[FIXED] Is there a way of determining how much GPU memory is in use by TensorFlow?

Issue

Tensorflow tends to preallocate the entire available memory on it's GPUs. For debugging, is there a way of telling how much of that memory is actually in use?

Solution

(1) There is some limited support with Timeline for logging memory allocations. Here is an example for its usage:

    run_options = tf.RunOptions(trace_level=tf.RunOptions.FULL_TRACE)
    run_metadata = tf.RunMetadata()
    summary, _ = sess.run([merged, train_step],
                          feed_dict=feed_dict(True),
                          options=run_options,
                          run_metadata=run_metadata)
    train_writer.add_run_metadata(run_metadata, 'step%03d' % i)
    train_writer.add_summary(summary, i)
    print('Adding run metadata for', i)
    tl = timeline.Timeline(run_metadata.step_stats)
    print(tl.generate_chrome_trace_format(show_memory=True))
    trace_file = tf.gfile.Open(name='timeline', mode='w')
    trace_file.write(tl.generate_chrome_trace_format(show_memory=True))

You can give this code a try with the MNIST example (mnist with summaries)

This will generate a tracing file named timeline, which you can open with chrome://tracing. Note that this only gives an approximated GPU memory usage statistics. It basically simulated a GPU execution, but doesn't have access to the full graph metadata. It also can't know how many variables have been assigned to the GPU.

(2) For a very coarse measure of GPU memory usage, nvidia-smi will show the total device memory usage at the time you run the command.

nvprof can show the on-chip shared memory usage and register usage at the CUDA kernel level, but doesn't show the global/device memory usage.

Here is an example command: nvprof --print-gpu-trace matrixMul

And more details here: http://docs.nvidia.com/cuda/profiler-users-guide/#abstract

Answered By - Yao Zhang