.. _performance: Fast Performance ================ Here are some best practices to increase your performance. ---------- Dataloaders ----------- When building your Dataloader set `num_workers` > 0 and `pin_memory=True` (only for GPUs). .. code-block:: python Dataloader(dataset, num_workers=8, pin_memory=True) num_workers ^^^^^^^^^^^ The question of how many `num_workers` is tricky. Here's a summary of some references, [`1 `_], and our suggestions. 1. num_workers=0 means ONLY the main process will load batches (that can be a bottleneck). 2. num_workers=1 means ONLY one worker (just not the main process) will load data but it will still be slow. 3. The num_workers depends on the batch size and your machine. 4. A general place to start is to set `num_workers` equal to the number of CPUs on that machine. .. warning:: Increasing num_workers will ALSO increase your CPU memory consumption. The best thing to do is to increase the `num_workers` slowly and stop once you see no more improvement in your training speed. Spawn ^^^^^ When using `distributed_backend=ddp_spawn` (the ddp default) or TPU training, the way multiple GPUs/TPU cores are used is by calling `.spawn()` under the hood. The problem is that PyTorch has issues with `num_workers` > 0 when using .spawn(). For this reason we recommend you use `distributed_backend=ddp` so you can increase the `num_workers`, however your script has to be callable like so: .. code-block:: bash python my_program.py --gpus X ---------- .item(), .numpy(), .cpu() ------------------------- Don't call .item() anywhere on your code. Use `.detach()` instead to remove the connected graph calls. Lightning takes a great deal of care to be optimized for this. ---------- empty_cache() ------------- Don't call this unnecessarily! Every time you call this ALL your GPUs have to wait to sync. ---------- Construct tensors directly on the device ---------------------------------------- LightningModules know what device they are on! Construct tensors on the device directly to avoid CPU->Device transfer. .. code-block:: python # bad t = torch.rand(2, 2).cuda() # good (self is LightningModule) t = torch.rand(2, 2, device=self.device) For tensors that need to be model attributes, it is best practice to register them as buffers in the modules's `__init__` method: .. code-block:: python # bad self.t = torch.rand(2, 2, device=self.device) # good self.register_buffer("t", torch.rand(2, 2)) ---------- Use DDP not DP -------------- DP performs three GPU transfers for EVERY batch: 1. Copy model to device. 2. Copy data to device. 3. Copy outputs of each device back to master. Whereas DDP only performs 1 transfer to sync gradients. Because of this, DDP is MUCH faster than DP. ---------- 16-bit precision ---------------- Use 16-bit to decrease the memory (and thus increase your batch size). On certain GPUs (V100s, 2080tis), 16-bit calculations are also faster. However, know that 16-bit and multi-processing (any DDP) can have issues. Here are some common problems. 1. `CUDA error: an illegal memory access was encountered `_. The solution is likely setting a specific CUDA, CUDNN, PyTorch version combination. 2. `CUDA error: device-side assert triggered`. This is a general catch-all error. To see the actual error run your script like so: .. code-block:: bash # won't see what the error is python main.py # will see what the error is CUDA_LAUNCH_BLOCKING=1 python main.py We also recommend using 16-bit native found in PyTorch 1.6. Just install this version and Lightning will automatically use it.