Flow is a computational framework for deep RL and control experiments for traffic microsimulation.

Last update: Jan 02, 2023

Overview

Flow

Flow is a computational framework for deep RL and control experiments for traffic microsimulation.

See our website for more information on the application of Flow to several mixed-autonomy traffic scenarios. Other results and videos are available as well.

More information

Technical questions

If you have a bug, please report it. Otherwise, join the Flow Users group on Slack!

Getting involved

We welcome your contributions.

Please report bugs and improvements by submitting GitHub issue.
Submit your contributions using pull requests. Please use this template for your pull requests.

Citing Flow

If you use Flow for academic research, you are highly encouraged to cite our paper:

C. Wu, A. Kreidieh, K. Parvate, E. Vinitsky, A. Bayen, "Flow: Architecture and Benchmarking for Reinforcement Learning in Traffic Control," CoRR, vol. abs/1710.05465, 2017. [Online]. Available: https://arxiv.org/abs/1710.05465

If you use the benchmarks, you are highly encouraged to cite our paper:

Vinitsky, E., Kreidieh, A., Le Flem, L., Kheterpal, N., Jang, K., Wu, F., ... & Bayen, A. M, Benchmarks for reinforcement learning in mixed-autonomy traffic. In Conference on Robot Learning (pp. 399-409). Available: http://proceedings.mlr.press/v87/vinitsky18a.html

Contributors

Flow is supported by the Mobile Sensing Lab at UC Berkeley and Amazon AWS Machine Learning research grants. The contributors are listed in Flow Team Page.

Comments

Failed to run examples/sumo/sugiyama.py

I followed the steps for the installation procedure (https://berkeleyflow.readthedocs.io/en/latest/flow_setup.html). However, when i tried to run the example ( in the "Testing the Installation" section) I get the following error:

Traceback (most recent call last): File "examples/sumo/sugiyama.py", line 11, in from flow.envs.loop.loop_accel import AccelEnv, ADDITIONAL_ENV_PARAMS File "/home/dkoutras/flow/flow/envs/init.py", line 4, in from flow.envs.multiagent_env import MultiEnv File "/home/dkoutras/flow/flow/envs/multiagent_env.py", line 10, in from ray.rllib.env import MultiAgentEnv ModuleNotFoundError: No module named 'ray'

I have not installed the ray library, but as mentioned in the corresponding section I sould not need it anyway. Any ideas ?

opened by dimikout3 38
Increasing the number of vehicles in Figure Eight scenario
Choose an option This is a:

[x] Question

[x] Feature request

Describe the issue Figure eight scenario only accommodates a maximum of 14 vehicles in Flow. Even if I give more than this, it is not effective. But I am planning to extend this to 100 vehicles involving RL and human vehicles. I just want to know the reason behind this limitation to be able to apply in a real world scenario. Can I add another route with this and expand the capacity_ Like merging one figure eight and one ring wave scenario or merging two figure eight scenarios to increase its capacity.

Expected behavior Many vehicles in a single road track

Desktop (please complete the following information):

OS: Ubuntu 18.04

Version [run python -c "import flow; print(flow.version)"]: Leave empty if not applicable.

Additional context https://flow.readthedocs.io/en/latest/examples.html#figure-eight
opened by pnp91 20
Observation outside expected value range when executing /examples/rllib/traffic_light_grid.py
#Bug Description

ValueError: ('Observation outside expected value range', Box(339,), array([0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.05 , 0.05 , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.37301541, 0.42857143, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 2. , 3. , 3. , 0. , 0. , 3. , 3. , 0. , 3. , 0. , 0. , 1. , 1. , 0. , 1. , 1. , 1. , 0. , 1. , 0. , 0. , 1. , 1. , 0. , 0. , 1. , 0. ]))

Bug Reproduce

/examples/rllib/traffic_light_grid.py

The exact command that you did run that caused the bug: python traffic_light_grid.py

Output Log

Traceback (most recent call last): File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/tune/trial_runner.py", line 498, in _process_trial result = self.trial_executor.fetch_result(trial) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/tune/ray_trial_executor.py", line 342, in fetch_result result = ray.get(trial_future[0]) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/worker.py", line 2247, in get raise value ray.exceptions.RayTaskError: [36mray_PPO:train()[39m (pid=10958, host=611dabai) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/agents/trainer.py", line 369, in train raise e File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/agents/trainer.py", line 358, in train result = Trainable.train(self) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/tune/trainable.py", line 171, in train result = self._train() File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/agents/trainer_template.py", line 126, in _train fetches = self.optimizer.step() File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/optimizers/multi_gpu_optimizer.py", line 140, in step self.num_envs_per_worker, self.train_batch_size) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/optimizers/rollout.py", line 29, in collect_samples next_sample = ray_get_and_free(fut_sample) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/utils/memory.py", line 33, in ray_get_and_free result = ray.get(object_ids) ray.exceptions.RayTaskError: [36mray_RolloutWorker:sample()[39m (pid=11185, host=611dabai) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/evaluation/rollout_worker.py", line 453, in sample batches = [self.input_reader.next()] File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/evaluation/sampler.py", line 56, in next batches = [self.get_data()] File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/evaluation/sampler.py", line 97, in get_data item = next(self.rollout_provider) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/evaluation/sampler.py", line 313, in _env_runner soft_horizon) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/evaluation/sampler.py", line 401, in _process_observations policy_id).transform(raw_obs) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/models/preprocessors.py", line 166, in transform self.check_shape(observation) File "/home/dl_e/anaconda3/envs/flow_backup/lib/python3.6/site-packages/ray/rllib/models/preprocessors.py", line 61, in check_shape self._obs_space, observation) ValueError: ('Observation outside expected value range', Box(339,), array([0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.05 , 0.05 , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0.37301541, 0.42857143, 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 2. , 3. , 3. , 0. , 0. , 3. , 3. , 0. , 3. , 0. , 0. , 1. , 1. , 0. , 1. , 1. , 1. , 0. , 1. , 0. , 0. , 1. , 1. , 0. , 0. , 1. , 0. ]))

Versions

OS: ubuntu 16.04

Flow version: 0.5.0.dev

Flow commit number: fatal: there is no submission

bug
opened by ZJP-c4cld 18
Error while running sumo/sugiyama.py
Hi, I don't know what happend but I am having trouble with flow right now. What use to work doesn't anymore. I 'm going to try to find out about that error but if anyone knows.

Warning: Cannot find local schema '/Users/username/flow/bin/data/xsd/types_file.xsd', will try website lookup.```
opened by lucfisc 18
Environment.yml is broken

Numpy=1.13.0 leads to some crazy looking error. This is extremely high priority, I will confirm that it passes on my machine and @AboudyKreidieh can you try it on your machine and confirm this works?

opened by eugenevinitsky 16
There are always errors in OSX following Installing procedure
Choose an option This is a:

[x] Question

Describe the issue When coming to the Installing Flow and SUMO, after downloading SUMO’s github directory, something goes wrong and I cannot figure it out.

I input the code into terminal export CPPFLAGS=-I/opt/X11/include export LDFLAGS=-L/opt/X11/lib ./configure CXX=clang++ CXXFLAGS="-stdlib=libc++ -std=gnu++11" --with-xerces=/usr/local --with-proj-gdal=/usr/local make -j$nproc But I got MacBook-Pro-3:sumo mac$ make -j$nproc make: *** No targets specified and no makefile found. Stop. I have tried many times and dont know why
opened by CYBruce 15

Better folder structure and file names

Pull request information

Status: In development
Kind of changes: File and class names of environments/scenarios, structure of example folders
Related PR or issue: #564

Description

This PR introduces major changes to file names and folder structures, as well as fo some class names, mainly for the following folders:

envs
multiagent_envs (now is a subfolder under envs)
scenarios
examples
removal of cooperative_merge (and loop_merge)

TODO:

[x] better names for environments --> NL
[x] merge multiagent_envs into envs --> AY
[x] better name for scenarios so that they match the environments --> AY
[x] better file names (e.g. sugiyama, loop,ring -> ring) --> AY, NL

This is how the new envs folder looks like (some classes names have also been changed):

envs
├── __init__.py
├── base.py
├── bay_bridge.py
├── bottleneck.py
├── merge.py
├── multiagent
│   ├── __init__.py
│   ├── base.py
│   ├── highway.py
│   ├── ring
│   │   ├── __init__.py
│   │   ├── accel.py
│   │   └── wave_attenuation.py
│   └── traffic_light_grid.py
├── ring
│   ├── __init__.py
│   ├── accel.py
│   ├── lane_change_accel.py
│   └── wave_attenuation.py
├── test.py
└── traffic_light_grid.py

This is how the new scenarios folder looks like (some classes names have also been changed):

scenarios/
├── __init__.py
├── base.py
├── bay_bridge.py
├── bay_bridge_toll.py
├── bottleneck.py
├── figure_eight.py
├── highway.py
├── merge.py
├── minicity.py
├── multi_ring.py
├── ring.py
└── traffic_light_grid.py

upgrade

opened by nathanlct 13

Having both RL and actuated traffic lights in a simple grid scenario
[QUESTION] Hello! I am trying to create a simple grid scenario containing at the same time actuated traffic lights and a group of RL-based traffic lights. I am modifying the following scripts:

flow/examples/rllib/green_wave.py (Experiment)

flow/scenatios/grid.py (Scenario)

flow/envs/green_wave_env.py (Environment)

According to the ADDITIONAL_ENV_PARAMS variable, either all traffic lights are "controlled" (RL) or either they are all actuated. I've tried to add every TL manually in the grid scenario but it seems that in the end the ADDITIONAL_ENV_PARAMS variable is who determines how traffic lights are managed in the simulation. Is there a way I could do this? I didn't find any documentation about it.

Thank you very much!

Antonio
opened by Antonio-Dominguez 13
use "conda env create -f environment.yml", but failed?
The conflict is caused by: The user requested redis~=2.10.6 ray 0.8.0 depends on redis>=3.3.2

To fix this you could try to:

loosen the range of package versions you've specified

remove package versions to allow pip attempt to solve the dependency conflict

Pip subprocess error: ERROR: Cannot install -r /Users/dulei/Documents/Flow/flow/condaenv.k_4i73t0.requirements.txt (line 18) and redis~=2.10.6 because these package versions have conflicting dependencies. ERROR: ResolutionImpossible: for help visit https://pip.pypa.io/en/latest/user_guide/#fixing-conflicting-dependencies

failed

CondaEnvException: Pip failed
bug
opened by Dukering 12
ImportError: No module named traci

Sorry for the problem again, I solved the problem yesterday and made sugiyama.py run correctly. However, when I rerun the sugiyama.py today, I got another problem saying "No module named traci". I'm sure I activate the source flow. Also I find the 'traci' direptory locally.

opened by CYBruce 12
Error with commandline when running examples/sumo/sugiyama.py

Versions: Ubuntu 16.04 Python 3.5

Problem: The following error codes occur after running examples/sumo/sugiyama.py

Loading configuration... done. Success. Error: On processing option '--num-clients': No option with the name 'num-clients' exists. Error: The parameter '1' is not allowed in this context. Switch or parameter name expected. Error: On processing option '--collision.check-junctions': No option with the name 'collision.check-junctions' exists. Error: The parameter 'true' is not allowed in this context. Switch or parameter name expected. Error: Could not parse commandline options. Quitting (on error). Could not connect to TraCI server at localhost:51587 [Errno 111] Connection refused Retrying in 1 seconds Could not connect to TraCI server at localhost:51587 [Errno 111] Connection refused

The sumo gui pops up with the above error codes. Flow had already been activated with 'source activate flow.' Any help would be appreciated.

Thanks!

opened by KidTrunks 11
Bump tensorflow from 1.15.2 to 2.9.3
Bumps tensorflow from 1.15.2 to 2.9.3.

Release notes

Sourced from tensorflow's releases.

TensorFlow 2.9.3

Release 2.9.3

This release introduces several vulnerability fixes:

Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)

Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)

Fixes an OOB write in grappler (CVE-2022-41902)

Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)

Fixes a CHECK fail in PyFunc (CVE-2022-41908)

Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)

Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)

Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)

Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)

Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

TensorFlow 2.9.2

Release 2.9.2

This releases introduces several vulnerability fixes:

Fixes a CHECK failure in tf.reshape caused by overflows (CVE-2022-35934)

Fixes a CHECK failure in SobolSample caused by missing validation (CVE-2022-35935)

Fixes an OOB read in Gather_nd op in TF Lite (CVE-2022-35937)

Fixes a CHECK failure in TensorListReserve caused by missing validation (CVE-2022-35960)

Fixes an OOB write in Scatter_nd op in TF Lite (CVE-2022-35939)

Fixes an integer overflow in RaggedRangeOp (CVE-2022-35940)

Fixes a CHECK failure in AvgPoolOp (CVE-2022-35941)

Fixes a CHECK failures in UnbatchGradOp (CVE-2022-35952)

Fixes a segfault TFLite converter on per-channel quantized transposed convolutions (CVE-2022-36027)

Fixes a CHECK failures in AvgPool3DGrad (CVE-2022-35959)

Fixes a CHECK failures in FractionalAvgPoolGrad (CVE-2022-35963)

Fixes a segfault in BlockLSTMGradV2 (CVE-2022-35964)

Fixes a segfault in LowerBound and UpperBound (CVE-2022-35965)

... (truncated)

Changelog

Sourced from tensorflow's changelog.

Release 2.9.3

This release introduces several vulnerability fixes:

Fixes an overflow in tf.keras.losses.poisson (CVE-2022-41887)

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

Fixes a heap OOB in FractionalAvgPool and FractionalMaxPool(CVE-2022-41900)

Fixes a CHECK_EQ in SparseMatrixNNZ (CVE-2022-41901)

Fixes an OOB write in grappler (CVE-2022-41902)

Fixes a overflow in ResizeNearestNeighborGrad (CVE-2022-41907)

Fixes a CHECK fail in PyFunc (CVE-2022-41908)

Fixes a segfault in CompositeTensorVariantToComponents (CVE-2022-41909)

Fixes a invalid char to bool conversion in printing a tensor (CVE-2022-41911)

Fixes a heap overflow in QuantizeAndDequantizeV2 (CVE-2022-41910)

Fixes a CHECK failure in SobolSample via missing validation (CVE-2022-35935)

Fixes a CHECK fail in TensorListScatter and TensorListScatterV2 in eager mode (CVE-2022-35935)

Release 2.8.4

This release introduces several vulnerability fixes:

Fixes a heap OOB failure in ThreadUnsafeUnigramCandidateSampler caused by missing validation (CVE-2022-41880)

Fixes a segfault in ndarray_tensor_bridge (CVE-2022-41884)

Fixes an overflow in FusedResizeAndPadConv2D (CVE-2022-41885)

Fixes a overflow in ImageProjectiveTransformV2 (CVE-2022-41886)

Fixes an FPE in tf.image.generate_bounding_box_proposals on GPU (CVE-2022-41888)

Fixes a segfault in pywrap_tfe_src caused by invalid attributes (CVE-2022-41889)

Fixes a CHECK fail in BCast (CVE-2022-41890)

Fixes a segfault in TensorListConcat (CVE-2022-41891)

Fixes a CHECK_EQ fail in TensorListResize (CVE-2022-41893)

Fixes an overflow in CONV_3D_TRANSPOSE on TFLite (CVE-2022-41894)

Fixes a heap OOB in MirrorPadGrad (CVE-2022-41895)

Fixes a crash in Mfcc (CVE-2022-41896)

Fixes a heap OOB in FractionalMaxPoolGrad (CVE-2022-41897)

Fixes a CHECK fail in SparseFillEmptyRowsGrad (CVE-2022-41898)

Fixes a CHECK fail in SdcaOptimizer (CVE-2022-41899)

... (truncated)

Commits

a5ed5f3 Merge pull request #58584 from tensorflow/vinila21-patch-2

258f9a1 Update py_func.cc

cd27cfb Merge pull request #58580 from tensorflow-jenkins/version-numbers-2.9.3-24474

3e75385 Update version numbers to 2.9.3

bc72c39 Merge pull request #58482 from tensorflow-jenkins/relnotes-2.9.3-25695

3506c90 Update RELEASE.md

8dcb48e Update RELEASE.md

4f34ec8 Merge pull request #58576 from pak-laura/c2.99f03a9d3bafe902c1e6beb105b2f2417...

6fc67e4 Replace CHECK with returning an InternalError on failing to create python tuple

5dbe90a Merge pull request #58570 from tensorflow/r2.9-7b174a0f2e4

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dependencies
opened by dependabot[bot] 0

Update requirements.txt and environment.yml

Bug Description

It is impossible to create an environment or install the proper dependencies for Flow to work, with the current environment.yml and requirements.txt. This is because the dependencies are mostly outdated and have conflicts, which need to be resolved during installation.

I personally had a hard time before I could actually get Flow to work on my Ubuntu 18.04 machine, so I'm posting the changes that MIGHT work for you as well (this totally depends on your machine as I have not tested it on any other OS).

environment.yml:

name: flow

dependencies:
    - python==3.7.3
    - pip:
        - scipy==1.1.0
        - lxml==4.4.1
        - six==1.11.0
        - path.py
        - python-dateutil==2.7.3
        - pip>=18.0
        - tensorflow==1.15.2
        - setuptools==41.0.0
        - plotly==2.4.0
        - gym==0.14.0
        - pyprind==2.11.2
        - nose2==0.8.0
        - cached_property
        - joblib==0.10.3
        - matplotlib==3.0.0
        - dill
        - lz4
        - ray==0.8.0
        - setproctitle
        - psutil
        - opencv-python
        - boto3==1.10.45
        - redis

requirements.txt

gym==0.14.0
numpy==1.18.4
scipy==1.1.0
lxml==4.4.1
pyprind==2.11.2
nose2==0.8.0
six==1.11.0
path.py
joblib==0.10.3
python-dateutil==2.7.3
cached_property
pyglet==1.3.2
matplotlib==3.1.0
imutils==0.5.1
numpydoc
ray==0.8.0
opencv-python
dill
lz4
setproctitle
psutil
opencv-python
boto3==1.10.45
redis
pandas==0.24.2
plotly==2.4.0
tabulate
tensorflow==1.15.2
awscli==1.16.309
torch==1.4.0
pytz
tensorboardX

If you still have some dependency issues, just make sure that you remove the versions from the above files and allow pip to resolve any conflicts. Another tip is to uninstall and reinstall packages if they throw ImportError, ModuleNotFoundError etc.

Also, any help regarding how to install Flow on Windows would be greatly appreciated.

bug

opened by bharath2438 1

Bump joblib from 0.10.3 to 1.2.0
Bumps joblib from 0.10.3 to 1.2.0.

Changelog

Sourced from joblib's changelog.

Release 1.2.0

Fix a security issue where eval(pre_dispatch) could potentially run arbitrary code. Now only basic numerics are supported. joblib/joblib#1327

Make sure that joblib works even when multiprocessing is not available, for instance with Pyodide joblib/joblib#1256

Avoid unnecessary warnings when workers and main process delete the temporary memmap folder contents concurrently. joblib/joblib#1263

Fix memory alignment bug for pickles containing numpy arrays. This is especially important when loading the pickle with mmap_mode != None as the resulting numpy.memmap object would not be able to correct the misalignment without performing a memory copy. This bug would cause invalid computation and segmentation faults with native code that would directly access the underlying data buffer of a numpy array, for instance C/C++/Cython code compiled with older GCC versions or some old OpenBLAS written in platform specific assembly. joblib/joblib#1254

Vendor cloudpickle 2.2.0 which adds support for PyPy 3.8+.

Vendor loky 3.3.0 which fixes several bugs including:

robustly forcibly terminating worker processes in case of a crash (joblib/joblib#1269);

avoiding leaking worker processes in case of nested loky parallel calls;

reliability spawn the correct number of reusable workers.

Release 1.1.0

Fix byte order inconsistency issue during deserialization using joblib.load in cross-endian environment: the numpy arrays are now always loaded to use the system byte order, independently of the byte order of the system that serialized the pickle. joblib/joblib#1181

Fix joblib.Memory bug with the ignore parameter when the cached function is a decorated function.

... (truncated)

Commits

5991350 Release 1.2.0

3fa2188 MAINT cleanup numpy warnings related to np.matrix in tests (#1340)

cea26ff CI test the future loky-3.3.0 branch (#1338)

8aca6f4 MAINT: remove pytest.warns(None) warnings in pytest 7 (#1264)

067ed4f XFAIL test_child_raises_parent_exits_cleanly with multiprocessing (#1339)

ac4ebd5 MAINT add back pytest warnings plugin (#1337)

a23427d Test child raises parent exits cleanly more reliable on macos (#1335)

ac09691 [MAINT] various test updates (#1334)

4a314b1 Vendor loky 3.2.0 (#1333)

bdf47e9 Make test_parallel_with_interactively_defined_functions_default_backend timeo...

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dependencies
opened by dependabot[bot] 0
Retrieving datas from SUMO simulation started with flow using TraCI from another terminal

I am using Flow to start a SUMO simulation integrating deep reinforcement learning inside the process. I am also using another computer to retrieve some data from the simulation using TraCI.

The first problem is that flow does not assign the port I choose correctly. Secondly, the other computer connects to the SUMO simulation using TraCI, but as soon as it starts it goes out and doesn't save anything. The simulation keeps going on on the other computer.

• THIS IS THE CODE I USE ON THE FIRST COMPUTER TO ASSIGN THE PORT AND THE CLIENTS FOR SUMO

sim_params = SumoParams(port=45678, render=VISUALIZE_SIM_IN_SUMO, sim_step=1, num_clients=2)

• THIS IS THE CODE I USE ON THE SECOND COMPUTER TO CONNECT WITH TRACI AND RETRIEVE DATA

PORT = 49745 ( I need to set it once I start the simulation on the first computer since Flow does not use the port I specified at the beginning)

traci.init(PORT,tc.DEFAULT_NUM_RETRIES,"second_computer.local") traci.setOrder(2) # number can be anything as long as each client gets its number

If this solution is not achievable, do you know how to get this data from another terminal ?

opened by LorenzoUccello 0
Bump lxml from 4.4.1 to 4.9.1
Bumps lxml from 4.4.1 to 4.9.1.

Changelog

Sourced from lxml's changelog.

4.9.1 (2022-07-01)

Bugs fixed

A crash was resolved when using iterwalk() (or canonicalize()) after parsing certain incorrect input. Note that iterwalk() can crash on valid input parsed with the same parser after failing to parse the incorrect input.

4.9.0 (2022-06-01)

Bugs fixed

GH#341: The mixin inheritance order in lxml.html was corrected. Patch by xmo-odoo.

Other changes

Built with Cython 0.29.30 to adapt to changes in Python 3.11 and 3.12.

Wheels include zlib 1.2.12, libxml2 2.9.14 and libxslt 1.1.35 (libxml2 2.9.12+ and libxslt 1.1.34 on Windows).

GH#343: Windows-AArch64 build support in Visual Studio. Patch by Steve Dower.

4.8.0 (2022-02-17)

Features added

GH#337: Path-like objects are now supported throughout the API instead of just strings. Patch by Henning Janssen.

The ElementMaker now supports QName values as tags, which always override the default namespace of the factory.

Bugs fixed

GH#338: In lxml.objectify, the XSI float annotation "nan" and "inf" were spelled in lower case, whereas XML Schema datatypes define them as "NaN" and "INF" respectively.

... (truncated)

Commits

d01872c Prevent parse failure in new test from leaking into later test runs.

d65e632 Prepare release of lxml 4.9.1.

86368e9 Fix a crash when incorrect parser input occurs together with usages of iterwa...

50c2764 Delete unused Travis CI config and reference in docs (GH-345)

8f0bf2d Try to speed up the musllinux AArch64 build by splitting the different CPytho...

b9f7074 Remove debug print from test.

b224e0f Try to install 'xz' in wheel builds, if available, since it's now needed to e...

897ebfa Update macOS deployment target version from 10.14 to 10.15 since 10.14 starts...

853c9e9 Prepare release of 4.9.0.

d3f77e6 Add a test for https://bugs.launchpad.net/lxml/+bug/1965070 leaving out the a...

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dependencies
opened by dependabot[bot] 0
Bump numpy from 1.18.4 to 1.22.0
Bumps numpy from 1.18.4 to 1.22.0.

Release notes

Sourced from numpy's releases.

v1.22.0

NumPy 1.22.0 Release Notes

NumPy 1.22.0 is a big release featuring the work of 153 contributors spread over 609 pull requests. There have been many improvements, highlights are:

Annotations of the main namespace are essentially complete. Upstream is a moving target, so there will likely be further improvements, but the major work is done. This is probably the most user visible enhancement in this release.

A preliminary version of the proposed Array-API is provided. This is a step in creating a standard collection of functions that can be used across application such as CuPy and JAX.

NumPy now has a DLPack backend. DLPack provides a common interchange format for array (tensor) data.

New methods for quantile, percentile, and related functions. The new methods provide a complete set of the methods commonly found in the literature.

A new configurable allocator for use by downstream projects.

These are in addition to the ongoing work to provide SIMD support for commonly used functions, improvements to F2PY, and better documentation.

The Python versions supported in this release are 3.8-3.10, Python 3.7 has been dropped. Note that 32 bit wheels are only provided for Python 3.8 and 3.9 on Windows, all other wheels are 64 bits on account of Ubuntu, Fedora, and other Linux distributions dropping 32 bit support. All 64 bit wheels are also linked with 64 bit integer OpenBLAS, which should fix the occasional problems encountered by folks using truly huge arrays.

Expired deprecations

Deprecated numeric style dtype strings have been removed

Using the strings "Bytes0", "Datetime64", "Str0", "Uint32", and "Uint64" as a dtype will now raise a TypeError.

(gh-19539)

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

numpy.loads was deprecated in v1.15, with the recommendation that users use pickle.loads instead. ndfromtxt and mafromtxt were both deprecated in v1.17 - users should use numpy.genfromtxt instead with the appropriate value for the usemask parameter.

(gh-19615)

... (truncated)

Commits

4adc87d Merge pull request #20685 from charris/prepare-for-1.22.0-release

fd66547 REL: Prepare for the NumPy 1.22.0 release.

125304b wip

c283859 Merge pull request #20682 from charris/backport-20416

5399c03 Merge pull request #20681 from charris/backport-20954

f9c45f8 Merge pull request #20680 from charris/backport-20663

794b36f Update armccompiler.py

d93b14e Update test_public_api.py

7662c07 Update init.py

311ab52 Update armccompiler.py

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dependencies
opened by dependabot[bot] 0

Releases(v0.4.1)

v0.4.1(Sep 8, 2019)
introduced support for a new RL library, stable-baselines: https://github.com/hill-a/stable-baselines

removed and deprecated all rllab features

introduced a multi-lane generic highway network with on- and off-ramps

added a tutorial on running the bottleneck experiments

added a tutorial for running RLlib experiments on EC2 servers

some refactoring to features in grid network

increased unit test coverage for renderer features

Source code(tar.gz)
Source code(zip)
v0.4.0(Jul 21, 2019)
upgraded to sumo 1.1.0, see PR #293

new router class, see PR #524

updated documentation for most methods and classes

Source code(tar.gz)
Source code(zip)
v0.3.1(Jun 11, 2019)
This is the final release before we are switching to a more recent version of SUMO. Some changes include:

Cleanup to many of the scripts from redundant or unsed pieces of code.

Modifications to aimsun functionality and inclusion of an aimsun scripting class.

Extended unit test coverage. Our coverage is now about 90%.

Tutorials on using OpenStreetMap and Network Template scenarios.

Updated behavior to some of the examples and benchmarks to be compatible with the newer version of SUMO.

Source code(tar.gz)
Source code(zip)
v0.3.0(Mar 7, 2019)
support for a new traffic simulator: Aimsun

simulator specific commands were abstracted and moved from the environments to a kernel class, consisting of simulation, scenario, vehicle, and traffic_light subkernels. The subkernels were implemented for SUMO and Aimsun, and further ones can be created to support a plethora of other traffic simulators

Source code(tar.gz)
Source code(zip)
v0.2.1(Nov 29, 2018)

Source code(tar.gz)
Source code(zip)
v0.2.0(Sep 21, 2018)

Source code(tar.gz)
Source code(zip)
0.1.0(Oct 18, 2017)

Source code(tar.gz)
Source code(zip)

Flow is a computational framework for deep RL and control experiments for traffic microsimulation.

Related tags

Overview

Flow

More information

Technical questions

Getting involved

Citing Flow

Contributors

Comments

Bug Reproduce

Output Log

Versions

Pull request information

Description

TensorFlow 2.9.3

Release 2.9.3

TensorFlow 2.9.2

Release 2.9.2

Release 2.9.3

Release 2.8.4

Bug Description

Release 1.2.0

Release 1.1.0

4.9.1 (2022-07-01)

Bugs fixed

4.9.0 (2022-06-01)

Bugs fixed

Other changes

4.8.0 (2022-02-17)

Features added

Bugs fixed

v1.22.0

NumPy 1.22.0 Release Notes

Expired deprecations

Deprecated numeric style dtype strings have been removed

Expired deprecations for loads, ndfromtxt, and mafromtxt in npyio

Releases(v0.4.1)

v0.4.1(Sep 8, 2019)

v0.4.0(Jul 21, 2019)

v0.3.1(Jun 11, 2019)

v0.3.0(Mar 7, 2019)

v0.2.1(Nov 29, 2018)

v0.2.0(Sep 21, 2018)

0.1.0(Oct 18, 2017)

Owner

EquiBind: Geometric Deep Learning for Drug Binding Structure Prediction

2021 CCF BDCI 全国信息检索挑战杯（CCIR-Cup）智能人机交互自然语言理解赛道第二名参赛解决方案

Official implementation of the paper 'High-Resolution Photorealistic Image Translation in Real-Time: A Laplacian Pyramid Translation Network' in CVPR 2021

object detection; robust detection; ACM MM21 grand challenge; Security AI Challenger Phase VII

NVIDIA container runtime

Official Implement of CVPR 2021 paper “Cross-Modal Collaborative Representation Learning and a Large-Scale RGBT Benchmark for Crowd Counting”

Generate vibrant and detailed images using only text.

This is a work in progress reimplementation of Instant Neural Graphics Primitives

Compressed Video Action Recognition

StellarGraph - Machine Learning on Graphs

Face Recognition and Emotion Detector Device

Camview - A CLI-tool used to stream CCTV online footage based on URL params

A PyTorch implementation of the continual learning experiments with deep neural networks

Official PyTorch(Geometric) implementation of DPGNN(DPGCN) in "Distance-wise Prototypical Graph Neural Network for Node Imbalance Classification"

CrossMLP - The repository offers the official implementation of our BMVC 2021 paper (oral) in PyTorch.

Cereal box identification in store shelves using computer vision and a single train image per model.

unet for image segmentation

Graph Robustness Benchmark: A scalable, unified, modular, and reproducible benchmark for evaluating the adversarial robustness of Graph Machine Learning.

Efficient 3D human pose estimation in video using 2D keypoint trajectories

Production First and Production Ready End-to-End Speech Recognition Toolkit

Expired deprecations for `loads`, `ndfromtxt`, and `mafromtxt` in npyio