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def job(request):
"""View for a single job.""" |
job_id = request.GET.get("job_id")
recent_jobs = JobRecord.objects.order_by("-start_time")[0:100]
recent_trials = TrialRecord.objects \
.filter(job_id=job_id) \
.order_by("-start_time")
trial_records = []
for recent_trial in recent_trials:
trial_records.append(get_trial_info... |
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def trial(request):
"""View for a single trial.""" |
job_id = request.GET.get("job_id")
trial_id = request.GET.get("trial_id")
recent_trials = TrialRecord.objects \
.filter(job_id=job_id) \
.order_by("-start_time")
recent_results = ResultRecord.objects \
.filter(trial_id=trial_id) \
.order_by("-date")[0:2000]
current_t... |
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def get_job_info(current_job):
"""Get job information for current job.""" |
trials = TrialRecord.objects.filter(job_id=current_job.job_id)
total_num = len(trials)
running_num = sum(t.trial_status == Trial.RUNNING for t in trials)
success_num = sum(t.trial_status == Trial.TERMINATED for t in trials)
failed_num = sum(t.trial_status == Trial.ERROR for t in trials)
if tot... |
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def get_trial_info(current_trial):
"""Get job information for current trial.""" |
if current_trial.end_time and ("_" in current_trial.end_time):
# end time is parsed from result.json and the format
# is like: yyyy-mm-dd_hh-MM-ss, which will be converted
# to yyyy-mm-dd hh:MM:ss here
time_obj = datetime.datetime.strptime(current_trial.end_time,
... |
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def get_winner(trials):
"""Get winner trial of a job.""" |
winner = {}
# TODO: sort_key should be customized here
sort_key = "accuracy"
if trials and len(trials) > 0:
first_metrics = get_trial_info(trials[0])["metrics"]
if first_metrics and not first_metrics.get("accuracy", None):
sort_key = "episode_reward"
max_metric = flo... |
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def to_argv(config):
"""Converts configuration to a command line argument format.""" |
argv = []
for k, v in config.items():
if "-" in k:
raise ValueError("Use '_' instead of '-' in `{}`".format(k))
if v is None:
continue
if not isinstance(v, bool) or v: # for argparse flags
argv.append("--{}".format(k.replace("_", "-")))
if is... |
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def create_trial_from_spec(spec, output_path, parser, **trial_kwargs):
"""Creates a Trial object from parsing the spec. Arguments: spec (dict):
A resolved exper... |
try:
args = parser.parse_args(to_argv(spec))
except SystemExit:
raise TuneError("Error parsing args, see above message", spec)
if "resources_per_trial" in spec:
trial_kwargs["resources"] = json_to_resources(
spec["resources_per_trial"])
return Trial(
# Submit... |
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def wait_for_compute_zone_operation(compute, project_name, operation, zone):
"""Poll for compute zone operation until finished.""" |
logger.info("wait_for_compute_zone_operation: "
"Waiting for operation {} to finish...".format(
operation["name"]))
for _ in range(MAX_POLLS):
result = compute.zoneOperations().get(
project=project_name, operation=operation["name"],
zone=zone... |
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def _get_task_id(source):
"""Return the task id associated to the generic source of the signal. Args: source: source of the signal, it can be either an object id... |
if type(source) is ray.actor.ActorHandle:
return source._ray_actor_id
else:
if type(source) is ray.TaskID:
return source
else:
return ray._raylet.compute_task_id(source) |
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def receive(sources, timeout=None):
"""Get all outstanding signals from sources. A source can be either (1) an object ID returned by the task (we want to receive... |
# If None, initialize the timeout to a huge value (i.e., over 30,000 years
# in this case) to "approximate" infinity.
if timeout is None:
timeout = 10**12
if timeout < 0:
raise ValueError("The 'timeout' argument cannot be less than 0.")
if not hasattr(ray.worker.global_worker, "s... |
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def reset():
""" Reset the worker state associated with any signals that this worker has received so far. If the worker calls receive() on a source next, it will... |
if hasattr(ray.worker.global_worker, "signal_counters"):
ray.worker.global_worker.signal_counters = defaultdict(lambda: b"0") |
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def log_once(key):
"""Returns True if this is the "first" call for a given key. Various logging settings can adjust the definition of "first". Example: """ |
global _last_logged
if _disabled:
return False
elif key not in _logged:
_logged.add(key)
_last_logged = time.time()
return True
elif _periodic_log and time.time() - _last_logged > 60.0:
_logged.clear()
_last_logged = time.time()
return False
... |
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def get(object_ids):
"""Get a single or a collection of remote objects from the object store. This method is identical to `ray.get` except it adds support for tu... |
if isinstance(object_ids, (tuple, np.ndarray)):
return ray.get(list(object_ids))
elif isinstance(object_ids, dict):
keys_to_get = [
k for k, v in object_ids.items() if isinstance(v, ray.ObjectID)
]
ids_to_get = [
v for k, v in object_ids.items() if isinst... |
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def _raise_deprecation_note(deprecated, replacement, soft=False):
"""User notification for deprecated parameter. Arguments: deprecated (str):
Deprecated paramet... |
error_msg = ("`{deprecated}` is deprecated. Please use `{replacement}`. "
"`{deprecated}` will be removed in future versions of "
"Ray.".format(deprecated=deprecated, replacement=replacement))
if soft:
logger.warning(error_msg)
else:
raise DeprecationWarnin... |
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def convert_to_experiment_list(experiments):
"""Produces a list of Experiment objects. Converts input from dict, single experiment, or list of experiments to lis... |
exp_list = experiments
# Transform list if necessary
if experiments is None:
exp_list = []
elif isinstance(experiments, Experiment):
exp_list = [experiments]
elif type(experiments) is dict:
exp_list = [
Experiment.from_json(name, spec)
for name, spec... |
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def from_json(cls, name, spec):
"""Generates an Experiment object from JSON. Args: name (str):
Name of Experiment. spec (dict):
JSON configuration of experimen... |
if "run" not in spec:
raise TuneError("No trainable specified!")
# Special case the `env` param for RLlib by automatically
# moving it into the `config` section.
if "env" in spec:
spec["config"] = spec.get("config", {})
spec["config"]["env"] = spec["... |
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def _register_if_needed(cls, run_object):
"""Registers Trainable or Function at runtime. Assumes already registered if run_object is a string. Does not register ... |
if isinstance(run_object, six.string_types):
return run_object
elif isinstance(run_object, types.FunctionType):
if run_object.__name__ == "<lambda>":
logger.warning(
"Not auto-registering lambdas - resolving as variant.")
retu... |
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def tsqr(a):
"""Perform a QR decomposition of a tall-skinny matrix. Args: a: A distributed matrix with shape MxN (suppose K = min(M, N)). Returns: A tuple of q (... |
if len(a.shape) != 2:
raise Exception("tsqr requires len(a.shape) == 2, but a.shape is "
"{}".format(a.shape))
if a.num_blocks[1] != 1:
raise Exception("tsqr requires a.num_blocks[1] == 1, but a.num_blocks "
"is {}".format(a.num_blocks))
num_... |
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def modified_lu(q):
"""Perform a modified LU decomposition of a matrix. This takes a matrix q with orthonormal columns, returns l, u, s such that q - s = l * u. ... |
q = q.assemble()
m, b = q.shape[0], q.shape[1]
S = np.zeros(b)
q_work = np.copy(q)
for i in range(b):
S[i] = -1 * np.sign(q_work[i, i])
q_work[i, i] -= S[i]
# Scale ith column of L by diagonal element.
q_work[(i + 1):m, i] /= q_work[i, i]
# Perform Schur co... |
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def _naturalize(string):
"""Provides a natural representation for string for nice sorting.""" |
splits = re.split("([0-9]+)", string)
return [int(text) if text.isdigit() else text.lower() for text in splits] |
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def _find_newest_ckpt(ckpt_dir):
"""Returns path to most recently modified checkpoint.""" |
full_paths = [
os.path.join(ckpt_dir, fname) for fname in os.listdir(ckpt_dir)
if fname.startswith("experiment_state") and fname.endswith(".json")
]
return max(full_paths) |
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def checkpoint(self):
"""Saves execution state to `self._metadata_checkpoint_dir`. Overwrites the current session checkpoint, which starts when self is instantia... |
if not self._metadata_checkpoint_dir:
return
metadata_checkpoint_dir = self._metadata_checkpoint_dir
if not os.path.exists(metadata_checkpoint_dir):
os.makedirs(metadata_checkpoint_dir)
runner_state = {
"checkpoints": list(
self.trial_... |
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def restore(cls, metadata_checkpoint_dir, search_alg=None, scheduler=None, trial_executor=None):
"""Restores all checkpointed trials from previous run. Requires ... |
newest_ckpt_path = _find_newest_ckpt(metadata_checkpoint_dir)
with open(newest_ckpt_path, "r") as f:
runner_state = json.load(f, cls=_TuneFunctionDecoder)
logger.warning("".join([
"Attempting to resume experiment from {}. ".format(
metadata_checkpoint_d... |
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def is_finished(self):
"""Returns whether all trials have finished running.""" |
if self._total_time > self._global_time_limit:
logger.warning("Exceeded global time limit {} / {}".format(
self._total_time, self._global_time_limit))
return True
trials_done = all(trial.is_finished() for trial in self._trials)
return trials_done and se... |
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def step(self):
"""Runs one step of the trial event loop. Callers should typically run this method repeatedly in a loop. They may inspect or modify the runner's ... |
if self.is_finished():
raise TuneError("Called step when all trials finished?")
with warn_if_slow("on_step_begin"):
self.trial_executor.on_step_begin()
next_trial = self._get_next_trial() # blocking
if next_trial is not None:
with warn_if_slow("start... |
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def add_trial(self, trial):
"""Adds a new trial to this TrialRunner. Trials may be added at any time. Args: trial (Trial):
Trial to queue. """ |
trial.set_verbose(self._verbose)
self._trials.append(trial)
with warn_if_slow("scheduler.on_trial_add"):
self._scheduler_alg.on_trial_add(self, trial)
self.trial_executor.try_checkpoint_metadata(trial) |
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def _get_next_trial(self):
"""Replenishes queue. Blocks if all trials queued have finished, but search algorithm is still not finished. """ |
trials_done = all(trial.is_finished() for trial in self._trials)
wait_for_trial = trials_done and not self._search_alg.is_finished()
self._update_trial_queue(blocking=wait_for_trial)
with warn_if_slow("choose_trial_to_run"):
trial = self._scheduler_alg.choose_trial_to_run(se... |
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def _checkpoint_trial_if_needed(self, trial):
"""Checkpoints trial based off trial.last_result.""" |
if trial.should_checkpoint():
# Save trial runtime if possible
if hasattr(trial, "runner") and trial.runner:
self.trial_executor.save(trial, storage=Checkpoint.DISK)
self.trial_executor.try_checkpoint_metadata(trial) |
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def _try_recover(self, trial, error_msg):
"""Tries to recover trial. Notifies SearchAlgorithm and Scheduler if failure to recover. Args: trial (Trial):
Trial to... |
try:
self.trial_executor.stop_trial(
trial,
error=error_msg is not None,
error_msg=error_msg,
stop_logger=False)
trial.result_logger.flush()
if self.trial_executor.has_resources(trial.resources):
... |
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def _requeue_trial(self, trial):
"""Notification to TrialScheduler and requeue trial. This does not notify the SearchAlgorithm because the function evaluation is... |
self._scheduler_alg.on_trial_error(self, trial)
self.trial_executor.set_status(trial, Trial.PENDING)
with warn_if_slow("scheduler.on_trial_add"):
self._scheduler_alg.on_trial_add(self, trial) |
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def _update_trial_queue(self, blocking=False, timeout=600):
"""Adds next trials to queue if possible. Note that the timeout is currently unexposed to the user. A... |
trials = self._search_alg.next_trials()
if blocking and not trials:
start = time.time()
# Checking `is_finished` instead of _search_alg.is_finished
# is fine because blocking only occurs if all trials are
# finished and search_algorithm is not yet finishe... |
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def stop_trial(self, trial):
"""Stops trial. Trials may be stopped at any time. If trial is in state PENDING or PAUSED, calls `on_trial_remove` for scheduler and... |
error = False
error_msg = None
if trial.status in [Trial.ERROR, Trial.TERMINATED]:
return
elif trial.status in [Trial.PENDING, Trial.PAUSED]:
self._scheduler_alg.on_trial_remove(self, trial)
self._search_alg.on_trial_complete(
trial.t... |
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def run_func(func, *args, **kwargs):
"""Helper function for running examples""" |
ray.init()
func = ray.remote(func)
# NOTE: kwargs not allowed for now
result = ray.get(func.remote(*args))
# Inspect the stack to get calling example
caller = inspect.stack()[1][3]
print("%s: %s" % (caller, str(result)))
return result |
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def example6():
"""Cython simple class""" |
ray.init()
cls = ray.remote(cyth.simple_class)
a1 = cls.remote()
a2 = cls.remote()
result1 = ray.get(a1.increment.remote())
result2 = ray.get(a2.increment.remote())
print(result1, result2) |
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def _adjust_nstep(n_step, gamma, obs, actions, rewards, new_obs, dones):
"""Rewrites the given trajectory fragments to encode n-step rewards. reward[i] = ( rewar... |
assert not any(dones[:-1]), "Unexpected done in middle of trajectory"
traj_length = len(rewards)
for i in range(traj_length):
for j in range(1, n_step):
if i + j < traj_length:
new_obs[i] = new_obs[i + j]
dones[i] = dones[i + j]
rewards[... |
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def _minimize_and_clip(optimizer, objective, var_list, clip_val=10):
"""Minimized `objective` using `optimizer` w.r.t. variables in `var_list` while ensure the n... |
gradients = optimizer.compute_gradients(objective, var_list=var_list)
for i, (grad, var) in enumerate(gradients):
if grad is not None:
gradients[i] = (tf.clip_by_norm(grad, clip_val), var)
return gradients |
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def _scope_vars(scope, trainable_only=False):
""" Get variables inside a scope The scope can be specified as a string Parameters scope: str or VariableScope scop... |
return tf.get_collection(
tf.GraphKeys.TRAINABLE_VARIABLES
if trainable_only else tf.GraphKeys.VARIABLES,
scope=scope if isinstance(scope, str) else scope.name) |
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def get_custom_getter(self):
"""Returns a custom getter that this class's methods must be called All methods of this class must be called under a variable scope ... |
def inner_custom_getter(getter, *args, **kwargs):
if not self.use_tf_layers:
return getter(*args, **kwargs)
requested_dtype = kwargs["dtype"]
if not (requested_dtype == tf.float32
and self.variable_dtype == tf.float16):
kw... |
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def switch_to_aux_top_layer(self):
"""Context that construct cnn in the auxiliary arm.""" |
if self.aux_top_layer is None:
raise RuntimeError("Empty auxiliary top layer in the network.")
saved_top_layer = self.top_layer
saved_top_size = self.top_size
self.top_layer = self.aux_top_layer
self.top_size = self.aux_top_size
yield
self.aux_top_lay... |
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def _pool(self, pool_name, pool_function, k_height, k_width, d_height, d_width, mode, input_layer, num_channels_in):
"""Construct a pooling layer.""" |
if input_layer is None:
input_layer = self.top_layer
else:
self.top_size = num_channels_in
name = pool_name + str(self.counts[pool_name])
self.counts[pool_name] += 1
if self.use_tf_layers:
pool = pool_function(
input_layer, [k_... |
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def mpool(self, k_height, k_width, d_height=2, d_width=2, mode="VALID", input_layer=None, num_channels_in=None):
"""Construct a max pooling layer.""" |
return self._pool("mpool", pooling_layers.max_pooling2d, k_height,
k_width, d_height, d_width, mode, input_layer,
num_channels_in) |
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def apool(self, k_height, k_width, d_height=2, d_width=2, mode="VALID", input_layer=None, num_channels_in=None):
"""Construct an average pooling layer.""" |
return self._pool("apool", pooling_layers.average_pooling2d, k_height,
k_width, d_height, d_width, mode, input_layer,
num_channels_in) |
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def _batch_norm_without_layers(self, input_layer, decay, use_scale, epsilon):
"""Batch normalization on `input_layer` without tf.layers.""" |
shape = input_layer.shape
num_channels = shape[3] if self.data_format == "NHWC" else shape[1]
beta = self.get_variable(
"beta", [num_channels],
tf.float32,
tf.float32,
initializer=tf.zeros_initializer())
if use_scale:
gamma = s... |
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def lrn(self, depth_radius, bias, alpha, beta):
"""Adds a local response normalization layer.""" |
name = "lrn" + str(self.counts["lrn"])
self.counts["lrn"] += 1
self.top_layer = tf.nn.lrn(
self.top_layer, depth_radius, bias, alpha, beta, name=name)
return self.top_layer |
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def _internal_kv_get(key):
"""Fetch the value of a binary key.""" |
worker = ray.worker.get_global_worker()
if worker.mode == ray.worker.LOCAL_MODE:
return _local.get(key)
return worker.redis_client.hget(key, "value") |
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def _internal_kv_put(key, value, overwrite=False):
"""Globally associates a value with a given binary key. This only has an effect if the key does not already ha... |
worker = ray.worker.get_global_worker()
if worker.mode == ray.worker.LOCAL_MODE:
exists = key in _local
if not exists or overwrite:
_local[key] = value
return exists
if overwrite:
updated = worker.redis_client.hset(key, "value", value)
else:
updated... |
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def init(self, aggregators):
"""Deferred init so that we can pass in previously created workers.""" |
assert len(aggregators) == self.num_aggregation_workers, aggregators
if len(self.remote_evaluators) < self.num_aggregation_workers:
raise ValueError(
"The number of aggregation workers should not exceed the "
"number of total evaluation workers ({} vs {})".f... |
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def free(object_ids, local_only=False, delete_creating_tasks=False):
"""Free a list of IDs from object stores. This function is a low-level API which should be u... |
worker = ray.worker.get_global_worker()
if ray.worker._mode() == ray.worker.LOCAL_MODE:
return
if isinstance(object_ids, ray.ObjectID):
object_ids = [object_ids]
if not isinstance(object_ids, list):
raise TypeError("free() expects a list of ObjectID, got {}".format(
... |
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def run(self):
"""Start the collector worker thread. If running in standalone mode, the current thread will wait until the collector thread ends. """ |
self.collector.start()
if self.standalone:
self.collector.join() |
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def init_logger(cls, log_level):
"""Initialize logger settings.""" |
logger = logging.getLogger("AutoMLBoard")
handler = logging.StreamHandler()
formatter = logging.Formatter("[%(levelname)s %(asctime)s] "
"%(filename)s: %(lineno)d "
"%(message)s")
handler.setFormatter(formatter... |
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def run(self):
"""Run the main event loop for collector thread. In each round the collector traverse the results log directory and reload trial information from ... |
self._initialize()
self._do_collect()
while not self._is_finished:
time.sleep(self._reload_interval)
self._do_collect()
self.logger.info("Collector stopped.") |
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def _initialize(self):
"""Initialize collector worker thread, Log path will be checked first. Records in DB backend will be cleared. """ |
if not os.path.exists(self._logdir):
raise CollectorError("Log directory %s not exists" % self._logdir)
self.logger.info("Collector started, taking %s as parent directory"
"for all job logs." % self._logdir)
# clear old records
JobRecord.objects.fi... |
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def sync_job_info(self, job_name):
"""Load information of the job with the given job name. 1. Traverse each experiment sub-directory and sync information for eac... |
job_path = os.path.join(self._logdir, job_name)
if job_name not in self._monitored_jobs:
self._create_job_info(job_path)
self._monitored_jobs.add(job_name)
else:
self._update_job_info(job_path)
expr_dirs = filter(lambda d: os.path.isdir(os.path.join... |
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def sync_trial_info(self, job_path, expr_dir_name):
"""Load information of the trial from the given experiment directory. Create or update the trial information,... |
expr_name = expr_dir_name[-8:]
expr_path = os.path.join(job_path, expr_dir_name)
if expr_name not in self._monitored_trials:
self._create_trial_info(expr_path)
self._monitored_trials.add(expr_name)
else:
self._update_trial_info(expr_path) |
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def _create_job_info(self, job_dir):
"""Create information for given job. Meta file will be loaded if exists, and the job information will be saved in db backend... |
meta = self._build_job_meta(job_dir)
self.logger.debug("Create job: %s" % meta)
job_record = JobRecord.from_json(meta)
job_record.save() |
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def _update_job_info(cls, job_dir):
"""Update information for given job. Meta file will be loaded if exists, and the job information in in db backend will be upd... |
meta_file = os.path.join(job_dir, JOB_META_FILE)
meta = parse_json(meta_file)
if meta:
logging.debug("Update job info for %s" % meta["job_id"])
JobRecord.objects \
.filter(job_id=meta["job_id"]) \
.update(end_time=timestamp2date(meta["end... |
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def _create_trial_info(self, expr_dir):
"""Create information for given trial. Meta file will be loaded if exists, and the trial information will be saved in db ... |
meta = self._build_trial_meta(expr_dir)
self.logger.debug("Create trial for %s" % meta)
trial_record = TrialRecord.from_json(meta)
trial_record.save() |
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def _update_trial_info(self, expr_dir):
"""Update information for given trial. Meta file will be loaded if exists, and the trial information in db backend will b... |
trial_id = expr_dir[-8:]
meta_file = os.path.join(expr_dir, EXPR_META_FILE)
meta = parse_json(meta_file)
result_file = os.path.join(expr_dir, EXPR_RESULT_FILE)
offset = self._result_offsets.get(trial_id, 0)
results, new_offset = parse_multiple_json(result_file, offset)... |
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def _build_job_meta(cls, job_dir):
"""Build meta file for job. Args: job_dir (str):
Directory path of the job. Return: A dict of job meta info. """ |
meta_file = os.path.join(job_dir, JOB_META_FILE)
meta = parse_json(meta_file)
if not meta:
job_name = job_dir.split("/")[-1]
user = os.environ.get("USER", None)
meta = {
"job_id": job_name,
"job_name": job_name,
... |
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def _build_trial_meta(cls, expr_dir):
"""Build meta file for trial. Args: expr_dir (str):
Directory path of the experiment. Return: A dict of trial meta info. "... |
meta_file = os.path.join(expr_dir, EXPR_META_FILE)
meta = parse_json(meta_file)
if not meta:
job_id = expr_dir.split("/")[-2]
trial_id = expr_dir[-8:]
params = parse_json(os.path.join(expr_dir, EXPR_PARARM_FILE))
meta = {
"trial_i... |
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def _add_results(self, results, trial_id):
"""Add a list of results into db. Args: results (list):
A list of json results. trial_id (str):
Id of the trial. """ |
for result in results:
self.logger.debug("Appending result: %s" % result)
result["trial_id"] = trial_id
result_record = ResultRecord.from_json(result)
result_record.save() |
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def add_time_dimension(padded_inputs, seq_lens):
"""Adds a time dimension to padded inputs. Arguments: padded_inputs (Tensor):
a padded batch of sequences. That... |
# Sequence lengths have to be specified for LSTM batch inputs. The
# input batch must be padded to the max seq length given here. That is,
# batch_size == len(seq_lens) * max(seq_lens)
padded_batch_size = tf.shape(padded_inputs)[0]
max_seq_len = padded_batch_size // tf.shape(seq_lens)[0]
# Dy... |
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def chop_into_sequences(episode_ids, unroll_ids, agent_indices, feature_columns, state_columns, max_seq_len, dynamic_max=True, _extra_padding=0):
"""Truncate and... |
prev_id = None
seq_lens = []
seq_len = 0
unique_ids = np.add(
np.add(episode_ids, agent_indices),
np.array(unroll_ids) << 32)
for uid in unique_ids:
if (prev_id is not None and uid != prev_id) or \
seq_len >= max_seq_len:
seq_lens.append(seq_len)... |
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def explore(config, mutations, resample_probability, custom_explore_fn):
"""Return a config perturbed as specified. Args: config (dict):
Original hyperparameter... |
new_config = copy.deepcopy(config)
for key, distribution in mutations.items():
if isinstance(distribution, dict):
new_config.update({
key: explore(config[key], mutations[key], resample_probability,
None)
})
elif isinstance(dis... |
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def make_experiment_tag(orig_tag, config, mutations):
"""Appends perturbed params to the trial name to show in the console.""" |
resolved_vars = {}
for k in mutations.keys():
resolved_vars[("config", k)] = config[k]
return "{}@perturbed[{}]".format(orig_tag, format_vars(resolved_vars)) |
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def _exploit(self, trial_executor, trial, trial_to_clone):
"""Transfers perturbed state from trial_to_clone -> trial. If specified, also logs the updated hyperpa... |
trial_state = self._trial_state[trial]
new_state = self._trial_state[trial_to_clone]
if not new_state.last_checkpoint:
logger.info("[pbt]: no checkpoint for trial."
" Skip exploit for Trial {}".format(trial))
return
new_config = explore(t... |
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def _quantiles(self):
"""Returns trials in the lower and upper `quantile` of the population. If there is not enough data to compute this, returns empty lists.""" |
trials = []
for trial, state in self._trial_state.items():
if state.last_score is not None and not trial.is_finished():
trials.append(trial)
trials.sort(key=lambda t: self._trial_state[t].last_score)
if len(trials) <= 1:
return [], []
el... |
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def _build_layers(self, inputs, num_outputs, options):
"""Process the flattened inputs. Note that dict inputs will be flattened into a vector. To define a model ... |
hiddens = options.get("fcnet_hiddens")
activation = get_activation_fn(options.get("fcnet_activation"))
with tf.name_scope("fc_net"):
i = 1
last_layer = inputs
for size in hiddens:
label = "fc{}".format(i)
last_layer = slim.fu... |
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def with_base_config(base_config, extra_config):
"""Returns the given config dict merged with a base agent conf.""" |
config = copy.deepcopy(base_config)
config.update(extra_config)
return config |
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def get_agent_class(alg):
"""Returns the class of a known agent given its name.""" |
try:
return _get_agent_class(alg)
except ImportError:
from ray.rllib.agents.mock import _agent_import_failed
return _agent_import_failed(traceback.format_exc()) |
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def determine_ip_address():
"""Return the first IP address for an ethernet interface on the system.""" |
addrs = [
x.address for k, v in psutil.net_if_addrs().items() if k[0] == "e"
for x in v if x.family == AddressFamily.AF_INET
]
return addrs[0] |
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def run(self):
"""Run the reporter.""" |
while True:
try:
self.perform_iteration()
except Exception:
traceback.print_exc()
pass
time.sleep(ray_constants.REPORTER_UPDATE_INTERVAL_MS / 1000) |
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def check_serializable(cls):
"""Throws an exception if Ray cannot serialize this class efficiently. Args: cls (type):
The class to be serialized. Raises: Except... |
if is_named_tuple(cls):
# This case works.
return
if not hasattr(cls, "__new__"):
print("The class {} does not have a '__new__' attribute and is "
"probably an old-stye class. Please make it a new-style class "
"by inheriting from 'object'.")
raise Ra... |
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def is_named_tuple(cls):
"""Return True if cls is a namedtuple and False otherwise.""" |
b = cls.__bases__
if len(b) != 1 or b[0] != tuple:
return False
f = getattr(cls, "_fields", None)
if not isinstance(f, tuple):
return False
return all(type(n) == str for n in f) |
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def register_trainable(name, trainable):
"""Register a trainable function or class. Args: name (str):
Name to register. trainable (obj):
Function or tune.Train... |
from ray.tune.trainable import Trainable
from ray.tune.function_runner import wrap_function
if isinstance(trainable, type):
logger.debug("Detected class for trainable.")
elif isinstance(trainable, FunctionType):
logger.debug("Detected function for trainable.")
trainable = wrap... |
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def register_env(name, env_creator):
"""Register a custom environment for use with RLlib. Args: name (str):
Name to register. env_creator (obj):
Function that ... |
if not isinstance(env_creator, FunctionType):
raise TypeError("Second argument must be a function.", env_creator)
_global_registry.register(ENV_CREATOR, name, env_creator) |
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def get_learner_stats(grad_info):
"""Return optimization stats reported from the policy graph. Example: """ |
if LEARNER_STATS_KEY in grad_info:
return grad_info[LEARNER_STATS_KEY]
multiagent_stats = {}
for k, v in grad_info.items():
if type(v) is dict:
if LEARNER_STATS_KEY in v:
multiagent_stats[k] = v[LEARNER_STATS_KEY]
return multiagent_stats |
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def collect_metrics(local_evaluator=None, remote_evaluators=[], timeout_seconds=180):
"""Gathers episode metrics from PolicyEvaluator instances.""" |
episodes, num_dropped = collect_episodes(
local_evaluator, remote_evaluators, timeout_seconds=timeout_seconds)
metrics = summarize_episodes(episodes, episodes, num_dropped)
return metrics |
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def collect_episodes(local_evaluator=None, remote_evaluators=[], timeout_seconds=180):
"""Gathers new episodes metrics tuples from the given evaluators.""" |
pending = [
a.apply.remote(lambda ev: ev.get_metrics()) for a in remote_evaluators
]
collected, _ = ray.wait(
pending, num_returns=len(pending), timeout=timeout_seconds * 1.0)
num_metric_batches_dropped = len(pending) - len(collected)
if pending and len(collected) == 0:
rai... |
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def _partition(episodes):
"""Divides metrics data into true rollouts vs off-policy estimates.""" |
from ray.rllib.evaluation.sampler import RolloutMetrics
rollouts, estimates = [], []
for e in episodes:
if isinstance(e, RolloutMetrics):
rollouts.append(e)
elif isinstance(e, OffPolicyEstimate):
estimates.append(e)
else:
raise ValueError("Unkno... |
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def set_status(self, trial, status):
"""Sets status and checkpoints metadata if needed. Only checkpoints metadata if trial status is a terminal condition. PENDIN... |
trial.status = status
if status in [Trial.TERMINATED, Trial.ERROR]:
self.try_checkpoint_metadata(trial) |
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def try_checkpoint_metadata(self, trial):
"""Checkpoints metadata. Args: trial (Trial):
Trial to checkpoint. """ |
if trial._checkpoint.storage == Checkpoint.MEMORY:
logger.debug("Not saving data for trial w/ memory checkpoint.")
return
try:
logger.debug("Saving trial metadata.")
self._cached_trial_state[trial.trial_id] = trial.__getstate__()
except Exception:... |
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def unpause_trial(self, trial):
"""Sets PAUSED trial to pending to allow scheduler to start.""" |
assert trial.status == Trial.PAUSED, trial.status
self.set_status(trial, Trial.PENDING) |
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def resume_trial(self, trial):
"""Resumes PAUSED trials. This is a blocking call.""" |
assert trial.status == Trial.PAUSED, trial.status
self.start_trial(trial) |
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def on_trial_complete(self, trial_id, result=None, error=False, early_terminated=False):
"""Passes the result to Nevergrad unless early terminated or errored. Th... |
ng_trial_info = self._live_trial_mapping.pop(trial_id)
if result:
self._nevergrad_opt.tell(ng_trial_info, -result[self._reward_attr]) |
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def start(self):
"""Start the import thread.""" |
self.t = threading.Thread(target=self._run, name="ray_import_thread")
# Making the thread a daemon causes it to exit
# when the main thread exits.
self.t.daemon = True
self.t.start() |
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def _process_key(self, key):
"""Process the given export key from redis.""" |
# Handle the driver case first.
if self.mode != ray.WORKER_MODE:
if key.startswith(b"FunctionsToRun"):
with profiling.profile("fetch_and_run_function"):
self.fetch_and_execute_function_to_run(key)
# Return because FunctionsToRun are the only t... |
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def fetch_and_execute_function_to_run(self, key):
"""Run on arbitrary function on the worker.""" |
(driver_id, serialized_function,
run_on_other_drivers) = self.redis_client.hmget(
key, ["driver_id", "function", "run_on_other_drivers"])
if (utils.decode(run_on_other_drivers) == "False"
and self.worker.mode == ray.SCRIPT_MODE
and driver_id != sel... |
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def clip_action(action, space):
"""Called to clip actions to the specified range of this policy. Arguments: action: Single action. space: Action space the action... |
if isinstance(space, gym.spaces.Box):
return np.clip(action, space.low, space.high)
elif isinstance(space, gym.spaces.Tuple):
if type(action) not in (tuple, list):
raise ValueError("Expected tuple space for actions {}: {}".format(
action, space))
out = []
... |
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def on_trial_complete(self, trial_id, result=None, error=False, early_terminated=False):
"""Passes the result to skopt unless early terminated or errored. The re... |
skopt_trial_info = self._live_trial_mapping.pop(trial_id)
if result:
self._skopt_opt.tell(skopt_trial_info, -result[self._reward_attr]) |
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def address_to_ip(address):
"""Convert a hostname to a numerical IP addresses in an address. This should be a no-op if address already contains an actual numeric... |
address_parts = address.split(":")
ip_address = socket.gethostbyname(address_parts[0])
# Make sure localhost isn't resolved to the loopback ip
if ip_address == "127.0.0.1":
ip_address = get_node_ip_address()
return ":".join([ip_address] + address_parts[1:]) |
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def get_node_ip_address(address="8.8.8.8:53"):
"""Determine the IP address of the local node. Args: address (str):
The IP address and port of any known live ser... |
ip_address, port = address.split(":")
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
try:
# This command will raise an exception if there is no internet
# connection.
s.connect((ip_address, int(port)))
node_ip_address = s.getsockname()[0]
except Exception as e:
... |
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def create_redis_client(redis_address, password=None):
"""Create a Redis client. Args: The IP address, port, and password of the Redis server. Returns: A Redis c... |
redis_ip_address, redis_port = redis_address.split(":")
# For this command to work, some other client (on the same machine
# as Redis) must have run "CONFIG SET protected-mode no".
return redis.StrictRedis(
host=redis_ip_address, port=int(redis_port), password=password) |
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def wait_for_redis_to_start(redis_ip_address, redis_port, password=None, num_retries=5):
"""Wait for a Redis server to be available. This is accomplished by crea... |
redis_client = redis.StrictRedis(
host=redis_ip_address, port=redis_port, password=password)
# Wait for the Redis server to start.
counter = 0
while counter < num_retries:
try:
# Run some random command and see if it worked.
logger.info(
"Waiting ... |
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Description:
def _autodetect_num_gpus():
"""Attempt to detect the number of GPUs on this machine. TODO(rkn):
This currently assumes Nvidia GPUs and Linux. Returns: The numbe... |
proc_gpus_path = "/proc/driver/nvidia/gpus"
if os.path.isdir(proc_gpus_path):
return len(os.listdir(proc_gpus_path))
return 0 |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
def _compute_version_info():
"""Compute the versions of Python, pyarrow, and Ray. Returns: A tuple containing the version information. """ |
ray_version = ray.__version__
python_version = ".".join(map(str, sys.version_info[:3]))
pyarrow_version = pyarrow.__version__
return ray_version, python_version, pyarrow_version |
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Description:
def check_version_info(redis_client):
"""Check if various version info of this process is correct. This will be used to detect if workers or drivers are started ... |
redis_reply = redis_client.get("VERSION_INFO")
# Don't do the check if there is no version information in Redis. This
# is to make it easier to do things like start the processes by hand.
if redis_reply is None:
return
true_version_info = tuple(json.loads(ray.utils.decode(redis_reply)))
... |
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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
def _start_redis_instance(executable, modules, port=None, redis_max_clients=None, num_retries=20, stdout_file=None, stderr_file=None, password=None, redis_max_mem... |
assert os.path.isfile(executable)
for module in modules:
assert os.path.isfile(module)
counter = 0
if port is not None:
# If a port is specified, then try only once to connect.
# This ensures that we will use the given port.
num_retries = 1
else:
port = new_p... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
def start_log_monitor(redis_address, logs_dir, stdout_file=None, stderr_file=None, redis_password=None):
"""Start a log monitor process. Args: redis_address (str... |
log_monitor_filepath = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "log_monitor.py")
command = [
sys.executable, "-u", log_monitor_filepath,
"--redis-address={}".format(redis_address),
"--logs-dir={}".format(logs_dir)
]
if redis_password:
command +=... |
<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
def start_reporter(redis_address, stdout_file=None, stderr_file=None, redis_password=None):
"""Start a reporter process. Args: redis_address (str):
The address ... |
reporter_filepath = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "reporter.py")
command = [
sys.executable, "-u", reporter_filepath,
"--redis-address={}".format(redis_address)
]
if redis_password:
command += ["--redis-password", redis_password]
try:
... |
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