My thought was that I could maintain the initial fps by running inference on each sub-image in a separate process using pytorch's multiprocessing module. So I had no experience with multi node multi gpu, but far as I know, if you’re playing LLM with huggingface, you can look at the device_map or TGI (text generation inference) or torchrun’s MP/nproc from llama2 github. The if __name__ == '__main__' part is necessary. Each epoch took ~63 seconds with a total training time of 74m10s. share_memory() or data. x) would help increase parallelism in this case. May 18, 2018 · Just that other people can find this: I run a grid-search / cross validation of a TensorFlow model. Multiprocessing allows you to leverage multiple CPU cores on your machine to train PyTorch models faster. Multithreading refers to the ability of a processor to execute multiple threads concurrently, where each thread runs a process. intra_op_parallelism_threads: Nodes that can use multiple threads to parallelize their execution will schedule the individual pieces into this pool. Oct 8, 2022 · 1. multiprocessing. Part2. Aug 6, 2016 · 0. Jan 9, 2022 · In the ‘__init__’ method, we specify the ‘CUDA_VISIBLE_DEVICES’ to ‘0’ (or any specific GPU device) so that it picks the GPU. Both the models are able to do inference on a single GPU perfectly fine with a large batch size of 32. import torchvision. parallel. 1-Enable the option at your gRPC server initialization ("grpc. py. So according to the documentation, the GPU can run multiple kernels concurrently. I want to run inference on multiple GPUs where one of the inputs is fixed, while the other changes. Hi, I am new to the machine learning community. I then executed the following command to train with all four of my Titan X GPUs: $ python train. Follow along with the video below or on youtube. cpu_count()=64) I am trying to get inference of multiple video files using a deep learning model. If someone come up with a multi gpu preset I could add it to the list for others to use. Nov 22, 2018 · It's actually quite simple. Setting up the distributed process group. Pool(2) pool. config. Since I have more than 1 GPU in my machine, I want to do parallel inference. Here is the code: Jan 16, 2019 · model. main. For that, I used torch DDP and huggingface accelerate. I want some files to get processed on each of the 8 GPUs. I In single-process mode there is only one instance since everything is running within the System UI. Like Distributed Data Parallel, every process in Horovod operates on a single GPU with a fixed subset of the data. g. Data Parallelism is implemented using torch. Multiprocessing. Feb 28, 2024 · Multi-GPU, short for multiple graphics processing units, refers to a computing configuration that involves the use of more than one GPU within a single system. Learn four techniques you can use to accelerate tensor computations with PyTorch multi GPU techniques—data parallelism, distributed data parallelism, model parallelism, and elastic training. If you don't need that (just want the threading part), then you can load the model and use concurrent. Data Parallelism is when we split the mini-batch of samples into multiple smaller mini-batches and run the computation for each of the smaller mini-batches in parallel. It is a type of parallel processing in which a program is divided into smaller jobs that can be carried out simultaneously. This part here restricts TensorFlow to a single GPU: os. Yes, you definitely can. png --gpus 4. gpu_options = tf. Jul 15, 2019 · Each process will have its own graph and session. By design, the Time Sharing mechanism does not save the total training time and it also adds a context switch overhead cost, which explains why the total training time with joblib multiprocessing is even longer for model training with GPU. 1. checkpoint = torch. In the previous tutorial, we got a high-level overview of how DDP works; now we see how to use DDP in code. , max_process_per_gpu==1), it always works fine. to(rank) 2) if it's on cpu then mdl. l1 and self. l2 simultaneously here. Jan 27, 2019 · distributed. Before compiling the model in keras. According to this, Pytorch’s multiprocessing package allows to parallelize CUDA code. com How to migrate a single-GPU training script to multi-GPU via DDP. 2, When we only has one testing process per GPU (i. Instead, I think you can use multi-processing to scan different patches, which you seems to have already managed to achieve. experimental. set_device, [0,1]) #set GPUs on processed. Jan 26, 2022 · When using normal multiprocessing pkg, I can not get parallelism with one GPU, as parallel processes will sequentially quest all the CUDA threads and then return to the next process. DistributedDataParallel module wrapper. start_processes to start multiple Python processes, one per device. Jun 29, 2023 · To do single-host, multi-device synchronous training with a Keras model, you would use the torch. from torch. Both of them crash with OOM eror for the 13b model and take 3X memory for the :return: TODO: does this have to be done outside or inside the process? my guess is that it doesn't matter because 1) if its on gpu once it's on the right proc it moves it to cpu with id rank via mdl. Jun 19, 2018 · In my research (RL), the model is often quite small, it only utilizes 10-20% of GPU power. multiprocessing is a wrapper around the native multiprocessing module. See full list on medium. Apr 29, 2019 · But these requests are actually different threads in the same process and should share one CUDA context. What I tried to do is something like this: The GPU will context-switch between the processes so there will be no actual increased parallelism. _share_cuda_() The following is a simplified example which can reproduce the errors. For debugging consider passing CUDA_LAUNCH_BLOCKING=1. Using torch. fork. For some reasons, I try to parallelly do inference using multi-core CPU and single GPU, however I just got following runtime errors. At inference time, I need to use two different models in an auto-regressive manner. According to nvidia-smi my GPU has 8081 MiB free so allocating 1024 MB for each process should be fine. こんにちは、NTT研究所の山口です。. (I think the problem is too small to get the benefit of the GPU. You're a beast! Oct 11, 2022 · Is it possble to train multiple models in parallel on single GPU? CUDA kernel errors might be asynchronously reported at some other API call,so the stacktrace below might be incorrect. Viewed 1k times 2 I am trying to parallelly do Feb 16, 2020 · It seems like a case where torch multi-processing fits perfectly. Compared to MPS on pre-Volta GPUs, Volta MPS provides a few key improvements: Nov 6, 2020 · NVIDIAのGPUリソース分割技術. Contexts only expose concurrency when using streams. I’m interested in parallel training of multiple instances of a neural network model, on a single GPU. Jul 27, 2015 · GPUs in "Exclusive Process" or "Exclusive Thread" compute modes will reject any attempts to create more than one process/context on a single device. Reload to refresh your session. Threads from the same process share a common context. model = make_parallel (model, 2) where 2 is the number of GPUs available. Multi-processing works on CPU, while model prediction happened in GPU, which there is only one. ThreadPoolExecutor(). It will be both way more efficient and readable. You can disable distributed mode and switch to threading based data parallel as follows: % python -m espnet2. I wonder if replacing the process with ray worker(gpu_nums=0. pool. Oct 8, 2022 · distributed. If you meet some errors with distributed mode, please try single gpu mode or multi-GPUs with --multiprocessing_distributed false before reporting the issue. Part3. import torch. Jun 30, 2017 · ncclInvalidUsage: This usually reflects invalid usage of NCCL library (such as too many async ops, too many collectives at once, mixing streams in a group, etc). nn. So I made global network on GPU and make 4 processes with that global network. At the OS-level, all pipelined processes run concurrently. This is the '. Just import the file in your code and your code will be executed on multiple GPUs. Distributed Data Parallel — Training code & Analysis Jul 29, 2022 · At any given time, only one single job is using the GPU (cuda). Furthermore, if you get a RuntimeError: context has already been set, it's a very easy fix by moving the set_start_method('spawn') method to the beginning of your if __name__ Dec 19, 2017 · I'm trying to fit multiple small Keras models in parallel on a single GPU. DataParallel . However GPU 0 is doing all the work. map_location = None if args. Jul 7, 2023 · Part 1. Input2: Files to process for Jan 27, 2019 · Multiprocessing failed with single GPU. Ask Question Asked 5 years, 4 months ago. Apr 4, 2022 · You could improve performance far more than any GPU if you wrote efficient code with minimal synchronization. Of course this is only relevant for small models which on their own, don’t utilize the GPU well enough. There are two aspects to it. 'N processes per node, which has N GPUs. Hyper-Q allows CUDA kernels to be processed concurrently on the same GPU; this can benefit performance when the GPU compute capacity is underutilized by a single application process. Once the tensor/storage is moved to shared_memory (see share_memory_() ), it will be possible to send So if you want to use the local batch size of each GPU, you need to multiply it with the number of GPUs. set_memory_growth(gpu, True) Both give me OOM errors. For example, splitting your data into chunks and using one core to process each chunk without any communication with other cores, locks or using common memory locations (which require locks and synchronization). Each process will load the same script as a module and subsequently Feb 2, 2019 · If you want to have multiple actors sharing a single GPU, then you need to specify that each actor requires less than 1 GPU, for example, if you wish to share one GPU among 4 actors, then you can have each actor require 1/4th of a GPU. For each GPU, I want a different 6 CPU cores utilized. It’s unecessary. I want to run self. Aug 20, 2019 · Different hardware and inference code require different multiprocessing strategy. The program is able to utilize all of the hardware Aug 25, 2023 · I am using accelerate to perform multiGPU inference of openllama models (3b/13b). It’s a two steps process. First gpu processes the input pair (a_1, b), the second processes (a_2, b) and so on. May 4, 2021 · Run multiple independent models on single GPU. All the outputs are saved as files, so I don’t Oct 1, 2013 · Too much piling up here to address in comments, so, where mp is multiprocessing:. The compute mode is modifiable in some cases using the nvidia-smi utility. So the idea in pseudocode is: Application starts, process uses the API to determine the number of usable GPUS (beware things like compute mode in Linux) Mar 26, 2023 · I can train with single GPU with a modified script but I was trying to follow the mmpose instructions to train on multiGPU (on single device) for a faster runtime. 👍 3. Method 2: Many processes Hi there, I ended up went with single node multi-GPU setup 3xL40. – To start, create a Python file and import torch. According to Tensorflow: The two configurations listed below are used to optimize CPU performance by adjusting the thread pools. I'm facing some issues with multi-GPU inference using pytorch and pytorch-lightning models. Each run is independent and lives on a single GPU. You signed out in another tab or window. function, setting multiprocessing. Here, the world_size corresponds to the number of GPUs we will be using at once. 01) mrange = 1000. Jul 6, 2020 · Gaoyuan_Yang (Gaoyuan Yang) July 6, 2020, 10:50am 1. remote(num_gpus=0. multiprocessing import Pool, set_start_method. Some platforms are funky, and this info isn't always easy to get. My code looks like this: num_models = 20. """ # if gpu avail do Jul 7, 2023 · Part 1. I cannot see how multi-processing can help you on prediction. To use 100% of all cores, do not create and destroy new processes. cpu_count() should return the number of processors. Technique 3: Model Parallelism. DataParallel won't enable multiple GPU because SentenceTransformer explicitly send the features to a single device. A machine with multiple GPUs (this tutorial uses an AWS p3. In each call, you can pass an image. 25) Oct 1, 2022 · It has optimized the GPU memory: A single classification only use a third of the memory limit but the RAM usage is greater because every notebook must have all libraries loaded. to(device) and the end of the line. In this article, you will learn: Technique 1: Data Parallelism. Multiprocessing package - torch. It works by creating separate processes, each running a copy of your training loop, and distributing the workload across them. But test it. This is a good setup for large-scale industry workflows, e. Running the code on multiple CPUs using torch multiprocessing takes more than 6 minutes to process the same 50 images. process_img,zip(frames)) The result is in nvtop I one of the processes on GPU1 with the others on 0. Jul 25, 2021 · I have 8 GPUs, 64 CPU cores (multiprocessing. I make SentenceTransformer utilize multiple GPUs by refactoring the source code. list_physical_devices('GPU') for gpu in gpus: tf. Mar 16, 2021 · print(e) # OPTION 2. set_start_method('spawn', force=True), importing TensorFlow within the spawned process upon creation and putting global tf right after the import. First gpu processes the input pair (a_1, b), the second 49. starmap(model. load(filepath, map_location=map_location) So, I will try CUDA 9. How can I allocate different GPUs to different processes(as in each model running on separate GPU)? Does Pytorch do this by default or does it run all processes on 1 GPU only unless specified? Aug 10, 2021 · Make sure to enable port re-use for your workers. 0 - each GPU has its own context, and each context must be established by a different host thread. If you want to run each model in parallel, then you have to load the same model in multiple GPUs. so_reuseport", 1) 2- In the _reserve_port context Jan 24, 2021 · As you follow along, we’ll efficiently train dozens of small neural networks in parallel on a single GPU using the vmap function from JAX . 1. Assets might also be duplicated in multi-process mode. 'fastest way to use PyTorch for either single node or '. As far as I remember, the installation is done with a conda environment setup the same way as suggested by the Dec 20, 2018 · I would advice first to try and write your code without the for loop using pytorch ops. ) But I also get a speed-up by simply vectorizing the function (this is actually improved by using the GPU since it's more intensive). multiprocessing as mp // number of GPUs equal to number of processes world_size = torch As far as I know, training on GPU is faster than CPU. Below python filename: inference_{gpu_id}. I want to train a bunch of small models on a single GPU in parallel. Running the code on single CPU (without multiprocessing) takes only 40 seconds to process nearly 50 images. cuda else 'cpu'. Data Parallel — Training code & issue between DP and NVLink. You signed in with another tab or window. Create a few processes per core and link them with a pipeline. Oct 30, 2017 · For this experiment, I trained on a single Titan X GPU on my NVIDIA DevBox. from kernel_2_0 import *. device(mydevice): . Add this line. fork () to fork the Python interpreter. However, I am a little fuzzy on how CUDA GPU's speed up inference in pytorch and if this would actually lead to boost in inference time. multiprocessing to set up the distributed process group and to spawn the processes for inference on each GPU. So, let’s say I use n GPUs, each of them has a copy of the model. This is the most common setup for researchers and small-scale industry workflows. multiprocessing as mp. 2. Abstract. However, the loss values as a function epoch and batch size were only logged from a single GPU. Start by initializing the Queue to contain 2 of each GPU ID, then get the GPU ID from the queue at the beginning of foo and put it back at the end. May 31, 2020 · 3. Dec 10, 2019 · When I train my network with a single GPU, the training process terminates successfully after 120 epochs. The CUDA multi-GPU model is pretty straightforward pre 4. #!/usr/bin/env python3 from pathlib import Path. However, if I use two GPUs, I get nan loss after a dozen epochs. os. You points about API clunkiness and hard-to-kill jobs are valid, we need to make it easier. Jul 8, 2019 · We can run this code by opening a terminal and typing python src/mnist. In this tutorial, we start with a single-GPU Multi-GPU Examples. You should also initialize a DiffusionPipeline: import torch. All we need to do is use a multiprocessing. View the code used in this tutorial on GitHub. What is also odd is both GPUs show memory allocated but GPU 0 is twice that of GPU 1. Distributed Data Parallel (this article) — Training code May 30, 2022 · We can use this to identify the individual processes and use the rank = 0 as the base process. ngimel added the oncall: distributed label on Apr 28, 2020. The description of fork is (as of 2022-10-15): The parent process uses os. Aug 4, 2023 · Multithreading vs. So, I gave it a try by commenting tf. Volta MPS The Volta architecture introduced new MPS capabilities. Even if you need extra computations/complex indexing, this is most likely going to be much faster. asr_train --ngpu 4 --multiprocessing_distributed false. Each process will run the per_device_launch_fn function. e. The Python multiprocessing documentation lists the three methods to create a process pool: spawn. Trying to do that many times in many processes doesn't help. Technique 2: Distributed Data Parallelism. In my experiment, the model is relatively small compared to the GPU capacity. Through this, it is possible to learn by sharing one network in four processes. Increasing the memory per process increases the speed of the process to run the model. I have a model that accepts two inputs. Here's how it works: We use torch. Merged. The next program does not work in a cell you need to save it and run with python in a terminal. Since I was not lucky with the standard multiprocessing module i use pathos. In this paper, we propose a parallelization scheme for dynamically balancing work load between multiple CPUs and GPUs. Hi, I want to run two lines in parallel inside forward function on single GPU. To do this with multiprocessing, we need a script that will launch a process for every GPU. forkserver. nn as nn. the batch dimension). For GPU allocation, we have 32 processes, and 4 GPU with 16GB memory each. But the memory is limited so we have to do a very tight optimization, by hand …. Because of reasons i need to get them out of a list and train them one step at a time. One can wrap a Module in DataParallel and it will be parallelized over multiple GPUs in the To create our training script, we use the PyTorch -provided wrapper of the vanilla Python multiprocessing module. The child process, when it begins, is effectively identical to the parent process. Input1: GPU_id. to(device) To use the specific GPU's by setting OS environment variable: Before executing the program, set CUDA_VISIBLE_DEVICES variable as follows: export CUDA_VISIBLE_DEVICES=1,3 (Assuming you want to select 2nd and 4th GPU) Then, within program, you can just use DataParallel() as though you want to use all the GPUs. Apr 28, 2020 · Those extra threads for multi-process single-GPU are used not for frivolous reason, but because single thread is usually not fast enough to feed multiple GPUs. And it actually works. (device, handle, storage_size_bytes, storage_offset_bytes) = storage. On a cluster of many machines, each hosting one or multiple GPUs (multi-worker distributed training). Indeed, performing N small algebraic operations in parallel is always slower and a larger single algebraic operation, and even more on GPU. Trained a model on GPU, then inferencing on CPU (s) by multiple processes. The two sub-processes are independent from each other. Multiprocessing: The use of two or more CPUs within a single computer system [4][5]. You might be able to get some degree of concurrency if you build your code to use a per thread default stream, but that is still using Jul 2, 2019 · In my tests, the original PyTorch code is faster on CPU vs GPU for the the test input you share. Feb 10, 2019 · Yes, exactly this. Queue to manage the available GPU IDs. That's why I want to run several parallel classifications using the multiprocessing function, but it doesn't work. From the diagram above, we can see that in Dec 19, 2017 · I am using python multiprocessing to spawn multiple processes which run on different model objects of their own. Oct 11, 2021 · Multithreading: The ability of a central processing unit (CPU) (or a single core in a multi-core processor) to provide multiple threads of execution concurrently, supported by the operating system [3]. ) Note that this is different that Apr 2, 2024 · Accelerate PyTorch Training on Multiple CPU Cores with Multiprocessing. Currently I can only run them sequentially leading to an underutilized GPU. Part 3: Multi-GPU training with DDP (code walkthrough) Watch on. This can be done by declaring the actor class with. spawn() will take care of spawning world_size processes. w. map(model. With the answer of eozd, a worker starts with the next set of parameters as soon as finished Jul 14, 2021 · We can decompose your problem into two subproblems: 1) launching multiple processes to utilize all the 4 GPUs; 2) Partition the input data using DataLoader. Here is the code for that replicates the situation in my case: from torch. from kernel_2_2 import *. Do not use multiple models unless they hold different parameters. High-level overview of how DDP works. I could load multiple ML models to run inference simultaneously on a single GPU. 'multi node data parallel training') # moco specific configs: Jun 8, 2023 · Multiprocessing is a technique in computer science by which a computer can perform multiple tasks or processes simultaneously using a multi-core CPU or multiple GPUs. def myfun(): with tf. def run_inference(rank, world_size): # create default process group. py -n 1 -g 1 -nr 0, which will train on a single gpu on a single node. Apr 17, 2022 · parser. When doing things like hyperparameter search which we need to train the network with different configurations, is it okay to open several multiprocessing. py: This is the main file which contains the driving Sep 9, 2016 · 11. May 6, 2017 · 4. The multiprocessing allows the programmer to fully leverage multiple processors. It registers custom reducers, that use shared memory to provide shared views on the same data in different processes. Prerequisites. ice-tong mentioned this issue on Aug 23, 2022. The dashboard displays system metrics such as temperature and utilization, that were tracked for both GPUs. May 16, 2019 · Using multiprocessing pool is a bad practice if using batch processing is possible. mp. multiprocessing, it is possible to train a model asynchronously, with parameters either shared all the time, or being periodically synchronized. You switched accounts on another tab or window. gpus = tf. By doing this, however, each process used the memory of the GPU and the GPU ran out of memory. The term also refers to the ability of a system to support Explore the W&B App UI to view an example dashboard of metrics tracked from a single process. multiprocessing instead of multiprocessing; At the beginning of your file: torch. Jun 17, 2020 · pool = mp. For single GPU I use a batch size of 2 and for 2 GPUs I use a batch size of 1 for each GPU. mydevice = "/gpu:0". When you use CUDA in the normal way in a single process, you are already using all of the parallel capabilities of the GPU. The less you write (and the more you delegate to the OS) the more likely you are to use as many resources as possible. Process and train them in parallel in a single GPU, given that the GPU memory is enough (quite small network. priyathamkat (Priyatham Kattakinda) October 8, 2022, 5:41pm 1. However, it'd appear that the "system crashes as it starts training" issue you're experiencing Jun 9, 2021 · use torch. In this tutorial, we start with a single-GPU training script and migrate that to running it on 4 GPUs on a single node. 前回の記事 でA100のMIGについて触れていますが、MIGを活用する際の Jan 14, 2021 · Can a single GPU be called by two host threads concurrently, if I didn't use CUDA Stream? Basically no. Modified 5 years, 4 months ago. I also have multiple GPUs available with me. If that is too much for one gpu, then wrap your model in DistributedDataParallel and let it handle the batched data. autograd import Variable. The other parameters are exactly the I wish I could. share_memory() is a no op if it's already in shared memory o. [Feat] Add distributed backends and unittests open-mmlab/mmeval#3. The config file is set up to train a custom model on coco pose data. If you forward of a single problem is very small, then the gpu is going to be very ineficient to run it, even if you parallelise the work of launching the jobs It is suitable for cases where testing a single sample needs long runtime. Today Ray doesn’t provide any isolation on GPU if you schedule multiple tasks/actors on the same GPU. GPUOptions(per_process_gpu_memory_fraction=0. On your multi-GPU system, specifying device=[0,1] should ideally distribute the workload across the first two GPUs. from kernel_2_1 import *. We would like to show you a description here but the site won’t allow us. Saving and loading models in a distributed setup. set_start_method('spawn') This should solve the problem. torch. See line 140 of the source code, notice there's a . GPUs are specialized processors designed to handle complex calculations and graphics rendering tasks, commonly used in applications such as video games, scientific simulations, and Horovod allows the same training script to be used for single-GPU, multi-GPU, and multi-node training. from multiprocessing import Pool, current_process, Queue. training high-resolution image classification models on tens of millions of images using 20-100 GPUs. 2 or 10 soon to make it work as intended. (similar to 1st May 31, 2020 · The simplest and probably the most efficient method whould be concatenate your samples in dimension 0 (i. The models are small enough so that I can easily fit 20 or more on the GPU. In the first case, we recommend sending over the whole model object, while in the latter, we advise to only send the state_dict() . Using these resources efficiently in a seamless way is a challenging issue. from multiprocessing import Process. For example, zero-shoting learning tasks and video recognition tasks. Dec 7, 2023 · 1. py --output multi_gpu. Gradients are averaged across all GPUs in parallel during the backward pass, then synchronously applied before beginning the Jun 21, 2023 · This works fine on a single-GPU system but may cause errors on a multi-GPU system, particularly if other processes are using this GPU. If this is the true, does it mean if I have a large amount of requests arrive at the same time, the GPU utilization can go up to 100%? Jul 12, 2022 · 1. Oct 19, 2020 · alexgo (Alex Golts) October 19, 2020, 1:57pm 1. The minimum code is as follows: import torch. The only thing I change is the batch size. Whether you are training ensembles, sweeping over hyperparameters, or averaging across random seeds, this technique can give you a 10x-100x improvement in computation time. Since the auto-regressive steps are computationally expensive, I wanted to split my dataset into smaller parts and send them to several GPUs so it can run in parallel Oct 14, 2019 · Wrapping SentenceTransformer with nn. The make_parallel function is available in this file. distributed as dist. 8xlarge instance) PyTorch installed with CUDA. I wrote the following piece of code to evaluate the effect of Python multiprocessing while using TensorFlow: import tensorflow as tf. environ["CUDA_VISIBLE_DEVICES"]=str(GPU_id). Multiprocessing refers to the ability of a system to run multiple processors in parallel, where each processor can run one or more threads. add_argument('--multiprocessing-distributed', action='store_true', help='Use multi-processing distributed training to launch '. @ray. bin. This can be mitigated though, by using shared image providers or by removing images from CPU memory once they are uploaded to GPU memory, via the QSG_TRANSIENT_IMAGES environment variable. Today, it is possible to associate multiple CPUs and multiple GPUs in a single shared memory architecture. Here is a fully working example of multi GPU training with a resnet50 model from the torchvision library using DataParallel. What I am doing is exposing a way to configure the multi gpu option of the accelerate launch cli… but I personally have no idea and experience with how to best configure multi gpu parameters for kohya_ss training. futures. environ['CUDA_VISIBLE_DEVICES']="". The Pool object parallelizes the execution of a function across multiple input values. Single GPU Example — Training ResNet34 on CIFAR10. In one of these modes, attempts by other processes to use a device already in use will result in a CUDA API reported failure. distributed and torch. With multiprocessing. wg di ik dz mh cg fb gg dx mn