How to Solve the GPU Out of Memory Error Message on Google Colab
As a data scientist or software engineer, you may have encountered the “GPU out of memory” error message while working on Google Colab. This error message occurs when the GPU runs out of memory while performing a task, such as training a deep learning model. In this blog post, we will discuss the reasons behind this error message and provide some solutions to help you resolve the issue.
What Is the “GPU Out of Memory” Error Message?
The “GPU out of memory” error message occurs when there is not enough memory available on the GPU to complete a task. This can happen when you are training a deep learning model on a large dataset or when you are using a complex model architecture that requires a lot of memory. When the GPU runs out of memory, it cannot allocate any more memory to the model, and the training process fails.
Why Does the “GPU Out of Memory” Error Occur?
There are several reasons why the “GPU out of memory” error can occur on Google Colab:
Large dataset: If you are working with a large dataset, it can consume a significant amount of GPU memory. When the dataset is too large, the GPU may run out of memory while loading the data.
Complex model architecture: If you are using a complex model architecture that requires a lot of memory, it can cause the GPU to run out of memory. For example, if you are using a deep neural network with multiple layers, each layer requires memory to store the weights and activations.
Small GPU memory: If you are using a GPU with a small amount of memory, it can easily run out of memory when performing complex tasks.
How to Solve the “GPU Out of Memory” Error Message
There are several solutions that can help you resolve the “GPU out of memory” error message on Google Colab:
1. Reduce Batch Size
One of the most effective ways to reduce GPU memory usage is to reduce the batch size. The batch size determines the number of samples that are processed in each iteration during training. By reducing the batch size, you can reduce the amount of memory required to store the activations and gradients.
For example, if you are using a batch size of 64, you can try reducing it to 32 or 16. However, keep in mind that reducing the batch size can also increase the training time.
2. Use a Smaller Model Architecture
If you are using a complex model architecture that requires a lot of memory, you can try using a smaller model architecture. For example, you can try reducing the number of layers or the number of neurons in each layer.
Alternatively, you can use a pre-trained model that has already been trained on a similar dataset. Pre-trained models are trained on large datasets and can be fine-tuned on your specific dataset. This can save a lot of time and memory during training.
3. Use Mixed Precision
Another way to reduce GPU memory usage is to use mixed precision. Mixed precision is a technique that uses lower-precision data types, such as half-precision floating-point numbers, to store the model weights and activations. This can reduce the memory usage by up to 50%.
To use mixed precision, you need to enable it in your code. Most deep learning frameworks, such as TensorFlow and PyTorch, support mixed precision.
4. Use Gradient Checkpointing
Gradient checkpointing is a technique that allows you to trade-off memory usage for compute time. Instead of storing all the activations and gradients in memory during backpropagation, you can store only a subset of them and recompute the rest on the fly.
To use gradient checkpointing, you need to enable it in your code. Most deep learning frameworks support gradient checkpointing.
5. Use a Larger GPU
If you are using a GPU with a small amount of memory, you can try using a larger GPU. Google Colab offers several GPU options, ranging from the Tesla K80 with 12GB of memory to the Tesla T4 with 16GB of memory.
To change the GPU, you need to go to the Runtime menu and select “Change runtime type”. Then, you can select a GPU from the “Hardware accelerator” dropdown.
Conclusion
The “GPU out of memory” error message can be frustrating, but there are several solutions that can help you resolve the issue. By reducing the batch size, using a smaller model architecture, using mixed precision, using gradient checkpointing, or using a larger GPU, you can reduce the memory usage and complete your tasks successfully on Google Colab.
Remember that each solution has its trade-offs, so you need to choose the one that best fits your needs. With these solutions, you can continue to work on your deep learning projects without worrying about the “GPU out of memory” error message.
Hey i wrote this Blog Post back in 2020 . and i was not able to provide the implementation based solution.
so i wrote new blog post , i suggest you to read new updated blog post which covers some common senario when this error occurs. and if your senario is not mentioned there then you can easily contact me and i will try my best to provide you solutio in next 24 hour.
Click on below blog post to read futher
Your session crashed after using all available RAM — Google Colab
��You can skip to How section if you just looking for solution Why Google Colab showing this error ? The main reason…
THIS BLOG IS OUTDATED. PLEASE CLICK ON ABOVE CARD TO READ THE NEW UPDATED VERSION OF THIS BLOG POST.
I faced the same issue while training the text generator machine learning model inside google colab.
so what are the solutions to overcome this problem?
I know you read those articles and post about how to increase the ram in google colab by saving it in drive and crashing it with the below code.
but this wont work.
Google is done providing free service. now they charge you to increase the ram. you have to buy google colab pro. which is costly.
so what are the other options?
- you can upgrade the ram of your laptop or pc(which cost money)
- google colab pro(cost money)
I have one solution. which won't cost you money.
so first let's talk about the problem.
google colab is running out of ram
because we are loading all data at once.or generating all data at once.
google colab having 12 GB of ram. and it running out of ram.
that means our preprocessing part generating data which is of the size of more than 12 GB.
Solution :
we can process that data in chunks. if the total size of the data is 12 GB. than we can divide it into chunk(file) of 1 Gb.
12 GB data = 12 chunks(files) of 1 Gb
so now we have to load only 1 GB file into ram. which won't crash our notebook.
we can load files one by one to train our model.
so this is the theory part. in theory, it is working.
so now the question is how can we implement this.
so there is one library in python which allows us to save our data into a file.
and we can get same data when we read that file.
if you save dataframe then it will return that data frame when you read it.
Pickle is a module installed for both Python 2 and Python 3 by default.
how to use it :
first, you have to import it
then by using pickle. dump you can save that data into a file
this will return the same list.
so now we can use this in our google colab. whenever you face this kind of problem just divide your data into smaller files then load it one by one into ram and train your model.
Your session crashed after using all available RAM — Google Colab
Photo by Greg Bulla / Unsplash
Why Google Colab showing this error ?
The main reason behind this error is either you are loading large file or loading very large database for model training with very high batch size.
Example : what happens when you overload lift and exceed the weight limit. it will not work.
similar is happening in google colab. the ram is filled with data and there is no space left to process anything and that’s why it is crashing.
What You can do to solve this error ?
Simple solution would be not loading large files at once in ram,
if it’s text file you can read the file line by line, if it’s csv file the you can also load it in chunks
if you are training machine learning model and dataset is images then you can decrease the batch size and it will solve the issue.
i can not cover all cases for which you are getting this error but the simple solution is processing it in batches.
in next section i will discuss few common situations where you will get this error and also discuss the solution with code.
How You can solve this error ?
in this section i will try to cover top possible section where you can get this error and also provide the implemented solution.
If you are getting this error while reading very large txt file.
You might be reading that file like this, below code will load whole txt file into ram and if the file size is bigger than your ram then it will crash the google colab.
wrong way
Solution : Read the file line by Line
Right way
in above code i am using readline function which will only read the one line from file, you can add your own logic to store few lines in bucket and process them .
if you are getting this error while training CNN Image model
To solve this you can adjust the batch size in code.
Example : in below example the batch size is 1000. so it is loading 1000 images in ram and then training the model,
The solution depends on how much ram your system has.
and you can get perfect by batch size by trial-error method.
batch_size=
you can try few different batch sizes. you can start with upper value and keep on decreasing until it stops crashing.
- batch_size=1024
- batch_size=512
- batch_size=256
- batch_size=128
- batch_size=64
keep on decreasing until it won’t crash.
Remember , sometimes the batch size affect the speed of training that is why i suggest you to choose high batch size first and keep on decreasing it until it stops crashing.
this batch size will be different for everyone becuase it depends on dataset size and ram .
If you are getting this error while reading very large csv file using pandas
in pandas you can load csv in Batches, check below implementation . below code will load batch_size number of rows from file in dataframe
and you can access all the function on that dataframe just like normally you do when you read file.
in below example i print the shape of df by using df.shape
Paid Solution :
above solution are free solutions. if you can afford monthly charge of google colab you can just purchase their pro plan and your existing code will work as expected.
Google Colab Pro Plans
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GPU out of memory error message on Google Colab
I’m using a GPU on Google Colab to run some deep learning code.
I have got 70% of the way through the training, but now I keep getting the following error:
I’m trying to understand what this means. Is it talking about RAM memory? If so, the code should just run the same as is has been doing shouldn’t it? When I try to restart it, the memory message appears immediately. Why would it be using more RAM when I start it today than it did when I started it yesterday or the day before?
Or is this message about hard disk space? I could understand that because the code saves things as it goes on and so the hard disk usage would be cumulative.
Any help would be much appreciated.
So if it’s just the GPU running out of memory — could someone explain why the error message says 10.38 GiB already allocated — how can there be memory already allocated when I start to run something. Could that be being used by someone else? Do I just need to wait and try again later?
Here is a screenshot of the GPU usage when I run the code, just before it runs out of memory:
I found this post in which people seem to be having similar problems. When I run a code suggested on that thread I see: