assert mdl is not None, TypeError: Cannot cast scalar from dtype('<U1') to dtype('int64') according to the rule 'safe', etc

[Lan131]

First of all, if you pip install -r dev.txt or any of the other requirements files you get this notice:

DEPRECATION: git+https://github.com/facebookresearch/mtenv.git@main#egg=mtenv[all] contains an egg fragment with a non-PEP 508 name pip 25.0 will enforce this behaviour change. A possible replacement is to use the req @ url syntax, and remove the egg fragment. Discussion can be found at https://github.com/pypa/pip/issues/11617

Installing all packages individually and running CMTA.sh I get this error

[2024-03-13 19:59:47,927][default_logger][INFO] - {"setup": {"seed": "", "setup": "metaworld", "algo": "CMTA_info2500_mt10", "base_path": "/home/research/lanier.m/Desktop/CMTA-main/scripts", "dir_name": "logs_fix", "save_dir": "${setup.base_path}/${setup.dir_name}/${setup.id}", "device": "cuda:0", "id": "CMTA_info2500_mt10seed", "description": "Sample Task", "tags": null, "git": {"commit_id": null, "has_uncommitted_changes": null, "issue_id": null}, "date": "2024-03-13 19:59:47", "slurm_id": "-1", "debug": {"should_enable": false}}, "experiment": {"name": "metaworld", "builder": {"target": "mtrl.experiment.${experiment.name}.Experiment"}, "init_steps": 3000, "num_train_steps": 2500000, "eval_freq": 3000, "num_eval_episodes": 1, "should_resume": true, "eval_only": false, "random_pos": false, "save": {"model": {"retain_last_n": 1}, "buffer": {"should_save": true, "size_per_chunk": 15000, "num_samples_to_save": -1}}, "save_dir": "${setup.save_dir}", "save_video": true, "envs_to_exclude_during_training": null}, "agent": {"name": "state_sac", "encoder_feature_dim": 64, "num_layers": 0, "num_filters": 0, "builder": {"target": "mtrl.agent.sac.Agent", "actor_cfg": "${agent.actor}", "critic_cfg": "${agent.critic}", "multitask_cfg": "${agent.multitask}", "alpha_optimizer_cfg": "${agent.optimizers.alpha}", "actor_optimizer_cfg": "${agent.optimizers.actor}", "critic_optimizer_cfg": "${agent.optimizers.critic}", "discount": 0.99, "init_temperature": 1.0, "actor_update_freq": 1, "critic_tau": 0.005, "critic_target_update_freq": 1, "encoder_tau": 0.05}, "actor": {"target": "mtrl.agent.components.actor.Actor", "num_layers": 3, "hidden_dim": 512, "log_std_bounds": [-20, 2], "encoder_cfg": "${agent.encoder}", "multitask_cfg": "${agent.multitask}"}, "critic": {"target": "mtrl.agent.components.critic.Critic", "hidden_dim": "${agent.actor.hidden_dim}", "num_layers": "${agent.actor.num_layers}", "encoder_cfg": "${agent.encoder}", "multitask_cfg": "${agent.multitask}"}, "encoder": {"type_to_select": "moe", "identity": {"type": "identity", "feature_dim": "${agent.encoder_feature_dim}"}, "feedforward": {"type": "feedforward", "hidden_dim": 64, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "lstm": {"type": "lstm", "hidden_dim": 64, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "film": {"type": "film", "hidden_dim": 64, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "moe": {"type": "moe", "encoder_cfg": {"type": "feedforward", "hidden_dim": 64, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "num_experts": 6, "task_id_to_encoder_id_cfg": {"mode": "rnn_attention", "num_envs": "${env.num_envs}", "gate": {"embedding_dim": 64, "hidden_dim": 64, "num_layers": 2, "temperature": 1.0, "should_use_soft_attention": false, "topk": 2, "task_encoder_cfg": {"should_use_task_encoding": true, "should_detach_task_encoding": true}}, "attention": {"embedding_dim": 64, "hidden_dim": 64, "num_layers": 1, "temperature": 1.0, "should_use_soft_attention": true, "task_encoder_cfg": {"should_use_task_encoding": true, "should_detach_task_encoding": false}}, "rnn_attention": {"rnn_hidden_dim": 64, "hidden_dim": 64, "num_layers": 1, "temperature": 1.0, "should_use_soft_attention": true}, "cluster": {"env_name": "${env.name}", "task_description": "${env.description}", "ordered_task_list": "${env.ordered_task_list}", "mapping_cfg": "${agent.task_to_encoder_cluster}", "num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}, "identity": {"num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}, "ensemble": {"num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}}}, "factorized_moe": {"type": "fmoe", "encoder_cfg": "${agent.encoder.feedforward}", "num_factors": 2, "num_experts_per_factor": [5, 5]}, "pixel": {"type": "pixel", "feature_dim": "${agent.encoder_feature_dim}", "num_filters": "${agent.num_filters}", "num_layers": "${agent.num_layers}"}}, "transition_model": {"target": "mtrl.agent.components.transition_model.make_transition_model", "transition_cfg": {"type": "", "feature_dim": "${agent.encoder_feature_dim}", "layer_width": 512}, "multitask_cfg": "${agent.multitask}"}, "mask": {"num_tasks": "${env.num_envs}", "num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}, "multitask": {"num_envs": 10, "should_use_disentangled_alpha": true, "should_use_reweighting": false, "should_use_task_encoder": true, "should_use_multi_head_policy": false, "should_use_disjoint_policy": false, "should_use_attention_multi_head_policy": false, "task_encoder_cfg": {"model_cfg": {"target": "mtrl.agent.components.task_encoder.TaskEncoder", "pretrained_embedding_cfg": {"should_use": false, "path_to_load_from": "/workspace/S/lansiming/mtrl/metadata/task_embedding/roberta_small/${env.name}.json", "ordered_task_list": "${env.ordered_task_list}"}, "num_embeddings": "${agent.multitask.num_envs}", "embedding_dim": 64, "hidden_dim": 64, "num_layers": 1, "output_dim": 64}, "optimizer_cfg": "${agent.optimizers.actor}", "losses_to_train": ["critic", "transition_reward", "decoder", "task_encoder"]}, "multi_head_policy_cfg": {"mask_cfg": "${agent.mask}"}, "actor_cfg": {"should_condition_model_on_task_info": false, "should_condition_encoder_on_task_info": true, "should_concatenate_task_info_with_encoder": true, "moe_cfg": {"mode": "soft_modularization", "num_experts": 4, "should_use": false}}, "critic_cfg": "${agent.multitask.actor_cfg}"}, "gradnorm": {"alpha": 1.0}, "task_to_encoder_cluster": {"mt10": {"cluster": {"action_close": ["close"], "action_default": ["insert", "pick and place", "press", "reach"], "action_open": ["open"], "action_push": ["push"], "object_default": ["button", "door", "peg", "revolving joint"], "object_drawer": ["drawer"], "object_goal": ["goal"], "object_puck": ["puck"], "object_window": ["window"]}}, "mt50": {"cluster": {"action_close": ["close"], "action_default": ["insert", "pick and place", "press", "reach"], "action_open": ["open"], "action_push": ["push"], "object_default": ["button", "door", "peg", "revolving joint"], "object_drawer": ["drawer"], "object_goal": ["goal"], "object_puck": ["puck"], "object_window": ["window"]}}}, "optimizers": {"actor": {"target": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}, "alpha": {"target": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}, "critic": {"target": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}, "decoder": {"target": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999], "weight_decay": 1e-07}, "encoder": {"target": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}}}, "env": {"name": "metaworld-mt10", "num_envs": 10, "benchmark": {"target": "metaworld.MT10"}, "builder": {"make_kwargs": {"should_perform_reward_normalization": true}}, "dummy": {"target": "metaworld.MT1", "env_name": "pick-place-v1"}, "description": {"reach-v1": "Reach a goal position. Randomize the goal positions.", "push-v1": "Push the puck to a goal. Randomize puck and goal positions.", "pick-place-v1": "Pick and place a puck to a goal. Randomize puck and goal positions.", "door-open-v1": "Open a door with a revolving joint. Randomize door positions.", "drawer-open-v1": "Open a drawer. Randomize drawer positions.", "drawer-close-v1": "Push and close a drawer. Randomize the drawer positions.", "button-press-topdown-v1": "Press a button from the top. Randomize button positions.", "peg-insert-side-v1": "Insert a peg sideways. Randomize peg and goal positions.", "window-open-v1": "Push and open a window. Randomize window positions.", "window-close-v1": "Push and close a window. Randomize window positions."}, "ordered_task_list": null}, "replay_buffer": {"target": "mtrl.replay_buffer.ReplayBuffer", "env_obs_shape": null, "action_shape": null, "capacity": 5000000, "batch_size": 1280}, "logger": {"target": "mtrl.logger.Logger", "logger_dir": "${setup.save_dir}", "use_tb": false}, "metrics": {"train": [["episode", "E", "int", "average"], ["step", "S", "int", "average"], ["duration", "D", "time", "average"], ["hours", "T", "time", "average"], ["episode_reward", "R", "float", "average"], ["success", "Su", "float", "average"], ["batch_reward", "BR", "float", "average"], ["actor_loss", "ALOSS", "float", "average"], ["critic_loss", "CLOSS", "float", "average"], ["ae_loss", "RLOSS", "float", "average"], ["ae_transition_loss", null, "float", "average"], ["reward_loss", null, "float", "average"], ["actor_target_entropy", null, "float", "average"], ["actor_entropy", null, "float", "average"], ["alpha_loss", null, "float", "average"], ["alphavalue", null, "float", "average"], ["contrastive_loss", "MLOSS", "float", "average"], ["max_rat", "MR", "float", "average"], ["env_index", "ENV", "str", "constant"], ["episode_reward_envindex", "R_", "float", "average"], ["success_envindex", "Su_", "float", "average"], ["envindex", "ENV_", "str", "constant"], ["batch_reward_agentindex", null, "float", "average"], ["critic_loss_agentindex", "AGENT_", "float", "average"], ["actor_distilled_agent_loss_agentindex", null, "float", "average"], ["actor_loss_agentindex", null, "float", "average"], ["actor_target_entropy_agentindex", null, "float", "average"], ["actor_entropy_agentindex", null, "float", "average"], ["alpha_loss_agentindex", null, "float", "average"], ["alpha_value_agentindex", null, "float", "average"], ["ae_loss_agentindex", null, "float", "average"], ["info_nce_loss", "IN", "float", "average"], ["accuracy", null, "float", "average"]], "eval": [["episode", "E", "int", "average"], ["step", "S", "int", "average"], ["episode_reward", "R", "float", "average"], ["env_index", "ENV", "str", "constant"], ["success", "Su", "float", "average"], ["episode_reward_envindex", "R_", "float", "average"], ["success_envindex", "Su_", "float", "average"], ["envindex", "ENV_", "str", "constant"], ["batch_reward_agentindex", "AGENT_", "float", "average"]]}, "logbook": {"target": "ml_logger.logbook.make_config", "write_to_console": false, "logger_dir": "${setup.save_dir}", "create_multiple_log_files": false}, "status": "RUNNING", "logbook_id": "0", "logbook_timestamp": "07:59:47PM CDT Mar 13, 2024", "logbook_type": "metadata"} Traceback (most recent call last): File "_mt19937.pyx", line 178, in numpy.random._mt19937.MT19937._legacy_seeding TypeError: 'str' object cannot be interpreted as an integer

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 198, in run_and_report return func() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 347, in lambda: hydra.run( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 107, in run return run_job( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/core/utils.py", line 128, in run_job ret.return_value = task_function(task_cfg) File "/home/research/lanier.m/Desktop/CMTA-main/scripts/../main.py", line 14, in launch return run(config) File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/app/run.py", line 37, in run experiment_utils.prepare_and_run(config=config) File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/experiment/utils.py", line 20, in prepare_and_run set_seed(seed=config.setup.seed) File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/utils/utils.py", line 89, in set_seed np.random.seed(seed) File "mtrand.pyx", line 244, in numpy.random.mtrand.RandomState.seed File "_mt19937.pyx", line 166, in numpy.random._mt19937.MT19937._legacy_seeding File "_mt19937.pyx", line 186, in numpy.random._mt19937.MT19937._legacy_seeding TypeError: Cannot cast scalar from dtype('<U1') to dtype('int64') according to the rule 'safe'

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/scripts/../main.py", line 18, in launch() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/main.py", line 32, in decorated_main _run_hydra( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 346, in _run_hydra run_and_report( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 237, in run_and_report assert mdl is not None AssertionError

[niiceMing]

We forgot to annotate the git mtenv code in base.txt, which might have caused some errors. We've made a new commit to fix that. And \$ seed \$ in Getting started should be replaced by any specific number such as 1,10,20 ... For instance, the running code can be bash CMTA.sh 10 for seed=10

[Lan131]

So after install base.txt, dev.txt, and gym I run and get ModuleNotFoundError: No module named 'mtenv'

If I try to run this line from base.txt:

"-e git+https://github.com/facebookresearch/mtenv.git@main#egg=mtenv[all] ` B5==0.7" or run this pip install -e git+https://github.com/facebookresearch/mtenv.git@main#egg=mtenv[all]==0.7

I get this error.

DEPRECATION: git+https://github.com/facebookresearch/mtenv.git@main#egg=mtenv[all]==0.7 contains an egg fragment with a non-PEP 508 name pip 25.0 will enforce this behaviour change. A possible replacement is to use the req @ url syntax, and remove the egg fragment. Discussion can be found at https://github.com/pypa/pip/issues/11617 Obtaining mtenv[all]==0.7 from git+https://github.com/facebookresearch/mtenv.git@main#egg=mtenv[all]==0.7 (from mtenv[all]==0.7) Updating ./src/mtenv clone (to revision main) Running command git fetch -q --tags Running command git reset --hard -q f9c52ad56c00bc3dff96b4e6c4979376aa24854b Preparing metadata (setup.py) ... error error: subprocess-exited-with-error

× python setup.py egg_info did not run successfully. │ exit code: 1 ╰─> [78 lines of output] Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/scripts/src/mtenv/mtenv/envs/control/setup.py", line 6, in from mtenv.utils.setup_utils import parse_dependency ModuleNotFoundError: No module named 'mtenv' Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/scripts/src/mtenv/mtenv/envs/hipbmdp/setup.py", line 6, in from mtenv.utils.setup_utils import parse_dependency ModuleNotFoundError: No module named 'mtenv' Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/scripts/src/mtenv/mtenv/envs/metaworld/setup.py", line 6, in from mtenv.utils.setup_utils import parse_dependency ModuleNotFoundError: No module named 'mtenv' Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/scripts/src/mtenv/mtenv/envs/mpte/setup.py", line 6, in from mtenv.utils.setup_utils import parse_dependency ModuleNotFoundError: No module named 'mtenv' Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/scripts/src/mtenv/mtenv/envs/tabular_mdp/setup.py", line 6, in from mtenv.utils.setup_utils import parse_dependency ModuleNotFoundError: No module named 'mtenv' running egg_info creating /tmp/pip-pip-egg-info-frtg4fc7/mtenv.egg-info writing /tmp/pip-pip-egg-info-frtg4fc7/mtenv.egg-info/PKG-INFO Traceback (most recent call last): File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_vendor/packaging/markers.py", line 199, in init self._markers = _normalize_extra_values(_parse_marker(marker)) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_vendor/packaging/_parser.py", line 255, in parse_marker return _parse_marker(Tokenizer(source, rules=DEFAULT_RULES)) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_vendor/packaging/_parser.py", line 262, in _parse_marker expression = [_parse_marker_atom(tokenizer)] File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_vendor/packaging/_parser.py", line 287, in _parse_marker_atom marker = _parse_marker_item(tokenizer) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_vendor/packaging/_parser.py", line 297, in _parse_marker_item marker_var_left = _parse_marker_var(tokenizer) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_vendor/packaging/_parser.py", line 315, in _parse_marker_var tokenizer.raise_syntax_error( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_vendor/packaging/_tokenizer.py", line 165, in raise_syntax_error raise ParserSyntaxError( setuptools.extern.packaging._tokenizer.ParserSyntaxError: Expected a marker variable or quoted string

      ^

  The above exception was the direct cause of the following exception:

  Traceback (most recent call last):
    File "<string>", line 2, in <module>
    File "<pip-setuptools-caller>", line 34, in <module>
    File "/home/research/lanier.m/Desktop/CMTA-main/scripts/src/mtenv/setup.py", line 63, in <module>
      setuptools.setup(
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/__init__.py", line 104, in setup
      return distutils.core.setup(**attrs)
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/core.py", line 185, in setup
      return run_commands(dist)
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/core.py", line 201, in run_commands
      dist.run_commands()
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/dist.py", line 969, in run_commands
      self.run_command(cmd)
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/dist.py", line 967, in run_command
      super().run_command(command)
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/dist.py", line 988, in run_command
      cmd_obj.run()
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/command/egg_info.py", line 314, in run
      writer(self, ep.name, os.path.join(self.egg_info, ep.name))
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/command/egg_info.py", line 677, in write_pkg_info
      metadata.write_pkg_info(cmd.egg_info)
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_core_metadata.py", line 135, in write_pkg_info
      self.write_pkg_file(f)
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_core_metadata.py", line 205, in write_pkg_file
      _write_requirements(self, file)
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_core_metadata.py", line 228, in _write_requirements
      r = _include_extra(req, extra, condition.strip())
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_core_metadata.py", line 241, in _include_extra
      r.marker = Marker(" and ".join(x for x in parts if x))
    File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_vendor/packaging/markers.py", line 217, in __init__
      raise InvalidMarker(str(e)) from e
  setuptools.extern.packaging.markers.InvalidMarker: Expected a marker variable or quoted string

      ^
  [end of output]

note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed

× Encountered error while generating package metadata. ╰─> See above for output.

note: This is an issue with the package mentioned above, not pip. hint: See above for details.

[Lan131]

Can you also please provide the scipy version used. Newer versions are incompatible with the provided numpy version and installing some of the pacakges will automaticlly install a newer version of scipy which is incompatible.

[niiceMing]

Since we modified mtenv, we should not pip install the mtenv library from github directly. After git clone our repository, we need to pip install -e src/mtenv/. We'll add this in the Readme. And the scipy version is 1.7.0 for our environment.

[Lan131]

Thank you for that. Do I need to run anything prior to pip install -e src/mtenv/.? When I download and run I get this error.

[lanier.m@ssh8 CMTA-main]$ pip install -e src/mtenv/ Obtaining file:///home/research/lanier.m/Desktop/CMTA-main/src/mtenv Preparing metadata (setup.py) ... error error: subprocess-exited-with-error

× python setup.py egg_info did not run successfully. │ exit code: 1 ╰─> [21 lines of output] Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/src/mtenv/mtenv/envs/control/setup.py", line 6, in from mtenv.utils.setup_utils import parse_dependency ModuleNotFoundError: No module named 'mtenv' Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/src/mtenv/mtenv/envs/metaworld/setup.py", line 6, in from mtenv.utils.setup_utils import parse_dependency ModuleNotFoundError: No module named 'mtenv' Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/src/mtenv/mtenv/envs/hipbmdp/setup.py", line 6, in from mtenv.utils.setup_utils import parse_dependency ModuleNotFoundError: No module named 'mtenv' Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/src/mtenv/mtenv/envs/mpte/setup.py", line 6, in from mtenv.utils.setup_utils import parse_dependency ModuleNotFoundError: No module named 'mtenv' Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/src/mtenv/mtenv/envs/tabular_mdp/setup.py", line 6, in from mtenv.utils.setup_utils import parse_dependency ModuleNotFoundError: No module named 'mtenv' error in mtenv setup command: 'extras_require' must be a dictionary whose values are strings or lists of strings containing valid project/version requirement specifiers. [end of output]

note: This error originates from a subprocess, and is likely not a problem with pip. error: metadata-generation-failed

× Encountered error while generating package metadata. ╰─> See above for output.

note: This is an issue with the package mentioned above, not pip. hint: See above for details.

[niiceMing]

It looks like you need to add mtenv to PYTHONPATH, or you can try this: cd src/mtenv; pip install -e .

[Lan131]

So when I do that and run it now I get a different error, but am able to install: mesg: ttyname failed: Inappropriate ioctl for device mesg: ttyname failed: Inappropriate ioctl for device setup: seed: 10 setup: metaworld algo: CMTA_info2500_mt10 base_path: /home/research/lanier.m/Desktop/CMTA-main/scripts dir_name: logs_fix save_dir: ${setup.base_path}/${setup.dir_name}/${setup.id} device: cuda:0 id: CMTA_info2500_mt10_seed_10 description: Sample Task tags: null git: commit_id: null has_uncommitted_changes: null issue_id: null date: '2024-03-18 13:35:43' slurm_id: '-1' debug: should_enable: false experiment: name: metaworld builder: target: mtrl.experiment.${experiment.name}.Experiment init_steps: 3000 num_train_steps: 2500000 eval_freq: 3000 num_eval_episodes: 1 should_resume: true eval_only: false random_pos: false save: model: retain_last_n: 1 buffer: should_save: true size_per_chunk: 15000 num_samples_to_save: -1 save_dir: ${setup.save_dir} save_video: true envs_to_exclude_during_training: null agent: name: state_sac encoder_feature_dim: 64 num_layers: 0 num_filters: 0 builder: target: mtrl.agent.sac.Agent actor_cfg: ${agent.actor} critic_cfg: ${agent.critic} multitask_cfg: ${agent.multitask} alpha_optimizer_cfg: ${agent.optimizers.alpha} actor_optimizer_cfg: ${agent.optimizers.actor} critic_optimizer_cfg: ${agent.optimizers.critic} discount: 0.99 init_temperature: 1.0 actor_update_freq: 1 critic_tau: 0.005 critic_target_update_freq: 1 encoder_tau: 0.05 actor: target: mtrl.agent.components.actor.Actor num_layers: 3 hidden_dim: 512 log_std_bounds:

• -20
• 2 encoder_cfg: ${agent.encoder} multitask_cfg: ${agent.multitask} critic: target: mtrl.agent.components.critic.Critic hidden_dim: ${agent.actor.hidden_dim} num_layers: ${agent.actor.num_layers} encoder_cfg: ${agent.encoder} multitask_cfg: ${agent.multitask} encoder: type_to_select: moe identity: type: identity feature_dim: ${agent.encoder_feature_dim} feedforward: type: feedforward hidden_dim: 64 num_layers: 2 feature_dim: ${agent.encoder_feature_dim} should_tie_encoders: true lstm: type: lstm hidden_dim: 64 num_layers: 2 feature_dim: ${agent.encoder_feature_dim} should_tie_encoders: true film: type: film hidden_dim: 64 num_layers: 2 feature_dim: ${agent.encoder_feature_dim} should_tie_encoders: true moe: type: moe encoder_cfg: type: feedforward hidden_dim: 64 num_layers: 2 feature_dim: ${agent.encoder_feature_dim} should_tie_encoders: true num_experts: 6 task_id_to_encoder_id_cfg: mode: rnn_attention num_envs: ${env.num_envs} gate: embedding_dim: 64 hidden_dim: 64 num_layers: 2 temperature: 1.0 should_use_soft_attention: false topk: 2 task_encoder_cfg: should_use_task_encoding: true should_detach_task_encoding: true attention: embedding_dim: 64 hidden_dim: 64 num_layers: 1 temperature: 1.0 should_use_soft_attention: true task_encoder_cfg: should_use_task_encoding: true should_detach_task_encoding: false rnn_attention: rnn_hidden_dim: 64 hidden_dim: 64 num_layers: 1 temperature: 1.0 should_use_soft_attention: true cluster: env_name: ${env.name} task_description: ${env.description} ordered_task_list: ${env.ordered_task_list} mapping_cfg: ${agent.task_to_encoder_cluster} num_eval_episodes: ${experiment.num_eval_episodes} batch_size: ${replay_buffer.batch_size} identity: num_eval_episodes: ${experiment.num_eval_episodes} batch_size: ${replay_buffer.batch_size} ensemble: num_eval_episodes: ${experiment.num_eval_episodes} batch_size: ${replay_buffer.batch_size} factorized_moe: type: fmoe encoder_cfg: ${agent.encoder.feedforward} num_factors: 2 num_experts_per_factor:
  • 5
  • 5 pixel: type: pixel feature_dim: ${agent.encoder_feature_dim} num_filters: ${agent.num_filters} num_layers: ${agent.num_layers} transition_model: target: mtrl.agent.components.transition_model.make_transition_model transition_cfg: type: '' feature_dim: ${agent.encoder_feature_dim} layer_width: 512 multitask_cfg: ${agent.multitask} mask: num_tasks: ${env.num_envs} num_eval_episodes: ${experiment.num_eval_episodes} batch_size: ${replay_buffer.batch_size} multitask: num_envs: 10 should_use_disentangled_alpha: true should_use_reweighting: false should_use_task_encoder: true should_use_multi_head_policy: false should_use_disjoint_policy: false should_use_attention_multi_head_policy: false task_encoder_cfg: model_cfg: target: mtrl.agent.components.task_encoder.TaskEncoder pretrained_embedding_cfg: should_use: false path_to_load_from: /workspace/S/lansiming/mtrl/metadata/task_embedding/roberta_small/${env.name}.json ordered_task_list: ${env.ordered_task_list} num_embeddings: ${agent.multitask.num_envs} embedding_dim: 64 hidden_dim: 64 num_layers: 1 output_dim: 64 optimizer_cfg: ${agent.optimizers.actor} losses_to_train:
  • critic
  • transition_reward
  • decoder
  • task_encoder multi_head_policy_cfg: mask_cfg: ${agent.mask} actor_cfg: should_condition_model_on_task_info: false should_condition_encoder_on_task_info: true should_concatenate_task_info_with_encoder: true moe_cfg: mode: soft_modularization num_experts: 4 should_use: false critic_cfg: ${agent.multitask.actor_cfg} gradnorm: alpha: 1.0 task_to_encoder_cluster: mt10: cluster: action_close:
    • close action_default:
    • insert
    • pick and place
    • press
    • reach action_open:
    • open action_push:
    • push object_default:
    • button
    • door
    • peg
    • revolving joint object_drawer:
    • drawer object_goal:
    • goal object_puck:
    • puck object_window:
    • window mt50: cluster: action_close:
    • close action_default:
    • insert
    • pick and place
    • press
    • reach action_open:
    • open action_push:
    • push object_default:
    • button
    • door
    • peg
    • revolving joint object_drawer:
    • drawer object_goal:
    • goal object_puck:
    • puck object_window:
    • window optimizers: actor: target: torch.optim.Adam lr: 0.0003 betas:
  • 0.9
  • 0.999 alpha: target: torch.optim.Adam lr: 0.0003 betas:
  • 0.9
  • 0.999 critic: target: torch.optim.Adam lr: 0.0003 betas:
  • 0.9
  • 0.999 decoder: target: torch.optim.Adam lr: 0.0003 betas:
  • 0.9
  • 0.999 weight_decay: 1.0e-07 encoder: target: torch.optim.Adam lr: 0.0003 betas:
  • 0.9
  • 0.999 env: name: metaworld-mt10 num_envs: 10 benchmark: target: metaworld.MT10 builder: make_kwargs: should_perform_reward_normalization: true dummy: target: metaworld.MT1 env_name: pick-place-v1 description: reach-v1: Reach a goal position. Randomize the goal positions. push-v1: Push the puck to a goal. Randomize puck and goal positions. pick-place-v1: Pick and place a puck to a goal. Randomize puck and goal positions. door-open-v1: Open a door with a revolving joint. Randomize door positions. drawer-open-v1: Open a drawer. Randomize drawer positions. drawer-close-v1: Push and close a drawer. Randomize the drawer positions. button-press-topdown-v1: Press a button from the top. Randomize button positions. peg-insert-side-v1: Insert a peg sideways. Randomize peg and goal positions. window-open-v1: Push and open a window. Randomize window positions. window-close-v1: Push and close a window. Randomize window positions. ordered_task_list: null replay_buffer: target: mtrl.replay_buffer.ReplayBuffer env_obs_shape: null action_shape: null capacity: 5000000 batch_size: 1280 logger: target: mtrl.logger.Logger logger_dir: ${setup.save_dir} use_tb: false metrics: train:
    • • episode
• E
• int
• average
  • • step
• S
• int
• average
  • • duration
• D
• time
• average
  • • hours
• T
• time
• average
  • • episode_reward
• R
• float
• average
  • • success
• Su
• float
• average
  • • batch_reward
• BR
• float
• average
  • • actor_loss
• ALOSS
• float
• average
  • • critic_loss
• CLOSS
• float
• average
  • • ae_loss
• RLOSS
• float
• average
  • • ae_transition_loss
• null
• float
• average
  • • reward_loss
• null
• float
• average
  • • actor_target_entropy
• null
• float
• average
  • • actor_entropy
• null
• float
• average
  • • alpha_loss
• null
• float
• average
  • • alphavalue
• null
• float
• average
  • • contrastive_loss
• MLOSS
• float
• average
  • • max_rat
• MR
• float
• average
  • • env_index
• ENV
• str
• constant
  • • episode_reward_envindex
• R_
• float
• average
  • • success_envindex
• Su_
• float
• average
  • • envindex
• ENV_
• str
• constant
  • • batch_reward_agentindex
• null
• float
• average
  • • critic_loss_agentindex
• AGENT_
• float
• average
  • • actor_distilled_agent_loss_agentindex
• null
• float
• average
  • • actor_loss_agentindex
• null
• float
• average
  • • actor_target_entropy_agentindex
• null
• float
• average
  • • actor_entropy_agentindex
• null
• float
• average
  • • alpha_loss_agentindex
• null
• float
• average
  • • alpha_value_agentindex
• null
• float
• average
  • • ae_loss_agentindex
• null
• float
• average
  • • info_nce_loss
• IN
• float
• average
  • • accuracy
• null
• float
• average eval:
  • • episode
• E
• int
• average
  • • step
• S
• int
• average
  • • episode_reward
• R
• float
• average
  • • env_index
• ENV
• str
• constant
  • • success
• Su
• float
• average
  • • episode_reward_envindex
• R_
• float
• average
  • • success_envindex
• Su_
• float
• average
  • • envindex
• ENV_
• str
• constant
  • • batch_reward_agentindex
• AGENT_
• float
• average logbook: target: ml_logger.logbook.make_config write_to_console: false logger_dir: ${setup.save_dir} create_multiple_log_files: false

[2024-03-18 13:35:44,101][default_logger][INFO] - {"setup": {"seed": 10, "setup": "metaworld", "algo": "CMTA_info2500_mt10", "base_path": "/home/research/lanier.m/Desktop/CMTA-main/scripts", "dir_name": "logs_fix", "save_dir": "${setup.base_path}/${setup.dir_name}/${setup.id}", "device": "cuda:0", "id": "CMTA_info2500_mt10_seed_10", "description": "Sample Task", "tags": null, "git": {"commit_id": null, "has_uncommitted_changes": null, "issue_id": null}, "date": "2024-03-18 13:35:43", "slurm_id": "-1", "debug": {"should_enable": false}}, "experiment": {"name": "metaworld", "builder": {"target": "mtrl.experiment.${experiment.name}.Experiment"}, "init_steps": 3000, "num_train_steps": 2500000, "eval_freq": 3000, "num_eval_episodes": 1, "should_resume": true, "eval_only": false, "random_pos": false, "save": {"model": {"retain_last_n": 1}, "buffer": {"should_save": true, "size_per_chunk": 15000, "num_samples_to_save": -1}}, "save_dir": "${setup.save_dir}", "save_video": true, "envs_to_exclude_during_training": null}, "agent": {"name": "state_sac", "encoder_feature_dim": 64, "num_layers": 0, "num_filters": 0, "builder": {"target": "mtrl.agent.sac.Agent", "actor_cfg": "${agent.actor}", "critic_cfg": "${agent.critic}", "multitask_cfg": "${agent.multitask}", "alpha_optimizer_cfg": "${agent.optimizers.alpha}", "actor_optimizer_cfg": "${agent.optimizers.actor}", "critic_optimizer_cfg": "${agent.optimizers.critic}", "discount": 0.99, "init_temperature": 1.0, "actor_update_freq": 1, "critic_tau": 0.005, "critic_target_update_freq": 1, "encoder_tau": 0.05}, "actor": {"target": "mtrl.agent.components.actor.Actor", "num_layers": 3, "hidden_dim": 512, "log_std_bounds": [-20, 2], "encoder_cfg": "${agent.encoder}", "multitask_cfg": "${agent.multitask}"}, "critic": {"target": "mtrl.agent.components.critic.Critic", "hidden_dim": "${agent.actor.hidden_dim}", "num_layers": "${agent.actor.num_layers}", "encoder_cfg": "${agent.encoder}", "multitask_cfg": "${agent.multitask}"}, "encoder": {"type_to_select": "moe", "identity": {"type": "identity", "feature_dim": "${agent.encoder_feature_dim}"}, "feedforward": {"type": "feedforward", "hidden_dim": 64, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "lstm": {"type": "lstm", "hidden_dim": 64, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "film": {"type": "film", "hidden_dim": 64, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "moe": {"type": "moe", "encoder_cfg": {"type": "feedforward", "hidden_dim": 64, "num_layers": 2, "feature_dim": "${agent.encoder_feature_dim}", "should_tie_encoders": true}, "num_experts": 6, "task_id_to_encoder_id_cfg": {"mode": "rnn_attention", "num_envs": "${env.num_envs}", "gate": {"embedding_dim": 64, "hidden_dim": 64, "num_layers": 2, "temperature": 1.0, "should_use_soft_attention": false, "topk": 2, "task_encoder_cfg": {"should_use_task_encoding": true, "should_detach_task_encoding": true}}, "attention": {"embedding_dim": 64, "hidden_dim": 64, "num_layers": 1, "temperature": 1.0, "should_use_soft_attention": true, "task_encoder_cfg": {"should_use_task_encoding": true, "should_detach_task_encoding": false}}, "rnn_attention": {"rnn_hidden_dim": 64, "hidden_dim": 64, "num_layers": 1, "temperature": 1.0, "should_use_soft_attention": true}, "cluster": {"env_name": "${env.name}", "task_description": "${env.description}", "ordered_task_list": "${env.ordered_task_list}", "mapping_cfg": "${agent.task_to_encoder_cluster}", "num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}, "identity": {"num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}, "ensemble": {"num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}}}, "factorized_moe": {"type": "fmoe", "encoder_cfg": "${agent.encoder.feedforward}", "num_factors": 2, "num_experts_per_factor": [5, 5]}, "pixel": {"type": "pixel", "feature_dim": "${agent.encoder_feature_dim}", "num_filters": "${agent.num_filters}", "num_layers": "${agent.num_layers}"}}, "transition_model": {"target": "mtrl.agent.components.transition_model.make_transition_model", "transition_cfg": {"type": "", "feature_dim": "${agent.encoder_feature_dim}", "layer_width": 512}, "multitask_cfg": "${agent.multitask}"}, "mask": {"num_tasks": "${env.num_envs}", "num_eval_episodes": "${experiment.num_eval_episodes}", "batch_size": "${replay_buffer.batch_size}"}, "multitask": {"num_envs": 10, "should_use_disentangled_alpha": true, "should_use_reweighting": false, "should_use_task_encoder": true, "should_use_multi_head_policy": false, "should_use_disjoint_policy": false, "should_use_attention_multi_head_policy": false, "task_encoder_cfg": {"model_cfg": {"target": "mtrl.agent.components.task_encoder.TaskEncoder", "pretrained_embedding_cfg": {"should_use": false, "path_to_load_from": "/workspace/S/lansiming/mtrl/metadata/task_embedding/roberta_small/${env.name}.json", "ordered_task_list": "${env.ordered_task_list}"}, "num_embeddings": "${agent.multitask.num_envs}", "embedding_dim": 64, "hidden_dim": 64, "num_layers": 1, "output_dim": 64}, "optimizer_cfg": "${agent.optimizers.actor}", "losses_to_train": ["critic", "transition_reward", "decoder", "task_encoder"]}, "multi_head_policy_cfg": {"mask_cfg": "${agent.mask}"}, "actor_cfg": {"should_condition_model_on_task_info": false, "should_condition_encoder_on_task_info": true, "should_concatenate_task_info_with_encoder": true, "moe_cfg": {"mode": "soft_modularization", "num_experts": 4, "should_use": false}}, "critic_cfg": "${agent.multitask.actor_cfg}"}, "gradnorm": {"alpha": 1.0}, "task_to_encoder_cluster": {"mt10": {"cluster": {"action_close": ["close"], "action_default": ["insert", "pick and place", "press", "reach"], "action_open": ["open"], "action_push": ["push"], "object_default": ["button", "door", "peg", "revolving joint"], "object_drawer": ["drawer"], "object_goal": ["goal"], "object_puck": ["puck"], "object_window": ["window"]}}, "mt50": {"cluster": {"action_close": ["close"], "action_default": ["insert", "pick and place", "press", "reach"], "action_open": ["open"], "action_push": ["push"], "object_default": ["button", "door", "peg", "revolving joint"], "object_drawer": ["drawer"], "object_goal": ["goal"], "object_puck": ["puck"], "object_window": ["window"]}}}, "optimizers": {"actor": {"target": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}, "alpha": {"target": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}, "critic": {"target": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}, "decoder": {"target": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999], "weight_decay": 1e-07}, "encoder": {"target": "torch.optim.Adam", "lr": 0.0003, "betas": [0.9, 0.999]}}}, "env": {"name": "metaworld-mt10", "num_envs": 10, "benchmark": {"target": "metaworld.MT10"}, "builder": {"make_kwargs": {"should_perform_reward_normalization": true}}, "dummy": {"target": "metaworld.MT1", "env_name": "pick-place-v1"}, "description": {"reach-v1": "Reach a goal position. Randomize the goal positions.", "push-v1": "Push the puck to a goal. Randomize puck and goal positions.", "pick-place-v1": "Pick and place a puck to a goal. Randomize puck and goal positions.", "door-open-v1": "Open a door with a revolving joint. Randomize door positions.", "drawer-open-v1": "Open a drawer. Randomize drawer positions.", "drawer-close-v1": "Push and close a drawer. Randomize the drawer positions.", "button-press-topdown-v1": "Press a button from the top. Randomize button positions.", "peg-insert-side-v1": "Insert a peg sideways. Randomize peg and goal positions.", "window-open-v1": "Push and open a window. Randomize window positions.", "window-close-v1": "Push and close a window. Randomize window positions."}, "ordered_task_list": null}, "replay_buffer": {"target": "mtrl.replay_buffer.ReplayBuffer", "env_obs_shape": null, "action_shape": null, "capacity": 5000000, "batch_size": 1280}, "logger": {"target": "mtrl.logger.Logger", "logger_dir": "${setup.save_dir}", "use_tb": false}, "metrics": {"train": [["episode", "E", "int", "average"], ["step", "S", "int", "average"], ["duration", "D", "time", "average"], ["hours", "T", "time", "average"], ["episode_reward", "R", "float", "average"], ["success", "Su", "float", "average"], ["batch_reward", "BR", "float", "average"], ["actor_loss", "ALOSS", "float", "average"], ["critic_loss", "CLOSS", "float", "average"], ["ae_loss", "RLOSS", "float", "average"], ["ae_transition_loss", null, "float", "average"], ["reward_loss", null, "float", "average"], ["actor_target_entropy", null, "float", "average"], ["actor_entropy", null, "float", "average"], ["alpha_loss", null, "float", "average"], ["alphavalue", null, "float", "average"], ["contrastive_loss", "MLOSS", "float", "average"], ["max_rat", "MR", "float", "average"], ["env_index", "ENV", "str", "constant"], ["episode_reward_envindex", "R_", "float", "average"], ["success_envindex", "Su_", "float", "average"], ["envindex", "ENV_", "str", "constant"], ["batch_reward_agentindex", null, "float", "average"], ["critic_loss_agentindex", "AGENT_", "float", "average"], ["actor_distilled_agent_loss_agentindex", null, "float", "average"], ["actor_loss_agentindex", null, "float", "average"], ["actor_target_entropy_agentindex", null, "float", "average"], ["actor_entropy_agentindex", null, "float", "average"], ["alpha_loss_agentindex", null, "float", "average"], ["alpha_value_agentindex", null, "float", "average"], ["ae_loss_agentindex", null, "float", "average"], ["info_nce_loss", "IN", "float", "average"], ["accuracy", null, "float", "average"]], "eval": [["episode", "E", "int", "average"], ["step", "S", "int", "average"], ["episode_reward", "R", "float", "average"], ["env_index", "ENV", "str", "constant"], ["success", "Su", "float", "average"], ["episode_reward_envindex", "R_", "float", "average"], ["success_envindex", "Su_", "float", "average"], ["envindex", "ENV_", "str", "constant"], ["batch_reward_agentindex", "AGENT_", "float", "average"]]}, "logbook": {"target": "ml_logger.logbook.make_config", "write_to_console": false, "logger_dir": "${setup.save_dir}", "create_multiple_log_files": false}, "status": "RUNNING", "logbook_id": "0", "logbook_timestamp": "01:35:44PM CDT Mar 18, 2024", "logbook_type": "metadata"} Starting Experiment at Mon Mar 18 13:35:44 2024 torch version = 1.7.1+cu110 Traceback (most recent call last): File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 529, in _locate module = import_module(mod) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/importlib/init.py", line 127, in import_module return _bootstrap._gcd_import(name[level:], package, level) File "", line 1027, in _gcd_import File "", line 961, in _sanity_check ValueError: Empty module name

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/utils.py", line 61, in call type_or_callable = _locate(cls) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 532, in _locate raise ImportError(f"Error loading module '{path}'") from e ImportError: Error loading module 'metaworld.MT10'

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/utils.py", line 63, in call return _instantiate_class(type_or_callable, config, *args, *kwargs) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 500, in _instantiate_class return clazz(args, **final_kwargs) File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/experiment/metaworld.py", line 20, in init super().init(config, experiment_id) File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/experiment/multitask.py", line 27, in init super().init(config, experiment_id) File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/experiment/experiment.py", line 33, in init self.envs, self.env_metadata = self.build_envs() File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/experiment/metaworld.py", line 44, in build_envs benchmark = hydra.utils.instantiate(self.config.env.benchmark) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/utils.py", line 70, in call raise HydraException(f"Error calling '{cls}' : {e}") from e hydra.errors.HydraException: Error calling 'metaworld.MT10' : Error loading module 'metaworld.MT10'

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/scripts/../main.py", line 18, in launch() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/main.py", line 32, in decorated_main _run_hydra( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 346, in _run_hydra run_and_report( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 201, in run_and_report raise ex File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 198, in run_and_report return func() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 347, in lambda: hydra.run( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 107, in run return run_job( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/core/utils.py", line 128, in run_job ret.return_value = task_function(task_cfg) File "/home/research/lanier.m/Desktop/CMTA-main/scripts/../main.py", line 14, in launch return run(config) File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/app/run.py", line 37, in run experiment_utils.prepare_and_run(config=config) File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/experiment/utils.py", line 23, in prepare_and_run experiment = hydra.utils.instantiate( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/utils.py", line 70, in call raise HydraException(f"Error calling '{cls}' : {e}") from e hydra.errors.HydraException: Error calling 'mtrl.experiment.metaworld.Experiment' : Error calling 'metaworld.MT10' : Error loading module 'metaworld.MT10'

edit: this looks potentially related https://github.com/facebookresearch/mtrl/issues/11

[anindyasarkarIITH]

I also got exactly same issue, any suggestions on how to fix this?

[niiceMing]

Before we can run our algorithm, we need to install metaworld successfully. You could find the solution in https://github.com/Farama-Foundation/Metaworld.

[Lan131]

Installing the commit on the page gives me an error related to mujoco:

pip install git+https://github.com/Farama-Foundation/Metaworld.git@af8417bfc82a3e249b4b02156518d775f29eb289

Your repo doesn't specify which version you used. Using mujoco_py==2.0.2.8, properly downloading the binaries and exporting the path, installing cython<3 lets me install metaworld:

Package Version Editable project location

---

absl-py 2.1.0 antlr4-python3-runtime 4.8 cffi 1.16.0 cloudpickle 1.6.0 Cython 0.29.37 Farama-Notifications 0.0.4 fasteners 0.19 future 1.0.0 glfw 2.7.0 gym 0.17.3 hydra-core 1.0.5 hydra-submitit-launcher 1.0.1 imageio 2.9.0 imageio-ffmpeg 0.4.3 importlib_metadata 7.0.2 metaworld 0.0.0 mllogger 0.7 mtenv 1.0 /home/research/lanier.m/Desktop/CMTA-main/src/mtenv mujoco 2.3.7 mujoco-py 2.0.2.8 numpy 1.19.5 omegaconf 2.0.6 opencv-python 4.5.1.48 Pillow 7.1.0 pip 24.0 pycparser 2.21 pyglet 1.5.0 PyOpenGL 3.1.7 PyYAML 6.0.1 scipy 1.7.0 setuptools 65.5.0 submitit 1.5.1 termcolor 1.1.0 torch 1.7.1+cu110 torchvision 0.8.2+cu110 typing_extensions 4.10.0 wheel 0.42.0 zipp 3.18.1

However I get errors both running the code provided as well as importing metaworld in python in general: hydra.errors.HydraException: Error calling 'metaworld.MT10' : Error loading module 'metaworld.MT10'

The above exception was the direct cause of the following exception:

Traceback (most recent call last): File "/home/research/lanier.m/Desktop/CMTA-main/scripts/../main.py", line 18, in launch() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/main.py", line 32, in decorated_main _run_hydra( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 346, in _run_hydra run_and_report( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 201, in run_and_report raise ex File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 198, in run_and_report return func() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/utils.py", line 347, in lambda: hydra.run( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/_internal/hydra.py", line 107, in run return run_job( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/core/utils.py", line 128, in run_job ret.return_value = task_function(task_cfg) File "/home/research/lanier.m/Desktop/CMTA-main/scripts/../main.py", line 14, in launch return run(config) File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/app/run.py", line 37, in run experiment_utils.prepare_and_run(config=config) File "/home/research/lanier.m/Desktop/CMTA-main/mtrl/experiment/utils.py", line 23, in prepare_and_run experiment = hydra.utils.instantiate( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/hydra/utils.py", line 70, in call raise HydraException(f"Error calling '{cls}' : {e}") from e hydra.errors.HydraException: Error calling 'mtrl.experiment.metaworld.Experiment' : Error calling 'metaworld.MT10' : Error loading module 'metaworld.MT10'

import metaworld running build_ext building 'mujoco_py.cymj' extension gcc -pthread -B /home/research/lanier.m/miniconda3/envs/FairRL/compiler_compat -Wno-unused-result -Wsign-compare -DNDEBUG -fwrapv -O2 -Wall -fPIC -O2 -isystem /home/research/lanier.m/miniconda3/envs/FairRL/include -fPIC -O2 -isystem /home/research/lanier.m/miniconda3/envs/FairRL/include -fPIC -I/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py -I/home/research/lanier.m/.mujoco/mujoco200/include -I/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/numpy/core/include -I/home/research/lanier.m/miniconda3/envs/FairRL/include/python3.9 -c /home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/cymj.c -o /home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/generated/_pyxbld_2.0.2.8_39_linuxcpuextensionbuilder/temp.linux-x86_64-cpython-39/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/cymj.o -fopenmp -w /home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/cymj.c: In function ‘pyx_f_9mujoco_py_4cymj_11PyMjvFigure__set’: /home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/cymj.c:77666:91: error: subscripted value is neither array nor pointer nor vector pyx_t_1 = ((PyObject *)pyx_f_9mujoco_py_4cymjwrap_float_1d((&(pyx_v_p->linewidth[0])), 0x64)); if (unlikely(!pyx_t_1)) PYX_ERR(4, 3829, __pyx_L1_error) ^ /home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/cymj.c: In function ‘pyx_pf_9mujoco_py_4cymj_11PyMjvFigure_9legendoff_get’: /home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/cymj.c:78306:55: error: ‘mjvFigure’ {aka ‘struct _mjvFigure’} has no member named ‘legendoff’; did you mean ‘legendoffset’? pyx_t_1 = __Pyx_PyInt_From_int(pyx_v_self->ptr->legendoff); if (unlikely(!pyx_t_1)) PYX_ERR(4, 3860, pyx_L1_error) ^~~~~ legendoffset /home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/cymj.c: In function ‘__pyx_pf_9mujoco_py_4cymj_11PyMjvFigure_9legendoff_2set’: /home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/cymj.c:78361:22: error: ‘mjvFigure’ {aka ‘struct _mjvFigure’} has no member named ‘legendoff’; did you mean ‘legendoffset’? pyx_v_self->ptr->legendoff = __pyx_v_x; ^~~~~ legendoffset Traceback (most recent call last): File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/unixccompiler.py", line 186, in _compile self.spawn(compiler_so + cc_args + [src, '-o', obj] + extra_postargs) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/ccompiler.py", line 1007, in spawn spawn(cmd, dry_run=self.dry_run, **kwargs) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/spawn.py", line 70, in spawn raise DistutilsExecError( distutils.errors.DistutilsExecError: command '/usr/bin/gcc' failed with exit code 1

During handling of the above exception, another exception occurred:

Traceback (most recent call last): File "", line 1, in File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/metaworld/init.py", line 7, in import metaworld.envs.mujoco.env_dict as _env_dict File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/metaworld/envs/mujoco/env_dict.py", line 3, in from metaworld.envs.mujoco.sawyer_xyz import ( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/metaworld/envs/mujoco/sawyer_xyz/init.py", line 1, in from metaworld.envs.mujoco.sawyer_xyz.sawyer_assembly_peg import SawyerNutAssemblyEnv File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/metaworld/envs/mujoco/sawyer_xyz/sawyer_assembly_peg.py", line 5, in from metaworld.envs.mujoco.sawyer_xyz.base import SawyerXYZEnv, _assert_task_is_set File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/metaworld/envs/mujoco/sawyer_xyz/base.py", line 7, in import mujoco_py File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/init.py", line 3, in from mujoco_py.builder import cymj, ignore_mujoco_warnings, functions, MujocoException File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/builder.py", line 506, in cymj = load_cython_ext(mujoco_path) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/builder.py", line 105, in load_cython_ext cext_so_path = builder.build() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/builder.py", line 221, in build built_so_file_path = self._build_impl() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/builder.py", line 273, in _build_impl so_file_path = super()._build_impl() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/builder.py", line 244, in _build_impl dist.run_commands() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/dist.py", line 968, in run_commands self.run_command(cmd) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/dist.py", line 1217, in run_command super().run_command(command) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/dist.py", line 987, in run_command cmd_obj.run() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/Cython/Distutils/old_build_ext.py", line 186, in run _build_ext.build_ext.run(self) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/command/build_ext.py", line 346, in run self.build_extensions() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/mujoco_py/builder.py", line 144, in build_extensions build_ext.build_extensions(self) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/Cython/Distutils/old_build_ext.py", line 195, in build_extensions _build_ext.build_ext.build_extensions(self) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/command/build_ext.py", line 466, in build_extensions self._build_extensions_serial() File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/command/build_ext.py", line 492, in _build_extensions_serial self.build_extension(ext) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/command/build_ext.py", line 547, in build_extension objects = self.compiler.compile( File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/ccompiler.py", line 599, in compile self._compile(obj, src, ext, cc_args, extra_postargs, pp_opts) File "/home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages/setuptools/_distutils/unixccompiler.py", line 188, in _compile raise CompileError(msg) distutils.errors.CompileError: command '/usr/bin/gcc' failed with exit code 1

Can you please provide the mujoco version used as well as any dependencies for that?

[niiceMing]

we use mujoco-py 2.1.2.14. Please refer to other tutorials to install metaworld and mujoco.

[Lan131]

The metaworld commit provided is incompatible with that version of mujoco.

(FairRL) [lanier.m@ssh8 Desktop]$ pip install git+https://github.com/Farama-Foundation/Metaworld.git@af8417bfc82a3e249b4b02156518d775f29eb289 Collecting git+https://github.com/Farama-Foundation/Metaworld.git@af8417bfc82a3e249b4b02156518d775f29eb289 Cloning https://github.com/Farama-Foundation/Metaworld.git (to revision af8417bfc82a3e249b4b02156518d775f29eb289) to /tmp/pip-req-build-0rqdf4v1 Running command git clone --filter=blob:none --quiet https://github.com/Farama-Foundation/Metaworld.git /tmp/pip-req-build-0rqdf4v1 Running command git rev-parse -q --verify 'sha^af8417bfc82a3e249b4b02156518d775f29eb289' Running command git fetch -q https://github.com/Farama-Foundation/Metaworld.git af8417bfc82a3e249b4b02156518d775f29eb289 Running command git checkout -q af8417bfc82a3e249b4b02156518d775f29eb289 Resolved https://github.com/Farama-Foundation/Metaworld.git to commit af8417bfc82a3e249b4b02156518d775f29eb289 Preparing metadata (setup.py) ... done Requirement already satisfied: gym>=0.15.4 in /home/research/lanier.m/miniconda3/envs/FairRL/lib/python3.9/site-packages (from metaworld==0.0.0) (0.17.3) Collecting mujoco-py<2.1,>=2.0 (from metaworld==0.0.0)

It automaticly tries to install a different version over the one existing. This doesn't appear reproducible.

[niiceMing]

Try this https://github.com/Farama-Foundation/Metaworld/issues/358#issuecomment-1020372602.

[Lan131]

I was able to get it running although I needed to install patchelf and info-nce-pytorch. Please close. Thank you for all your efforts.