code stringlengths 66 870k | docstring stringlengths 19 26.7k | func_name stringlengths 1 138 | language stringclasses 1
value | repo stringlengths 7 68 | path stringlengths 5 324 | url stringlengths 46 389 | license stringclasses 7
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def get_task_description(task_instance: task_eval.TaskEval) -> str:
"""Get the description for a task.
Args:
task_instance: Task instance
Returns:
Task description
"""
task_name = task_instance.__class__.__name__
try:
# First try to use get_instruction(... | Get the description for a task.
Args:
task_instance: Task instance
Returns:
Task description
| get_task_description | python | droidrun/droidrun | eval/utils/task_manager.py | https://github.com/droidrun/droidrun/blob/master/eval/utils/task_manager.py | MIT |
def check_task_success(env, task_instance: task_eval.TaskEval) -> bool:
"""Check if a task was completed successfully.
Args:
env: AndroidWorld environment
task_instance: Task instance
Returns:
True if task was successful, False otherwise
"""
task_name = task_ins... | Check if a task was completed successfully.
Args:
env: AndroidWorld environment
task_instance: Task instance
Returns:
True if task was successful, False otherwise
| check_task_success | python | droidrun/droidrun | eval/utils/task_manager.py | https://github.com/droidrun/droidrun/blob/master/eval/utils/task_manager.py | MIT |
def teardown_task(env, task_instance: task_eval.TaskEval) -> bool:
"""Tear down a task.
Args:
env: AndroidWorld environment
task_instance: Task instance
Returns:
True if teardown was successful, False otherwise
"""
task_name = task_instance.__class__.__name__
... | Tear down a task.
Args:
env: AndroidWorld environment
task_instance: Task instance
Returns:
True if teardown was successful, False otherwise
| teardown_task | python | droidrun/droidrun | eval/utils/task_manager.py | https://github.com/droidrun/droidrun/blob/master/eval/utils/task_manager.py | MIT |
def __init__(self, path, target_column=None,
ndarray=True, **kwargs):
"""
:param str path:
The *path* represents a filesystem path or URL that's passed
on as the *filepath_or_buffer* argument to
:func:`read_table`.
:param str target_column:
... |
:param str path:
The *path* represents a filesystem path or URL that's passed
on as the *filepath_or_buffer* argument to
:func:`read_table`.
:param str target_column:
The column in the table to load that represents the target
value. This column will n... | __init__ | python | ottogroup/palladium | palladium/dataset.py | https://github.com/ottogroup/palladium/blob/master/palladium/dataset.py | Apache-2.0 |
def __init__(self, url, sql, target_column=None, ndarray=True, **kwargs):
"""
:param str url:
The database *url* that'll be used to make a connection.
Format follows RFC-1738.
:param str sql:
SQL query to be executed or database table name.
:param str targ... |
:param str url:
The database *url* that'll be used to make a connection.
Format follows RFC-1738.
:param str sql:
SQL query to be executed or database table name.
:param str target_column:
The name of the column used as the target. (All other
... | __init__ | python | ottogroup/palladium | palladium/dataset.py | https://github.com/ottogroup/palladium/blob/master/palladium/dataset.py | Apache-2.0 |
def __init__(self,
impl,
update_cache_rrule,
):
"""
:param palladium.interfaces.DatasetLoader impl:
The underlying (decorated) dataset loader object.
:param dict update_cache_rrule:
Keyword arguments for a :class:`dateutil.r... |
:param palladium.interfaces.DatasetLoader impl:
The underlying (decorated) dataset loader object.
:param dict update_cache_rrule:
Keyword arguments for a :class:`dateutil.rrule.rrule` that
determines when the cache will be updated. See
:class:`~palladium.util.R... | __init__ | python | ottogroup/palladium | palladium/dataset.py | https://github.com/ottogroup/palladium/blob/master/palladium/dataset.py | Apache-2.0 |
def test_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Test a model.
Uses 'dataset_loader_test' and 'model_persister' from the
configuration to load a test dataset to test the accuracy of a trained
model with.
Usage:
pld-test [options]
Options:
-h --help Show this screen.
--model-versi... | Test a model.
Uses 'dataset_loader_test' and 'model_persister' from the
configuration to load a test dataset to test the accuracy of a trained
model with.
Usage:
pld-test [options]
Options:
-h --help Show this screen.
--model-version=<version> The version of the model to be tested. If
... | test_cmd | python | ottogroup/palladium | palladium/eval.py | https://github.com/ottogroup/palladium/blob/master/palladium/eval.py | Apache-2.0 |
def fit_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Fit a model and save to database.
Will use 'dataset_loader_train', 'model', and 'model_perister' from
the configuration file, to load a dataset to train a model with, and
persist it.
Usage:
pld-fit [options]
Options:
-n --no-save Don't per... | Fit a model and save to database.
Will use 'dataset_loader_train', 'model', and 'model_perister' from
the configuration file, to load a dataset to train a model with, and
persist it.
Usage:
pld-fit [options]
Options:
-n --no-save Don't persist the fitted model to disk.
--no-activate D... | fit_cmd | python | ottogroup/palladium | palladium/fit.py | https://github.com/ottogroup/palladium/blob/master/palladium/fit.py | Apache-2.0 |
def admin_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Activate or delete models.
Models are usually made active right after fitting (see command
pld-fit). The 'activate' command allows you to explicitly set the
currently active model. Use 'pld-list' to get an overview of all
available models along with thei... | Activate or delete models.
Models are usually made active right after fitting (see command
pld-fit). The 'activate' command allows you to explicitly set the
currently active model. Use 'pld-list' to get an overview of all
available models along with their version identifiers.
Deleting a model will simply remove it ... | admin_cmd | python | ottogroup/palladium | palladium/fit.py | https://github.com/ottogroup/palladium/blob/master/palladium/fit.py | Apache-2.0 |
def grid_search_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Grid search parameters for the model.
Uses 'dataset_loader_train', 'model', and 'grid_search' from the
configuration to load a training dataset, and run a grid search on the
model using the grid of hyperparameters.
Usage:
pld-grid-search [options]... | Grid search parameters for the model.
Uses 'dataset_loader_train', 'model', and 'grid_search' from the
configuration to load a training dataset, and run a grid search on the
model using the grid of hyperparameters.
Usage:
pld-grid-search [options]
Options:
--save-results=<fname> Save results to CSV file
--pe... | grid_search_cmd | python | ottogroup/palladium | palladium/fit.py | https://github.com/ottogroup/palladium/blob/master/palladium/fit.py | Apache-2.0 |
def __call__(self):
"""Loads the data and returns a tuple *(data, target)*, or
*(X, y)*.
:return:
A tuple *(data, target*).
*data* is a two dimensional numpy array with shape n x m
(one row per example).
*target* is a one dimensional array with n target... | Loads the data and returns a tuple *(data, target)*, or
*(X, y)*.
:return:
A tuple *(data, target*).
*data* is a two dimensional numpy array with shape n x m
(one row per example).
*target* is a one dimensional array with n target values.
*target* ma... | __call__ | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def __iter__(self):
"""
:return:
Tuples of train/test indices.
""" |
:return:
Tuples of train/test indices.
| __iter__ | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def fit(self, X, y=None):
"""Fit to data array *X* and possibly a target array *y*.
:return: self
""" | Fit to data array *X* and possibly a target array *y*.
:return: self
| fit | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def predict(self, X, **kw):
"""Predict classes for data array *X* with shape n x m.
Some models may accept additional keyword arguments.
:return:
A numpy array of length n with the predicted classes (for
classification problems) or numeric values (for regression
p... | Predict classes for data array *X* with shape n x m.
Some models may accept additional keyword arguments.
:return:
A numpy array of length n with the predicted classes (for
classification problems) or numeric values (for regression
problems).
:raises:
M... | predict | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def read(self, version=None):
"""Returns a :class:`Model` instance.
:param str version:
*version* may be used to read a specific version of a model.
If *version* is ``None``, returns the active model.
:return:
The model object.
:raises:
LookupEr... | Returns a :class:`Model` instance.
:param str version:
*version* may be used to read a specific version of a model.
If *version* is ``None``, returns the active model.
:return:
The model object.
:raises:
LookupError if no model was available.
| read | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def write(self, model):
"""Persists a :class:`Model` and returns a new version number.
It is the :class:`ModelPersister`'s responsibility to annotate
the 'version' information onto the model before it is saved.
The new model will initially be inactive. Use
:meth:`ModelPersiste... | Persists a :class:`Model` and returns a new version number.
It is the :class:`ModelPersister`'s responsibility to annotate
the 'version' information onto the model before it is saved.
The new model will initially be inactive. Use
:meth:`ModelPersister.activate` to activate the model.
... | write | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def activate(self, version):
"""Set the model with the given *version* to be the active
one.
Implies that any previously active model becomes inactive.
:param str version:
The *version* of the model that's activated.
:raises:
LookupError if no model with gi... | Set the model with the given *version* to be the active
one.
Implies that any previously active model becomes inactive.
:param str version:
The *version* of the model that's activated.
:raises:
LookupError if no model with given *version* exists.
| activate | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def delete(self, version):
"""Delete the model with the given *version* from the
database.
:param str version:
The *version* of the model that's activated.
:raises:
LookupError if no model with given *version* exists.
""" | Delete the model with the given *version* from the
database.
:param str version:
The *version* of the model that's activated.
:raises:
LookupError if no model with given *version* exists.
| delete | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def list_models(self):
"""List metadata of all available models.
:return:
A list of dicts, with each dict containing information about
one of the available models. Each dict is guaranteed to
contain the ``version`` key, which is the same version
number that :met... | List metadata of all available models.
:return:
A list of dicts, with each dict containing information about
one of the available models. Each dict is guaranteed to
contain the ``version`` key, which is the same version
number that :meth:`ModelPersister.read` accepts fo... | list_models | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def list_properties(self):
"""List properties of :class:`ModelPersister` itself.
:return:
A dictionary of key and value pairs, where both keys and
values are of type ``str``. Properties will usually include
``active-model`` and ``db-version`` entries.
""" | List properties of :class:`ModelPersister` itself.
:return:
A dictionary of key and value pairs, where both keys and
values are of type ``str``. Properties will usually include
``active-model`` and ``db-version`` entries.
| list_properties | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def upgrade(self, from_version=None, to_version=__version__):
"""Upgrade the underlying database to the latest version.
Newer versions of Palladium may require changes to the
:class:`ModelPersister`'s database. This method provides an
opportunity to run the necessary upgrade steps.
... | Upgrade the underlying database to the latest version.
Newer versions of Palladium may require changes to the
:class:`ModelPersister`'s database. This method provides an
opportunity to run the necessary upgrade steps.
It's the :class:`ModelPersister`'s responsibility to keep
t... | upgrade | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def __call__(self, model, request):
"""
Use the model to run a prediction with the requested data.
:param model:
The :class:`~Model` instance to use for making predictions.
:param request:
A werkzeug ``request`` object. A dictionary with query
parameters ... |
Use the model to run a prediction with the requested data.
:param model:
The :class:`~Model` instance to use for making predictions.
:param request:
A werkzeug ``request`` object. A dictionary with query
parameters is available at *request.values*.
:ret... | __call__ | python | ottogroup/palladium | palladium/interfaces.py | https://github.com/ottogroup/palladium/blob/master/palladium/interfaces.py | Apache-2.0 |
def __init__(self, fit_func, predict_func,
fit_kwargs=None, predict_kwargs=None,
encode_labels=False):
"""
Instantiates a model with the given *fit_func* and
*predict_func* written in Julia.
:param str fit_func:
The dotted name of the Julia fu... |
Instantiates a model with the given *fit_func* and
*predict_func* written in Julia.
:param str fit_func:
The dotted name of the Julia function to use for fitting.
The function must take as its first two arguments the *X*
and *y* arrays. All elements of the option... | __init__ | python | ottogroup/palladium | palladium/julia.py | https://github.com/ottogroup/palladium/blob/master/palladium/julia.py | Apache-2.0 |
def open(self, path, mode='r'):
"""Return a file handle
For normal files, the implementation is:
```python
return open(path, mode)
```
""" | Return a file handle
For normal files, the implementation is:
```python
return open(path, mode)
```
| open | python | ottogroup/palladium | palladium/persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/persistence.py | Apache-2.0 |
def exists(self, path):
"""Test whether a path exists
For normal files, the implementation is:
```python
return os.path.exists(path)
```
""" | Test whether a path exists
For normal files, the implementation is:
```python
return os.path.exists(path)
```
| exists | python | ottogroup/palladium | palladium/persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/persistence.py | Apache-2.0 |
def remove(self, path):
"""Remove a file
For normal files, the implementation is:
```python
os.remove(path)
```
""" | Remove a file
For normal files, the implementation is:
```python
os.remove(path)
```
| remove | python | ottogroup/palladium | palladium/persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/persistence.py | Apache-2.0 |
def __init__(self, path, io):
"""
:param str path:
The *path* template that I will use to store models,
e.g. ``/path/to/model-{version}``.
:param FileLikeIO io:
Used to access low level file handle operations.
"""
if '{version}' not in path:
... |
:param str path:
The *path* template that I will use to store models,
e.g. ``/path/to/model-{version}``.
:param FileLikeIO io:
Used to access low level file handle operations.
| __init__ | python | ottogroup/palladium | palladium/persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/persistence.py | Apache-2.0 |
def __init__(
self, url, poolclass=None, chunk_size=1024 ** 2 * 100,
table_postfix=''):
"""
:param str url:
The database *url* that'll be used to make a connection.
Format follows RFC-1738. I'll create a table ``models`` to
store the pickles in if i... |
:param str url:
The database *url* that'll be used to make a connection.
Format follows RFC-1738. I'll create a table ``models`` to
store the pickles in if it doesn't exist yet.
:param sqlalchemy.pool.Pool poolclass:
A class specifying DB connection behavior of... | __init__ | python | ottogroup/palladium | palladium/persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/persistence.py | Apache-2.0 |
def __init__(self,
impl,
update_cache_rrule=None,
check_version=True,
):
"""
:param ModelPersister impl:
The underlying (decorated) persister object.
:param dict update_cache_rrule:
Optional keyword argument... |
:param ModelPersister impl:
The underlying (decorated) persister object.
:param dict update_cache_rrule:
Optional keyword arguments for a
:class:`dateutil.rrule.rrule` that determines when the cache
will be updated. See :class:`~palladium.util.RruleThread` for
... | __init__ | python | ottogroup/palladium | palladium/persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/persistence.py | Apache-2.0 |
def make_ujson_response(obj, status_code=200):
"""Encodes the given *obj* to json and wraps it in a response.
:return:
A Flask response.
"""
json_encoded = ujson.encode(obj, ensure_ascii=False)
resp = make_response(json_encoded)
resp.mimetype = 'application/json'
resp.content_type = '... | Encodes the given *obj* to json and wraps it in a response.
:return:
A Flask response.
| make_ujson_response | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def __init__(
self,
mapping,
params=(),
entry_point='/predict',
decorator_list_name='predict_decorators',
predict_proba=False,
unwrap_sample=False,
**kwargs
):
"""
:param mapping:
A list of query parameters and their type that... |
:param mapping:
A list of query parameters and their type that should be
included in the request. These will be processed in the
:meth:`sample_from_data` method to construct a sample
that can be used for prediction. An example that expects
two request paramet... | __init__ | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def sample_from_data(self, model, data):
"""Convert incoming sample *data* into a numpy array.
:param model:
The :class:`~Model` instance to use for making predictions.
:param data:
A dict-like with the sample's data, typically retrieved from
``request.args`` or si... | Convert incoming sample *data* into a numpy array.
:param model:
The :class:`~Model` instance to use for making predictions.
:param data:
A dict-like with the sample's data, typically retrieved from
``request.args`` or similar.
| sample_from_data | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def params_from_data(self, model, data):
"""Retrieve additional parameters (keyword arguments) for
``model.predict`` from request *data*.
:param model:
The :class:`~Model` instance to use for making predictions.
:param data:
A dict-like with the parameter data, typic... | Retrieve additional parameters (keyword arguments) for
``model.predict`` from request *data*.
:param model:
The :class:`~Model` instance to use for making predictions.
:param data:
A dict-like with the parameter data, typically retrieved
from ``request.args`` or si... | params_from_data | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def response_from_prediction(self, y_pred, single=True):
"""Turns a model's prediction in *y_pred* into a JSON
response.
"""
result = y_pred.tolist()
if single:
result = result[0]
response = {
'metadata': get_metadata(),
'result': resul... | Turns a model's prediction in *y_pred* into a JSON
response.
| response_from_prediction | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def create_predict_function(
route, predict_service, decorator_list_name, config):
"""Creates a predict function and registers it to
the Flask app using the route decorator.
:param str route:
Path of the entry point.
:param palladium.interfaces.PredictService predict_service:
The p... | Creates a predict function and registers it to
the Flask app using the route decorator.
:param str route:
Path of the entry point.
:param palladium.interfaces.PredictService predict_service:
The predict service to be registered to this entry point.
:param str decorator_list_name:
Th... | create_predict_function | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def devserver_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Serve the web API for development.
Usage:
pld-devserver [options]
Options:
-h --help Show this screen.
--host=<host> The host to use [default: 0.0.0.0].
--port=<port> The port to use [default: 5000].
--de... | Serve the web API for development.
Usage:
pld-devserver [options]
Options:
-h --help Show this screen.
--host=<host> The host to use [default: 0.0.0.0].
--port=<port> The port to use [default: 5000].
--debug=<debug> Whether or not to use debug mode [default: 0].
| devserver_cmd | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def listen(self, io_in, io_out, io_err):
"""Listens to provided io stream and writes predictions
to output. In case of errors, the error stream will be used.
"""
for line in io_in:
if line.strip().lower() == 'exit':
break
try:
y_pr... | Listens to provided io stream and writes predictions
to output. In case of errors, the error stream will be used.
| listen | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def stream_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Start the streaming server, which listens to stdin, processes line
by line, and returns predictions.
The input should consist of a list of json objects, where each object
will result in a prediction. Each line is processed in a batch.
Example input (mus... | Start the streaming server, which listens to stdin, processes line
by line, and returns predictions.
The input should consist of a list of json objects, where each object
will result in a prediction. Each line is processed in a batch.
Example input (must be on a single line):
[{"sepal length": 1.0, "sepal width":... | stream_cmd | python | ottogroup/palladium | palladium/server.py | https://github.com/ottogroup/palladium/blob/master/palladium/server.py | Apache-2.0 |
def apply_kwargs(func, **kwargs):
"""Call *func* with kwargs, but only those kwargs that it accepts.
"""
new_kwargs = {}
params = signature(func).parameters
for param_name in params.keys():
if param_name in kwargs:
new_kwargs[param_name] = kwargs[param_name]
return func(**new... | Call *func* with kwargs, but only those kwargs that it accepts.
| apply_kwargs | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def args_from_config(func):
"""Decorator that injects parameters from the configuration.
"""
func_args = signature(func).parameters
@wraps(func)
def wrapper(*args, **kwargs):
config = get_config()
for i, argname in enumerate(func_args):
if len(args) > i or argname in kwa... | Decorator that injects parameters from the configuration.
| args_from_config | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def session_scope(session):
"""Provide a transactional scope around a series of operations."""
try:
yield session
session.commit()
except:
session.rollback()
raise
finally:
session.close() | Provide a transactional scope around a series of operations. | session_scope | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def __init__(self, func, rrule, sleep_between_checks=60):
"""
:param callable func:
The function that I will call periodically.
:param rrule rrule:
The :class:`dateutil.rrule.rrule` recurrence rule that
defines when I will do the calls. See the `python-dateutil
... |
:param callable func:
The function that I will call periodically.
:param rrule rrule:
The :class:`dateutil.rrule.rrule` recurrence rule that
defines when I will do the calls. See the `python-dateutil
docs <https://labix.org/python-dateutil>`_ for details on
... | __init__ | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def memory_usage_psutil():
"""Return the current process memory usage in MB.
"""
process = psutil.Process(os.getpid())
mem = process.memory_info()[0] / float(2 ** 20)
mem_vms = process.memory_info()[1] / float(2 ** 20)
return mem, mem_vms | Return the current process memory usage in MB.
| memory_usage_psutil | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def upgrade_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Upgrade the database to the latest version.
Usage:
pld-ugprade [options]
Options:
--from=<v> Upgrade from a specific version, overriding
the version stored in the database.
--to=<v> Upgrade... | Upgrade the database to the latest version.
Usage:
pld-ugprade [options]
Options:
--from=<v> Upgrade from a specific version, overriding
the version stored in the database.
--to=<v> Upgrade to a specific version instead of the
... | upgrade_cmd | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def export_cmd(argv=sys.argv[1:]): # pragma: no cover
"""\
Export a model from one model persister to another.
The model persister to export to is supposed to be available in the
configuration file under the 'model_persister_export' key.
Usage:
pld-export [options]
Options:
--version=<v> Export a... | Export a model from one model persister to another.
The model persister to export to is supposed to be available in the
configuration file under the 'model_persister_export' key.
Usage:
pld-export [options]
Options:
--version=<v> Export a specific version rather than the active
... | export_cmd | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def Partial(func, **kwargs):
"""Allows the use of partially applied functions in the
configuration.
"""
if isinstance(func, str):
func = resolve_dotted_name(func)
partial_func = partial(func, **kwargs)
update_wrapper(partial_func, func)
return partial_func | Allows the use of partially applied functions in the
configuration.
| Partial | python | ottogroup/palladium | palladium/util.py | https://github.com/ottogroup/palladium/blob/master/palladium/util.py | Apache-2.0 |
def test_upload(self, mocked_requests, persister):
""" test upload of model and metadata """
model = Dummy(name='mymodel')
get_md_url = "%s/mymodel-metadata.json" % (self.base_url,)
mocked_requests.head(get_md_url, status_code=404)
put_model_body = None
def handle_put_m... | test upload of model and metadata | test_upload | python | ottogroup/palladium | palladium/tests/test_persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/tests/test_persistence.py | Apache-2.0 |
def test_download(self, mocked_requests, persister):
""" test download and activation of a model """
expected = Dummy(name='mymodel', __metadata__={})
zipped_model = gzip.compress(pickle.dumps(expected))
get_md_url = "%s/mymodel-metadata.json" % (self.base_url,)
mocked_requests.... | test download and activation of a model | test_download | python | ottogroup/palladium | palladium/tests/test_persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/tests/test_persistence.py | Apache-2.0 |
def test_delete(self, mocked_requests, persister):
""" test deleting a model and metadata update """
get_md_url = "%s/mymodel-metadata.json" % (self.base_url,)
mocked_requests.head(get_md_url, status_code=200)
mocked_requests.get(
get_md_url,
json={"models": [{"v... | test deleting a model and metadata update | test_delete | python | ottogroup/palladium | palladium/tests/test_persistence.py | https://github.com/ottogroup/palladium/blob/master/palladium/tests/test_persistence.py | Apache-2.0 |
def flask_app_test(request, config):
"""A Flask app where _url_map, _view_functions, _rules, and
_rules_by_end_point will be reset to the previous values after
running the test.
"""
from palladium.server import app
orig_rules = app.url_map._rules
app.url_map._rules = [rule for rule in app.u... | A Flask app where _url_map, _view_functions, _rules, and
_rules_by_end_point will be reset to the previous values after
running the test.
| flask_app_test | python | ottogroup/palladium | palladium/tests/__init__.py | https://github.com/ottogroup/palladium/blob/master/palladium/tests/__init__.py | Apache-2.0 |
def get_task(benchmark, env_id):
"""Get a task by env_id.
Return None if the benchmark doesn't have the env.
"""
return next(
filter(lambda task: task['env_id'] == env_id, benchmark['tasks']),
None) | Get a task by env_id.
Return None if the benchmark doesn't have the env.
| get_task | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/benchmarks.py | MIT |
def find_task_for_env_id_in_any_benchmark(env_id):
"""Find task for env id in any benchmark."""
for bm in _BENCHMARKS:
for task in bm['tasks']:
if task['env_id'] == env_id:
return bm, task
return None, None | Find task for env id in any benchmark. | find_task_for_env_id_in_any_benchmark | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/benchmarks.py | MIT |
def continuous_mlp_policy_tf_ddpg_benchmarks():
"""Run benchmarking experiments for Continuous MLP Policy on TF-DDPG."""
seeds = random.sample(range(100), 5)
iterate_experiments(continuous_mlp_policy, MuJoCo1M_ENV_SET, seeds=seeds) | Run benchmarking experiments for Continuous MLP Policy on TF-DDPG. | continuous_mlp_policy_tf_ddpg_benchmarks | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/benchmark_policies.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/benchmark_policies.py | MIT |
def benchmark(exec_func=None, *, plot=True, auto=False):
"""Decorator for benchmark function.
Args:
exec_func (func): The experiment function.
plot (bool): Whether the result of this run needs to be plotted.
PNG files will be generated in sub folder /plot.
auto (auto): Wheth... | Decorator for benchmark function.
Args:
exec_func (func): The experiment function.
plot (bool): Whether the result of this run needs to be plotted.
PNG files will be generated in sub folder /plot.
auto (auto): Whether this is automatic benchmarking. JSON files
will b... | benchmark | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def iterate_experiments(func,
env_ids,
snapshot_config=None,
seeds=None,
xcolumn='TotalEnvSteps',
xlabel='Total Environment Steps',
ycolumn='Evaluation/AverageReturn',
... | Iterate experiments for benchmarking over env_ids and seeds.
Args:
env_ids (list[str]): List of environment ids.
snapshot_config (garage.experiment.SnapshotConfig): The experiment
configuration used by :class:`~Trainer` to create the
:class:`~Snapshotter`.
seeds (lis... | iterate_experiments | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def _get_log_dir(exec_func_name):
"""Get the log directory given the experiment name.
Args:
exec_func_name (str): The function name which runs benchmarks.
Returns:
str: Log directory.
"""
cwd = pathlib.Path.cwd()
return str(cwd.joinpath('data', 'local', 'benchmarks', exec_func... | Get the log directory given the experiment name.
Args:
exec_func_name (str): The function name which runs benchmarks.
Returns:
str: Log directory.
| _get_log_dir | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def _read_csv(log_dir, xcolumn, ycolumn):
"""Read csv files and return xs and ys.
Args:
log_dir (str): Log directory for csv file.
xcolumn (str): Which column should be the JSON x axis.
ycolumn (str): Which column should be the JSON y axis.
Returns:
list: List of x axis poi... | Read csv files and return xs and ys.
Args:
log_dir (str): Log directory for csv file.
xcolumn (str): Which column should be the JSON x axis.
ycolumn (str): Which column should be the JSON y axis.
Returns:
list: List of x axis points.
list: List of y axis points.
| _read_csv | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def _export_to_json(json_name, xs, xlabel, ys, ylabel, ys_std):
"""Save selected csv column to JSON preparing for automatic benchmarking.
Args:
json_name (str): The JSON file name.
xs (list): List of x axis points
xlabel (str): Label name for x axis.
ys (np.array): List of y axi... | Save selected csv column to JSON preparing for automatic benchmarking.
Args:
json_name (str): The JSON file name.
xs (list): List of x axis points
xlabel (str): Label name for x axis.
ys (np.array): List of y axis points
ylabel (str): Label name for y axis.
ys_std (n... | _export_to_json | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def _upload_to_gcp_storage(exec_dir):
"""Upload all files to GCP storage under exec_dir folder.
Args:
exec_dir (str): The execution directory.
"""
_bucket = storage.Client().bucket('resl-garage-benchmarks')
exec_name = os.path.basename(exec_dir)
for folder_name in os.listdir(exec_dir)... | Upload all files to GCP storage under exec_dir folder.
Args:
exec_dir (str): The execution directory.
| _upload_to_gcp_storage | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/helper.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/helper.py | MIT |
def run(names):
"""Run selected benchmarks.
Args:
names (tuple): Benchmark names.
Raises:
BadParameter: if any run name is invalid or duplicated.
"""
if not names:
raise click.BadParameter('Empty names!')
if len(names) != len(set(names)):
raise click.BadParame... | Run selected benchmarks.
Args:
names (tuple): Benchmark names.
Raises:
BadParameter: if any run name is invalid or duplicated.
| run | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/run_benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/run_benchmarks.py | MIT |
def _get_all_options():
"""Return a dict containing all benchmark options.
Dict of (str: obj) representing benchmark name and its function object.
Returns:
dict: Benchmark options.
"""
d = {}
d.update(_get_runs_dict(benchmark_algos))
d.update(_get_runs_dict(benchmark_policies))
... | Return a dict containing all benchmark options.
Dict of (str: obj) representing benchmark name and its function object.
Returns:
dict: Benchmark options.
| _get_all_options | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/run_benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/run_benchmarks.py | MIT |
def _get_runs_dict(module):
"""Return a dict containing benchmark options of the module.
Dict of (str: obj) representing benchmark name and its function object.
Args:
module (object): Module object.
Returns:
dict: Benchmark options of the module.
"""
d = {}
for name, obj ... | Return a dict containing benchmark options of the module.
Dict of (str: obj) representing benchmark name and its function object.
Args:
module (object): Module object.
Returns:
dict: Benchmark options of the module.
| _get_runs_dict | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/run_benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/run_benchmarks.py | MIT |
def _echo_run_names(header, d):
"""Echo run names to the command line.
Args:
header (str): The header name.
d (dict): The dict containing benchmark options.
"""
click.echo('-----' + header + '-----')
for name in d:
click.echo(name)
click.echo() | Echo run names to the command line.
Args:
header (str): The header name.
d (dict): The dict containing benchmark options.
| _echo_run_names | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/run_benchmarks.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/run_benchmarks.py | MIT |
def ddpg_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow DDPG model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Rand... | Create garage TensorFlow DDPG model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| ddpg_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/ddpg_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/ddpg_garage_tf.py | MIT |
def her_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow HER model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random... | Create garage TensorFlow HER model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| her_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/her_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/her_garage_tf.py | MIT |
def ppo_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch PPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (... | Create garage PyTorch PPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| ppo_garage_pytorch | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/ppo_garage_pytorch.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/ppo_garage_pytorch.py | MIT |
def ppo_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow PPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (in... | Create garage TensorFlow PPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
... | ppo_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/ppo_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/ppo_garage_tf.py | MIT |
def td3_garage_pytorch(ctxt, env_id, seed):
"""Create garage TensorFlow TD3 model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Localtrainer to create the
snapshotter.
env_id (str): Environment id of the task.
... | Create garage TensorFlow TD3 model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Localtrainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial... | td3_garage_pytorch | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/td3_garage_pytorch.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/td3_garage_pytorch.py | MIT |
def td3_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow TD3 model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random... | Create garage TensorFlow TD3 model and training.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| td3_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/td3_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/td3_garage_tf.py | MIT |
def trpo_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch TRPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
... | Create garage PyTorch TRPO model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the tria... | trpo_garage_pytorch | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/trpo_garage_pytorch.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/trpo_garage_pytorch.py | MIT |
def trpo_garage_tf(ctxt, env_id, seed):
"""Create garage Tensorflow TROI model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (... | Create garage Tensorflow TROI model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
... | trpo_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/trpo_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/trpo_garage_tf.py | MIT |
def vpg_garage_pytorch(ctxt, env_id, seed):
"""Create garage PyTorch VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (... | Create garage PyTorch VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| vpg_garage_pytorch | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/vpg_garage_pytorch.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/vpg_garage_pytorch.py | MIT |
def vpg_garage_tf(ctxt, env_id, seed):
"""Create garage TensorFlow VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (in... | Create garage TensorFlow VPG model and training.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
... | vpg_garage_tf | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/algos/vpg_garage_tf.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/algos/vpg_garage_tf.py | MIT |
def continuous_mlp_baseline(ctxt, env_id, seed):
"""Create Continuous MLP Baseline on TF-PPO.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Ran... | Create Continuous MLP Baseline on TF-PPO.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| continuous_mlp_baseline | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/baselines/continuous_mlp_baseline.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/baselines/continuous_mlp_baseline.py | MIT |
def gaussian_cnn_baseline(ctxt, env_id, seed):
"""Create Gaussian CNN Baseline on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int... | Create Gaussian CNN Baseline on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| gaussian_cnn_baseline | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/baselines/gaussian_cnn_baseline.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/baselines/gaussian_cnn_baseline.py | MIT |
def gaussian_mlp_baseline(ctxt, env_id, seed):
"""Create Gaussian MLP Baseline on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int... | Create Gaussian MLP Baseline on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| gaussian_mlp_baseline | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/baselines/gaussian_mlp_baseline.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/baselines/gaussian_mlp_baseline.py | MIT |
def categorical_cnn_policy(ctxt, env_id, seed):
"""Create Categorical CNN Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (i... | Create Categorical CNN Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| categorical_cnn_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/categorical_cnn_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/categorical_cnn_policy.py | MIT |
def categorical_gru_policy(ctxt, env_id, seed):
"""Create Categorical CNN Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (i... | Create Categorical CNN Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| categorical_gru_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/categorical_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/categorical_gru_policy.py | MIT |
def categorical_lstm_policy(ctxt, env_id, seed):
"""Create Categorical LSTM Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed ... | Create Categorical LSTM Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| categorical_lstm_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/categorical_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/categorical_lstm_policy.py | MIT |
def categorical_mlp_policy(ctxt, env_id, seed):
"""Create Categorical MLP Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (i... | Create Categorical MLP Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| categorical_mlp_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/categorical_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/categorical_mlp_policy.py | MIT |
def continuous_mlp_policy(ctxt, env_id, seed):
"""Create Continuous MLP Policy on TF-DDPG.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random... | Create Continuous MLP Policy on TF-DDPG.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| continuous_mlp_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/continuous_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/continuous_mlp_policy.py | MIT |
def gaussian_gru_policy(ctxt, env_id, seed):
"""Create Gaussian GRU Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): R... | Create Gaussian GRU Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| gaussian_gru_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/gaussian_gru_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/gaussian_gru_policy.py | MIT |
def gaussian_lstm_policy(ctxt, env_id, seed):
"""Create Gaussian LSTM Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int):... | Create Gaussian LSTM Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| gaussian_lstm_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/gaussian_lstm_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/gaussian_lstm_policy.py | MIT |
def gaussian_mlp_policy(ctxt, env_id, seed):
"""Create Gaussian MLP Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): R... | Create Gaussian MLP Policy on TF-PPO.
Args:
ctxt (garage.experiment.ExperimentContext): The experiment
configuration used by Trainer to create the
snapshotter.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| gaussian_mlp_policy | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/policies/gaussian_mlp_policy.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/policies/gaussian_mlp_policy.py | MIT |
def continuous_mlp_q_function(ctxt, env_id, seed):
"""Create Continuous MLP QFunction on TF-DDPG.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int):... | Create Continuous MLP QFunction on TF-DDPG.
Args:
ctxt (ExperimentContext): The experiment configuration used by
:class:`~Trainer` to create the :class:`~Snapshotter`.
env_id (str): Environment id of the task.
seed (int): Random positive integer for the trial.
| continuous_mlp_q_function | python | rlworkgroup/garage | benchmarks/src/garage_benchmarks/experiments/q_functions/continuous_mlp_q_function.py | https://github.com/rlworkgroup/garage/blob/master/benchmarks/src/garage_benchmarks/experiments/q_functions/continuous_mlp_q_function.py | MIT |
def setup(self, algo, env):
"""Set up trainer for algorithm and environment.
This method saves algo and env within trainer and creates a sampler.
Note:
After setup() is called all variables in session should have been
initialized. setup() respects existing values in ses... | Set up trainer for algorithm and environment.
This method saves algo and env within trainer and creates a sampler.
Note:
After setup() is called all variables in session should have been
initialized. setup() respects existing values in session so
policy weights can ... | setup | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def obtain_episodes(self,
itr,
batch_size=None,
agent_update=None,
env_update=None):
"""Obtain one batch of episodes.
Args:
itr (int): Index of iteration (epoch).
batch_size (int): Nu... | Obtain one batch of episodes.
Args:
itr (int): Index of iteration (epoch).
batch_size (int): Number of steps in batch. This is a hint that the
sampler may or may not respect.
agent_update (object): Value which will be passed into the
`agent_up... | obtain_episodes | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def obtain_samples(self,
itr,
batch_size=None,
agent_update=None,
env_update=None):
"""Obtain one batch of samples.
Args:
itr (int): Index of iteration (epoch).
batch_size (int): Number o... | Obtain one batch of samples.
Args:
itr (int): Index of iteration (epoch).
batch_size (int): Number of steps in batch.
This is a hint that the sampler may or may not respect.
agent_update (object): Value which will be passed into the
`agent_upd... | obtain_samples | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def save(self, epoch):
"""Save snapshot of current batch.
Args:
epoch (int): Epoch.
Raises:
NotSetupError: if save() is called before the trainer is set up.
"""
if not self._has_setup:
raise NotSetupError('Use setup() to setup trainer before... | Save snapshot of current batch.
Args:
epoch (int): Epoch.
Raises:
NotSetupError: if save() is called before the trainer is set up.
| save | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def restore(self, from_dir, from_epoch='last'):
"""Restore experiment from snapshot.
Args:
from_dir (str): Directory of the pickle file
to resume experiment from.
from_epoch (str or int): The epoch to restore from.
Can be 'first', 'last' or a numb... | Restore experiment from snapshot.
Args:
from_dir (str): Directory of the pickle file
to resume experiment from.
from_epoch (str or int): The epoch to restore from.
Can be 'first', 'last' or a number.
Not applicable when snapshot_mode='last... | restore | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def log_diagnostics(self, pause_for_plot=False):
"""Log diagnostics.
Args:
pause_for_plot (bool): Pause for plot.
"""
logger.log('Time %.2f s' % (time.time() - self._start_time))
logger.log('EpochTime %.2f s' % (time.time() - self._itr_start_time))
tabular.r... | Log diagnostics.
Args:
pause_for_plot (bool): Pause for plot.
| log_diagnostics | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def train(self,
n_epochs,
batch_size=None,
plot=False,
store_episodes=False,
pause_for_plot=False):
"""Start training.
Args:
n_epochs (int): Number of epochs.
batch_size (int or None): Number of environment st... | Start training.
Args:
n_epochs (int): Number of epochs.
batch_size (int or None): Number of environment steps in one batch.
plot (bool): Visualize an episode from the policy after each epoch.
store_episodes (bool): Save episodes in snapshot.
pause_for... | train | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def step_epochs(self):
"""Step through each epoch.
This function returns a magic generator. When iterated through, this
generator automatically performs services such as snapshotting and log
management. It is used inside train() in each algorithm.
The generator initializes two ... | Step through each epoch.
This function returns a magic generator. When iterated through, this
generator automatically performs services such as snapshotting and log
management. It is used inside train() in each algorithm.
The generator initializes two variables: `self.step_itr` and
... | step_epochs | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def resume(self,
n_epochs=None,
batch_size=None,
plot=None,
store_episodes=None,
pause_for_plot=None):
"""Resume from restored experiment.
This method provides the same interface as train().
If not specified, an argumen... | Resume from restored experiment.
This method provides the same interface as train().
If not specified, an argument will default to the
saved arguments from the last call to train().
Args:
n_epochs (int): Number of epochs.
batch_size (int): Number of environment... | resume | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def get_env_copy(self):
"""Get a copy of the environment.
Returns:
Environment: An environment instance.
"""
if self._env:
return cloudpickle.loads(cloudpickle.dumps(self._env))
else:
return None | Get a copy of the environment.
Returns:
Environment: An environment instance.
| get_env_copy | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def __enter__(self):
"""Set self.sess as the default session.
Returns:
TFTrainer: This trainer.
"""
if tf.compat.v1.get_default_session() is not self.sess:
self.sess.__enter__()
self.sess_entered = True
return self | Set self.sess as the default session.
Returns:
TFTrainer: This trainer.
| __enter__ | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def __exit__(self, exc_type, exc_val, exc_tb):
"""Leave session.
Args:
exc_type (str): Type.
exc_val (object): Value.
exc_tb (object): Traceback.
"""
if tf.compat.v1.get_default_session(
) is self.sess and self.sess_entered:
self.... | Leave session.
Args:
exc_type (str): Type.
exc_val (object): Value.
exc_tb (object): Traceback.
| __exit__ | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def setup(self, algo, env):
"""Set up trainer and sessions for algorithm and environment.
This method saves algo and env within trainer and creates a sampler,
and initializes all uninitialized variables in session.
Note:
After setup() is called all variables in session shou... | Set up trainer and sessions for algorithm and environment.
This method saves algo and env within trainer and creates a sampler,
and initializes all uninitialized variables in session.
Note:
After setup() is called all variables in session should have been
initialized. s... | setup | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def initialize_tf_vars(self):
"""Initialize all uninitialized variables in session."""
with tf.name_scope('initialize_tf_vars'):
uninited_set = [
e.decode() for e in self.sess.run(
tf.compat.v1.report_uninitialized_variables())
]
se... | Initialize all uninitialized variables in session. | initialize_tf_vars | python | rlworkgroup/garage | src/garage/trainer.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/trainer.py | MIT |
def get_step_type(cls, step_cnt, max_episode_length, done):
"""Determines the step type based on step cnt and done signal.
Args:
step_cnt (int): current step cnt of the environment.
max_episode_length (int): maximum episode length.
done (bool): the done signal return... | Determines the step type based on step cnt and done signal.
Args:
step_cnt (int): current step cnt of the environment.
max_episode_length (int): maximum episode length.
done (bool): the done signal returned by Environment.
Returns:
StepType: the step typ... | get_step_type | python | rlworkgroup/garage | src/garage/_dtypes.py | https://github.com/rlworkgroup/garage/blob/master/src/garage/_dtypes.py | MIT |
Subsets and Splits
Django Code with Docstrings
Filters Python code examples from Django repository that contain Django-related code, helping identify relevant code snippets for understanding Django framework usage patterns.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves Python code examples from Django repository that contain 'django' in the code, which helps identify Django-specific code snippets but provides limited analytical insights beyond basic filtering.
SQL Console for Shuu12121/python-treesitter-filtered-datasetsV2
Retrieves specific code examples from the Flask repository but doesn't provide meaningful analysis or patterns beyond basic data retrieval.
HTTPX Repo Code and Docstrings
Retrieves specific code examples from the httpx repository, which is useful for understanding how particular libraries are used but doesn't provide broader analytical insights about the dataset.
Requests Repo Docstrings & Code
Retrieves code examples with their docstrings and file paths from the requests repository, providing basic filtering but limited analytical value beyond finding specific code samples.
Quart Repo Docstrings & Code
Retrieves code examples with their docstrings from the Quart repository, providing basic code samples but offering limited analytical value for understanding broader patterns or relationships in the dataset.