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Started a local Ray instance. View the dashboard at http://127.0.0.1:8265
2024-01-26 18:46:32,146 INFO tune.py:220 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call ray.init(...) before tune.run(...).
2024-01-26 18:46:32,149 INFO tune.py:592 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see ray-project/ray#36949
Traceback (most recent call last):
File "src/models/train_model.py", line 124, in
main(args)
File "src/models/train_model.py", line 100, in main
result = tune.run(
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/tune.py", line 620, in run
os.environ["RAY_AIR_LOCAL_CACHE_DIR"] = local_dir
File "/usr/lib/python3.8/os.py", line 680, in setitem
value = self.encodevalue(value)
File "/usr/lib/python3.8/os.py", line 750, in encode
raise TypeError("str expected, not %s" % type(value).name)
TypeError: str expected, not PosixPath
snehil@snehil-VirtualBox:~/Downloads/ifcnet-models-master$ python3 src/models/train_model.py MVCNN
2024-01-26 18:57:01,787 INFO worker.py:1715 -- Started a local Ray instance. View the dashboard at http://127.0.0.1:8265
2024-01-26 18:57:02,406 INFO tune.py:220 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call ray.init(...) before tune.run(...).
2024-01-26 18:57:02,408 INFO tune.py:592 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see ray-project/ray#36949
Traceback (most recent call last):
File "src/models/train_model.py", line 124, in
main(args)
File "src/models/train_model.py", line 100, in main
result = tune.run(
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/tune.py", line 772, in run
experiments[i] = Experiment(
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/experiment/experiment.py", line 149, in init
self._run_identifier = Experiment.register_if_needed(run)
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/experiment/experiment.py", line 345, in register_if_needed
register_trainable(name, run_object)
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/registry.py", line 105, in register_trainable
trainable = wrap_function(trainable, warn=warn)
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/trainable/function_trainable.py", line 287, in wrap_function
raise DeprecationWarning(_CHECKPOINT_DIR_ARG_DEPRECATION_MSG)
DeprecationWarning: Accepting a checkpoint_dir argument in your training function is deprecated.
Please use ray.train.get_checkpoint() to access your checkpoint as a ray.train.Checkpoint object instead. See below for an example:
Before
from ray import tune
def train_fn(config, checkpoint_dir=None):
if checkpoint_dir:
torch.load(os.path.join(checkpoint_dir, "checkpoint.pt"))
...
tuner = tune.Tuner(train_fn)
tuner.fit()
After
from ray import train, tune
def train_fn(config):
checkpoint: train.Checkpoint = train.get_checkpoint()
if checkpoint:
with checkpoint.as_directory() as checkpoint_dir:
torch.load(os.path.join(checkpoint_dir, "checkpoint.pt"))
...
tuner = tune.Tuner(train_fn)
tuner.fit()
The text was updated successfully, but these errors were encountered:
Started a local Ray instance. View the dashboard at http://127.0.0.1:8265
2024-01-26 18:46:32,146 INFO tune.py:220 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call
ray.init(...)
beforetune.run(...)
.2024-01-26 18:46:32,149 INFO tune.py:592 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see ray-project/ray#36949
Traceback (most recent call last):
File "src/models/train_model.py", line 124, in
main(args)
File "src/models/train_model.py", line 100, in main
result = tune.run(
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/tune.py", line 620, in run
os.environ["RAY_AIR_LOCAL_CACHE_DIR"] = local_dir
File "/usr/lib/python3.8/os.py", line 680, in setitem
value = self.encodevalue(value)
File "/usr/lib/python3.8/os.py", line 750, in encode
raise TypeError("str expected, not %s" % type(value).name)
TypeError: str expected, not PosixPath
snehil@snehil-VirtualBox:~/Downloads/ifcnet-models-master$ python3 src/models/train_model.py MVCNN
2024-01-26 18:57:01,787 INFO worker.py:1715 -- Started a local Ray instance. View the dashboard at http://127.0.0.1:8265
2024-01-26 18:57:02,406 INFO tune.py:220 -- Initializing Ray automatically. For cluster usage or custom Ray initialization, call
ray.init(...)
beforetune.run(...)
.2024-01-26 18:57:02,408 INFO tune.py:592 -- [output] This will use the new output engine with verbosity 2. To disable the new output and use the legacy output engine, set the environment variable RAY_AIR_NEW_OUTPUT=0. For more information, please see ray-project/ray#36949
Traceback (most recent call last):
File "src/models/train_model.py", line 124, in
main(args)
File "src/models/train_model.py", line 100, in main
result = tune.run(
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/tune.py", line 772, in run
experiments[i] = Experiment(
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/experiment/experiment.py", line 149, in init
self._run_identifier = Experiment.register_if_needed(run)
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/experiment/experiment.py", line 345, in register_if_needed
register_trainable(name, run_object)
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/registry.py", line 105, in register_trainable
trainable = wrap_function(trainable, warn=warn)
File "/home/snehil/.local/lib/python3.8/site-packages/ray/tune/trainable/function_trainable.py", line 287, in wrap_function
raise DeprecationWarning(_CHECKPOINT_DIR_ARG_DEPRECATION_MSG)
DeprecationWarning: Accepting a
checkpoint_dir
argument in your training function is deprecated.Please use
ray.train.get_checkpoint()
to access your checkpoint as aray.train.Checkpoint
object instead. See below for an example:Before
from ray import tune
def train_fn(config, checkpoint_dir=None):
if checkpoint_dir:
torch.load(os.path.join(checkpoint_dir, "checkpoint.pt"))
...
tuner = tune.Tuner(train_fn)
tuner.fit()
After
from ray import train, tune
def train_fn(config):
checkpoint: train.Checkpoint = train.get_checkpoint()
if checkpoint:
with checkpoint.as_directory() as checkpoint_dir:
torch.load(os.path.join(checkpoint_dir, "checkpoint.pt"))
...
tuner = tune.Tuner(train_fn)
tuner.fit()
The text was updated successfully, but these errors were encountered: