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Obviously, we also need a training script.
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Let’s assume we have downloaded and placed our dataset at some location in the filesystem, we’ll use “D:\imagenet-small” for this demonstration. We use a small subset of ImageNet 2012 as the dataset. With the necessary knowledge in our backpack, let’s get started with the actual training. If the process with rank 0 doesn’t exist, the entire training is a no-go. Note that a process with rank 0 is always needed because it will act like the “controller” which coordinates all the processes. Rank can be seen as an index number of each process, which can be used to identify one specific process. Pretty straightforward, right? Now let’s talk about “rank”. Thus, world size also equals to the total number of GPUs used. As we mentioned before, each process is responsible for one dedicated GPU. World size is essentially the number of processes participating in the training job. Other concepts that might be a bit confusing are “world size” and “rank”. See the PyTorch documentation to find out more about “store”.
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This is called “store” in PyTorch (–dist-url in the script parameter). And finally, we need a place for the backend to exchange information. See the PyTorch documentation to find more information about “backend”. In PyTorch 1.8 we will be using Gloo as the backend because NCCL and MPI backends are currently not available on Windows. This is called “backend” in PyTorch (–dist-backend in the script parameter). Additionally, we need some method to coordinate the group of processes (more importantly, the GPUs behind them), so that they can communicate with each other. Each of the processes is responsible for the training workload of one dedicated GPU. A process group is, as the name suggests, a group of processes. One important concept we need to understand is “process group”, which is the fundamental tool that powers DDP. To better understand how DDP works, here are some basic concepts we need to learn first. In this article, we use the size “Standard NC24s_v3”, which puts four NVIDIA Tesla V100 GPUs at our disposal. You can also follow the normal VM creation process and choose the desired DSVM image: You can search directly for this resource: At the time of writing, PyTorch 1.8.1(Anaconda) is included in the DSVM image, which will be what we use for demonstration. This is a handy VM image with a lot of machine learning tools preinstalled. We use this very nice resource in Azure called a Data Science Virtual Machine (DSVM).
#LOGIC PRO X PARALLEL FOR WINDOWS CODE#
Walkthroughįor reference, we’ll set up two machines with the same spec on Azure, with one being Windows and the other being Linux, then perform model training with the same code and dataset. In this article, we’d like to show you how it can help with the training experience on Windows. In PyTorch 1.7 the support for DDP on Windows was introduced by Microsoft and has since then been continuously improved. DDP can utilize all the GPUs you have to maximize the computing power, thus significantly shorten the time needed for training.įor a reasonably long time, DDP was only available on Linux. You can have multiple GPUs on a single machine, or multiple machines separately.
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DDP performs model training across multiple GPUs, in a transparent fashion. If you have the luxury (especially at this moment of time) of having multiple GPUs, you are likely to find Distributed Data Parallel (DDP) helpful in terms of model training. It takes quite a long time and people can’t really do anything about it. Model training has been and will be in the foreseeable future one of the most frustrating things machine learning developers face.