How can I solve 'ran out of gpu memory' in TensorFlow

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I ran the MNIST demo in TensorFlow with 2 conv layers and a full-conect layer, I got an message that 'ran out of memeory trying to allocate 2.59GiB' , but it shows that total memory is 4.69GiB, and free memory is 3.22GiB, how can it stop with 2.59GiB? And with larger network, how can I manage gpu memory? I concern only how to make best use of the gpu memory and wanna know how it happened, not how to pre-allocating memory

It's not about that. first of all you can see how much memory it gets when it runs by monitoring your gpu. for example if you have a nvidia gpu u can check that with watch -n 1 nvidia-smi command. But in most cases if you didn't set the maximum fraction of gpu memory, it allocates almost the whole free memory. your problem is lack of enough memory for your gpu. cnn networks are totally heavy. When you are trying to feed your network DO NOT do it with your whole data. DO this feeding procedure in low batch sizes.

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I was encountering out of memory errors when training a small CNN on a GTX 970. Through somewhat of a fluke, I discovered that telling TensorFlow to allocate memory on the GPU as needed (instead of up front) resolved all my issues. This can be accomplished using the following Python code:

    config = tf.ConfigProto()
    config.gpu_options.allow_growth = True
    sess = tf.Session(config=config)

Previously, TensorFlow would pre-allocate ~90% of GPU memory. For some unknown reason, this would later result in out-of-memory errors even though the model could fit entirely in GPU memory. By using the above code, I no longer have OOM errors.

Note: If the model is too big to fit in GPU memory, this probably won't help!

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By default, TensorFlow maps nearly all of the GPU memory of all GPUs (subject to CUDA_VISIBLE_DEVICES) visible to the process. This is done to more efficiently use the relatively precious GPU memory resources on the devices by reducing memory fragmentation.

TensorFlow provides two Config options on the Session to control this.

The first is the allow_growth option, which attempts to allocate only as much GPU memory based on runtime allocations:

config = tf.ConfigProto()
config.gpu_options.allow_growth = True
session = tf.Session(config=config)

The second method is the per_process_gpu_memory_fraction option, which determines the fraction of the overall amount of memory that each visible GPU should be allocated. For example, you can tell TensorFlow to only allocate 40% of the total memory of each GPU by:

config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = 0.4
session = tf.Session(config=config)

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  • Possible duplicate of How to prevent tensorflow from allocating the totality of a GPU memory?
  • I saw it before, but it refers to pre-allocating gpu memory, not lacking of memory
  • I have a rather large network (CNN+LSTM). My input data is of size, batch_size = 5, (5x396x396) -- it's a 3D volume. So a rather small batch size. I'm running on a GTX 1070 with 8GB RAM, but I'm still running out of memory. Are there any workarounds you know of? Any tutorials that outline workarounds?
  • This worked for me as well on keras and conda install.
  • Nah, didn't work for me, using Keras.
  • In my defense, the question does not reference or tag Keras.
  • @nickandross "For some unknown reason..." I just wanted to add that the reason is to avoid unnecessary/additional data transfer from main RAM to GPU memory, since the data transfer is much slower than the computations themselves and can become the bottleneck. It therefore can save time to transfer as much data as possible first, instead of allocating a little bit, computing some (fast), waiting for more data to be transferred (relatively slowly), then compute fast, than wait again for more data to arrive at the GPU etc...
  • A small note... this information is obtained via the tensorflow guide on how to use it with gpu: