This is memory error.
If I understand right, you use TF and MXNet within the same script.
I had kind of that problem: when used together, different frameworks fight for
the GPU memory (and in my experience, tensorflow is much more greedy on it).
Depending on model (how much GPU memory it consumes), I was able use MXNet and
TF models together within different scripts/processes.
My method to block TF from taking all the GPU memory:
def _prevent_tf_gpu_crash(fraction=.5):
config = tf.compat.v1.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = fraction
tf.compat.v1.keras.backend.set_session(tf.compat.v1.Session(config=config))
Maybe I can tell you more if you provide more details.
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