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Make DeepSpeech.py executable and call Python interpreter explicitly in docs

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X-DeepSpeech: NOBUILD
This commit is contained in:
Reuben Morais 2020-04-29 15:53:42 +02:00
parent aa143e1b9e
commit 6eb784bd3e
2 changed files with 3 additions and 3 deletions
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DeepSpeech.py Normal file → Executable file
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doc/TRAINING.rst
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@ -112,7 +112,7 @@ If, for example, Common Voice language ``en`` was extracted to ``../data/CV/en/`
.. code-block:: bash
./DeepSpeech.py --train_files ../data/CV/en/clips/train.csv --dev_files ../data/CV/en/clips/dev.csv --test_files ../data/CV/en/clips/test.csv
python3 DeepSpeech.py --train_files ../data/CV/en/clips/train.csv --dev_files ../data/CV/en/clips/dev.csv --test_files ../data/CV/en/clips/test.csv
Training a model
^^^^^^^^^^^^^^^^
@ -121,7 +121,7 @@ The central (Python) script is ``DeepSpeech.py`` in the project's root directory
.. code-block:: bash
./DeepSpeech.py --helpfull
python3 DeepSpeech.py --helpfull
To get the output of this in a slightly better-formatted way, you can also look at the flag definitions in :ref:`training-flags`.
@ -163,7 +163,7 @@ Automatic Mixed Precision (AMP) training on GPU for TensorFlow has been recently
Mixed precision training makes use of both FP32 and FP16 precisions where appropriate. FP16 operations can leverage the Tensor cores on NVIDIA GPUs (Volta, Turing or newer architectures) for improved throughput. Mixed precision training also often allows larger batch sizes. DeepSpeech GPU automatic mixed precision training can be enabled via the flag value `--auto_mixed_precision=True`.
```
DeepSpeech.py --train_files ./train.csv --dev_files ./dev.csv --test_files ./test.csv --automatic_mixed_precision=True
python3 DeepSpeech.py --train_files ./train.csv --dev_files ./dev.csv --test_files ./test.csv --automatic_mixed_precision=True
```
On a Volta generation V100 GPU, automatic mixed precision speeds up DeepSpeech training and evaluation by ~30%-40%.