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ray/doc/source/tune-usage.rst
Richard Liaw 1eaa57c98f
[tune] Distributed example + walkthrough (#5157)
2019-08-02 09:17:20 -07:00

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Tune User Guide
===============
Tune Overview
-------------
.. image:: images/tune-api.svg
Tune schedules a number of *trials* in a cluster. Each trial runs a user-defined Python function or class and is parameterized either by a *config* variation from Tune's Variant Generator or a user-specified **search algorithm**. The trials are scheduled and managed by a **trial scheduler**.
More information about Tune's `search algorithms can be found here <tune-searchalg.html>`__. More information about Tune's `trial schedulers can be found here <tune-schedulers.html>`__.
Experiment Configuration
------------------------
This section will cover the main steps needed to modify your code to run Tune: using the `Training API <tune-usage.html#training-api>`__ and `executing your Tune experiment <tune-usage.html#specifying-experiments>`__.
You can checkout out our `examples page <tune-examples.html>`__ for more code examples.
Training API
~~~~~~~~~~~~
Training can be done with either the **Trainable Class API** or **function-based API**.
**Python classes** passed into Tune will need to subclass ``ray.tune.Trainable``. The Trainable interface `can be found here <tune-package-ref.html#ray.tune.Trainable>`__. Here is an example:
.. code-block:: python
class Example(Trainable):
def _setup(self, config):
...
def _train(self):
# run training code
result_dict = {"accuracy": 0.5, "f1": 0.1, ...}
return result_dict
**Python functions** will need to have the following signature and call ``tune.track.log``, which will allow you to report metrics used for scheduling, search, or early stopping.:
.. code-block:: python
def trainable(config):
"""
Args:
config (dict): Parameters provided from the search algorithm
or variant generation.
"""
while True:
# ...
tune.track.log(**kwargs)
Tune will run this function on a separate thread in a Ray actor process. Note that this API is not checkpointable, since the thread will never return control back to its caller. ``tune.track`` documentation can be `found here <tune-package-ref.html#module-ray.tune.track>`__.
Both the Trainable and function-based API will have `autofilled metrics <tune-usage.html#auto-filled-results>`__ in addition to the metrics reported.
.. note::
If you have a lambda function that you want to train, you will need to first register the function: ``tune.register_trainable("lambda_id", lambda x: ...)``. You can then use ``lambda_id`` in place of ``my_trainable``.
.. note::
See previous versions of the documentation for the ``reporter`` API.
Launching an Experiment
~~~~~~~~~~~~~~~~~~~~~~~
Tune provides a ``run`` function that generates and runs the trials.
.. code-block:: python
tune.run(
trainable,
name="example-experiment",
num_samples=10,
)
This function will report status on the command line until all Trials stop:
::
== Status ==
Using FIFO scheduling algorithm.
Resources used: 4/8 CPUs, 0/0 GPUs
Result logdir: ~/ray_results/my_experiment
- train_func_0_lr=0.2,momentum=1: RUNNING [pid=6778], 209 s, 20604 ts, 7.29 acc
- train_func_1_lr=0.4,momentum=1: RUNNING [pid=6780], 208 s, 20522 ts, 53.1 acc
- train_func_2_lr=0.6,momentum=1: TERMINATED [pid=6789], 21 s, 2190 ts, 100 acc
- train_func_3_lr=0.2,momentum=2: RUNNING [pid=6791], 208 s, 41004 ts, 8.37 acc
- train_func_4_lr=0.4,momentum=2: RUNNING [pid=6800], 209 s, 41204 ts, 70.1 acc
- train_func_5_lr=0.6,momentum=2: TERMINATED [pid=6809], 10 s, 2164 ts, 100 acc
All results reported by the trainable will be logged locally to a unique directory per experiment, e.g. ``~/ray_results/example-experiment`` in the above example. On a cluster, incremental results will be synced to local disk on the head node.
Analyzing Results
-----------------
Tune provides an ``ExperimentAnalysis`` object for analyzing results from ``tune.run``.
.. code-block:: python
analysis = tune.run(
trainable,
name="example-experiment",
num_samples=10,
)
You can use the ``ExperimentAnalysis`` object to obtain the best configuration of the experiment:
.. code-block:: python
>>> print("Best config is", analysis.get_best_config(metric="mean_accuracy"))
Best config is: {'lr': 0.011537575723482687, 'momentum': 0.8921971713692662}
Here are some example operations for obtaining a summary of your experiment:
.. code-block:: python
# Get a dataframe for the last reported results of all of the trials
df = analysis.dataframe()
# Get a dataframe for the max accuracy seen for each trial
df = analysis.dataframe(metric="mean_accuracy", mode="max")
# Get a dict mapping {trial logdir -> dataframes} for all trials in the experiment.
all_dataframes = analysis.trial_dataframes
# Get a list of trials
trials = analysis.trials
You may want to get a summary of multiple experiments that point to the same ``local_dir``. For this, you can use the ``Analysis`` class.
.. code-block:: python
from ray.tune import Analysis
analysis = Analysis("~/ray_results/example-experiment")
See the `full documentation <tune-package-ref.html#ray.tune.Analysis>`_ for the ``Analysis`` object.
Training Features
-----------------
Tune Search Space (Default)
~~~~~~~~~~~~~~~~~~~~~~~~~~~
You can use ``tune.grid_search`` to specify an axis of a grid search. By default, Tune also supports sampling parameters from user-specified lambda functions, which can be used independently or in combination with grid search.
.. note::
If you specify an explicit Search Algorithm such as any SuggestionAlgorithm, you may not be able to specify lambdas or grid search with this interface, as the search algorithm may require a different search space declaration.
The following shows grid search over two nested parameters combined with random sampling from two lambda functions, generating 9 different trials. Note that the value of ``beta`` depends on the value of ``alpha``, which is represented by referencing ``spec.config.alpha`` in the lambda function. This lets you specify conditional parameter distributions.
.. code-block:: python
:emphasize-lines: 4-11
tune.run(
my_trainable,
name="my_trainable",
config={
"alpha": tune.sample_from(lambda spec: np.random.uniform(100)),
"beta": tune.sample_from(lambda spec: spec.config.alpha * np.random.normal()),
"nn_layers": [
tune.grid_search([16, 64, 256]),
tune.grid_search([16, 64, 256]),
],
}
)
.. note::
Use ``tune.sample_from(...)`` to sample from a function during trial variant generation. If you need to pass a literal function in your config, use ``tune.function(...)`` to escape it.
For more information on variant generation, see `basic_variant.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/suggest/basic_variant.py>`__.
Custom Trial Names
~~~~~~~~~~~~~~~~~~
To specify custom trial names, you can pass use the ``trial_name_creator`` argument
to `tune.run`. This takes a function with the following signature, and
be sure to wrap it with `tune.function`:
.. code-block:: python
def trial_name_string(trial):
"""
Args:
trial (Trial): A generated trial object.
Returns:
trial_name (str): String representation of Trial.
"""
return str(trial)
tune.run(
MyTrainableClass,
name="example-experiment",
num_samples=1,
trial_name_creator=tune.function(trial_name_string)
)
An example can be found in `logging_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__.
Sampling Multiple Times
~~~~~~~~~~~~~~~~~~~~~~~
By default, each random variable and grid search point is sampled once. To take multiple random samples, add ``num_samples: N`` to the experiment config. If `grid_search` is provided as an argument, the grid will be repeated `num_samples` of times.
.. code-block:: python
:emphasize-lines: 12
tune.run(
my_trainable,
name="my_trainable",
config={
"alpha": tune.sample_from(lambda spec: np.random.uniform(100)),
"beta": tune.sample_from(lambda spec: spec.config.alpha * np.random.normal()),
"nn_layers": [
tune.grid_search([16, 64, 256]),
tune.grid_search([16, 64, 256]),
],
},
num_samples=10
)
E.g. in the above, ``num_samples=10`` repeats the 3x3 grid search 10 times, for a total of 90 trials, each with randomly sampled values of ``alpha`` and ``beta``.
Using GPUs (Resource Allocation)
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Tune will allocate the specified GPU and CPU ``resources_per_trial`` to each individual trial (defaulting to 1 CPU per trial). Under the hood, Tune runs each trial as a Ray actor, using Ray's resource handling to allocate resources and place actors. A trial will not be scheduled unless at least that amount of resources is available in the cluster, preventing the cluster from being overloaded.
Fractional values are also supported, (i.e., ``"gpu": 0.2``). You can find an example of this in the `Keras MNIST example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_keras.py>`__.
If GPU resources are not requested, the ``CUDA_VISIBLE_DEVICES`` environment variable will be set as empty, disallowing GPU access.
Otherwise, it will be set to the GPUs in the list (this is managed by Ray).
If your trainable function / class creates further Ray actors or tasks that also consume CPU / GPU resources, you will also want to set ``extra_cpu`` or ``extra_gpu`` to reserve extra resource slots for the actors you will create. For example, if a trainable class requires 1 GPU itself, but will launch 4 actors each using another GPU, then it should set ``"gpu": 1, "extra_gpu": 4``.
.. code-block:: python
:emphasize-lines: 4-8
tune.run(
my_trainable,
name="my_trainable",
resources_per_trial={
"cpu": 1,
"gpu": 1,
"extra_gpu": 4
}
)
Saving and Recovery
-------------------
When running a hyperparameter search, Tune can automatically and periodically save/checkpoint your model. Checkpointing is used for
* saving a model at the end of training
* modifying a model in the middle of training
* fault-tolerance in experiments with pre-emptible machines.
* enables certain Trial Schedulers such as HyperBand and PBT.
To enable checkpointing, you must implement a `Trainable class <tune-usage.html#training-api>`__ (Trainable functions are not checkpointable, since they never return control back to their caller). The easiest way to do this is to subclass the pre-defined ``Trainable`` class and implement ``_save``, and ``_restore`` abstract methods, as seen in `this example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/hyperband_example.py>`__.
For TensorFlow model training, this would look something like this `tensorflow example <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/tune_mnist_ray_hyperband.py>`__:
.. code-block:: python
class MyTrainableClass(Trainable):
def _setup(self, config):
self.saver = tf.train.Saver()
self.sess = ...
def _train(self):
return {"mean_accuracy: self.sess.run(...)}
def _save(self, checkpoint_dir):
return self.saver.save(self.sess, os.path.join(checkpoint_dir, save))
def _restore(self, checkpoint_prefix):
self.saver.restore(self.sess, checkpoint_prefix)
Checkpoints will be saved by training iteration to ``local_dir/exp_name/trial_name/checkpoint_<iter>``. You can restore a single trial checkpoint by using ``tune.run(restore=<checkpoint_dir>)``. To test if your Trainable will checkpoint and restore correctly, you can use ``tune.util.validate_save_restore`` as follows:
.. code-block:: python
from ray.tune.util import validate_save_restore
validate_save_restore(MyTrainableClass)
validate_save_restore(MyTrainableClass, use_object_store=True)
Trainable (Trial) Checkpointing
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
Checkpointing assumes that the model state will be saved to disk on whichever node the Trainable is running on. You can checkpoint with three different mechanisms: manually, periodically, and at termination.
**Manual Checkpointing**: A custom Trainable can manually trigger checkpointing by returning ``should_checkpoint: True`` (or ``tune.result.SHOULD_CHECKPOINT: True``) in the result dictionary of `_train`. This can be especially helpful in spot instances:
.. code-block:: python
def _train(self):
# training code
result = {"mean_accuracy": accuracy}
if detect_instance_preemption():
result.update(should_checkpoint=True)
return result
**Periodic Checkpointing**: periodic checkpointing can be used to provide fault-tolerance for experiments. This can be enabled by setting ``checkpoint_freq=<int>`` and ``max_failures=<int>`` to checkpoint trials every *N* iterations and recover from up to *M* crashes per trial, e.g.:
.. code-block:: python
tune.run(
my_trainable,
checkpoint_freq=10,
max_failures=5,
)
**Checkpointing at Termination**: The checkpoint_freq may not coincide with the exact end of an experiment. If you want a checkpoint to be created at the end
of a trial, you can additionally set the ``checkpoint_at_end=True``:
.. code-block:: python
:emphasize-lines: 5
tune.run(
my_trainable,
checkpoint_freq=10,
checkpoint_at_end=True,
max_failures=5,
)
The checkpoint will be saved at a path that looks like ``local_dir/exp_name/trial_name/checkpoint_x/``, where the x is the number of iterations so far when the checkpoint is saved. To restore the checkpoint, you can use the ``restore`` argument and specify a checkpoint file. By doing this, you can change whatever experiments' configuration such as the experiment's name, the training iteration or so:
.. code-block:: python
# Restored previous trial from the given checkpoint
tune.run(
"PG",
name="RestoredExp", # The name can be different.
stop={"training_iteration": 10}, # train 5 more iterations than previous
restore="~/ray_results/Original/PG_<xxx>/checkpoint_5/checkpoint-5",
config={"env": "CartPole-v0"},
)
Fault Tolerance
~~~~~~~~~~~~~~~
Tune will automatically restart trials from the last checkpoint in case of trial failures/error (if ``max_failures`` is set), both in the single node and distributed setting.
In the distributed setting, if using the autoscaler with ``rsync`` enabled, Tune will automatically sync the trial folder with the driver. For example, if a node is lost while a trial (specifically, the corresponding Trainable actor of the trial) is still executing on that node and a checkpoint of the trial exists, Tune will wait until available resources are available to begin executing the trial again.
If the trial/actor is placed on a different node, Tune will automatically push the previous checkpoint file to that node and restore the remote trial actor state, allowing the trial to resume from the latest checkpoint even after failure.
Take a look at `an example <tune-distributed.html#example-for-using-spot-instances-aws>`_.
Recovering From Failures
~~~~~~~~~~~~~~~~~~~~~~~~
Tune automatically persists the progress of your entire experiment (a ``tune.run`` session), so if an experiment crashes or is otherwise cancelled, it can be resumed by passing one of True, False, "LOCAL", "REMOTE", or "PROMPT" to ``tune.run(resume=...)``. Note that this only works if trial checkpoints are detected, whether it be by manual or periodic checkpointing.
**Settings:**
- The default setting of ``resume=False`` creates a new experiment.
- ``resume="LOCAL"`` and ``resume=True`` restore the experiment from ``local_dir/[experiment_name]``.
- ``resume="REMOTE"`` syncs the upload dir down to the local dir and then restores the experiment from ``local_dir/experiment_name``.
- ``resume="PROMPT"`` will cause Tune to prompt you for whether you want to resume. You can always force a new experiment to be created by changing the experiment name.
Note that trials will be restored to their last checkpoint. If trial checkpointing is not enabled, unfinished trials will be restarted from scratch.
E.g.:
.. code-block:: python
tune.run(
my_trainable,
checkpoint_freq=10,
local_dir="~/path/to/results",
resume=True
)
Upon a second run, this will restore the entire experiment state from ``~/path/to/results/my_experiment_name``. Importantly, any changes to the experiment specification upon resume will be ignored. For example, if the previous experiment has reached its termination, then resuming it with a new stop criterion makes no effect: the new experiment will terminate immediately after initialization. If you want to change the configuration, such as training more iterations, you can do so restore the checkpoint by setting ``restore=<path-to-checkpoint>`` - note that this only works for a single trial.
.. warning::
This feature is still experimental, so any provided Trial Scheduler or Search Algorithm will not be preserved. Only ``FIFOScheduler`` and ``BasicVariantGenerator`` will be supported.
Handling Large Datasets
-----------------------
You often will want to compute a large object (e.g., training data, model weights) on the driver and use that object within each trial. Tune provides a ``pin_in_object_store`` utility function that can be used to broadcast such large objects. Objects pinned in this way will never be evicted from the Ray object store while the driver process is running, and can be efficiently retrieved from any task via ``get_pinned_object``.
.. code-block:: python
import ray
from ray import tune
from ray.tune.util import pin_in_object_store, get_pinned_object
import numpy as np
ray.init()
# X_id can be referenced in closures
X_id = pin_in_object_store(np.random.random(size=100000000))
def f(config, reporter):
X = get_pinned_object(X_id)
# use X
tune.run(f)
Auto-Filled Results
-------------------
During training, Tune will automatically fill certain fields if not already provided. All of these can be used as stopping conditions or in the Scheduler/Search Algorithm specification.
.. literalinclude:: ../../python/ray/tune/result.py
:language: python
:start-after: __sphinx_doc_begin__
:end-before: __sphinx_doc_end__
The following fields will automatically show up on the console output, if provided:
1. ``episode_reward_mean``
2. ``mean_loss``
3. ``mean_accuracy``
4. ``timesteps_this_iter`` (aggregated into ``timesteps_total``).
.. code-block:: bash
Example_0: TERMINATED [pid=68248], 179 s, 2 iter, 60000 ts, 94 rew
Visualizing Results
-------------------
To visualize learning in tensorboard, install TensorFlow:
.. code-block:: bash
$ pip install tensorflow
Then, after you run a experiment, you can visualize your experiment with TensorBoard by specifying the output directory of your results. Note that if you running Ray on a remote cluster, you can forward the tensorboard port to your local machine through SSH using ``ssh -L 6006:localhost:6006 <address>``:
.. code-block:: bash
$ tensorboard --logdir=~/ray_results/my_experiment
If you are running Ray on a remote multi-user cluster where you do not have sudo access, you can run the following commands to make sure tensorboard is able to write to the tmp directory:
.. code-block:: bash
$ export TMPDIR=/tmp/$USER; mkdir -p $TMPDIR; tensorboard --logdir=~/ray_results
.. image:: ray-tune-tensorboard.png
To use rllab's VisKit (you may have to install some dependencies), run:
.. code-block:: bash
$ git clone https://github.com/rll/rllab.git
$ python rllab/rllab/viskit/frontend.py ~/ray_results/my_experiment
.. image:: ray-tune-viskit.png
Logging
-------
You can pass in your own logging mechanisms to output logs in custom formats as follows:
.. code-block:: python
from ray.tune.logger import DEFAULT_LOGGERS
tune.run(
MyTrainableClass
name="experiment_name",
loggers=DEFAULT_LOGGERS + (CustomLogger1, CustomLogger2)
)
These loggers will be called along with the default Tune loggers. All loggers must inherit the `Logger interface <tune-package-ref.html#ray.tune.logger.Logger>`__. Tune has default loggers for Tensorboard, CSV, and JSON formats. You can also check out `logger.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/logger.py>`__ for implementation details. An example can be found in `logging_example.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/examples/logging_example.py>`__.
Uploading/Syncing
-----------------
Tune automatically syncs the trial folder on remote nodes back to the head node. This requires the ray cluster to be started with the `autoscaler <autoscaling.html>`__.
By default, local syncing requires rsync to be installed. You can customize the sync command with the ``sync_to_driver`` argument in ``tune.run`` by providing either a function or a string.
If a string is provided, then it must include replacement fields ``{source}`` and ``{target}``, like ``rsync -savz -e "ssh -i ssh_key.pem" {source} {target}``. Alternatively, a function can be provided with the following signature (and must be wrapped with ``tune.function``):
.. code-block:: python
def custom_sync_func(source, target):
sync_cmd = "rsync {source} {target}".format(
source=source,
target=target)
sync_process = subprocess.Popen(sync_cmd, shell=True)
sync_process.wait()
tune.run(
MyTrainableClass,
name="experiment_name",
sync_to_driver=tune.function(custom_sync_func),
)
When syncing results back to the driver, the source would be a path similar to ``ubuntu@192.0.0.1:/home/ubuntu/ray_results/trial1``, and the target would be a local path.
This custom sync command would be also be used in node failures, where the source argument would be the path to the trial directory and the target would be a remote path. The `sync_to_driver` would be invoked to push a checkpoint to new node for a queued trial to resume.
If an upload directory is provided, Tune will automatically sync results to the given directory, natively supporting standard S3/gsutil commands.
You can customize this to specify arbitrary storages with the ``sync_to_cloud`` argument. This argument is similar to ``sync_to_cloud`` in that it supports strings with the same replacement fields and arbitrary functions. See `syncer.py <https://github.com/ray-project/ray/blob/master/python/ray/tune/syncer.py>`__ for implementation details.
.. code-block:: python
tune.run(
MyTrainableClass,
name="experiment_name",
sync_to_cloud=tune.function(custom_sync_func),
)
Tune Client API
---------------
You can interact with an ongoing experiment with the Tune Client API. The Tune Client API is organized around REST, which includes resource-oriented URLs, accepts form-encoded requests, returns JSON-encoded responses, and uses standard HTTP protocol.
To allow Tune to receive and respond to your API calls, you have to start your experiment with ``with_server=True``:
.. code-block:: python
tune.run(..., with_server=True, server_port=4321)
The easiest way to use the Tune Client API is with the built-in TuneClient. To use TuneClient, verify that you have the ``requests`` library installed:
.. code-block:: bash
$ pip install requests
Then, on the client side, you can use the following class. If on a cluster, you may want to forward this port (e.g. ``ssh -L <local_port>:localhost:<remote_port> <address>``) so that you can use the Client on your local machine.
.. autoclass:: ray.tune.web_server.TuneClient
:members:
For an example notebook for using the Client API, see the `Client API Example <https://github.com/ray-project/ray/tree/master/python/ray/tune/TuneClient.ipynb>`__.
The API also supports curl. Here are the examples for getting trials (``GET /trials/[:id]``):
.. code-block:: bash
$ curl http://<address>:<port>/trials
$ curl http://<address>:<port>/trials/<trial_id>
And stopping a trial (``PUT /trials/:id``):
.. code-block:: bash
$ curl -X PUT http://<address>:<port>/trials/<trial_id>
Debugging (Single Process)
--------------------------
By default, Tune will run hyperparameter evaluations on multiple processes. However, if you need to debug your training process, it may be easier to do everything on a single process. You can force all Ray functions to occur on a single process with ``local_mode`` by calling the following before ``tune.run``.
.. code-block:: python
ray.init(local_mode=True)
Note that some behavior such as writing to files by depending on the current working directory in a Trainable and setting global process variables may not work as expected. Local mode with multiple configuration evaluations will interleave computation, so it is most naturally used when running a single configuration evaluation.
Tune CLI (Experimental)
-----------------------
``tune`` has an easy-to-use command line interface (CLI) to manage and monitor your experiments on Ray. To do this, verify that you have the ``tabulate`` library installed:
.. code-block:: bash
$ pip install tabulate
Here are a few examples of command line calls.
- ``tune list-trials``: List tabular information about trials within an experiment. Empty columns will be dropped by default. Add the ``--sort`` flag to sort the output by specific columns. Add the ``--filter`` flag to filter the output in the format ``"<column> <operator> <value>"``. Add the ``--output`` flag to write the trial information to a specific file (CSV or Pickle). Add the ``--columns`` and ``--result-columns`` flags to select specific columns to display.
.. code-block:: bash
$ tune list-trials [EXPERIMENT_DIR] --output note.csv
+------------------+-----------------------+------------+
| trainable_name | experiment_tag | trial_id |
|------------------+-----------------------+------------|
| MyTrainableClass | 0_height=40,width=37 | 87b54a1d |
| MyTrainableClass | 1_height=21,width=70 | 23b89036 |
| MyTrainableClass | 2_height=99,width=90 | 518dbe95 |
| MyTrainableClass | 3_height=54,width=21 | 7b99a28a |
| MyTrainableClass | 4_height=90,width=69 | ae4e02fb |
+------------------+-----------------------+------------+
Dropped columns: ['status', 'last_update_time']
Please increase your terminal size to view remaining columns.
Output saved at: note.csv
$ tune list-trials [EXPERIMENT_DIR] --filter "trial_id == 7b99a28a"
+------------------+-----------------------+------------+
| trainable_name | experiment_tag | trial_id |
|------------------+-----------------------+------------|
| MyTrainableClass | 3_height=54,width=21 | 7b99a28a |
+------------------+-----------------------+------------+
Dropped columns: ['status', 'last_update_time']
Please increase your terminal size to view remaining columns.
- ``tune list-experiments``: List tabular information about experiments within a project. Empty columns will be dropped by default. Add the ``--sort`` flag to sort the output by specific columns. Add the ``--filter`` flag to filter the output in the format ``"<column> <operator> <value>"``. Add the ``--output`` flag to write the trial information to a specific file (CSV or Pickle). Add the ``--columns`` flag to select specific columns to display.
.. code-block:: bash
$ tune list-experiments [PROJECT_DIR] --output note.csv
+----------------------+----------------+------------------+---------------------+
| name | total_trials | running_trials | terminated_trials |
|----------------------+----------------+------------------+---------------------|
| pbt_test | 10 | 0 | 0 |
| test | 1 | 0 | 0 |
| hyperband_test | 1 | 0 | 1 |
+----------------------+----------------+------------------+---------------------+
Dropped columns: ['error_trials', 'last_updated']
Please increase your terminal size to view remaining columns.
Output saved at: note.csv
$ tune list-experiments [PROJECT_DIR] --filter "total_trials <= 1" --sort name
+----------------------+----------------+------------------+---------------------+
| name | total_trials | running_trials | terminated_trials |
|----------------------+----------------+------------------+---------------------|
| hyperband_test | 1 | 0 | 1 |
| test | 1 | 0 | 0 |
+----------------------+----------------+------------------+---------------------+
Dropped columns: ['error_trials', 'last_updated']
Please increase your terminal size to view remaining columns.
Further Questions or Issues?
----------------------------
You can post questions or issues or feedback through the following channels:
1. `ray-dev@googlegroups.com`_: For discussions about development or any general
questions and feedback.
2. `StackOverflow`_: For questions about how to use Ray.
3. `GitHub Issues`_: For bug reports and feature requests.
.. _`ray-dev@googlegroups.com`: https://groups.google.com/forum/#!forum/ray-dev
.. _`StackOverflow`: https://stackoverflow.com/questions/tagged/ray
.. _`GitHub Issues`: https://github.com/ray-project/ray/issues
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