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inefficiency    音标拼音: [ɪnɪf'ɪʃənsi]
n. 无效率,无能

无效率,无能

inefficiency
n 1: unskillfulness resulting from a lack of efficiency [ant:
{efficiency}]

Inefficiency \In`ef*fi"cien*cy\, n.
The quality of being inefficient; lack of power or energy
sufficient for the desired effect; inefficacy; incapacity;
as, he was discharged from his position for inefficiency.
[1913 Webster]


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  • python - how to programmatically determine available GPU memory with tensorflow . . .
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  • Use a GPU - TensorFlow Core
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    You can see how to limit the GPU memory here: https: www tensorflow org guide gpu#limiting_gpu_memory_growth gpus = tf config experimental list_physical_devices('GPU')
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    To limit TensorFlow to a specific set of GPUs we use the tf config experimental set_visible_devices method Due to the default setting of TensorFlow, even if a model can be executed on far less
  • Limit TensorFlow GPU Memory Usage: A Practical Guide
    When working with TensorFlow, especially with large models or datasets, you might encounter "Resource Exhausted: OOM" errors indicating insufficient GPU memory This article provides a practical guide with six effective methods to resolve these out-of-memory issues and optimize your TensorFlow code for smoother execution
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    I want to find out how much GPU memory my Tensorflow model needs at inference So I used tf contrib memory_stats MaxBytesInUse which returned 6168 MB But with config gpu_options per_process_gpu_memory_fraction I can use a way smaller fraction of my GPU and the model still runs fine without needing more time for one inference step
  • How to quickly determine memory requirements for model
    You can find this info by checking the size of pytorch_model bin (or tf_model h5 flax_model msgpack for TF Flax models) These files can be sharded sometimes (if pytorch_model bin index json is present), in which case you need to sum up all the shards listed in the index file
  • Configuring TensorFlow GPU and CPU Settings - Sling Academy
    By default, TensorFlow allocates the entire memory of all GPUs This may not be desirable in a shared environment To avoid exhausting the entire GPU memory, you can configure TensorFlow to use GPU memory as-needed: if physical_devices: for gpu in physical_devices: tf config experimental set_memory_growth(gpu, True)
  • Using GPU in TensorFlow Model - DZone
    TensorFlow GPU offers two configuration options to control the allocation of a subset of memory if and when required by the processor to save memory, and these TensorFlow GPU optimizations are
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    Instead of dynamic GPU allocation, a fixed memory allocation can be done by specifying the fraction of memory needed using set_per_process_memory_growth config = tf ConfigProto ()





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