740970 Epoch 9 ; Time: 5.179614 ; Training: accuracy=0.661887 ; Validation: accuracy=0.750836 Epoch 1 ; Time: 1.618978 ; Training: accuracy=0.036318 ; Validation: accuracy=0.038552 Epoch 1 ; Time: 0.631645 ; Training: accuracy=0.422562 ; Validation: accuracy=0.676134 Epoch 2 ; Time: 1.199739 ; Training: accuracy=0.523967 ; Validation: accuracy=0.716807 Epoch 3 ; Time: 1.771595 ; Training: accuracy=0.547521 ; Validation: accuracy=0.709580 Epoch 4 ; Time: 2.350334 ; Training: accuracy=0.559835 ; Validation: accuracy=0.723866 Epoch 5 ; Time: 2.924589 ; Training: accuracy=0.580826 ; Validation: accuracy=0.728739 Epoch 6 ; Time: 3.560243 ; Training: accuracy=0.589008 ; Validation: accuracy=0.757479 Epoch 7 ; Time: 4.141949 ; Training: accuracy=0.585041 ; Validation: accuracy=0.754286 Epoch 8 ; Time: 4.742064 ; Training: accuracy=0.588430 ; Validation: accuracy=0.753109 Epoch 9 ; Time: 5.323142 ; Training: accuracy=0.609339 ; Validation: accuracy=0.752101 Epoch 1 ; Time: 0.348901 ; Training: accuracy=0.083554 ; Validation: accuracy=0.060438 Epoch 1 ; Time: 0.550358 ; Training: accuracy=0.040368 ; Validation: accuracy=0.033478 Epoch 1 ; Time: 0.421942 ; Training: accuracy=0.326778 ; Validation: accuracy=0.625708 Epoch 2 ; Time: 0.779967 ; Training: accuracy=0.442122 ; Validation: accuracy=0.680986 Epoch 3 ; Time: 1.137134 ; Training: accuracy=0.471949 ; Validation: accuracy=0.682651 Epoch 1 ; Time: 0.376085 ; Training: accuracy=0.451456 ; Validation: accuracy=0.705557 Epoch 2 ; Time: 0.693395 ; Training: accuracy=0.575613 ; Validation: accuracy=0.695347 Epoch 3 ; Time: 1.009008 ; Training: accuracy=0.588695 ; Validation: accuracy=0.683964 Epoch 1 ; Time: 0.637191 ; Training: accuracy=0.102950 ; Validation: accuracy=0.183339 Epoch 1 ; Time: 0.330724 ; Training: accuracy=0.255921 ; Validation: accuracy=0.496343 Epoch 2 ; Time: 0.591566 ; Training: accuracy=0.431743 ; Validation: accuracy=0.590426 Epoch 3 ; Time: 0.876340 ; Training: accuracy=0.490461 ; Validation: accuracy=0.632812 Epoch 1 ; Time: 0.418901 ; Training: accuracy=0.411633 ; Validation: accuracy=0.682151 Epoch 2 ; Time: 0.740164 ; Training: accuracy=0.630340 ; Validation: accuracy=0.740593 Epoch 3 ; Time: 1.064412 ; Training: accuracy=0.675370 ; Validation: accuracy=0.768731 Epoch 4 ; Time: 1.385570 ; Training: accuracy=0.696274 ; Validation: accuracy=0.787379 Epoch 5 ; Time: 1.703562 ; Training: accuracy=0.713955 ; Validation: accuracy=0.803530 Epoch 6 ; Time: 2.022915 ; Training: accuracy=0.719078 ; Validation: accuracy=0.814186 Epoch 7 ; Time: 2.342436 ; Training: accuracy=0.731306 ; Validation: accuracy=0.818182 Epoch 8 ; Time: 2.655205 ; Training: accuracy=0.739651 ; Validation: accuracy=0.824675 Epoch 9 ; Time: 2.976654 ; Training: accuracy=0.745105 ; Validation: accuracy=0.821012 Epoch 1 ; Time: 0.568247 ; Training: accuracy=0.284332 ; Validation: accuracy=0.640879 Epoch 2 ; Time: 1.057711 ; Training: accuracy=0.422489 ; Validation: accuracy=0.690888 Epoch 3 ; Time: 1.538753 ; Training: accuracy=0.466970 ; Validation: accuracy=0.714717 Epoch 4 ; Time: 2.017047 ; Training: accuracy=0.477305 ; Validation: accuracy=0.720759 Epoch 5 ; Time: 2.499402 ; Training: accuracy=0.493262 ; Validation: accuracy=0.713207 Epoch 6 ; Time: 2.978205 ; Training: accuracy=0.499215 ; Validation: accuracy=0.723947 Epoch 7 ; Time: 3.475100 ; Training: accuracy=0.514924 ; Validation: accuracy=0.727807 Epoch 8 ; Time: 3.956110 ; Training: accuracy=0.513683 ; Validation: accuracy=0.727303 Epoch 9 ; Time: 4.434170 ; Training: accuracy=0.523687 ; Validation: accuracy=0.739386 Epoch 1 ; Time: 0.300867 ; Training: accuracy=0.145724 ; Validation: accuracy=0.513132 Epoch 1 ; Time: 0.633698 ; Training: accuracy=0.133284 ; Validation: accuracy=0.388135 Epoch 1 ; Time: 0.380906 ; Training: accuracy=0.203834 ; Validation: accuracy=0.487845 Epoch 1 ; Time: 0.304531 ; Training: accuracy=0.106250 ; Validation: accuracy=0.178358 Epoch 1 ; Time: 0.311316 ; Training: accuracy=0.414824 ; Validation: accuracy=0.701003 Epoch 2 ; Time: 0.600304 ; Training: accuracy=0.626584 ; Validation: accuracy=0.752676 Epoch 3 ; Time: 0.855273 ; Training: accuracy=0.670311 ; Validation: accuracy=0.784950 Epoch 4 ; Time: 1.170644 ; Training: accuracy=0.711553 ; Validation: accuracy=0.810033 Epoch 5 ; Time: 1.421779 ; Training: accuracy=0.737226 ; Validation: accuracy=0.830936 Epoch 6 ; Time: 1.670517 ; Training: accuracy=0.757350 ; Validation: accuracy=0.839967 Epoch 7 ; Time: 1.920087 ; Training: accuracy=0.772257 ; Validation: accuracy=0.855686 Epoch 8 ; Time: 2.179702 ; Training: accuracy=0.776646 ; Validation: accuracy=0.866388 Epoch 9 ; Time: 2.447286 ; Training: accuracy=0.792050 ; Validation: accuracy=0.872575 Epoch 1 ; Time: 0.397302 ; Training: accuracy=0.358558 ; Validation: accuracy=0.606426 Epoch 2 ; Time: 0.761423 ; Training: accuracy=0.656296 ; Validation: accuracy=0.707162 Epoch 3 ; Time: 1.108681 ; Training: accuracy=0.733042 ; Validation: accuracy=0.769076 Epoch 4 ; Time: 1.447953 ; Training: accuracy=0.773725 ; Validation: accuracy=0.803715 Epoch 5 ; Time: 1.781177 ; Training: accuracy=0.801205 ; Validation: accuracy=0.822959 Epoch 6 ; Time: 2.155372 ; Training: accuracy=0.826622 ; Validation: accuracy=0.835843 Epoch 7 ; Time: 2.515275 ; Training: accuracy=0.840238 ; Validation: accuracy=0.851908 Epoch 8 ; Time: 2.888833 ; Training: accuracy=0.859383 ; Validation: accuracy=0.867135 Epoch 9 ; Time: 3.239732 ; Training: accuracy=0.868460 ; Validation: accuracy=0.876841 Epoch 1 ; Time: 0.564663 ; Training: accuracy=0.101025 ; Validation: accuracy=0.170699 Epoch 1 ; Time: 0.291451 ; Training: accuracy=0.367434 ; Validation: accuracy=0.627992 Epoch 2 ; Time: 0.523886 ; Training: accuracy=0.537171 ; Validation: accuracy=0.725399 Epoch 3 ; Time: 0.750020 ; Training: accuracy=0.586760 ; Validation: accuracy=0.749003 Epoch 4 ; Time: 0.982082 ; Training: accuracy=0.610773 ; Validation: accuracy=0.767453 Epoch 5 ; Time: 1.211215 ; Training: accuracy=0.642105 ; Validation: accuracy=0.781582 Epoch 6 ; Time: 1.444432 ; Training: accuracy=0.654934 ; Validation: accuracy=0.803025 Epoch 7 ; Time: 1.668773 ; Training: accuracy=0.663405 ; Validation: accuracy=0.786237 Epoch 8 ; Time: 1.895919 ; Training: accuracy=0.681250 ; Validation: accuracy=0.824136 Epoch 9 ; Time: 2.221158 ; Training: accuracy=0.697451 ; Validation: accuracy=0.800864 Epoch 1 ; Time: 0.691166 ; Training: accuracy=0.409061 ; Validation: accuracy=0.644657 Epoch 2 ; Time: 1.289294 ; Training: accuracy=0.623347 ; Validation: accuracy=0.729503 Epoch 3 ; Time: 1.900382 ; Training: accuracy=0.686425 ; Validation: accuracy=0.771673 Epoch 4 ; Time: 2.599052 ; Training: accuracy=0.716766 ; Validation: accuracy=0.786290 Epoch 5 ; Time: 3.277293 ; Training: accuracy=0.742146 ; Validation: accuracy=0.807964 Epoch 6 ; Time: 3.935235 ; Training: accuracy=0.759011 ; Validation: accuracy=0.823421 Epoch 7 ; Time: 4.589851 ; Training: accuracy=0.773892 ; Validation: accuracy=0.835349 Epoch 8 ; Time: 5.233963 ; Training: accuracy=0.786045 ; Validation: accuracy=0.849462 Epoch 9 ; Time: 5.910367 ; Training: accuracy=0.792328 ; Validation: accuracy=0.856351 Epoch 1 ; Time: 0.288102 ; Training: accuracy=0.405139 ; Validation: accuracy=0.701793 Epoch 2 ; Time: 0.520498 ; Training: accuracy=0.622379 ; Validation: accuracy=0.768805 Epoch 3 ; Time: 0.757052 ; Training: accuracy=0.675673 ; Validation: accuracy=0.787234 Epoch 4 ; Time: 0.990964 ; Training: accuracy=0.702942 ; Validation: accuracy=0.807673 Epoch 5 ; Time: 1.214895 ; Training: accuracy=0.716038 ; Validation: accuracy=0.819233 Epoch 6 ; Time: 1.439561 ; Training: accuracy=0.735930 ; Validation: accuracy=0.834143 Epoch 7 ; Time: 1.666994 ; Training: accuracy=0.743722 ; Validation: accuracy=0.846708 Epoch 8 ; Time: 1.908782 ; Training: accuracy=0.752176 ; Validation: accuracy=0.852739 Epoch 9 ; Time: 2.175746 ; Training: accuracy=0.755574 ; Validation: accuracy=0.851734 Epoch 1 ; Time: 0.384220 ; Training: accuracy=0.418733 ; Validation: accuracy=0.717406 Epoch 2 ; Time: 0.689044 ; Training: accuracy=0.598876 ; Validation: accuracy=0.777218 Epoch 3 ; Time: 0.990826 ; Training: accuracy=0.648727 ; Validation: accuracy=0.795027 Epoch 4 ; Time: 1.283572 ; Training: accuracy=0.673280 ; Validation: accuracy=0.820060 Epoch 5 ; Time: 1.578003 ; Training: accuracy=0.701802 ; Validation: accuracy=0.832157 Epoch 6 ; Time: 1.870872 ; Training: accuracy=0.715112 ; Validation: accuracy=0.847110 Epoch 7 ; Time: 2.225747 ; Training: accuracy=0.733383 ; Validation: accuracy=0.857023 Epoch 8 ; Time: 2.548026 ; Training: accuracy=0.745949 ; Validation: accuracy=0.867776 Epoch 9 ; Time: 2.934313 ; Training: accuracy=0.743882 ; Validation: accuracy=0.858535 Epoch 1 ; Time: 0.510480 ; Training: accuracy=0.623262 ; Validation: accuracy=0.791667 Epoch 2 ; Time: 0.878617 ; Training: accuracy=0.746523 ; Validation: accuracy=0.837833 Epoch 3 ; Time: 1.245148 ; Training: accuracy=0.775993 ; Validation: accuracy=0.863833 Epoch 4 ; Time: 1.604610 ; Training: accuracy=0.792964 ; Validation: accuracy=0.878500 Epoch 5 ; Time: 2.042608 ; Training: accuracy=0.814983 ; Validation: accuracy=0.886000 Epoch 6 ; Time: 2.405598 ; Training: accuracy=0.819950 ; Validation: accuracy=0.877667 Epoch 7 ; Time: 2.781806 ; Training: accuracy=0.822599 ; Validation: accuracy=0.880000 Epoch 8 ; Time: 3.150146 ; Training: accuracy=0.832368 ; Validation: accuracy=0.875167 Epoch 9 ; Time: 3.502203 ; Training: accuracy=0.837748 ; Validation: accuracy=0.899000 Epoch 1 ; Time: 0.310339 ; Training: accuracy=0.645217 ; Validation: accuracy=0.791806 Epoch 2 ; Time: 0.567796 ; Training: accuracy=0.814990 ; Validation: accuracy=0.843478 Epoch 3 ; Time: 0.850517 ; Training: accuracy=0.846377 ; Validation: accuracy=0.860368 Epoch 4 ; Time: 1.100746 ; Training: accuracy=0.865921 ; Validation: accuracy=0.879097 Epoch 5 ; Time: 1.365263 ; Training: accuracy=0.877847 ; Validation: accuracy=0.886120 Epoch 6 ; Time: 1.610470 ; Training: accuracy=0.885052 ; Validation: accuracy=0.890635 Epoch 7 ; Time: 1.855182 ; Training: accuracy=0.896480 ; Validation: accuracy=0.895151 Epoch 8 ; Time: 2.103321 ; Training: accuracy=0.899793 ; Validation: accuracy=0.905853 Epoch 9 ; Time: 2.353834 ; Training: accuracy=0.902112 ; Validation: accuracy=0.901171 Epoch 1 ; Time: 0.635832 ; Training: accuracy=0.282077 ; Validation: accuracy=0.614310 Epoch 1 ; Time: 0.288144 ; Training: accuracy=0.273931 ; Validation: accuracy=0.592088 Epoch 1 ; Time: 3.987203 ; Training: accuracy=0.629559 ; Validation: accuracy=0.783647 Epoch 2 ; Time: 8.175726 ; Training: accuracy=0.776442 ; Validation: accuracy=0.844886 Epoch 3 ; Time: 12.323493 ; Training: accuracy=0.821452 ; Validation: accuracy=0.870121 Epoch 4 ; Time: 16.648568 ; Training: accuracy=0.836704 ; Validation: accuracy=0.881561 Epoch 5 ; Time: 20.911560 ; Training: accuracy=0.850464 ; Validation: accuracy=0.902423 Epoch 6 ; Time: 25.113778 ; Training: accuracy=0.858256 ; Validation: accuracy=0.901750 Epoch 7 ; Time: 29.231350 ; Training: accuracy=0.871601 ; Validation: accuracy=0.897880 Epoch 8 ; Time: 33.995565 ; Training: accuracy=0.873591 ; Validation: accuracy=0.908479 Epoch 9 ; Time: 38.424927 ; Training: accuracy=0.880056 ; Validation: accuracy=0.915209 Analysing the results ~~~~~~~~~~~~~~~~~~~~~ The training history is stored in the ``results_df``, the main fields are the runtime and ``'best'`` (the objective). **Note**: You will get slightly different curves for different pairs of scheduler/searcher, the ``time_out`` here is a bit too short to really see the difference in a significant way (it would be better to set it to >1000s). Generally speaking though, hyperband stopping / promotion + model will tend to significantly outperform other combinations given enough time. .. code:: python results_df.head() .. raw:: html
bracket elapsed_time epoch error eval_time objective runtime searcher_data_size searcher_params_kernel_covariance_scale searcher_params_kernel_inv_bw0 ... searcher_params_kernel_inv_bw7 searcher_params_kernel_inv_bw8 searcher_params_mean_mean_value searcher_params_noise_variance target_epoch task_id time_since_start time_step time_this_iter best
0 0 0.503835 1 0.468750 0.498833 0.531250 0.794780 NaN 1.0 1.0 ... 1.0 1.0 0.0 0.001 9 0 0.797088 1.614126e+09 0.533082 0.468750
1 0 0.929302 2 0.344753 0.420733 0.655247 1.220247 1.0 1.0 1.0 ... 1.0 1.0 0.0 0.001 9 0 1.221023 1.614126e+09 0.425449 0.344753
2 0 1.341724 3 0.305314 0.410199 0.694686 1.632669 1.0 1.0 1.0 ... 1.0 1.0 0.0 0.001 9 0 1.633596 1.614126e+09 0.412425 0.305314
3 0 1.754769 4 0.288937 0.410367 0.711063 2.045714 2.0 1.0 1.0 ... 1.0 1.0 0.0 0.001 9 0 2.046513 1.614126e+09 0.413044 0.288937
4 0 2.169484 5 0.273061 0.412882 0.726939 2.460428 2.0 1.0 1.0 ... 1.0 1.0 0.0 0.001 9 0 2.461493 1.614126e+09 0.414714 0.273061

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.. code:: python import matplotlib.pyplot as plt plt.figure(figsize=(12, 8)) runtime = results_df['runtime'].values objective = results_df['best'].values plt.plot(runtime, objective, lw=2) plt.xticks(fontsize=12) plt.xlim(0, 120) plt.ylim(0, 0.5) plt.yticks(fontsize=12) plt.xlabel("Runtime [s]", fontsize=14) plt.ylabel("Objective", fontsize=14) .. parsed-literal:: :class: output Text(0, 0.5, 'Objective') Diving Deeper ------------- Now, you are ready to try HPO on your own machine learning models (if you use PyTorch, have a look at :ref:`sec_customstorch`). While AutoGluon comes with well-chosen defaults, it can pay off to tune it to your specific needs. Here are some tips which may come useful. Logging the Search Progress ~~~~~~~~~~~~~~~~~~~~~~~~~~~ First, it is a good idea in general to switch on ``debug_log``, which outputs useful information about the search progress. This is already done in the example above. The outputs show which configurations are chosen, stopped, or promoted. For BO and BOHB, a range of information is displayed for every ``get_config`` decision. This log output is very useful in order to figure out what is going on during the search. Configuring ``HyperbandScheduler`` ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The most important knobs to turn with ``HyperbandScheduler`` are ``max_t``, ``grace_period``, ``reduction_factor``, ``brackets``, and ``type``. The first three determine the rung levels at which stopping or promotion decisions are being made. - The maximum resource level ``max_t`` (usually, resource equates to epochs, so ``max_t`` is the maximum number of training epochs) is typically hardcoded in ``train_fn`` passed to the scheduler (this is ``run_mlp_openml`` in the example above). As already noted above, the value is best fixed in the ``ag.args`` decorator as ``epochs=XYZ``, it can then be accessed as ``args.epochs`` in the ``train_fn`` code. If this is done, you do not have to pass ``max_t`` when creating the scheduler. - ``grace_period`` and ``reduction_factor`` determine the rung levels, which are ``grace_period``, ``grace_period * reduction_factor``, ``grace_period * (reduction_factor ** 2)``, etc. All rung levels must be less or equal than ``max_t``. It is recommended to make ``max_t`` equal to the largest rung level. For example, if ``grace_period = 1``, ``reduction_factor = 3``, it is in general recommended to use ``max_t = 9``, ``max_t = 27``, or ``max_t = 81``. Choosing a ``max_t`` value "off the grid" works against the successive halving principle that the total resources spent in a rung should be roughly equal between rungs. If in the example above, you set ``max_t = 10``, about a third of configurations reaching 9 epochs are allowed to proceed, but only for one more epoch. - With ``reduction_factor``, you tune the extent to which successive halving filtering is applied. The larger this integer, the fewer configurations make it to higher number of epochs. Values 2, 3, 4 are commonly used. - Finally, ``grace_period`` should be set to the smallest resource (number of epochs) for which you expect any meaningful differentiation between configurations. While ``grace_period = 1`` should always be explored, it may be too low for any meaningful stopping decisions to be made at the first rung. - ``brackets`` sets the maximum number of brackets in Hyperband (make sure to study the Hyperband paper or follow-ups for details). For ``brackets = 1``, you are running successive halving (single bracket). Higher brackets have larger effective ``grace_period`` values (so runs are not stopped until later), yet are also chosen with less probability. We recommend to always consider successive halving (``brackets = 1``) in a comparison. - Finally, with ``type`` (values ``stopping``, ``promotion``) you are choosing different ways of extending successive halving scheduling to the asynchronous case. The method for the default ``stopping`` is simpler and seems to perform well, but ``promotion`` is more careful promoting configurations to higher resource levels, which can work better in some cases. Asynchronous BOHB ~~~~~~~~~~~~~~~~~ Finally, here are some ideas for tuning asynchronous BOHB, apart from tuning its ``HyperbandScheduling`` component. You need to pass these options in ``search_options``. - We support a range of different surrogate models over the criterion functions across resource levels. All of them are jointly dependent Gaussian process models, meaning that data collected at all resource levels are modelled together. The surrogate model is selected by ``gp_resource_kernel``, values are ``matern52``, ``matern52-res-warp``, ``exp-decay-sum``, ``exp-decay-combined``, ``exp-decay-delta1``. These are variants of either a joint Matern 5/2 kernel over configuration and resource, or the exponential decay model. Details about the latter can be found `here `__. - Fitting a Gaussian process surrogate model to data encurs a cost which scales cubically with the number of datapoints. When applied to expensive deep learning workloads, even multi-fidelity asynchronous BOHB is rarely running up more than 100 observations or so (across all rung levels and brackets), and the GP computations are subdominant. However, if you apply it to cheaper ``train_fn`` and find yourself beyond 2000 total evaluations, the cost of GP fitting can become painful. In such a situation, you can explore the options ``opt_skip_period`` and ``opt_skip_num_max_resource``. The basic idea is as follows. By far the most expensive part of a ``get_config`` call (picking the next configuration) is the refitting of the GP model to past data (this entails re-optimizing hyperparameters of the surrogate model itself). The options allow you to skip this expensive step for most ``get_config`` calls, after some initial period. Check the docstrings for details about these options. If you find yourself in such a situation and gain experience with these skipping features, make sure to contact the AutoGluon developers -- we would love to learn about your use case.