Runtimeerror: cuda out of memory. tried to allocate

Time needed: 3 minutes

Here are some solutions to solve the error Runtimeerror: cuda out of memory. tried to allocate.

• Solution 1: Reduce batch size

  Reduce batch size will be able to resolve this error.
  
  Reducing the batch size is the simple and most effective solution to reduce memory usage.
  
  Also, by reducing the batch size, you reduce the amount of memory needed to store the gradients for backpropagation.

• Solution 2: Reduce model size

  The other way to solve this error is by reducing model size.
  
  Reducing the number of parameters in your model will be able to help reduce memory usage.
  
  This can be done by reducing the number of layers or by using smaller filters in convolutional layers.

• Solution 3: Use smaller images

  Using smaller input images can also help reduce memory usage.
  
  If you can reduce the resolution of your images without sacrificing too much efficiency.
  
  You can necessarily reduce the amount of memory required to store them.

• Solution 4: Use mixed precision training

  Using the mixed precision training it will help to reduce memory usage.
  
  Mixed precision training involves using lower-precision floating-point numbers for certain parts of the computation.
  
  Which can necessarily reduce memory usage without sacrificing too much accuracy.

• Solution 5: Using gradient accumulation

  Another solution to solve this error is to use gradient accumulation.
  
  The gradient accumulation involves accumulating gradients over a lot of batches before updating the model’s parameters.
  
  This can help reduce memory usage by reducing the number of gradients that are required to be stored in memory.

• Solution 6: Use data parallelism

  Using data parallelism can help to solve this error.
  
  Because data parallelism involves splitting the batch across several GPUs and computing the gradients independently on each GPU.
  
  This can help reduce memory usage by distributing the memory usage across multiple GPUs.

• Solution 7: Use model parallelism

  Using the model parallelism can help you to solve this issue.
  
  Because model parallelism involves splitting the model across multiple GPUs and computing the forward and backward passes independently on each GPU.
  
  This will help to lessen the memory usage by distributing the memory usage across multiple GPUs.

• Solution 8: Use checkpointing

  By using checkpointing involves storing intermediate activations on disk instead of in GPU memory.
  
  This can help reduce memory usage by reducing the amount of memory needed to store intermediate activations.

• Solution 9: Upgrade to a GPU with more memory

  Upgrading to a GPU with more memory is the most easiest way to increase the amount of memory available to your model.