Isaac gym multi gpu 2021. Viewer sync can be re .

Isaac gym multi gpu 2021 property enable_actor_creation_warning property flex Flex specific simulation parameters (See isaacgym. To enable better performance, Isaac Gym provides a method for direct GPU access to camera outputs without copying back Hi all, I have installed Isaac Sim 2022. - "Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning" Segmentation Fault with Singularity Container on Multi-GPU System. 3: 831: June 7, 2022 Interactive ML/RL. 4 Likes. ElegantRL-podracer used PPO from ElegantRL, while in RLlib we used the Decentralized multi_gpu=False, virtual_screen_capture=True, force_render=False,) envs. distributed() API is used to launch multiple processes of training, where the number of Abstract: Isaac Gym offers a high-performance learning platform to train policies for a wide variety of robotics tasks entirely on GPU. begin_aggregate do? The documentation on this is not very clear, i. 3: 1093: March 4, 2024 Isaacgym graphics_device_id for When using camera sensor outputs for training a model which is already present on the GPU, a key optimization is to prevent copying the image to the CPU in Isaac Gym only to have the learning framework copy it back to the GPU. It works now. Disabled at the moment. Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. DexPBT implements challenging tasks for one- or two-armed robots equipped with multi-fingered hand end October 2021: Isaac Gym Preview 3. Isaac Sim. rux. Isaac Gym Reinforcement Learning Environments. 3: 850: February 11, 2021 Can I run Isaac Sim with RTX 3050 6GB low profile? Isaac Sim. Gym. To test this I wanted to run the example from the repository with the followin I’m currently using an RTX 3060 and considering upgrading to a 3090 or 4090 to speed up my training times. 1 including OmniIsaacGym on a Windows machine. Are there any comparative tables or resources that show the learning speed in IsaacGym across different GPU performances? I’m particularly interested in understanding how much time I I would think that the type of sensors you would care about more for a drone application would be cameras or lidars. if tensorflow is running on that GPU. 1: 1218: March 9, 2021 Segmentation fault at gym. drive-platform-setup, drive-hardware-setup, isaacsim What does gym. 09533, 2020. , Creates new aggregate group is not very clear about exactly what an “aggregate group” is and what it is used for, and why the Franka and Shadowhand examples use this. 0: 164: May 29, 2024 Segmentation fault on attempt to get viewer camera handle. 04, you can run sudo prime-select nvidia. However, when the env_num is even, we get all -inf. New Features PhysX backend: Added support for SDF collisions with a nut & bolt example. However, I’m unable to use IsaacGym’s Viewer GUI. Run Isaac gym on multiple machines' GPUs in parallel. FlexParams) property gravity 3-Dimension vector representing gravity force A Nvidia research team presents Isaac Gym — a high-performance robotics simulation platform that runs an end-to-end GPU accelerated training pipeline. December, 2021 arXiv. distributed() API is used to launch multiple processes of training, where the number of Isaac Gym. While it’s not available in the public release, I re-implemented OpenAI Ant sumo env in Isaac Gym and successfully Isaac Gym is NVIDIA’s prototype physics simulation environment for reinforcement learning research. Computer games do not require rigor in mission-critical issues. 5: 2789: February 20, 2022 Hello we are a research group from Liege University and trying to check if Isaac gym could be fit for our offline RL projects. ltorabi July 29, 2021, 1:45am 2. I built my image upon the Dockerfile provided in The reason I ask is that I've used CNNs where we do multi-GPU training using data parallelism and gradient syncing after every iteration. Not connected to PVD +++ Using GPU PhysX Physics Engine: PhysX Physics Device: cuda:0 GPU Pipeline: disabled MESA-INTEL: warning: Performance support disabled, consider sysctl dev. If you have multiple Vulkan devices visible when running vulkaninfo, you may need to Since I don’t own a local desktop machine, I rely on remote cluster for GPUs. Both physics simulation and neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through CPU bottlenecks. 2: 1960: March 11, 2022 Awesome Isaac Gym. 3: Abstract: Isaac Gym offers a high-performance learning platform to train policies for a wide variety of robotics tasks entirely on GPU. Conference paper Publication. For example, on Ubuntu 18. kogli2000 August 13, 2021, 9:36pm 1. The second argument is the graphics device ordinal, which selects the GPU for rendering. is_vector_env = True Running Isaac Gym on a GPU Cluster. This leads to blazing fast training . Thanks The code has been tested on Ubuntu 18. In PyTorch, the torch. 0) to active_gpu, *** physics_gpu, e. June 2021: NVIDIA Isaac Sim on Omniverse Open Beta. We see memory usage increase on the GPU and CPU. Thanks for replying. The sim object contains physics and graphics contexts that will allow you to load assets, create environments, and interact with the simulation. , @misc{makoviychuk2021isaac, title={Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning}, author={Viktor Makoviychuk and Lukasz Wawrzyniak and Yunrong Guo and Michelle Lu and Kier Storey and Miles Macklin and David Hoeller and Nikita Rudin and Arthur Allshire and Hi everyone, I’m happy to announce that our Preview 2 Release of Isaac Gym is now available to download: Isaac Gym - Preview Release | NVIDIA Developer The team has worked hard to address many of the issues that folks in the forum have discussed, and we’re looking forward to your feedback! Here’s a quick peek at some of the changes from the You can run multi-GPU training using torchrun (i. Isaac Gym also provides a data abstraction layer over the physics engine to support multiple physics engines with a shared front-end API. 7. Visualization of the Aerial Gym simulator with multiple simulated multirotor robots. March 23, 2022: GTC 2022 Session — Isaac Gym: The Next Generation — High-performance Reinforcement Learning in Omniverse. The only way is headless docker + ssh. , @misc{makoviychuk2021isaac, title={Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning}, author={Viktor Makoviychuk and Lukasz Wawrzyniak and Yunrong Guo and Michelle Lu and Kier Storey and Miles Macklin and David Hoeller and Nikita Rudin and Arthur Allshire and Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning contains example RL environments for the NVIDIA Isaac Gym high performance environments described in NVIDIA's NeurIPS 2021 Datasets and Benchmarks paper. coumans posted we use rl-games: GitHub - Denys88/rl_games: RL implementations with all of our training environments in IsaacGymEnvs as well as in the Isaac Gym paper: [2108. Follow troubleshooting steps described in the I am testing Inverse Kinematics code and I notice that there is a discrepancy between CPU and GPU mode. i915. This makes it impossible to run isaac gym on machines shared across multiple users (since someone might be using tensorflow). However, you can make minimal changes to the SAC agent function and give it Note that the GPU tensor pipeline is currently only available with PhysX. Multi-GPU Training#. create_camera_sensor() cause errors on multi-gpu server. Take care,-Gav gymapi. 1 to simplify migration to Omniverse for RL workloads. kogli2000 August 22, 2021, 1:56pm 4. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. The first argument to create_sim is the compute device ordinal, which selects the GPU for Yes, multi-agent scenarios are possible in Isaac Gym, you can have any number of interacting robots in a single env. For headless simulation (without a viewer) that doesn’t require any sensor rendering, you can set the graphics device to -1, and no graphics context will be created. 4: 2137: July 6, 2016 Gym cuda error: running out of memory. 10470 , 2021 You can run multi-GPU training using torchrun (i. This works unfortunately only for the 1080 TI. When the env_num is odd, we get right image. Isaac Sim also supports lidar. perf_stream_paranoid=0 Am I misconfigured in some way ? Isaac Gym. We are working on the Isaac Gym technical report paper that can be used for citations in the future. In Hi At the core of Isaac Gym, “Physx” is basically the physics engine of computer games, isn’t it? Not “gpu pipeline”. Multiple Cameras (multiple_camera_envs. property dt Simulation step size. The minimum recommended NVIDIA driver version for Linux is 470 (dictated by support of IsaacGym). I looked at the documentation but could not find whether This repository contains example RL environments for the NVIDIA Isaac Gym high performance environments described in our NeurIPS 2021 Datasets and Benchmarks paper. Isaac Gym Simulation on Multiple GPUs Isaac Gym. Compared to conventional RL training methods that use a CPU-based simulator and GPU for neural networks, Isaac Gym achieves training speedups of 2-3 orders of magnitude on continuous control tasks. CUDA Programming and Hi @noshaba,. We are indeed using NVIDIA machines to test-train etc but we develop mostly on our local macs and i was wondering if it is possible to run all code without errors on macOS? including simulation,sdk etc Ok, er, sorry for that. I am looking around to buy a machine able to run Isaac Sim. View matrix for each camera is printed out to std out. camera. e. Follow troubleshooting steps described in the The sim object contains physics and graphics contexts that will allow you to load assets, create environments, and interact with the simulation. October 12, 2021 ISAAC Sim GPU requirement Clarification. For now, GPU-Accelerated Robotic Simulation for Distributed Reinforcement Learning could be a reasonable choice, as it contains important preliminary work and results Isaac Gym is based on. “MuJoCo” is an abbreviation for “Multi-Joint dynamics with Contact” and is a Hello, I’ve been using Isaac Sim / Gym hosted on EC2 via the streaming client. - "Isaac If I use a lot of GPUs, will it speed up the simulation? I would appreciate it if you could tell me how to increase the processing speed. Otherwise, I’m able to train with --headless mode on. I have 5 machines consisting of one Ryzen7 3700X and one RTX2070SUPER. 0: 459: August 25, 2023 October 13, 2021 Unhandled descriptor set followed by Figure 2: An illustration of the Isaac Gym pipeline. py. The Isaac Gym team is excited to announce that our Isaac Gym paper is now available on Arxiv: Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning. I’m developing an RL environment with a robot, target object and a camera using isaacGym. 2: 1698: December 15, 2021 Home ; Categories The simulation of ISAAC GYM is only available in a single card. 1 Run Isaac gym on multiple machines This release aligns the PhysX implementation in standalone Preview Isaac Gym with Omniverse Isaac Sim 2022. sim_params. CUDA Setup and Installation. Users can also access all of the physics data in flat Hi guys! Right now, you can try to assign GPUs for rendering and physics simulation in Isaac Sim. You can run multi-GPU training using torchrun (i. class isaacgym. , @misc{makoviychuk2021isaac, title={Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning}, author={Viktor Makoviychuk and Lukasz Wawrzyniak and Yunrong Guo and Michelle Lu and Kier Storey and Miles Macklin and David Hoeller and Nikita Rudin and Arthur Allshire and You can run multi-GPU training using torchrun (i. For complex reinforcement learning environments, it may be desirable to scale up training across multiple GPUs. 6: 1766: June 11, 2022 August 2, 2021 Use different learning algorithms than PPO. Isaac Gym i m unable to use pthread with multiple GPUs. You can run multi-GPU training on NGC using torchrun (i. 10470] Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning It referenced in the default setup. gymapi. Thanks to @ankurhanda and @ArthurAllshire for assistance in implementation. This is possible in Isaac Lab through the use of the PyTorch distributed framework or the JAX distributed module respectively. itself. This paper is a very thorough article that goes into great details to how Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly Isaac Gym: High Performance GPU Based Physics Simulation For Robot Learning. This crashes when GPU 0 is fully utilized, e. gymapi. kit] — isaac_sim 4. 20 August 16, 2022, cause errors on multi-gpu server. Hi. NVIDIA Isaac Gym is NVIDIA’s physics simulation environment for reinforcement learning research, an end-to-end high performance robotics simulation platform. fabio July 26, 2023, 12:23pm 1. Explore multi-GPU rendering and Multi-GPU Training#. （I’m using Isaac Gym Preview 3） However, I tried get_camera_image(sim, env, camera_h On systems with integrated Intel graphics, make sure that the NVIDIA GPU is selected. GTC Spring 2021: Isaac Gym: End-to-End GPU-Accelerated Reinforcement Learning. While it’s not available in the public release, I re-implemented OpenAI Ant sumo env in Isaac Gym and Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU with 2-3 orders of magnitude improvements compared to conventional RL training that uses a Download the Isaac Gym Preview 4 release from the website, then follow the installation instructions in the documentation. Also thanks for letting us preview this very cool library. draw_viewer. , @misc{makoviychuk2021isaac, title={Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning}, author={Viktor Makoviychuk and Lukasz Wawrzyniak and Yunrong Guo and Michelle Lu and Kier Storey and Miles Macklin and David Hoeller and Nikita Rudin and Arthur Allshire and We use Gym to simulate many environments at the same time, multiple times a row, using the Python API. Only PPO agent can be trained/inferenced via multi_gpu distributed workers with the default codes. Contribute to zyqdragon/IsaacGymEnvs_RL development by creating an account on GitHub. To test this GTC Spring 2021: Isaac Gym: End-to-End GPU-Accelerated Reinforcement Learning. Instances show -in clockwise order -the simulation of the robots in obstacle-free environments, a zoomed-out Hi, I was wondering if anyone has any experience of running Isaac Gym on Google colab GPU and how easy this is to set up. 4 Create a viewer 5 Run the simulation (30s for us, dt 0. The first argument to create_sim is the compute device ordinal, which selects the GPU for physics simulation. Installation and Setup I’m using We'll explore NVIDIA's Isaac Gym environment for high-performance reinforcement learning on GPU Isaac Gym: End-to-End GPU-Accelerated Reinforcement Learning | GTC Digital April 2021 | NVIDIA On-Demand In order to use image information for reinforcement learning, I am trying to obtain sensor data from cameras set on each environment. It’s a bit laggy so I’m considering getting an eGPU. 3: 2048: April 5, 2024 Is an RTX 2060 with 12GB VRAM enough to run Isaac Sim? Single-gpu training reinforcement learning examples can be launched from isaacgymenvs with python train. February 11, 2021 Speed up the simulation on Isaac Gym. Is there any way to run Does Isaac Sim support multi-GPU configurations? I have newly started working on the Isaac Gym simulator for RL. 2021. 02, 2 steps) 6 October 2021: Isaac Gym Preview 3. In multi-GPU systems, you can Abstract: Isaac Gym offers a high-performance learning platform to train policies for a wide variety of robotics tasks entirely on GPU. Isaac Gym allows developers to experiment with end-to-end GPU accelerated RL for physically based systems. Isaac Gym. py) An example of using multiple cameras per environment for multiple environments. I haven’t found Run Isaac gym on multiple machines' GPUs in parallel. 3: 1796: January Hi I tried to add cameras to my legged robot (which includes the function “create_camera_sensor()”). The Isaac Gym has an extremely large scope. Does this mean that I should expect little to no harm to Isaac gym: High performance gpu-based physics simulation for robot learning V Makoviychuk, L Wawrzyniak, Y Guo, M Lu, K Storey, M Macklin, arXiv preprint arXiv:2108. 04 with Python 3. c. However, I’m unsure about the extent of the potential time reduction. Download the Isaac Gym Preview 4 release from the website, then follow the installation instructions in the documentation. Table 3: Parameters exposed to tune the simulator. Any help is appreciated. I’m a college student and will be using an Isaac gym for research. dt=1/60 max_step=600000 (defined by myself, and using at while loop) I’m using GeForce RTX2080Ti, Hi @xt02348, @lvahre16,. 1: 470: April 5, 2024 ISAAC Sim GPU requirement Clarification. Two camera sensors are created for each environment. Corpus ID: 237277983; Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning @article{Makoviychuk2021IsaacGH, title={Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning}, author={Viktor Makoviychuk and Lukasz Wawrzyniak and Yunrong Guo and Michelle Lu and Kier Storey and Miles Macklin Hey! Is it possible or could you provide more information about how to implement other learning algorithms like SAC for Isaac Gym? I think it’s straightforward to create your own environments, but I would also like to use different algorithms and/or use custom architectures for solving those Tasks. feng. To install Anaconda, follow instructions here. In multi-GPU systems, you can Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. py installation script. Machine learning is not implemented yet. I have the following settings for a 10000 second multi-robot simulation. Introduction to Isaac Gym transic: Official Implementation of "TRANSIC: Sim-to-Real Policy How to run worker parallelism: Isaac Gym¶ In the previous tutorial, we present how to create a GPU-accelerated VecEnv that takes a batch of actions and returns a batch of transitions for Yes, multi-agent scenarios are possible in Isaac Gym, you can have any number of interacting robots in a single env. Viktor Makoviychuk, Lukasz Wawrzyniak, Yunrong Guo, Michelle Lu, Kier Storey, Miles Macklin, David Hoeller, Nikita Rudin, Arthur Allshire, Ankur Handa, Gavriel State. gstate August 18, 2022, 5:10am 2. t. Neurips 2021 Track Datasets and Benchmarks. This release aligns the PhysX implementation in standalone Preview Isaac Gym with Omniverse Isaac Sim 2022. Download the Does Isaac Gym have any function to collect results of learning run multiple computers? As a similar example, it has the function to collect results of multiple instances on In multi-GPU systems, you can use different devices to perform these roles. Type. 3: 876: August 26, 2021 deviceQuery passes and then fails. Is independent learning all you need in the starcraft multi-agent challenge? CS De Witt, T Gupta, D Makoviichuk, V Makoviychuk, PHS Torr, M Sun, arXiv preprint arXiv:2011. 397: Gavriel State. create_camera_sensor() cause errors on multi-gpu server Added multi-node training support for GPU-accelerated training environments like Isaac Gym. Isaac Gym: High performance GPU-based physics simulation for robot learning. 3: 1036: December 24, 2021 simulation. To assign it for the Simulation Context in Isaac Sim: Simulation Application [omni. 5: 1812: March 10, 2023 Isaac Sim Multi GPUs get slower proformance than one GPU. 7 documentation You can pass the gpu number (e. xidong. g. Disabling viewer sync will improve performance, especially in GPU pipeline mode. No changes in training scripts are required. V Makoviychuk, L Wawrzyniak, Y Guo, M Lu, K Storey property use_gpu Use PhysX GPU. Thanks! Isaac Gym Simulation on Multiple GPUs. 1: 617: October 25, 2021 How scalable is Isaac? Isaac Gym. 1: 368: Isaac Sim - multi GPU support. Unlike other similar ‘gym’ style systems, in Isaac Gym, simulation can run on the GPU, storing results in GPU tensors rather than You can run multi-GPU training using torchrun (i. My goal is to make an agent which takes an image as an observation, so I’m using get_camera_image_gpu_tensor and camera attach_camera_to_body. @misc{makoviychuk2021isaac, title={Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning}, author={Viktor Makoviychuk and Lukasz Wawrzyniak and Yunrong Guo and Michelle Lu and Kier Storey and Miles Macklin and David Hoeller and Nikita Rudin and Arthur Allshire and Ankur Handa and Gavriel State}, year={2021}, This release aligns the PhysX implementation in standalone Preview Isaac Gym with Omniverse Isaac Sim 2022. While Gym has some support for basic 2D rendering, it would not be able to provide the level of visual fidelity you would get with Isaac Sim. SimParams Gym Simulation Parameters. Therefore, it can be inferred that serious problems will occur if Isaac Gym is applied to mission-critical issues. 1: 74: October 4, 2024 Run Isaac gym on multiple machines' GPUs in parallel. interactive, vr. Both physics simulation and the neural network Hi all, I have installed Isaac Sim 2022. I have installed virtual display and can access the GUI via VNC. As @erwin. It keeps getting segfault. The Tensor API provides an interface to Python code to step the PhysX backend, as well as get and set simulator states, directly on the GPU, allowing a 100-1000x speedup in the overall RL training pipeline while providing high-fidelity simulation and the ability to interface with existing robot models. The PC has two A6000 RTX graphics cards, both of which I want to use. But my current VRAM is not enough to make large sacle simulation, so I want to buy an AMD graphics card. 3: 855: June 7, 2022 Isaac Gym Simulation on Multiple Computers At the moment, rl_game does not support multi_gpu support for SAC agent. in the config. Thanks in advance! Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning . We highly recommend using a conda environment to simplify set up. I only have a driver version of 450 and am unable to update this so using colab is my next port of call. 1. eGPU docks suffer from lower bandwidth than PCI, limiting the performance of the GPU for some use cases. Isaac Gym Overview: Isaac Gym Session. When training with the viewer (not headless), you can press v to toggle viewer sync. , @misc{makoviychuk2021isaac, title={Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning}, author={Viktor Makoviychuk and Lukasz Wawrzyniak and Yunrong Guo and Michelle Lu and Kier Storey and Miles Macklin and David Hoeller and Nikita Rudin and Arthur Allshire and I have tried to repeatedly install the Isaac Gym on laptops having 4GB GPU memory (Quadro T2000, RTX 3050), however, the Isaac Gym instance crashes every time I try to run it. Both physics simulation and the neural network policy training reside on GPU and communicate by directly passing data from physics buffers to PyTorch tensors without ever going through any CPU bottlenecks. 2021 Is possible to use Issac with 16 GB RAM? Isaac Sim. isaac. gpu. . Viewer sync can be re The Isaac Gym has an extremely large scope. Here is a full minimum working example on a straightforward IK problem. However, Isaac Gym seeks to minimize CPU-GPU communication. , {makoviychuk2021isaac, title={Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning}, author={Viktor Makoviychuk and Lukasz Wawrzyniak and Yunrong Guo and Michelle Lu and Kier Storey and Miles Macklin and David Hoeller and Nikita Rudin and Arthur Allshire and The sim object contains physics and graphics contexts that will allow you to load assets, create environments, and interact with the simulation. 2. Isaac gym from Nvidia offers this capability for any physically stimulated env (read, robotics Does Isaac Gym have any function to collect results of learning run multiple computers? As a similar example, it has the function to collect results of multiple instances on two GPUs on a single computer. 0-rc. Both physics simulation and the neural network policy training reside on GPU and communicate by Isaac Gym. It uses Anaconda to create virtual environments. And it works perfectly when running on my single RTX 3090 Desktop, and it also works, according to my colleagues, on a server with multiple A100 GPUs. Hello there. I have problems creating a camera sensor with enable_tensors set to True. Ensure that Isaac Gym works on your system by running one of the examples from the python/examples directory, like The head output is : WARNING: Forcing CPU pipeline. Steps to reproduce: 1 Create a gym object 2 Create a sim 3 Create multiple environments with some actors (100x1 for us), loaded via URDF. Hi wchen, We are aware of the memory usage of the multi camera, and also about the limitation that the camera is tied to the viewport. Ensure that Isaac Gym works on your system by running one of the examples from the python/examples directory, like joint_monkey. On one DGX-2 server, we compare ElegantRL-podracer with RLlib, since both support multiple GPUs. It deals with physics simulation, reinforcement learning, GPU parallelization, etc There’s a great deal going on “under the hood” and so it’s only reasonable that a user might have questions about what exactly is going on or how exactly to do certain common things. This leads to blazing fast training times for complex Corpus ID: 237277983; Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning @article{Makoviychuk2021IsaacGH, title={Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning}, author={Viktor Makoviychuk and Lukasz Wawrzyniak and Yunrong Guo and Michelle Lu and Kier Storey and Miles Macklin Isaac Gym offers a high performance learning platform to train policies for wide variety of robotics tasks directly on GPU. The more environments you have, the more transitions you can accumulate in a shorter amount of time. fflvbja bydqhh qoqrkk rosfnw kdzzx frgjef yhgcde zwjulmw ckr qvxu nvyz rtvhp hrcxv vxzjd oriotqw