Duplicate from pierretassel/JobShopCPRL

Co-authored-by: Pierre Tassel <pierretassel@users.noreply.huggingface.co>

.gitattributes

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+*.7z filter=lfs diff=lfs merge=lfs -text
+*.arrow filter=lfs diff=lfs merge=lfs -text
+*.bin filter=lfs diff=lfs merge=lfs -text
+*.bz2 filter=lfs diff=lfs merge=lfs -text
+*.ckpt filter=lfs diff=lfs merge=lfs -text
+*.ftz filter=lfs diff=lfs merge=lfs -text
+*.gz filter=lfs diff=lfs merge=lfs -text
+*.h5 filter=lfs diff=lfs merge=lfs -text
+*.joblib filter=lfs diff=lfs merge=lfs -text
+*.lfs.* filter=lfs diff=lfs merge=lfs -text
+*.mlmodel filter=lfs diff=lfs merge=lfs -text
+*.model filter=lfs diff=lfs merge=lfs -text
+*.msgpack filter=lfs diff=lfs merge=lfs -text
+*.npy filter=lfs diff=lfs merge=lfs -text
+*.npz filter=lfs diff=lfs merge=lfs -text
+*.onnx filter=lfs diff=lfs merge=lfs -text
+*.ot filter=lfs diff=lfs merge=lfs -text
+*.parquet filter=lfs diff=lfs merge=lfs -text
+*.pb filter=lfs diff=lfs merge=lfs -text
+*.pickle filter=lfs diff=lfs merge=lfs -text
+*.pkl filter=lfs diff=lfs merge=lfs -text
+*.pt filter=lfs diff=lfs merge=lfs -text
+*.pth filter=lfs diff=lfs merge=lfs -text
+*.rar filter=lfs diff=lfs merge=lfs -text
+*.safetensors filter=lfs diff=lfs merge=lfs -text
+saved_model/**/* filter=lfs diff=lfs merge=lfs -text
+*.tar.* filter=lfs diff=lfs merge=lfs -text
+*.tflite filter=lfs diff=lfs merge=lfs -text
+*.tgz filter=lfs diff=lfs merge=lfs -text
+*.wasm filter=lfs diff=lfs merge=lfs -text
+*.xz filter=lfs diff=lfs merge=lfs -text
+*.zip filter=lfs diff=lfs merge=lfs -text
+*.zst filter=lfs diff=lfs merge=lfs -text
+*tfevents* filter=lfs diff=lfs merge=lfs -text

.gitignore

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+### JetBrains template
+# Covers JetBrains IDEs: IntelliJ, RubyMine, PhpStorm, AppCode, PyCharm, CLion, Android Studio, WebStorm and Rider
+# Reference: https://intellij-support.jetbrains.com/hc/en-us/articles/206544839
+
+# User-specific stuff
+.idea/**/workspace.xml
+.idea/**/tasks.xml
+.idea/**/usage.statistics.xml
+.idea/**/dictionaries
+.idea/**/shelf
+
+# Generated files
+.idea/**/contentModel.xml
+
+# Sensitive or high-churn files
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+.idea/**/dataSources.ids
+.idea/**/dataSources.local.xml
+.idea/**/sqlDataSources.xml
+.idea/**/dynamic.xml
+.idea/**/uiDesigner.xml
+.idea/**/dbnavigator.xml
+
+# Gradle
+.idea/**/gradle.xml
+.idea/**/libraries
+
+# Gradle and Maven with auto-import
+# When using Gradle or Maven with auto-import, you should exclude module files,
+# since they will be recreated, and may cause churn.  Uncomment if using
+# auto-import.
+# .idea/artifacts
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+# .idea/jarRepositories.xml
+# .idea/modules.xml
+# .idea/*.iml
+# .idea/modules
+# *.iml
+# *.ipr
+
+# CMake
+cmake-build-*/
+
+# Mongo Explorer plugin
+.idea/**/mongoSettings.xml
+
+# File-based project format
+*.iws
+
+# IntelliJ
+out/
+
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+.idea_modules/
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+
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+com_crashlytics_export_strings.xml
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+
+### Python template
+# Byte-compiled / optimized / DLL files
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+*$py.class
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
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+.installed.cfg
+*.egg
+MANIFEST
+
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+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
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+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
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+
+# Django stuff:
+*.log
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+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
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+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
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+__pypackages__/
+
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+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/
+

MyDummyVecEnv.py

@@ -0,0 +1,124 @@
+from collections import OrderedDict
+from typing import Any, Callable, List, Optional, Sequence, Type, Union
+
+import gym
+import numpy as np
+
+from stable_baselines3.common.vec_env.base_vec_env import VecEnv, VecEnvIndices, VecEnvObs, VecEnvStepReturn
+from stable_baselines3.common.vec_env.util import dict_to_obs, obs_space_info
+
+import torch
+
+
+class MyDummyVecEnv(VecEnv):
+    """
+    Creates a simple vectorized wrapper for multiple environments, calling each environment in sequence on the current
+    Python process. This is useful for computationally simple environment such as ``cartpole-v1``,
+    as the overhead of multiprocess or multithread outweighs the environment computation time.
+    This can also be used for RL methods that
+    require a vectorized environment, but that you want a single environments to train with.
+
+    :param env_fns: a list of functions
+        that return environments to vectorize
+    """
+
+    def __init__(self, env_fns: List[Callable[[], gym.Env]], device):
+        self.envs = [fn() for fn in env_fns]
+        env = self.envs[0]
+        VecEnv.__init__(self, len(env_fns), env.observation_space, env.action_space)
+        obs_space = env.observation_space
+        self.keys, shapes, dtypes = obs_space_info(obs_space)
+        self.device = device
+
+        self.buf_obs = OrderedDict(
+            [(k, torch.zeros((self.num_envs,) + tuple(shapes[k]), dtype=torch.float, device=self.device)) for k in self.keys])
+        self.buf_dones = np.zeros((self.num_envs,), dtype=bool)
+        self.buf_rews = np.zeros((self.num_envs,), dtype=np.float32)
+        self.buf_infos = [{} for _ in range(self.num_envs)]
+        self.actions = None
+
+    def step_async(self, actions: np.ndarray) -> None:
+        self.actions = actions
+
+    def step_wait(self) -> VecEnvStepReturn:
+        for env_idx in range(self.num_envs):
+            obs, self.buf_rews[env_idx], self.buf_dones[env_idx], self.buf_infos[env_idx] = self.envs[env_idx].step(
+                self.actions[env_idx]
+            )
+            if self.buf_dones[env_idx]:
+                # save final observation where user can get it, then reset
+                self.buf_infos[env_idx]["terminal_observation"] = obs
+                obs = self.envs[env_idx].reset()
+            self._save_obs(env_idx, obs)
+        return (self._obs_from_buf(), self.buf_rews, self.buf_dones, self.buf_infos)
+
+    def seed(self, seed: Optional[int] = None) -> List[Union[None, int]]:
+        seeds = list()
+        for idx, env in enumerate(self.envs):
+            seeds.append(env.seed(seed + idx))
+        return seeds
+
+    def reset(self) -> VecEnvObs:
+        for env_idx in range(self.num_envs):
+            obs = self.envs[env_idx].reset()
+            self._save_obs(env_idx, obs)
+        return self._obs_from_buf()
+
+    def close(self) -> None:
+        for env in self.envs:
+            env.close()
+
+    def get_images(self) -> Sequence[np.ndarray]:
+        return [env.render(mode="rgb_array") for env in self.envs]
+
+    def render(self, mode: str = "human") -> Optional[np.ndarray]:
+        """
+        Gym environment rendering. If there are multiple environments then
+        they are tiled together in one image via ``BaseVecEnv.render()``.
+        Otherwise (if ``self.num_envs == 1``), we pass the render call directly to the
+        underlying environment.
+
+        Therefore, some arguments such as ``mode`` will have values that are valid
+        only when ``num_envs == 1``.
+
+        :param mode: The rendering type.
+        """
+        if self.num_envs == 1:
+            return self.envs[0].render(mode=mode)
+        else:
+            return super().render(mode=mode)
+
+    def _save_obs(self, env_idx: int, obs: VecEnvObs) -> None:
+        for key in self.keys:
+            self.buf_obs[key][env_idx] = torch.from_numpy(obs[key]).to(self.device, non_blocking=True)
+
+    def _obs_from_buf(self) -> VecEnvObs:
+        return dict_to_obs(self.observation_space, self.buf_obs)
+
+    def get_attr(self, attr_name: str, indices: VecEnvIndices = None) -> List[Any]:
+        """Return attribute from vectorized environment (see base class)."""
+        target_envs = self._get_target_envs(indices)
+        return [getattr(env_i, attr_name) for env_i in target_envs]
+
+    def set_attr(self, attr_name: str, value: Any, indices: VecEnvIndices = None) -> None:
+        """Set attribute inside vectorized environments (see base class)."""
+        target_envs = self._get_target_envs(indices)
+        for env_i in target_envs:
+            setattr(env_i, attr_name, value)
+
+    def env_method(self, method_name: str, *method_args, indices: VecEnvIndices = None, **method_kwargs) -> List[Any]:
+        """Call instance methods of vectorized environments."""
+        target_envs = self._get_target_envs(indices)
+        return [getattr(env_i, method_name)(*method_args, **method_kwargs) for env_i in target_envs]
+
+    def env_is_wrapped(self, wrapper_class: Type[gym.Wrapper], indices: VecEnvIndices = None) -> List[bool]:
+        """Check if worker environments are wrapped with a given wrapper"""
+        target_envs = self._get_target_envs(indices)
+        # Import here to avoid a circular import
+        from stable_baselines3.common import env_util
+
+        return [env_util.is_wrapped(env_i, wrapper_class) for env_i in target_envs]
+
+    def _get_target_envs(self, indices: VecEnvIndices) -> List[gym.Env]:
+        indices = self._get_indices(indices)
+        return [self.envs[i] for i in indices]

README.md

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+---
+title: JobShopCPRL
+emoji: 🐨
+colorFrom: gray
+colorTo: red
+sdk: gradio
+sdk_version: 3.11.0
+app_file: app.py
+pinned: false
+license: mit
+duplicated_from: pierretassel/JobShopCPRL
+---
+
+Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference

app.py

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+import datetime
+import os
+import random
+import time
+
+import plotly.figure_factory as ff
+import json
+
+import pandas as pd
+
+from compiled_jss.CPEnv import CompiledJssEnvCP
+
+from stable_baselines3.common.vec_env import VecEnvWrapper
+from torch.distributions import Categorical
+
+import torch
+import numpy as np
+
+from MyDummyVecEnv import MyDummyVecEnv
+
+import gradio as gr
+
+
+class VecPyTorch(VecEnvWrapper):
+
+    def __init__(self, venv, device):
+        super(VecPyTorch, self).__init__(venv)
+        self.device = device
+
+    def reset(self):
+        return self.venv.reset()
+
+    def step_async(self, actions):
+        self.venv.step_async(actions)
+
+    def step_wait(self):
+        return self.venv.step_wait()
+
+
+def make_env(seed, instance):
+    def thunk():
+        _env = CompiledJssEnvCP(instance)
+        return _env
+
+    return thunk
+
+
+def solve(file, num_workers, seed):
+    seed = int(abs(seed))
+    random.seed(seed)
+    np.random.seed(seed)
+    torch.manual_seed(seed)
+    with torch.inference_mode():
+        device = torch.device('cpu')
+        actor = torch.jit.load('actor.pt', map_location=device)
+        actor.eval()
+        start_time = time.time()
+        fn_env = [make_env(0, file.name)
+                  for _ in range(num_workers)]
+        async_envs = MyDummyVecEnv(fn_env, device)
+        envs = VecPyTorch(async_envs, device)
+        current_solution_cost = float('inf')
+        current_solution = ''
+        obs = envs.reset()
+        total_episode = 0
+        while total_episode < envs.num_envs:
+            logits = actor(obs['interval_rep'], obs['attention_interval_mask'], obs['job_resource_mask'],
+                           obs['action_mask'], obs['index_interval'], obs['start_end_tokens'])
+            # temperature vector
+            if num_workers >= 4:
+                temperature = torch.arange(0.5, 2.0, step=(1.5 / num_workers), device=device)
+            else:
+                temperature = torch.ones(num_workers, device=device)
+            logits = logits / temperature[:, None]
+            probs = Categorical(logits=logits).probs
+            # random sample based on logits
+            actions = torch.multinomial(probs, probs.shape[1]).cpu().numpy()
+            obs, reward, done, infos = envs.step(actions)
+            total_episode += done.sum()
+            # total_actions += 1
+            # print(f'Episode {total_episode} / {envs.num_envs} - Actions {total_actions}', end='\r')
+            for env_idx, info in enumerate(infos):
+                if 'makespan' in info and int(info['makespan']) < current_solution_cost:
+                    current_solution_cost = int(info['makespan'])
+                    current_solution = json.loads(info['solution'])
+        total_time = time.time() - start_time
+        pretty_output = ""
+        for job_id in range(len(current_solution)):
+            pretty_output += f"Job {job_id}: {current_solution[job_id]}\n"
+
+        jobs_data = []
+        file.seek(0)
+        line_str: str = file.readline()
+        line_cnt: int = 1
+        jobs_count: int = 0
+        machines_count: int = 0
+        while line_str:
+            data = []
+            split_data = line_str.split()
+            if line_cnt == 1:
+                jobs_count, machines_count = int(split_data[0]), int(
+                    split_data[1]
+                )
+            else:
+                i = 0
+                this_job_op_count = 0
+                while i < len(split_data):
+                    machine, op_time = int(split_data[i]), int(split_data[i + 1])
+                    data.append((machine, op_time))
+                    i += 2
+                    this_job_op_count += 1
+                jobs_data.append(data)
+            line_str = file.readline()
+            line_cnt += 1
+        # convert to integer the current_solution
+        current_solution = [[int(x) for x in y] for y in current_solution]
+        df = []
+        for job_id in range(jobs_count):
+            for task_id in range(len(current_solution[job_id])):
+                dict_op = dict()
+                dict_op["Task"] = "Job {}".format(job_id)
+                start_sec = current_solution[job_id][task_id]
+                finish_sec = start_sec + jobs_data[job_id][task_id][1]
+                dict_op["Start"] = datetime.datetime.fromtimestamp(start_sec)
+                dict_op["Finish"] = datetime.datetime.fromtimestamp(finish_sec)
+                dict_op["Resource"] = "Machine {}".format(
+                    jobs_data[job_id][task_id][0]
+                )
+                df.append(dict_op)
+                i += 1
+        fig = None
+        colors = [
+            tuple([random.random() for _ in range(3)]) for _ in range(machines_count)
+        ]
+        if len(df) > 0:
+            df = pd.DataFrame(df)
+            fig = ff.create_gantt(
+                df,
+                index_col="Resource",
+                colors=colors,
+                show_colorbar=True,
+                group_tasks=True,
+            )
+            fig.update_yaxes(
+                autorange=True
+            )
+        return current_solution_cost, str(total_time) + " seconds", pretty_output, fig
+
+
+title = "Job-Shop Scheduling CP environment with RL dispatching"
+description = """A Job-Shop Scheduling Reinforcement Learning based solver using an underlying CP model as an 
+environment. <br> 
+For fast inference, 
+check out the cached examples below.<br> Any Job-Shop Scheduling instance following the standard specification is 
+compatible. <a href='http://jobshop.jjvh.nl/index.php'>Check out this website for more instances</a>.<br>
+Increasing the number of workers will provide better solutions, but will slow down the solving time.
+This behavior is different than the one from the paper repository as here agents are run sequentially, 
+whereas we run agents in parallel (technical limitation due to the platform here). <br>
+<br>
+For large instance, we recommend running the approach locally outside the interface, as it causes a lot 
+of overhead and the resource available on this platform are low (1 vCPU and no GPU).<br> """
+
+article = "<p style='text-align: center'>Article Under Review</p>"
+# list all non-hidden files in the 'instances' directory
+examples = [['instances/' + f, 16, 0] for f in os.listdir('instances') if not f.startswith('.')]
+iface = gr.Interface(fn=solve,
+                     inputs=[gr.File(label="Instance File"),
+                             gr.Slider(8, 32, value=16, label="Number of Workers", step=1),
+                             gr.Number(0, label="Random Seed", precision=0)],
+                     outputs=[gr.Text(label="Makespan"), gr.Text(label="Elapsed Time"), gr.Text(label="Solution"),
+                              gr.Plot(label="Solution's Gantt Chart")],
+                     title=title,
+                     description=description,
+                     article=article,
+                     examples=examples,
+                     allow_flagging="never")
+iface.launch(enable_queue=True)

instances/dmu01.txt

@@ -0,0 +1,21 @@
+20	15 
+0	160	13	5	6	139	11	99	12	9	5	98	2	28	1	107	3	196	10	165	7	114	4	7	14	34	8	133	9	76
+14	105	7	160	3	19	2	189	11	25	1	95	12	15	0	122	4	165	9	2	10	66	13	111	8	51	6	83	5	183
+11	61	5	11	9	130	4	147	13	106	12	1	6	141	7	136	10	33	0	13	2	15	8	10	14	62	3	4	1	142
+13	117	1	11	4	162	0	192	5	35	8	172	3	4	14	193	2	141	11	139	6	62	9	12	12	1	7	135	10	25
+5	53	9	89	10	168	12	41	11	121	1	181	3	43	0	118	4	61	14	193	2	124	6	176	13	28	8	125	7	136
+5	152	0	115	2	122	14	5	12	46	13	144	11	29	7	176	1	115	6	18	4	23	9	26	3	175	8	110	10	75
+6	50	1	62	3	186	12	57	11	156	10	32	2	134	9	141	4	189	13	118	0	102	7	3	8	177	14	43	5	41
+13	35	0	171	14	160	9	32	7	5	11	154	8	195	3	113	12	162	5	152	6	140	2	72	4	16	10	104	1	171
+13	68	8	54	6	116	4	9	14	99	12	155	10	22	5	135	0	67	1	165	9	100	11	47	3	46	7	55	2	12
+1	135	5	105	9	49	8	4	12	176	3	52	11	128	7	188	6	170	10	170	2	169	4	62	0	120	13	28	14	70
+2	93	1	172	13	124	6	72	7	189	14	122	5	38	0	120	12	114	11	51	9	77	8	65	4	176	3	171	10	169
+3	122	6	21	4	6	13	189	14	75	5	5	9	180	0	160	1	14	11	73	12	45	2	61	7	148	10	96	8	194
+9	94	12	198	8	100	5	194	2	127	10	95	4	43	3	52	6	166	1	31	14	100	13	104	7	166	11	139	0	143
+5	4	3	78	11	199	8	119	12	167	0	54	9	38	14	114	13	10	4	115	7	101	1	104	2	61	6	75	10	175
+10	18	11	115	6	166	8	41	14	124	12	101	7	38	13	29	0	91	2	118	9	40	5	55	1	82	4	89	3	100
+11	2	9	107	14	99	3	152	7	51	4	13	10	112	0	96	1	150	6	97	13	67	5	57	2	45	8	17	12	184
+1	176	11	15	3	92	9	9	14	77	12	4	7	83	10	195	4	156	6	102	2	91	13	65	8	19	5	163	0	93
+8	38	0	32	14	80	11	109	9	71	1	100	12	139	7	52	3	163	13	40	4	5	6	28	2	105	5	186	10	186
+11	1	3	73	0	106	4	80	12	150	13	5	5	71	9	145	1	138	6	148	10	168	7	60	2	107	14	164	8	178
+1	14	10	5	4	115	2	70	11	112	5	76	9	20	0	104	7	167	13	58	8	193	12	30	6	132	3	6	14	19

instances/dmu02.txt

@@ -0,0 +1,21 @@
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instances/dmu03.txt

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instances/dmu04.txt

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instances/dmu05.txt

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instances/la01.txt

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instances/la02.txt

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instances/la03.txt

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instances/la04.txt

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instances/la05.txt

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instances/ta01

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instances/ta02

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instances/ta03

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instances/ta04

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instances/ta05

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requirements.txt

@@ -0,0 +1,5 @@
+job-shop-cp-env==1.0.0
+ray[default]~=1.5.0
+ray[rllib]~=1.5.0
+stable-baselines3==1.6.2
+plotly==5.11.0