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Max is the smartest boy in his class. He learns so quickly that he is frequently bored to death. One day a weird creature appears next to him in school. It is Magic.
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The player with the double starts the first round by placing the double in the center of the table. This domino serves as the "engine" for the round. Each later round starts with the player who has drawn the next-lowest double. The 13th and final round begins with the player who draws the double-blank. If there is a situation where no player has drawn the tile required to begin the round, players take turns drawing from the boneyard until it is found. The player who draws it starts the round.

💥 Training Neural Nets on Larger Batches: Practical Tips for 1-GPU, Multi-GPU & Distributed setups

The first player now starts to build a train a single row of dominoes starting from the center domino and moving toward the player. The end of the domino placed near the engine must match the engine's double number. For e xample: If the engine is a double, the end of the domino placed near the engine must be a The other end can be anything at all. NOTE: For the first turn, and only the first turn, players may place as many dominoes as they wish as long as they continue forming a valid train.

When the ends of the adjacent dominoes match, a valid train has been formed. If a player can play all of his dominoes on the first turn, the round ends after each player has had one turn. If a player cannot start a train on the first turn, they'll place a marker where a domino would have been placed to indicate that they could not begin. They do not draw any dominoes from the pile of face down dominoes know as the boneyard. On the second turn and all subsequent turns, any player can play dominoes in marked areas.

On the first turn, the only options for each player are to start a train or to pass.

Beginning with the second turn, any player can start the Mexican Train by placing a new train in the same way that they started their own train i. After the first turn, play continues in a clockwise direction. However, each player is limited to placing a single domino per turn, unless the domino is a double. The single domino played may be added to the player's own train, to another player's train if that train is marked indicating the owner could not play a domino on the previous move , or to the Mexican Train, sometimes called the caboose.

The Mexican Train is always open to all players. If a player cannot place a domino, he must draw one from the boneyard. He may then play this domino if a legal play exists. If he cannot play, he places a marker on the end of his train, and the next player takes a turn. If no dominoes remain in the boneyard, the player marks his train.

Starting with the second turn, anytime a player places a double tile both ends have the same number of pips , he must play a second domino.


This one can be played in any legal position on his train, an opponent's train, or the Mexican Train. If the player cannot play the second or third, etc. If the boneyard domino cannot be played, or none is available, he must place a marker on the end of his train, and the play moves on to the next player. If a turn ends with a double being open on the end of a train, the next player to go must "satisfy the double," which means that he must play a tile to that open double. This must be done even if the move would otherwise be illegal.

If the next player cannot satisfy the double, he must draw a domino from the boneyard if one is available. When one player places his final domino, or when no player has a legal play, the game ends. We need to double that to store the associated gradient tensors, our model output thus requires 2,4 GB of memory! But we can make sure the memory load is more evenly distributed among the GPUs.

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There are two main solution to the imbalanced GPU usage issue:. We well need to distribute our loss criterion computation as well to be able to compute and back propagate our loss.

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The difference between DataParallelModel and torch. It computes the loss function in parallel on each GPU, splitting the target label tensor the same way the model input was chunked by DataParallel. Here is how to handle two particular cases you may encounter:. Now how can we harness the power of several servers to train on even larger batches? The simplest option is to use PyTorch DistributedDataParallel which is meant to be almost a drop-in replacement for DataParallel discussed above.

But be careful: while the code looks similar, training your model in a distributed setting will change your workflow because you will actually have to start an independent python training script on each node these scripts are all identical.

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As we will see, once started, these training scripts will be synchronized together by PyTorch distributed backend. In practice, this means that each training script will have:. In these settings, DistributedDataParallel can advantageously replace DataParallel even on a single-machine setup. DistributedDataParallel is build on top of torch. We will consider a simple but general setup with two 4-GPU servers nodes :. First we need to adapt our script so that it can be run separately on each machine node.

We are actually going to go fully distributed and run a separate process for each GPU of each node, so 8 process in total. Our training script is a bit longer as we need to initialize the distributed backend for synchronization, encapsulate the model and prepare the data to train each process on a separate subset of the data each process is independent so we have to care of having each of them handle a different slice of the dataset ourselves. Here is the updated code:.

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We are almost done now. We just have to start an instance of our training script on each server. The first machine will be our master, it need to be accessible from all the other machine and thus have an accessible IP address On this first machine, we run our training script using torch.

On the second machine we similarly start our script:. The process of running a bunch of almost identical commands on a cluster of machine might looks a bit tedious. So now is probably a good time to learn about the magic of… GNU parallel :. One exciting improvement of the coming PyTorch v1.

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  6. I will update this short introduction when v1. This conclude our quick post on a few tips, tricks and tools to train your model on larger batches in a variety of settings. I hope you enjoyed this more technical post! Sign in.