TensorLayer官方中文文档1.7.4：CLI – 命令行界面

所属分类：TensorLayer

CLI - 命令行界面¶

The tensorlayer.cli module provides a command-line tool for some common tasks.

tl train¶

(Alpha release - usage might change later)
The tensorlayer.cli.train module provides the

tl train

subcommand.
It helps the user bootstrap a TensorFlow/TensorLayer program for distributed training
using multiple GPU cards or CPUs on a computer.
You need to first setup the CUDA_VISIBLE_DEVICES
to tell

tl train

which GPUs are available. If the CUDA_VISIBLE_DEVICES is not given,

tl train

would try best to discover all available GPUs.
In distribute training, each TensorFlow program needs a TF_CONFIG environment variable to describe
the cluster. It also needs a master daemon to
monitor all trainers.

tl train

is responsible
for automatically managing these two tasks.

Usage¶

tl train [-h] [-p NUM_PSS] [-c CPU_TRAINERS] <file> [args [args ...]]# example of using GPU 0 and 1 for training mnist
CUDA_VISIBLE_DEVICES="0,1"
tl train example/tutorial_mnist_distributed.py

# example of using CPU trainers for inception v3
tl train -c 16 example/tutorial_imagenet_inceptionV3_distributed.py

# example of using GPU trainers for inception v3 with customized arguments
# as CUDA_VISIBLE_DEVICES is not given, tl would try to discover all available GPUs
tl train example/tutorial_imagenet_inceptionV3_distributed.py -- --batch_size 16

Parameters¶

file

: python file path.

NUM_PSS

: The number of parameter servers.

CPU_TRAINERS

: The number of CPU trainers.It is recommended that

NUM_PSS + CPU_TRAINERS <= cpu count

args

: Any parameter after

--

would be passed to the python program.

Notes¶

A parallel training program would require multiple parameter servers
to help parallel trainers to exchange intermediate gradients.
The best number of parameter servers is often proportional to the
size of your model as well as the number of CPUs available.
You can control the number of parameter servers using the

-p

parameter.
If you have a single computer with massive CPUs, you can use the

-c

parameter
to enable CPU-only parallel training.
The reason we are not supporting GPU-CPU co-training is because GPU and
CPU are running at different speeds. Using them together in training would
incur stragglers.
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