CLI - 命令行界面¶
The tensorlayer.cli module provides a command-line tool for some common tasks.
(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
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.
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
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.
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
If you have a single computer with massive CPUs, you can use the
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