update docker hub init (#367)
* update docker hub init * replace personal account with maro-team * update hello files for CIM * update docker repository name * update docker file name * fix bugs in notebook, rectify docs * fix doc build issue * remove docs from playground; fix citibike lp example Event issue * update the exampel for vector env * update vector env example * update README due to PR comments * add link to playground above MARO installation in README * fix some typos Co-authored-by: Jinyu Wang <Wang.Jinyu@microsoft.com>
This commit is contained in:
Jinyu-W
GitHub
Родитель
dca9280b91
Коммит
d94899777e
|
|
@ -38,7 +38,7 @@ Steps to reproduce the behavior:
|
|||
- How you installed MARO (`pip`, `source`):
|
||||
- OS (`Linux`, `Windows`, `macOS`):
|
||||
- Python version (`3.6`, `3.7`):
|
||||
- Docker image (e.g., arthursjiang/maro:cpu[5f36ed]):
|
||||
- Docker image (e.g., maro2020/maro:latest):
|
||||
- CPU/GPU:
|
||||
- Any other relevant information:
|
||||
|
||||
|
|
|
|||
|
|
@ -30,7 +30,7 @@ jobs:
|
|||
run: |
|
||||
pip install -r ./maro/requirements.build.txt
|
||||
cython ./maro/backends/backend.pyx ./maro/backends/np_backend.pyx ./maro/backends/raw_backend.pyx ./maro/backends/frame.pyx --cplus -3 -E NODES_MEMORY_LAYOUT=ONE_BLOCK -X embedsignature=True
|
||||
cat ./maro/__misc__.py | grep __version__ | egrep -o [0-9].[0-9].[0-9,a-z]+ | { read version; docker build -f ./docker_files/cpu.play.df . -t ${{ secrets.DOCKER_HUB_USERNAME }}/maro:cpu -t ${{ secrets.DOCKER_HUB_USERNAME }}/maro:latest -t ${{ secrets.DOCKER_HUB_USERNAME }}/maro:cpu-$version; }
|
||||
cat ./maro/__misc__.py | grep __version__ | egrep -o [0-9].[0-9].[0-9,a-z]+ | { read version; docker build -f ./docker_files/cpu.playground.df . -t ${{ secrets.DOCKER_HUB_USERNAME }}/maro:cpu -t ${{ secrets.DOCKER_HUB_USERNAME }}/maro:latest -t ${{ secrets.DOCKER_HUB_USERNAME }}/maro:cpu-$version; }
|
||||
|
||||
- name: Login docker hub
|
||||
run: |
|
||||
|
|
|
|||
27
README.md
27
README.md
|
|
@ -2,13 +2,13 @@
|
|||
[](https://pypi.org/project/pymaro/)
|
||||
[](https://pypi.org/project/pymaro/#files)
|
||||
[](https://github.com/microsoft/maro)
|
||||
[](https://hub.docker.com/repository/docker/arthursjiang/maro/tags?page=1)
|
||||
[](https://hub.docker.com/repository/docker/maro2020/maro/tags?page=1)
|
||||
[](https://github.com/microsoft/maro/issues)
|
||||
[](https://github.com/microsoft/maro/pulls)
|
||||
[](https://libraries.io/pypi/pymaro)
|
||||
[](https://github.com/microsoft/maro/actions?query=workflow%3Atest)
|
||||
[](https://github.com/microsoft/maro/actions?query=workflow%3Abuild)
|
||||
[](https://hub.docker.com/repository/docker/arthursjiang/maro)
|
||||
[](https://hub.docker.com/repository/docker/maro2020/maro)
|
||||
[](https://maro.readthedocs.io/)
|
||||
[](https://pypi.org/project/pymaro/#files)
|
||||
[](https://pypi.org/project/pymaro/#files)
|
||||
|
|
@ -23,8 +23,8 @@
|
|||
[](https://github.com/microsoft/maro/actions?query=workflow%3Alint)
|
||||
[](https://codecov.io/gh/microsoft/maro)
|
||||
[](https://pypi.org/project/pymaro/#files)
|
||||
[](https://hub.docker.com/repository/docker/arthursjiang/maro)
|
||||
[](https://hub.docker.com/r/arthursjiang/maro)
|
||||
[](https://hub.docker.com/repository/docker/maro2020/maro)
|
||||
[](https://hub.docker.com/r/maro2020/maro)
|
||||
|
||||
# [](https://maro.readthedocs.io/en/latest/)
|
||||
|
||||
|
|
@ -58,6 +58,8 @@ of user-defined functions for message auto-handling, cluster provision, and job
|
|||
| `examples` | Showcase of MARO. |
|
||||
| `notebooks` | MARO quick-start notebooks. |
|
||||
|
||||
*Try [MARO playground](#run-playground) to have a quick experience.*
|
||||
|
||||
## Install MARO from [PyPI](https://pypi.org/project/pymaro/#files)
|
||||
|
||||
*Notes: The CLI commands (including the visualization tool) are not included in pymaro package. To enable these support, you need to install from source.*
|
||||
|
|
@ -185,14 +187,13 @@ maro inspector dashboard --source_path ./dump_data/YOUR_SNAPSHOT_DUMP_FOLDER
|
|||
|
||||
## Run Playground
|
||||
|
||||
- Pull from [Docker Hub](https://hub.docker.com/repository/registry-1.docker.io/arthursjiang/maro/tags?page=1)
|
||||
- Pull from [Docker Hub](https://hub.docker.com/r/maro2020/playground)
|
||||
|
||||
```sh
|
||||
# Run playground container.
|
||||
# Redis commander (GUI for redis) -> http://127.0.0.1:40009
|
||||
# Local host docs -> http://127.0.0.1:40010
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40011
|
||||
docker run -p 40009:40009 -p 40010:40010 -p 40011:40011 arthursjiang/maro:cpu
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40010
|
||||
docker run -p 40009:40009 -p 40010:40010 maro2020/playground
|
||||
```
|
||||
|
||||
- Build from source
|
||||
|
|
@ -204,9 +205,8 @@ maro inspector dashboard --source_path ./dump_data/YOUR_SNAPSHOT_DUMP_FOLDER
|
|||
|
||||
# Run playground container.
|
||||
# Redis commander (GUI for redis) -> http://127.0.0.1:40009
|
||||
# Local host docs -> http://127.0.0.1:40010
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40011
|
||||
docker run -p 40009:40009 -p 40010:40010 -p 40011:40011 maro/playground:cpu
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40010
|
||||
docker run -p 40009:40009 -p 40010:40010 maro2020/playground
|
||||
```
|
||||
|
||||
- Windows
|
||||
|
|
@ -217,9 +217,8 @@ maro inspector dashboard --source_path ./dump_data/YOUR_SNAPSHOT_DUMP_FOLDER
|
|||
|
||||
# Run playground container.
|
||||
# Redis commander (GUI for redis) -> http://127.0.0.1:40009
|
||||
# Local host docs -> http://127.0.0.1:40010
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40011
|
||||
docker run -p 40009:40009 -p 40010:40010 -p 40011:40011 maro/playground:cpu
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40010
|
||||
docker run -p 40009:40009 -p 40010:40010 maro2020/playground
|
||||
```
|
||||
|
||||
## Contributing
|
||||
|
|
|
|||
|
|
@ -1,20 +1,8 @@
|
|||
FROM python:3.6
|
||||
FROM python:3.7
|
||||
|
||||
WORKDIR /maro_playground
|
||||
|
||||
# Setup notebook
|
||||
ADD ./notebooks ./notebooks
|
||||
RUN /usr/local/bin/python -m pip install --upgrade pip
|
||||
RUN pip install -r ./notebooks/requirements.nb.txt
|
||||
RUN jupyter contrib nbextension install --system
|
||||
RUN jt -t onedork -fs 95 -altp -tfs 11 -nfs 115 -cellw 88% -T
|
||||
|
||||
|
||||
# Install redis
|
||||
RUN wget http://download.redis.io/releases/redis-6.0.6.tar.gz; tar xzf redis-6.0.6.tar.gz; cd redis-6.0.6; make
|
||||
RUN rm redis-6.0.6.tar.gz
|
||||
|
||||
# Install others
|
||||
# Install zsh and other packages for the terminal usage
|
||||
RUN apt-get -o Acquire::Check-Valid-Until=false -o Acquire::Check-Date=false update
|
||||
RUN apt-get install -y zsh
|
||||
RUN apt-get install -y htop
|
||||
|
|
@ -23,42 +11,36 @@ RUN wget https://github.com/robbyrussell/oh-my-zsh/raw/master/tools/install.sh -
|
|||
RUN chsh -s `which zsh` && wget https://raw.githubusercontent.com/ArthurJiang/config/master/.zshrc -O ~/.zshrc
|
||||
RUN apt-get install -y npm
|
||||
RUN rm -rf /var/lib/apt/lists/*
|
||||
|
||||
# Install redis
|
||||
RUN wget http://download.redis.io/releases/redis-6.0.6.tar.gz; tar xzf redis-6.0.6.tar.gz; cd redis-6.0.6; make
|
||||
RUN rm redis-6.0.6.tar.gz
|
||||
RUN npm install -g redis-commander
|
||||
|
||||
# Setup notebook
|
||||
ADD ./notebooks ./notebooks
|
||||
RUN /usr/local/bin/python -m pip install --upgrade pip
|
||||
RUN pip install -r ./notebooks/requirements.nb.txt
|
||||
RUN jupyter contrib nbextension install --system
|
||||
RUN jt -t onedork -fs 95 -altp -tfs 11 -nfs 115 -cellw 88% -T
|
||||
RUN rm ./notebooks/*.txt
|
||||
RUN rm ./notebooks/*.sh
|
||||
|
||||
# Add examples
|
||||
ADD ./examples ./examples
|
||||
ADD ./examples/requirements.ex.txt ./examples/requirements.ex.txt
|
||||
RUN pip install -r ./examples/requirements.ex.txt
|
||||
RUN rm ./examples/requirements.ex.txt
|
||||
|
||||
# Add local docs
|
||||
ADD ./docs ./docs
|
||||
ADD ./maro ./maro
|
||||
ADD setup.py setup.py
|
||||
ADD ./scripts ./scripts
|
||||
RUN bash scripts/install_maro.sh
|
||||
RUN pip install -U -r ./docs/requirements.docs.txt
|
||||
RUN cd docs; make html
|
||||
RUN rm -rf ./maro
|
||||
RUN rm setup.py
|
||||
RUN rm -rf ./scripts
|
||||
# Install MARO
|
||||
RUN pip install pymaro
|
||||
ENV PYTHONPATH ./
|
||||
|
||||
# Add run cmd
|
||||
ADD ./scripts/run_playground.sh ./run.sh
|
||||
|
||||
# Add readme
|
||||
# Add README
|
||||
ADD ./playground.md ./README.md
|
||||
|
||||
# Clean
|
||||
RUN rm ./notebooks/*.txt
|
||||
RUN rm ./notebooks/*.sh
|
||||
RUN rm -r ./docs/source
|
||||
RUN rm ./docs/make.bat
|
||||
RUN rm ./docs/Makefile
|
||||
RUN rm ./docs/README.md
|
||||
RUN rm ./docs/requirements.docs.txt
|
||||
RUN rm -rf ./build
|
||||
RUN rm -rf ./pymaro.egg-info
|
||||
|
||||
# Install maro
|
||||
RUN pip install pymaro
|
||||
|
||||
# Start service
|
||||
CMD ["/bin/bash", "./run.sh"]
|
||||
|
|
@ -76,4 +76,4 @@ This environment is driven by `real trip history data <https://s3.amazonaws.com/
|
|||
|
||||
.. note::
|
||||
|
||||
All related code snippets are supported in `maro playground <https://hub.docker.com/r/arthursjiang/maro>`_.
|
||||
All related code snippets are supported in `maro playground <https://hub.docker.com/r/maro2020/playground>`_.
|
||||
|
|
|
|||
|
|
@ -19,7 +19,7 @@ in the roll-out loop. In this example,
|
|||
to or unloaded from the vessel) to action objects that can be executed by the environment.
|
||||
* ``get_offline_reward`` computes the reward of a given action as a linear combination of fulfillment and
|
||||
shortage within a future time frame.
|
||||
* ``on_finish`` processes a complete trajectory into data that can be used directly by the learning agents.
|
||||
* ``on_finish`` processes a complete trajectory into data that can be used directly by the learning agents.
|
||||
|
||||
|
||||
.. code-block:: python
|
||||
|
|
@ -27,7 +27,7 @@ in the roll-out loop. In this example,
|
|||
def __init__(
|
||||
self, env, *, port_attributes, vessel_attributes, action_space, look_back, max_ports_downstream,
|
||||
reward_time_window, fulfillment_factor, shortage_factor, time_decay,
|
||||
finite_vessel_space=True, has_early_discharge=True
|
||||
finite_vessel_space=True, has_early_discharge=True
|
||||
):
|
||||
super().__init__(env)
|
||||
self.port_attributes = port_attributes
|
||||
|
|
@ -113,7 +113,7 @@ Agent
|
|||
|
||||
The out-of-the-box DQN is used as our agent.
|
||||
|
||||
.. code-block:: python
|
||||
.. code-block:: python
|
||||
agent_config = {
|
||||
"model": ...,
|
||||
"optimization": ...,
|
||||
|
|
@ -149,7 +149,7 @@ The learner's side requires a concrete learner class that inherits from ``AbsLea
|
|||
method which contains the main training loop. Here the implementation is similar to the single-threaded version
|
||||
except that the ``collect`` method is used to obtain roll-out data from the actors (since the roll-out executors
|
||||
are located on the actors' side). The agents created here are where training occurs and hence always contains the
|
||||
latest policies.
|
||||
latest policies.
|
||||
|
||||
.. code-block:: python
|
||||
def cim_dqn_learner():
|
||||
|
|
@ -165,4 +165,4 @@ latest policies.
|
|||
|
||||
.. note::
|
||||
|
||||
All related code snippets are supported in `maro playground <https://hub.docker.com/r/arthursjiang/maro>`_.
|
||||
All related code snippets are supported in `maro playground <https://hub.docker.com/r/maro2020/playground>`_.
|
||||
|
|
|
|||
|
|
@ -32,45 +32,35 @@ Quick Start
|
|||
.. code-block:: python
|
||||
|
||||
from maro.simulator import Env
|
||||
from maro.simulator.scenarios.cim.common import Action
|
||||
from maro.simulator.scenarios.cim.common import Action, ActionType, DecisionEvent
|
||||
|
||||
# Initialize an environment with a specific scenario, related topology.
|
||||
# In Container Inventory Management, 1 tick means 1 day, here durations=100 means a length of 100 days
|
||||
from random import randint
|
||||
|
||||
# Initialize an Env for cim scenario
|
||||
env = Env(scenario="cim", topology="toy.5p_ssddd_l0.0", start_tick=0, durations=100)
|
||||
|
||||
# Query environment summary, which includes business instances, intra-instance attributes, etc.
|
||||
print(env.summary)
|
||||
metrics: object = None
|
||||
decision_event: DecisionEvent = None
|
||||
is_done: bool = False
|
||||
action: Action = None
|
||||
|
||||
for ep in range(2):
|
||||
# Gym-like step function.
|
||||
metrics, decision_event, is_done = env.step(None)
|
||||
# Start the env with a None Action
|
||||
metrics, decision_event, is_done = env.step(None)
|
||||
|
||||
while not is_done:
|
||||
past_week_ticks = [
|
||||
x for x in range(decision_event.tick - 7, decision_event.tick)
|
||||
]
|
||||
decision_port_idx = decision_event.port_idx
|
||||
intr_port_infos = ["booking", "empty", "shortage"]
|
||||
while not is_done:
|
||||
# Generate a random Action according to the action_scope in DecisionEvent
|
||||
action_scope = decision_event.action_scope
|
||||
to_discharge = action_scope.discharge > 0 and randint(0, 1) > 0
|
||||
|
||||
# Query the snapshot list of the environment to get the information of
|
||||
# the booking, empty container inventory, shortage of the decision port in the past week.
|
||||
past_week_info = env.snapshot_list["ports"][
|
||||
past_week_ticks : decision_port_idx : intr_port_infos
|
||||
]
|
||||
action = Action(
|
||||
decision_event.vessel_idx,
|
||||
decision_event.port_idx,
|
||||
randint(0, action_scope.discharge if to_discharge else action_scope.load),
|
||||
ActionType.DISCHARGE if to_discharge else ActionType.LOAD
|
||||
)
|
||||
|
||||
dummy_action = Action(
|
||||
vessel_idx=decision_event.vessel_idx,
|
||||
port_idx=decision_event.port_idx,
|
||||
quantity=0
|
||||
)
|
||||
|
||||
# Drive environment with dummy action (no repositioning).
|
||||
metrics, decision_event, is_done = env.step(dummy_action)
|
||||
|
||||
# Query environment business metrics at the end of an episode,
|
||||
# it is your optimized object (usually includes multi-target).
|
||||
print(f"ep: {ep}, environment metrics: {env.metrics}")
|
||||
env.reset()
|
||||
# Respond the environment with the generated Action
|
||||
metrics, decision_event, is_done = env.step(action)
|
||||
|
||||
Contents
|
||||
----------
|
||||
|
|
|
|||
|
|
@ -2,16 +2,15 @@
|
|||
Playground Docker Image
|
||||
=======================
|
||||
|
||||
Pull from `Docker Hub <https://hub.docker.com/repository/registry-1.docker.io/arthursjiang/maro/tags?page=1>`_
|
||||
Pull from `Docker Hub <https://hub.docker.com/repository/registry-1.docker.io/maro2020/playground/tags?page=1>`_
|
||||
------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
# Run playground container.
|
||||
# Redis commander (GUI for redis) -> http://127.0.0.1:40009
|
||||
# Local host docs -> http://127.0.0.1:40010
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40011
|
||||
docker run -p 40009:40009 -p 40010:40010 -p 40011:40011 arthursjiang/maro:cpu
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40010
|
||||
docker run -p 40009:40009 -p 40010:40010 maro2020/playground
|
||||
|
||||
Run from Source
|
||||
---------------
|
||||
|
|
@ -25,9 +24,8 @@ Run from Source
|
|||
|
||||
# Run playground container.
|
||||
# Redis commander (GUI for redis) -> http://127.0.0.1:40009
|
||||
# Local host docs -> http://127.0.0.1:40010
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40011
|
||||
docker run -p 40009:40009 -p 40010:40010 -p 40011:40011 maro/playground:cpu
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40010
|
||||
docker run -p 40009:40009 -p 40010:40010 maro2020/playground
|
||||
|
||||
* Windows
|
||||
|
||||
|
|
@ -38,9 +36,8 @@ Run from Source
|
|||
|
||||
# Run playground container.
|
||||
# Redis commander (GUI for redis) -> http://127.0.0.1:40009
|
||||
# Local host docs -> http://127.0.0.1:40010
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40011
|
||||
docker run -p 40009:40009 -p 40010:40010 -p 40011:40011 maro/playground:cpu
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40010
|
||||
docker run -p 40009:40009 -p 40010:40010 maro2020/playground
|
||||
|
||||
Major Services in Playground
|
||||
----------------------------
|
||||
|
|
@ -54,15 +51,12 @@ Major Services in Playground
|
|||
* - ``Redis Commander``
|
||||
- Redis web GUI.
|
||||
- http://127.0.0.1:40009
|
||||
* - ``Read the Docs``
|
||||
- Local host docs.
|
||||
- http://127.0.0.1:40010
|
||||
* - ``Jupyter Lab``
|
||||
- Jupyter lab with MARO environment, examples, notebooks.
|
||||
- http://127.0.0.1:40011
|
||||
- http://127.0.0.1:40010
|
||||
|
||||
|
||||
*(If you use other port mapping, remember to change the port number.)*
|
||||
*(Remember to change ports if you use different ports mapping.)*
|
||||
|
||||
Major Materials in Root Folder
|
||||
------------------------------
|
||||
|
|
@ -78,4 +72,4 @@ Major Materials in Root Folder
|
|||
- Quick-start tutorial.
|
||||
|
||||
|
||||
*(Those not mentioned in the table can be ignored.)*
|
||||
*(The ones not mentioned in this table can be ignored.)*
|
||||
|
|
|
|||
|
|
@ -571,10 +571,8 @@ Once we get a ``DecisionEvent`` from the environment, we should respond with an
|
|||
|
||||
* **vessel_idx** (int): The id of the vessel/operation object of the port/agent.
|
||||
* **port_idx** (int): The id of the port/agent that take this action.
|
||||
* **quantity** (int): The sign of this value denotes different meanings:
|
||||
|
||||
* Positive quantity means discharging empty containers from vessel to port.
|
||||
* Negative quantity means loading empty containers from port to vessel.
|
||||
* **action_type** (ActionType): Whether to load or discharge empty containers in this action.
|
||||
* **quantity** (int): The quantity of empty containers to be loaded/discharged.
|
||||
|
||||
Example
|
||||
^^^^^^^
|
||||
|
|
@ -583,61 +581,36 @@ Here we will show you a simple example of interaction with the environment in
|
|||
random mode, we hope this could help you learn how to use the environment interfaces:
|
||||
|
||||
.. code-block:: python
|
||||
from maro.simulator import Env
|
||||
from maro.simulator.scenarios.cim.common import Action, ActionType, DecisionEvent
|
||||
|
||||
from maro.simulator import Env
|
||||
from maro.simulator.scenarios.cim.common import Action, DecisionEvent
|
||||
from random import randint
|
||||
|
||||
import random
|
||||
# Initialize an Env for cim scenario
|
||||
env = Env(scenario="cim", topology="toy.5p_ssddd_l0.0", start_tick=0, durations=100)
|
||||
|
||||
# Initialize an environment of CIM scenario, with a specific topology.
|
||||
# In Container Inventory Management, 1 tick means 1 day, durations=100 here indicates a length of 100 days.
|
||||
env = Env(scenario="cim", topology="toy.5p_ssddd_l0.0", start_tick=0, durations=100)
|
||||
metrics: object = None
|
||||
decision_event: DecisionEvent = None
|
||||
is_done: bool = False
|
||||
action: Action = None
|
||||
|
||||
# Query for the environment summary, the business instances and intra-instance attributes
|
||||
# will be listed in the output for your reference.
|
||||
print(env.summary)
|
||||
# Start the env with a None Action
|
||||
metrics, decision_event, is_done = env.step(None)
|
||||
|
||||
metrics: object = None
|
||||
decision_event: DecisionEvent = None
|
||||
is_done: bool = False
|
||||
action: Action = None
|
||||
while not is_done:
|
||||
# Generate a random Action according to the action_scope in DecisionEvent
|
||||
action_scope = decision_event.action_scope
|
||||
to_discharge = action_scope.discharge > 0 and randint(0, 1) > 0
|
||||
|
||||
num_episode = 2
|
||||
for ep in range(num_episode):
|
||||
# Gym-like step function.
|
||||
metrics, decision_event, is_done = env.step(None)
|
||||
action = Action(
|
||||
decision_event.vessel_idx,
|
||||
decision_event.port_idx,
|
||||
randint(0, action_scope.discharge if to_discharge else action_scope.load),
|
||||
ActionType.DISCHARGE if to_discharge else ActionType.LOAD
|
||||
)
|
||||
|
||||
while not is_done:
|
||||
past_week_ticks = [
|
||||
x for x in range(decision_event.tick - 7, decision_event.tick)
|
||||
]
|
||||
decision_port_idx = decision_event.port_idx
|
||||
intr_port_infos = ["booking", "empty", "shortage"]
|
||||
|
||||
# Query the snapshot list of this environment to get the information of
|
||||
# the booking, empty, shortage of the decision port in the past week.
|
||||
past_week_info = env.snapshot_list["ports"][
|
||||
past_week_ticks : decision_port_idx : intr_port_infos
|
||||
]
|
||||
|
||||
# Generate a random Action according to the action_scope in DecisionEvent.
|
||||
random_quantity = random.randint(
|
||||
-decision_event.action_scope.load,
|
||||
decision_event.action_scope.discharge
|
||||
)
|
||||
action = Action(
|
||||
vessel_idx=decision_event.vessel_idx,
|
||||
port_idx=decision_event.port_idx,
|
||||
quantity=random_quantity
|
||||
)
|
||||
|
||||
# Drive the environment with the random action.
|
||||
metrics, decision_event, is_done = env.step(action)
|
||||
|
||||
# Query for the environment business metrics at the end of each episode,
|
||||
# it is usually users' optimized object in CIM scenario (usually includes multi-target).
|
||||
print(f"ep: {ep}, environment metrics: {env.metrics}")
|
||||
env.reset()
|
||||
# Respond the environment with the generated Action
|
||||
metrics, decision_event, is_done = env.step(action)
|
||||
|
||||
Jump to `this notebook <https://github.com/microsoft/maro/tree/master/notebooks/container_inventory_management/interact_with_environment.ipynb>`_
|
||||
for a quick experience.
|
||||
|
|
@ -646,7 +619,7 @@ Visualization
|
|||
-------------
|
||||
|
||||
The resource holders in this scenario is the port and vessel.
|
||||
In order to facilitate users to select specific data and
|
||||
In order to facilitate users to select specific data and
|
||||
observe the overall or partial data trend, the
|
||||
visualization tool provides data selection options in two dimensions:
|
||||
Inter-epoch view & Intra-epoch view.
|
||||
|
|
|
|||
|
|
@ -23,7 +23,7 @@ sys.path.insert(0, cim_example_path)
|
|||
from common import CIMTrajectory, common_config
|
||||
from dqn.config import agent_config, training_config
|
||||
|
||||
log_dir = join(cim_dqn_path, "logs")
|
||||
log_dir = join(cim_dqn_path, "log")
|
||||
makedirs(log_dir, exist_ok=True)
|
||||
|
||||
|
||||
|
|
@ -74,7 +74,7 @@ if __name__ == "__main__":
|
|||
"-w", "--whoami", type=int, choices=[0, 1, 2], default=0,
|
||||
help="Identity of this process: 0 - multi-process mode, 1 - learner, 2 - actor"
|
||||
)
|
||||
|
||||
|
||||
args = parser.parse_args()
|
||||
if args.whoami == 0:
|
||||
actor_processes = [Process(target=cim_dqn_actor) for _ in range(training_config["num_actors"])]
|
||||
|
|
|
|||
|
|
@ -1,25 +1,19 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT license.
|
||||
|
||||
from random import seed, randint
|
||||
|
||||
from maro.simulator import Env
|
||||
from maro.simulator.scenarios.cim.common import Action, ActionType
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
start_tick = 0
|
||||
durations = 100 # 100 days
|
||||
|
||||
opts = dict()
|
||||
"""
|
||||
enable-dump-snapshot parameter means business_engine needs dump snapshot data before reset.
|
||||
If you leave value to empty string, it will dump to current folder.
|
||||
For getting dump data, please uncomment below line and specify dump destination folder.
|
||||
"""
|
||||
# Initialize an environment with a specific scenario, related topology.
|
||||
env = Env(scenario="cim", topology="global_trade.22p_l0.1",
|
||||
start_tick=start_tick, durations=durations)
|
||||
env = Env(scenario="cim", topology="global_trade.22p_l0.1", start_tick=0, durations=100)
|
||||
|
||||
# To reset environmental data before starting a new experiment.
|
||||
env.reset()
|
||||
|
||||
# Query environment summary, which includes business instances, intra-instance attributes, etc.
|
||||
print(env.summary)
|
||||
|
||||
|
|
@ -28,25 +22,18 @@ if __name__ == "__main__":
|
|||
metrics, decision_event, is_done = env.step(None)
|
||||
|
||||
while not is_done:
|
||||
past_week_ticks = [x for x in range(
|
||||
max(decision_event.tick - 7, 0), decision_event.tick)]
|
||||
decision_port_idx = decision_event.port_idx
|
||||
intr_port_infos = ["booking", "empty", "shortage"]
|
||||
action_scope = decision_event.action_scope
|
||||
to_discharge = action_scope.discharge > 0 and randint(0, 1) > 0
|
||||
|
||||
# Query the decision port booking, empty container inventory, shortage information in the past week
|
||||
past_week_info = env.snapshot_list["ports"][past_week_ticks:
|
||||
decision_port_idx:
|
||||
intr_port_infos]
|
||||
|
||||
dummy_action = Action(
|
||||
random_action = Action(
|
||||
decision_event.vessel_idx,
|
||||
decision_event.port_idx,
|
||||
0,
|
||||
ActionType.LOAD
|
||||
randint(0, action_scope.discharge if to_discharge else action_scope.load),
|
||||
ActionType.DISCHARGE if to_discharge else ActionType.LOAD
|
||||
)
|
||||
|
||||
# Drive environment with dummy action (no repositioning)
|
||||
metrics, decision_event, is_done = env.step(dummy_action)
|
||||
metrics, decision_event, is_done = env.step(random_action)
|
||||
|
||||
# Query environment business metrics at the end of an episode,
|
||||
# it is your optimized object (usually includes multi-target).
|
||||
|
|
|
|||
|
|
@ -9,32 +9,25 @@ os.environ["MARO_STREAMIT_ENABLED"] = "true"
|
|||
os.environ["MARO_STREAMIT_EXPERIMENT_NAME"] = "experiment_example"
|
||||
|
||||
|
||||
from random import seed, randint
|
||||
|
||||
from maro.simulator import Env
|
||||
from maro.simulator.scenarios.cim.common import Action, ActionType
|
||||
from maro.simulator.scenarios.cim.common import Action, ActionScope, ActionType
|
||||
from maro.streamit import streamit
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
start_tick = 0
|
||||
durations = 100 # 100 days
|
||||
seed(0)
|
||||
NUM_EPISODE = 2
|
||||
|
||||
opts = dict()
|
||||
with streamit:
|
||||
"""
|
||||
enable-dump-snapshot parameter means business_engine needs dump snapshot data before reset.
|
||||
If you leave value to empty string, it will dump to current folder.
|
||||
For getting dump data, please uncomment below line and specify dump destination folder.
|
||||
"""
|
||||
|
||||
# Initialize an environment with a specific scenario, related topology.
|
||||
env = Env(scenario="cim", topology="global_trade.22p_l0.1",
|
||||
start_tick=start_tick, durations=durations, options=opts)
|
||||
env = Env(scenario="cim", topology="global_trade.22p_l0.1", start_tick=0, durations=100)
|
||||
|
||||
# To reset environmental data before starting a new experiment.
|
||||
env.reset()
|
||||
# Query environment summary, which includes business instances, intra-instance attributes, etc.
|
||||
print(env.summary)
|
||||
|
||||
for ep in range(2):
|
||||
for ep in range(NUM_EPISODE):
|
||||
# Tell streamit we are in a new episode.
|
||||
streamit.episode(ep)
|
||||
|
||||
|
|
@ -42,25 +35,18 @@ if __name__ == "__main__":
|
|||
metrics, decision_event, is_done = env.step(None)
|
||||
|
||||
while not is_done:
|
||||
past_week_ticks = [x for x in range(
|
||||
max(decision_event.tick - 7, 0), decision_event.tick)]
|
||||
decision_port_idx = decision_event.port_idx
|
||||
intr_port_infos = ["booking", "empty", "shortage"]
|
||||
action_scope = decision_event.action_scope
|
||||
to_discharge = action_scope.discharge > 0 and randint(0, 1) > 0
|
||||
|
||||
# Query the decision port booking, empty container inventory, shortage information in the past week
|
||||
past_week_info = env.snapshot_list["ports"][past_week_ticks:
|
||||
decision_port_idx:
|
||||
intr_port_infos]
|
||||
|
||||
dummy_action = Action(
|
||||
random_action = Action(
|
||||
decision_event.vessel_idx,
|
||||
decision_event.port_idx,
|
||||
0,
|
||||
ActionType.LOAD
|
||||
randint(0, action_scope.discharge if to_discharge else action_scope.load),
|
||||
ActionType.DISCHARGE if to_discharge else ActionType.LOAD
|
||||
)
|
||||
|
||||
# Drive environment with dummy action (no repositioning)
|
||||
metrics, decision_event, is_done = env.step(dummy_action)
|
||||
metrics, decision_event, is_done = env.step(random_action)
|
||||
|
||||
# Query environment business metrics at the end of an episode,
|
||||
# it is your optimized object (usually includes multi-target).
|
||||
|
|
|
|||
|
|
@ -1,13 +1,15 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT license.
|
||||
|
||||
from random import seed, randint
|
||||
|
||||
from maro.simulator import Env
|
||||
from maro.simulator.scenarios.cim.common import Action, ActionType
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
start_tick = 0
|
||||
durations = 100 # 100 days
|
||||
seed(0)
|
||||
NUM_EPISODE = 2
|
||||
|
||||
opts = dict()
|
||||
"""
|
||||
|
|
@ -20,37 +22,29 @@ if __name__ == "__main__":
|
|||
# Initialize an environment with a specific scenario, related topology.
|
||||
env = Env(
|
||||
scenario="cim", topology="global_trade.22p_l0.1",
|
||||
start_tick=start_tick, durations=durations, options=opts
|
||||
start_tick=0, durations=100, options=opts
|
||||
)
|
||||
|
||||
# To reset environmental data before starting a new experiment.
|
||||
env.reset()
|
||||
# Query environment summary, which includes business instances, intra-instance attributes, etc.
|
||||
print(env.summary)
|
||||
|
||||
for ep in range(2):
|
||||
for ep in range(NUM_EPISODE):
|
||||
# Gym-like step function.
|
||||
metrics, decision_event, is_done = env.step(None)
|
||||
|
||||
while not is_done:
|
||||
past_week_ticks = [x for x in range(
|
||||
max(decision_event.tick - 7, 0), decision_event.tick)]
|
||||
decision_port_idx = decision_event.port_idx
|
||||
intr_port_infos = ["booking", "empty", "shortage"]
|
||||
action_scope = decision_event.action_scope
|
||||
to_discharge = action_scope.discharge > 0 and randint(0, 1) > 0
|
||||
|
||||
# Query the decision port booking, empty container inventory, shortage information in the past week
|
||||
past_week_info = env.snapshot_list["ports"][past_week_ticks:
|
||||
decision_port_idx:
|
||||
intr_port_infos]
|
||||
|
||||
dummy_action = Action(
|
||||
random_action = Action(
|
||||
decision_event.vessel_idx,
|
||||
decision_event.port_idx,
|
||||
0,
|
||||
ActionType.LOAD
|
||||
randint(0, action_scope.discharge if to_discharge else action_scope.load),
|
||||
ActionType.DISCHARGE if to_discharge else ActionType.LOAD
|
||||
)
|
||||
|
||||
# Drive environment with dummy action (no repositioning)
|
||||
metrics, decision_event, is_done = env.step(dummy_action)
|
||||
metrics, decision_event, is_done = env.step(random_action)
|
||||
|
||||
# Query environment business metrics at the end of an episode,
|
||||
# it is your optimized object (usually includes multi-target).
|
||||
|
|
|
|||
|
|
@ -0,0 +1 @@
|
|||
PuLP==2.1
|
||||
|
|
@ -1,46 +1,66 @@
|
|||
# Copyright (c) Microsoft Corporation.
|
||||
# Licensed under the MIT license.
|
||||
|
||||
from maro.simulator.scenarios.cim.common import Action, DecisionEvent
|
||||
from enum import Enum
|
||||
from maro.simulator.scenarios.cim.common import Action, ActionType, DecisionEvent
|
||||
from maro.vector_env import VectorEnv
|
||||
|
||||
class VectorEnvUsage(Enum):
|
||||
PUSH_ONE_FORWARD = "push the first environment forward and left others behind"
|
||||
PUSH_ALL_FORWARD = "push all environments forward together"
|
||||
|
||||
USAGE = VectorEnvUsage.PUSH_ONE_FORWARD
|
||||
|
||||
if __name__ == "__main__":
|
||||
with VectorEnv(batch_num=4, scenario="cim", topology="toy.5p_ssddd_l0.0", durations=100) as env:
|
||||
for ep in range(2):
|
||||
print("current episode:", ep)
|
||||
for USAGE in [VectorEnvUsage.PUSH_ONE_FORWARD, VectorEnvUsage.PUSH_ALL_FORWARD]:
|
||||
print(f"******************************************************")
|
||||
print(f"Mode: {USAGE} ({USAGE.value})")
|
||||
intermediate_status_reported = False
|
||||
|
||||
metrics, decision_event, is_done = (None, None, False)
|
||||
|
||||
while not is_done:
|
||||
action = None
|
||||
|
||||
# Usage:
|
||||
# 1. Only push speicified (1st for this example) environment, leave others behind
|
||||
# if decision_event:
|
||||
# env0_dec: DecisionEvent = decision_event[0]
|
||||
|
||||
# # 1.1 After 1st environment is done, then others will push forward.
|
||||
# if env0_dec:
|
||||
# ss0 = env.snapshot_list["vessels"][env0_dec.tick:env0_dec.vessel_idx:"remaining_space"]
|
||||
# action = {0: Action(env0_dec.vessel_idx, env0_dec.port_idx, -env0_dec.action_scope.load)}
|
||||
|
||||
# 2. Only pass action to 1st environment (give None to other environments),
|
||||
# but keep pushing all the environment, until the end
|
||||
if decision_event:
|
||||
env0_dec: DecisionEvent = decision_event[0]
|
||||
|
||||
# Showcase: how to access information from snapshot list in vector env.
|
||||
if env0_dec:
|
||||
ss0 = env.snapshot_list["vessels"][env0_dec.tick:env0_dec.vessel_idx:"remaining_space"]
|
||||
|
||||
action = [None] * env.batch_number
|
||||
# 1. Only push specified (1st for this example) environment, leave others behind.
|
||||
if USAGE == VectorEnvUsage.PUSH_ONE_FORWARD and env0_dec:
|
||||
# Only action for the 1st Env. After 1st environment is done, then others will push forward.
|
||||
action = {
|
||||
0: Action(
|
||||
vessel_idx=env0_dec.vessel_idx,
|
||||
port_idx=env0_dec.port_idx,
|
||||
quantity=env0_dec.action_scope.load,
|
||||
action_type=ActionType.LOAD
|
||||
)
|
||||
}
|
||||
|
||||
# with a list of action, will push all environment to next step
|
||||
action[0] = Action(env0_dec.vessel_idx, env0_dec.port_idx, -env0_dec.action_scope.load)
|
||||
# 2. Only pass action to 1st environment (give None to other environments),
|
||||
# but keep pushing all the environment, until the end
|
||||
elif USAGE == VectorEnvUsage.PUSH_ALL_FORWARD and env0_dec:
|
||||
# With a list of action, will push all environment to next step.
|
||||
action = [None] * env.batch_number
|
||||
action[0] = Action(
|
||||
vessel_idx=env0_dec.vessel_idx,
|
||||
port_idx=env0_dec.port_idx,
|
||||
quantity=env0_dec.action_scope.load,
|
||||
action_type=ActionType.LOAD
|
||||
)
|
||||
|
||||
metrics, decision_event, is_done = env.step(action)
|
||||
|
||||
print("Final tick for each environment:", env.tick)
|
||||
print("Final frame index for each environment:", env.frame_index)
|
||||
if not intermediate_status_reported and env.tick[0] >= 99:
|
||||
print(f"When env 0 reach tick {env.tick[0]}, ticks for each environment: {env.tick}")
|
||||
intermediate_status_reported = True
|
||||
|
||||
print(f"Final tick for each environment: {env.tick}")
|
||||
print(f"Final frame index for each environment: {env.frame_index}")
|
||||
|
||||
env.reset()
|
||||
|
|
|
|||
|
|
@ -11,7 +11,7 @@
|
|||
|
||||
|
||||
.. image:: https://github.com/microsoft/maro/workflows/docker/badge.svg
|
||||
:target: https://hub.docker.com/repository/docker/arthursjiang/maro
|
||||
:target: https://hub.docker.com/repository/docker/maro2020/maro
|
||||
:alt: docker
|
||||
|
||||
|
||||
|
|
@ -84,14 +84,14 @@ Install MARO from `PyPI <https://pypi.org/project/pymaro/#files>`_
|
|||
----------------------------------------------------------------------
|
||||
|
||||
|
||||
*
|
||||
*
|
||||
Max OS / Linux
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
pip install pymaro
|
||||
|
||||
*
|
||||
*
|
||||
Windows
|
||||
|
||||
.. code-block:: powershell
|
||||
|
|
@ -105,20 +105,20 @@ Install MARO from Source (\ `Editable Mode <https://pip.pypa.io/en/stable/refere
|
|||
------------------------------------------------------------------------------------------------------------------------
|
||||
|
||||
|
||||
*
|
||||
*
|
||||
Prerequisites
|
||||
|
||||
|
||||
* C++ Compiler
|
||||
|
||||
* Linux or Mac OS X: ``gcc``
|
||||
* Windows: `Build Tools for Visual Studio 2017 <https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=15>`_
|
||||
* Windows: `Build Tools for Visual Studio 2017 <https://visualstudio.microsoft.com/thank-you-downloading-visual-studio/?sku=BuildTools&rel=15>`_
|
||||
|
||||
*
|
||||
*
|
||||
Enable Virtual Environment
|
||||
|
||||
|
||||
*
|
||||
*
|
||||
Mac OS / Linux
|
||||
|
||||
.. code-block:: sh
|
||||
|
|
@ -127,7 +127,7 @@ Install MARO from Source (\ `Editable Mode <https://pip.pypa.io/en/stable/refere
|
|||
python -m venv maro_venv
|
||||
source ./maro_venv/bin/activate
|
||||
|
||||
*
|
||||
*
|
||||
Windows
|
||||
|
||||
.. code-block:: powershell
|
||||
|
|
@ -139,11 +139,11 @@ Install MARO from Source (\ `Editable Mode <https://pip.pypa.io/en/stable/refere
|
|||
# You may need this for SecurityError in PowerShell.
|
||||
Set-ExecutionPolicy -Scope CurrentUser -ExecutionPolicy Unrestricted
|
||||
|
||||
*
|
||||
*
|
||||
Install MARO
|
||||
|
||||
|
||||
*
|
||||
*
|
||||
Mac OS / Linux
|
||||
|
||||
.. code-block:: sh
|
||||
|
|
@ -151,7 +151,7 @@ Install MARO from Source (\ `Editable Mode <https://pip.pypa.io/en/stable/refere
|
|||
# Install MARO from source.
|
||||
bash scripts/install_maro.sh
|
||||
|
||||
*
|
||||
*
|
||||
Windows
|
||||
|
||||
.. code-block:: powershell
|
||||
|
|
@ -179,22 +179,23 @@ Run Playground
|
|||
--------------
|
||||
|
||||
|
||||
*
|
||||
Pull from `Docker Hub <https://hub.docker.com/repository/registry-1.docker.io/arthursjiang/maro/tags?page=1>`_
|
||||
*
|
||||
Pull from `Docker Hub <https://hub.docker.com/r/maro2020/playground>`_
|
||||
.. code-block:: sh
|
||||
docker pull maro2020/playground
|
||||
|
||||
.. code-block:: sh
|
||||
|
||||
# Run playground container.
|
||||
# Redis commander (GUI for redis) -> http://127.0.0.1:40009
|
||||
# Local host docs -> http://127.0.0.1:40010
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40011
|
||||
docker run -p 40009:40009 -p 40010:40010 -p 40011:40011 arthursjiang/maro:cpu
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40010
|
||||
docker run -p 40009:40009 -p 40010:40010 maro2020/playground
|
||||
|
||||
*
|
||||
*
|
||||
Build from source
|
||||
|
||||
|
||||
*
|
||||
*
|
||||
Mac OS / Linux
|
||||
|
||||
.. code-block:: sh
|
||||
|
|
@ -204,11 +205,10 @@ Run Playground
|
|||
|
||||
# Run playground container.
|
||||
# Redis commander (GUI for redis) -> http://127.0.0.1:40009
|
||||
# Local host docs -> http://127.0.0.1:40010
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40011
|
||||
docker run -p 40009:40009 -p 40010:40010 -p 40011:40011 maro/playground:cpu
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40010
|
||||
docker run -p 40009:40009 -p 40010:40010 maro2020/playground
|
||||
|
||||
*
|
||||
*
|
||||
Windows
|
||||
|
||||
.. code-block:: powershell
|
||||
|
|
@ -218,9 +218,8 @@ Run Playground
|
|||
|
||||
# Run playground container.
|
||||
# Redis commander (GUI for redis) -> http://127.0.0.1:40009
|
||||
# Local host docs -> http://127.0.0.1:40010
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40011
|
||||
docker run -p 40009:40009 -p 40010:40010 -p 40011:40011 maro/playground:cpu
|
||||
# Jupyter lab with maro -> http://127.0.0.1:40010
|
||||
docker run -p 40009:40009 -p 40010:40010 maro2020/playground
|
||||
|
||||
Contributing
|
||||
------------
|
||||
|
|
|
|||
|
|
@ -77,13 +77,13 @@ def start_geo_vis(start: str, experiment_name: str, front_end_port: int, **kwarg
|
|||
|
||||
# Start front-end docker container.
|
||||
exec_path = os.path.dirname(inspect.getfile(inspect.currentframe()))
|
||||
os.system("docker pull meroychen/geo_front_service")
|
||||
os.system("docker pull maro2020/geo_front_service")
|
||||
os.system("docker stop geo-vis")
|
||||
os.system("docker rm geo-vis")
|
||||
if front_end_port is not None:
|
||||
os.system(f"docker run -d -p {front_end_port}:8080 --name geo-vis meroychen/geo_front_service")
|
||||
os.system(f"docker run -d -p {front_end_port}:8080 --name geo-vis maro2020/geo_front_service")
|
||||
else:
|
||||
os.system("docker run -d -p 8080:8080 --name geo-vis meroychen/geo_front_service")
|
||||
os.system("docker run -d -p 8080:8080 --name geo-vis maro2020/geo_front_service")
|
||||
back_end_path = f"{exec_path}/back_end/vis_app/app.py"
|
||||
os.system(f"python {back_end_path}")
|
||||
|
||||
|
|
|
|||
|
|
@ -6,5 +6,6 @@ from .cascade_event import CascadeEvent
|
|||
from .event_buffer import EventBuffer
|
||||
from .event_state import EventState
|
||||
from .maro_events import MaroEvents
|
||||
from .typings import Event
|
||||
|
||||
__all__ = ["AtomEvent", "CascadeEvent", "EventBuffer", "EventState", "MaroEvents"]
|
||||
__all__ = ["AtomEvent", "CascadeEvent", "Event", "EventBuffer", "EventState", "MaroEvents"]
|
||||
|
|
|
|||
|
|
@ -25,10 +25,11 @@ class Action:
|
|||
Args:
|
||||
vessel_idx (int): Which vessel will take action.
|
||||
port_idx (int): Which port will take action.
|
||||
quantity (int): How many containers can be moved from vessel to port (negative in reverse).
|
||||
action_type (ActionType): Whether the action is a Load or a Discharge.
|
||||
quantity (int): How many containers are loaded/discharged in this Action.
|
||||
"""
|
||||
|
||||
summary_key = ["port_idx", "vessel_idx", "quantity"]
|
||||
summary_key = ["port_idx", "vessel_idx", "action_type", "quantity"]
|
||||
|
||||
def __init__(self, vessel_idx: int, port_idx: int, quantity: int, action_type: ActionType):
|
||||
self.vessel_idx = vessel_idx
|
||||
|
|
|
|||
|
|
@ -18,11 +18,7 @@
|
|||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"12:03:56 | WARNING | \u001b[33mBinary data files for scenario: citi_bike topology: toy.3s_4t not found.\u001b[0m\n",
|
||||
"12:03:56 | WARNING | \u001b[33mGenerating temp binary data file for scenario: citi_bike topology: toy.3s_4t pid: 129013. If you want to keep the data, please use MARO CLI command 'maro env data generate -s citi_bike -t toy.3s_4t' to generate the binary data files first.\u001b[0m\n",
|
||||
"12:03:56 | INFO | \u001b[32mGenerating trip data for topology toy.3s_4t .\u001b[0m\n",
|
||||
"12:03:57 | INFO | \u001b[32mBuilding binary data from ~/.maro/data/citi_bike/.source/.clean/toy.3s_4t/b37f36f2de334eb3/trips.csv to ~/.maro/data/citi_bike/.build/toy.3s_4t/b37f36f2de334eb3/trips.bin\u001b[0m\n",
|
||||
"{'trip_requirements': 2156, 'bike_shortage': 1163, 'operation_number': 0}\n"
|
||||
"{'trip_requirements': 2169, 'bike_shortage': 1199, 'operation_number': 0}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -73,7 +69,7 @@
|
|||
"output_type": "stream",
|
||||
"text": [
|
||||
"'The available scenarios in MARO:'\n",
|
||||
"['cim', 'citi_bike']\n",
|
||||
"['cim', 'citi_bike', 'vm_scheduling']\n",
|
||||
"\n",
|
||||
"'The predefined topologies in Citi Bike:'\n",
|
||||
"['ny.201801',\n",
|
||||
|
|
@ -144,17 +140,28 @@
|
|||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"12:04:06 | WARNING | \u001b[33mBinary data files for scenario: citi_bike topology: toy.3s_4t not found.\u001b[0m\n",
|
||||
"12:04:06 | WARNING | \u001b[33mGenerating temp binary data file for scenario: citi_bike topology: toy.3s_4t pid: 129013. If you want to keep the data, please use MARO CLI command 'maro env data generate -s citi_bike -t toy.3s_4t' to generate the binary data files first.\u001b[0m\n",
|
||||
"12:04:06 | INFO | \u001b[32mGenerating trip data for topology toy.3s_4t .\u001b[0m\n",
|
||||
"12:04:07 | INFO | \u001b[32mBuilding binary data from ~/.maro/data/citi_bike/.source/.clean/toy.3s_4t/0ad85988e84a43bd/trips.csv to ~/.maro/data/citi_bike/.build/toy.3s_4t/0ad85988e84a43bd/trips.bin\u001b[0m\n",
|
||||
"The current tick: 0.\n",
|
||||
"The current frame index: 0.\n",
|
||||
"There are 3 agents in this Env.\n",
|
||||
"There will be 48 snapshots in total.\n",
|
||||
"\n",
|
||||
"Env Summary:\n",
|
||||
"{'node_detail': {'matrices': {'attributes': {'trips_adj': {'slots': 9,\n",
|
||||
"{'event_payload': {'DeliverBike': ['from_station_idx',\n",
|
||||
" 'to_station_idx',\n",
|
||||
" 'number'],\n",
|
||||
" 'RebalanceBike': ['station_idx',\n",
|
||||
" 'tick',\n",
|
||||
" 'frame_index',\n",
|
||||
" 'type',\n",
|
||||
" 'action_scope'],\n",
|
||||
" 'RequireBike': ['timestamp',\n",
|
||||
" 'durations',\n",
|
||||
" 'src_station',\n",
|
||||
" 'dest_station'],\n",
|
||||
" 'ReturnBike': ['from_station_idx',\n",
|
||||
" 'to_station_idx',\n",
|
||||
" 'number']},\n",
|
||||
" 'node_detail': {'matrices': {'attributes': {'trips_adj': {'slots': 9,\n",
|
||||
" 'type': 'i'}},\n",
|
||||
" 'number': 1},\n",
|
||||
" 'stations': {'attributes': {'bikes': {'slots': 1, 'type': 'i'},\n",
|
||||
|
|
@ -275,19 +282,21 @@
|
|||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"12:04:16 | WARNING | \u001b[33mBinary data files for scenario: citi_bike topology: toy.3s_4t not found.\u001b[0m\n",
|
||||
"12:04:16 | WARNING | \u001b[33mGenerating temp binary data file for scenario: citi_bike topology: toy.3s_4t pid: 129013. If you want to keep the data, please use MARO CLI command 'maro env data generate -s citi_bike -t toy.3s_4t' to generate the binary data files first.\u001b[0m\n",
|
||||
"12:04:16 | INFO | \u001b[32mGenerating trip data for topology toy.3s_4t .\u001b[0m\n",
|
||||
"12:04:17 | INFO | \u001b[32mBuilding binary data from ~/.maro/data/citi_bike/.source/.clean/toy.3s_4t/56e2ca55ba6d4881/trips.csv to ~/.maro/data/citi_bike/.build/toy.3s_4t/56e2ca55ba6d4881/trips.bin\u001b[0m\n",
|
||||
"*************\n",
|
||||
"DecisionEvent(tick=79, station_idx=1, type=DecisionType.Demand, action_scope={0: 0, 2: 0, 1: 30})\n",
|
||||
"Action(from_station_idx=2, to_station_idx=1, number=0)\n",
|
||||
"DecisionEvent {station_idx: 2, type: 'DecisionType.Demand', action_scope:{1: 0, 0: 0, 2: 30}}\n",
|
||||
"Action {from_station_idx: 0, to_station_idx: '2', number:0}\n",
|
||||
"*************\n",
|
||||
"DecisionEvent(tick=799, station_idx=2, type=DecisionType.Demand, action_scope={0: 0, 1: 0, 2: 30})\n",
|
||||
"Action(from_station_idx=1, to_station_idx=2, number=0)\n",
|
||||
"DecisionEvent {station_idx: 0, type: 'DecisionType.Demand', action_scope:{2: 1, 1: 1, 0: 30}}\n",
|
||||
"Action {from_station_idx: 1, to_station_idx: '0', number:0}\n",
|
||||
"*************\n",
|
||||
"DecisionEvent(tick=959, station_idx=2, type=DecisionType.Demand, action_scope={0: 1, 1: 1, 2: 30})\n",
|
||||
"Action(from_station_idx=0, to_station_idx=2, number=0)\n"
|
||||
"DecisionEvent {station_idx: 1, type: 'DecisionType.Demand', action_scope:{2: 0, 0: 0, 1: 29}}\n",
|
||||
"Action {from_station_idx: 0, to_station_idx: '1', number:0}\n",
|
||||
"*************\n",
|
||||
"DecisionEvent {station_idx: 1, type: 'DecisionType.Demand', action_scope:{2: 0, 0: 0, 1: 29}}\n",
|
||||
"Action {from_station_idx: 2, to_station_idx: '1', number:0}\n",
|
||||
"*************\n",
|
||||
"DecisionEvent {station_idx: 0, type: 'DecisionType.Demand', action_scope:{2: 1, 1: 0, 0: 30}}\n",
|
||||
"Action {from_station_idx: 2, to_station_idx: '0', number:0}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
|
|
@ -316,11 +325,16 @@
|
|||
" target_idx_dock_tuple_list = [\n",
|
||||
" (k, v) for k, v in decision_event.action_scope.items() if k != decision_event.station_idx\n",
|
||||
" ]\n",
|
||||
" # Randomly choose a target station weighted by the quantity of empty docks\n",
|
||||
" target_idx, target_dock = random.choices(\n",
|
||||
" target_idx_dock_tuple_list,\n",
|
||||
" weights=[item[1] for item in target_idx_dock_tuple_list]\n",
|
||||
" )[0]\n",
|
||||
" weights=[item[1] for item in target_idx_dock_tuple_list]\n",
|
||||
" if sum(weights) == 0:\n",
|
||||
" target_idx = random.choices(target_idx_dock_tuple_list)[0][0]\n",
|
||||
" target_dock = 0\n",
|
||||
" else:\n",
|
||||
" # Randomly choose a target station weighted by the quantity of empty docks\n",
|
||||
" target_idx, target_dock = random.choices(\n",
|
||||
" target_idx_dock_tuple_list,\n",
|
||||
" weights=weights\n",
|
||||
" )[0]\n",
|
||||
" # Generate the corresponding random Action\n",
|
||||
" action = Action(\n",
|
||||
" from_station_idx=decision_event.station_idx,\n",
|
||||
|
|
@ -335,11 +349,16 @@
|
|||
" target_idx_inventory_tuple_list = [\n",
|
||||
" (k, v) for k, v in decision_event.action_scope.items() if k != decision_event.station_idx\n",
|
||||
" ]\n",
|
||||
" # Randomly choose a target station weighted by the bike inventory\n",
|
||||
" target_idx, target_inventory = random.choices(\n",
|
||||
" target_idx_inventory_tuple_list,\n",
|
||||
" weights=[item[1] for item in target_idx_inventory_tuple_list]\n",
|
||||
" )[0]\n",
|
||||
" weights = [item[1] for item in target_idx_inventory_tuple_list]\n",
|
||||
" if sum(weights) == 0:\n",
|
||||
" target_idx = random.choices(target_idx_inventory_tuple_list)[0][0]\n",
|
||||
" target_inventory = 0\n",
|
||||
" else:\n",
|
||||
" # Randomly choose a target station weighted by the bike inventory\n",
|
||||
" target_idx, target_inventory = random.choices(\n",
|
||||
" target_idx_inventory_tuple_list,\n",
|
||||
" weights=weights\n",
|
||||
" )[0]\n",
|
||||
" # Generate the corresponding random Action\n",
|
||||
" action = Action(\n",
|
||||
" from_station_idx=target_idx,\n",
|
||||
|
|
@ -385,10 +404,6 @@
|
|||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"12:04:26 | WARNING | \u001b[33mBinary data files for scenario: citi_bike topology: toy.3s_4t not found.\u001b[0m\n",
|
||||
"12:04:26 | WARNING | \u001b[33mGenerating temp binary data file for scenario: citi_bike topology: toy.3s_4t pid: 129013. If you want to keep the data, please use MARO CLI command 'maro env data generate -s citi_bike -t toy.3s_4t' to generate the binary data files first.\u001b[0m\n",
|
||||
"12:04:26 | INFO | \u001b[32mGenerating trip data for topology toy.3s_4t .\u001b[0m\n",
|
||||
"12:04:27 | INFO | \u001b[32mBuilding binary data from ~/.maro/data/citi_bike/.source/.clean/toy.3s_4t/278c7458875b4474/trips.csv to ~/.maro/data/citi_bike/.build/toy.3s_4t/278c7458875b4474/trips.bin\u001b[0m\n",
|
||||
"{'matrices': {'attributes': {...}, 'number': 1},\n",
|
||||
" 'stations': {'attributes': {...}, 'number': 3}}\n",
|
||||
"\n",
|
||||
|
|
@ -427,18 +442,14 @@
|
|||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"12:04:35 | WARNING | \u001b[33mBinary data files for scenario: citi_bike topology: toy.3s_4t not found.\u001b[0m\n",
|
||||
"12:04:35 | WARNING | \u001b[33mGenerating temp binary data file for scenario: citi_bike topology: toy.3s_4t pid: 129013. If you want to keep the data, please use MARO CLI command 'maro env data generate -s citi_bike -t toy.3s_4t' to generate the binary data files first.\u001b[0m\n",
|
||||
"12:04:35 | INFO | \u001b[32mGenerating trip data for topology toy.3s_4t .\u001b[0m\n",
|
||||
"12:04:36 | INFO | \u001b[32mBuilding binary data from ~/.maro/data/citi_bike/.source/.clean/toy.3s_4t/80aa59d9c58d4b5e/trips.csv to ~/.maro/data/citi_bike/.build/toy.3s_4t/80aa59d9c58d4b5e/trips.bin\u001b[0m\n",
|
||||
"array([11., 0., 11., 15., 0., 15., 16., 0., 16., 16., 0., 16.],\n",
|
||||
"array([15., 0., 15., 16., 0., 16., 16., 0., 16., 18., 0., 18.],\n",
|
||||
" dtype=float32)\n"
|
||||
]
|
||||
}
|
||||
|
|
@ -496,7 +507,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.8.5"
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -67,7 +67,7 @@
|
|||
"output_type": "stream",
|
||||
"text": [
|
||||
"'The available scenarios in MARO:'\n",
|
||||
"['cim', 'citi_bike']\n",
|
||||
"['cim', 'citi_bike', 'vm_scheduling']\n",
|
||||
"\n",
|
||||
"'The predefined topologies in CIM:'\n",
|
||||
"['global_trade.22p_l0.0',\n",
|
||||
|
|
@ -147,7 +147,34 @@
|
|||
"There will be 100 snapshots in total.\n",
|
||||
"\n",
|
||||
"Env Summary:\n",
|
||||
"{'node_detail': {'matrices': {'attributes': {'full_on_ports': {'slots': 25,\n",
|
||||
"{'event_payload': {'DISCHARGE_EMPTY': ['port_idx',\n",
|
||||
" 'vessel_idx',\n",
|
||||
" 'action_type',\n",
|
||||
" 'quantity'],\n",
|
||||
" 'DISCHARGE_FULL': ['vessel_idx',\n",
|
||||
" 'port_idx',\n",
|
||||
" 'from_port_idx',\n",
|
||||
" 'quantity'],\n",
|
||||
" 'LOAD_EMPTY': ['port_idx',\n",
|
||||
" 'vessel_idx',\n",
|
||||
" 'action_type',\n",
|
||||
" 'quantity'],\n",
|
||||
" 'LOAD_FULL': ['port_idx', 'vessel_idx'],\n",
|
||||
" 'ORDER': ['tick',\n",
|
||||
" 'src_port_idx',\n",
|
||||
" 'dest_port_idx',\n",
|
||||
" 'quantity'],\n",
|
||||
" 'PENDING_DECISION': ['tick',\n",
|
||||
" 'port_idx',\n",
|
||||
" 'vessel_idx',\n",
|
||||
" 'snapshot_list',\n",
|
||||
" 'action_scope',\n",
|
||||
" 'early_discharge'],\n",
|
||||
" 'RETURN_EMPTY': ['port_idx', 'quantity'],\n",
|
||||
" 'RETURN_FULL': ['src_port_idx', 'dest_port_idx', 'quantity'],\n",
|
||||
" 'VESSEL_ARRIVAL': ['port_idx', 'vessel_idx'],\n",
|
||||
" 'VESSEL_DEPARTURE': ['port_idx', 'vessel_idx']},\n",
|
||||
" 'node_detail': {'matrices': {'attributes': {'full_on_ports': {'slots': 25,\n",
|
||||
" 'type': 'i'},\n",
|
||||
" 'full_on_vessels': {'slots': 30,\n",
|
||||
" 'type': 'i'},\n",
|
||||
|
|
@ -184,8 +211,12 @@
|
|||
" 'type': 'i'},\n",
|
||||
" 'future_stop_tick_list': {'slots': 3,\n",
|
||||
" 'type': 'i'},\n",
|
||||
" 'is_parking': {'slots': 1,\n",
|
||||
" 'type': 'i2'},\n",
|
||||
" 'last_loc_idx': {'slots': 1,\n",
|
||||
" 'type': 'i'},\n",
|
||||
" 'loc_port_idx': {'slots': 1,\n",
|
||||
" 'type': 'i'},\n",
|
||||
" 'next_loc_idx': {'slots': 1,\n",
|
||||
" 'type': 'i'},\n",
|
||||
" 'past_stop_list': {'slots': 4,\n",
|
||||
|
|
@ -279,9 +310,8 @@
|
|||
"- A valid `Action` instance, including:\n",
|
||||
" - **vessel_idx**: (int) The id of the vessel/operation object of the port/agent;\n",
|
||||
" - **port_idx**: (int) The id of the port/agent that take this action;\n",
|
||||
" - **quantity**: (int) The sign of this value denotes different meanings:\n",
|
||||
" - Positive quantity means unloading empty containers from vessel to port;\n",
|
||||
" - Negative quantity means loading empty containers from port to vessel.\n",
|
||||
" - **action_type**: (ActionType) Whether to load or discharge empty containers in this action;\n",
|
||||
" - **quantity**: (int) The quantity of empty containers to be loaded/discharged.\n",
|
||||
"\n",
|
||||
"## Generate random actions based on the DecisionEvent\n",
|
||||
"\n",
|
||||
|
|
@ -298,19 +328,32 @@
|
|||
"output_type": "stream",
|
||||
"text": [
|
||||
"*************\n",
|
||||
"DecisionEvent(tick=14, port_idx=0, vessel_idx=5, action_scope=ActionScope(load=8856, discharge=1981))\n",
|
||||
"Action(port_idx=0, vessel_idx=5, quantity=-2667)\n",
|
||||
"DecisionEvent {port_idx: 4, vessel_idx: 5, action_scope: ActionScope {load: 6599, discharge: 0}, early_discharge: 0}\n",
|
||||
"Action {action_type: 'ActionType.LOAD', port_idx: 4, vessel_idx: 5, quantity: 1960}\n",
|
||||
"*************\n",
|
||||
"DecisionEvent(tick=21, port_idx=2, vessel_idx=1, action_scope=ActionScope(load=15997, discharge=3061))\n",
|
||||
"Action(port_idx=2, vessel_idx=1, quantity=-11608)\n"
|
||||
"DecisionEvent {port_idx: 1, vessel_idx: 3, action_scope: ActionScope {load: 0, discharge: 6027}, early_discharge: 0}\n",
|
||||
"Action {action_type: 'ActionType.DISCHARGE', port_idx: 1, vessel_idx: 3, quantity: 3396}\n",
|
||||
"*************\n",
|
||||
"DecisionEvent {port_idx: 3, vessel_idx: 2, action_scope: ActionScope {load: 4730, discharge: 14383}, early_discharge: 0}\n",
|
||||
"Action {action_type: 'ActionType.DISCHARGE', port_idx: 3, vessel_idx: 2, quantity: 10070}\n",
|
||||
"*************\n",
|
||||
"DecisionEvent {port_idx: 0, vessel_idx: 3, action_scope: ActionScope {load: 0, discharge: 2205}, early_discharge: 0}\n",
|
||||
"Action {action_type: 'ActionType.LOAD', port_idx: 0, vessel_idx: 3, quantity: 0}\n",
|
||||
"*************\n",
|
||||
"DecisionEvent {port_idx: 4, vessel_idx: 1, action_scope: ActionScope {load: 0, discharge: 14495}, early_discharge: 0}\n",
|
||||
"Action {action_type: 'ActionType.DISCHARGE', port_idx: 4, vessel_idx: 1, quantity: 8300}\n",
|
||||
"*************\n",
|
||||
"DecisionEvent {port_idx: 4, vessel_idx: 3, action_scope: ActionScope {load: 13824, discharge: 2205}, early_discharge: 0}\n",
|
||||
"Action {action_type: 'ActionType.DISCHARGE', port_idx: 4, vessel_idx: 3, quantity: 841}\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from maro.simulator import Env\n",
|
||||
"from maro.simulator.scenarios.cim.common import Action, DecisionEvent\n",
|
||||
"from maro.simulator.scenarios.cim.common import Action, ActionType, DecisionEvent\n",
|
||||
"\n",
|
||||
"import random\n",
|
||||
"from random import randint\n",
|
||||
"\n",
|
||||
"# Initialize an Env for cim scenario\n",
|
||||
"env = Env(scenario=\"cim\", topology=\"toy.5p_ssddd_l0.0\", start_tick=0, durations=100)\n",
|
||||
|
|
@ -325,20 +368,20 @@
|
|||
"\n",
|
||||
"while not is_done:\n",
|
||||
" # Generate a random Action according to the action_scope in DecisionEvent\n",
|
||||
" random_quantity = random.randint(\n",
|
||||
" -decision_event.action_scope.load,\n",
|
||||
" decision_event.action_scope.discharge\n",
|
||||
" )\n",
|
||||
" action_scope = decision_event.action_scope\n",
|
||||
" to_discharge = action_scope.discharge > 0 and randint(0, 1) > 0\n",
|
||||
"\n",
|
||||
" action = Action(\n",
|
||||
" vessel_idx=decision_event.vessel_idx,\n",
|
||||
" port_idx=decision_event.port_idx,\n",
|
||||
" quantity=random_quantity\n",
|
||||
" decision_event.vessel_idx,\n",
|
||||
" decision_event.port_idx,\n",
|
||||
" randint(0, action_scope.discharge if to_discharge else action_scope.load),\n",
|
||||
" ActionType.DISCHARGE if to_discharge else ActionType.LOAD\n",
|
||||
" )\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" # Randomly sample some records to show in the output\n",
|
||||
" if random.random() > 0.95:\n",
|
||||
" print(f\"*************\\n{decision_event}\\n{action}\")\n",
|
||||
" \n",
|
||||
"\n",
|
||||
" # Respond the environment with the generated Action\n",
|
||||
" metrics, decision_event, is_done = env.step(action)"
|
||||
]
|
||||
|
|
@ -458,10 +501,13 @@
|
|||
}
|
||||
],
|
||||
"metadata": {
|
||||
"interpreter": {
|
||||
"hash": "767d51c1340bd893661ea55ea3124f6de3c7a262a8b4abca0554b478b1e2ff90"
|
||||
},
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3.7.7 64-bit",
|
||||
"display_name": "Python 3",
|
||||
"language": "python",
|
||||
"name": "python37764bita867a8fada044a15b631200655c7181c"
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
|
|
@ -473,7 +519,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.7.7"
|
||||
"version": "3.7.0"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
|
|
@ -1,18 +1,21 @@
|
|||
# Playground
|
||||
Playground provides a standalone MARO running environment, it also includes examples of some predefined scenarios and quick-start tutorial.
|
||||
|
||||
## Main service in playground
|
||||
The playground provides a standalone MARO environment along with several predefined scenario examples and quick-start tutorials.
|
||||
|
||||
## Main service in the playground
|
||||
|
||||
| Service | Description | Host |
|
||||
|-------------------|------------------------------------------------------------|----------------------------|
|
||||
| `Redis Commander` | Redis Web GUI. | http://127.0.0.1:40009 |
|
||||
| `Read the Docs` | Local host docs. | http://127.0.0.1:40010 |
|
||||
| `Jupyter Lab` | Jupyter lab with MARO environment, examples, notebooks. | http://127.0.0.1:40011 |
|
||||
*(Remember change ports, if you used different ports mapping.)*
|
||||
| `Jupyter Lab` | Jupyter lab with MARO environment, examples, notebooks. | http://127.0.0.1:40010 |
|
||||
|
||||
## Major materials in root folder
|
||||
*(Remember to change ports if you use different ports mapping.)*
|
||||
|
||||
## Major materials in the root folder
|
||||
|
||||
| Folder | Description |
|
||||
|-------------------|--------------------------------------------|
|
||||
| `examples` | Showcases of predefined scenarios. |
|
||||
| `notebooks` | Quick-start tutorial. |
|
||||
*(Others can be ignored, which aren't mentioned in the table.)*
|
||||
|
||||
*(The ones not mentioned in this table can be ignored.)*
|
||||
|
|
|
|||
|
|
@ -1,8 +1,8 @@
|
|||
|
||||
rem script to build docker for playground image on Windows, this require the source code of maro
|
||||
|
||||
chdir "%~dp0.."
|
||||
|
||||
call .\scripts\compile_cython.bat
|
||||
|
||||
docker build -f ./docker_files/cpu.play.df . -t maro/playground:cpu
|
||||
|
||||
rem script to build docker for playground image on Windows, this require the source code of maro
|
||||
|
||||
chdir "%~dp0.."
|
||||
|
||||
call .\scripts\compile_cython.bat
|
||||
|
||||
docker build -f ./docker_files/cpu.playground.df . -t maro2020/playground
|
||||
|
|
|
|||
|
|
@ -10,4 +10,4 @@ fi
|
|||
|
||||
bash ./scripts/compile_cython.sh
|
||||
|
||||
docker build -f ./docker_files/cpu.play.df . -t maro/playground:cpu
|
||||
docker build -f ./docker_files/cpu.playground.df . -t maro2020/playground
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
#!/bin/bash
|
||||
|
||||
# Script to install maro in editable mode on linux/darwin,
|
||||
# Script to install maro in editable mode on linux/darwin,
|
||||
# usually for development.
|
||||
|
||||
if [[ "$OSTYPE" == "linux-gnu"* ]]; then
|
||||
|
|
@ -16,4 +16,4 @@ pip install -r ./maro/requirements.build.txt
|
|||
bash scripts/compile_cython.sh
|
||||
|
||||
# Install MARO in editable mode.
|
||||
pip install -e .
|
||||
pip install -e .
|
||||
|
|
|
|||
|
|
@ -5,8 +5,5 @@
|
|||
./redis-6.0.6/src/redis-server --port 6379 &
|
||||
redis-commander --port 40009 &
|
||||
|
||||
# Python 3.6
|
||||
cd ./docs/_build/html; python -m http.server 40010 -b 0.0.0.0 &
|
||||
|
||||
# It's only for your play locally or in an internal network environment, so disable the token for convenience
|
||||
cd ../../..; jupyter lab --port 40011 --allow-root --ip 0.0.0.0 --NotebookApp.token=''
|
||||
jupyter lab --port 40010 --allow-root --ip 0.0.0.0 --NotebookApp.token=''
|
||||
|
|
|
|||
4
setup.py
4
setup.py
|
|
@ -87,8 +87,8 @@ setup(
|
|||
description="MARO Python Package",
|
||||
long_description=readme,
|
||||
long_description_content_type="text/x-rst",
|
||||
author="Arthur Jiang",
|
||||
author_email="shujia.jiang@microsoft.com",
|
||||
author="MARO Team",
|
||||
author_email="maro-team@microsoft.com",
|
||||
url="https://github.com/microsoft/maro",
|
||||
project_urls={
|
||||
"Code": "https://github.com/microsoft/maro",
|
||||
|
|
|
|||
Загрузка…
Ссылка в новой задаче