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test=develop, add add_multi_gpu_install_check (#18157)

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* test=develop, add add_multi_gpu_install_check

* test=develop, refine warning doc

* test=develop, refine warning doc

* test=develop, refine warning doc

* test=develop, support multi cpu
revert-18229-add_multi_gpu_install_check
Jiabin Yang 6 years ago committed by GitHub
parent bbc292920c
commit 991c94f135
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GPG Key ID: 4AEE18F83AFDEB23
2 changed files with 99 additions and 22 deletions
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117
python/paddle/fluid/install_check.py
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@ -19,9 +19,12 @@ from . import layers
from . import backward
from .dygraph import Layer, nn
from . import executor
from . import optimizer
from . import core
from . import compiler
import logging
import numpy as np
import os
__all__ = ['run_check']
@ -45,25 +48,95 @@ def run_check():
This func should not be called only if you need to verify installation
'''
print("Running Verify Fluid Program ... ")
prog = Program()
startup_prog = Program()
scope = core.Scope()
with executor.scope_guard(scope):
with program_guard(prog, startup_prog):
with unique_name.guard():
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
inp = layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
simple_layer = SimpleLayer("simple_layer")
out = simple_layer(inp)
param_grads = backward.append_backward(
out, parameter_list=[simple_layer._fc1._w.name])[0]
exe = executor.Executor(core.CPUPlace(
) if not core.is_compiled_with_cuda() else core.CUDAPlace(0))
exe.run(default_startup_program())
exe.run(feed={inp.name: np_inp},
fetch_list=[out.name, param_grads[1].name])
use_cuda = False if not core.is_compiled_with_cuda() else True
place = core.CPUPlace() if not core.is_compiled_with_cuda(
) else core.CUDAPlace(0)
np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
if use_cuda:
if core.get_cuda_device_count() > 1:
os.environ['CUDA_VISIBLE_DEVICES'] = "0,1"
else:
os.environ['CUDA_VISIBLE_DEVICES'] = "0"
def test_parallerl_exe():
train_prog = Program()
startup_prog = Program()
scope = core.Scope()
if not use_cuda:
os.environ['CPU_NUM'] = "2"
with executor.scope_guard(scope):
with program_guard(train_prog, startup_prog):
with unique_name.guard():
places = []
build_strategy = compiler.BuildStrategy()
build_strategy.enable_inplace = True
build_strategy.memory_optimize = True
inp = layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
simple_layer = SimpleLayer("simple_layer")
out = simple_layer(inp)
exe = executor.Executor(place)
if use_cuda:
places = [core.CUDAPlace(0), core.CUDAPlace(1)]
else:
places = [core.CPUPlace(), core.CPUPlace()]
loss = layers.mean(out)
loss.persistable = True
optimizer.SGD(learning_rate=0.01).minimize(loss)
startup_prog.random_seed = 1
compiled_prog = compiler.CompiledProgram(
train_prog).with_data_parallel(
build_strategy=build_strategy,
loss_name=loss.name,
places=places)
exe.run(startup_prog)
exe.run(compiled_prog,
feed={inp.name: np_inp},
fetch_list=[loss.name])
def test_simple_exe():
train_prog = Program()
startup_prog = Program()
scope = core.Scope()
if not use_cuda:
os.environ['CPU_NUM'] = "1"
with executor.scope_guard(scope):
with program_guard(train_prog, startup_prog):
with unique_name.guard():
inp0 = layers.data(
name="inp", shape=[2, 2], append_batch_size=False)
simple_layer0 = SimpleLayer("simple_layer")
out0 = simple_layer0(inp0)
param_grads = backward.append_backward(
out0, parameter_list=[simple_layer0._fc1._w.name])[0]
exe0 = executor.Executor(core.CPUPlace()
if not core.is_compiled_with_cuda()
else core.CUDAPlace(0))
exe0.run(startup_prog)
exe0.run(feed={inp0.name: np_inp},
fetch_list=[out0.name, param_grads[1].name])
test_simple_exe()
print("Your Paddle Fluid works well on SINGLE GPU or CPU.")
try:
test_parallerl_exe()
print("Your Paddle Fluid works well on MUTIPLE GPU or CPU.")
print(
"Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now"
)
except Exception as e:
logging.warning(
"Your Paddle Fluid has some problem with multiple GPU. This may be caused by:"
"\n 1. There is only 1 GPU visible on your Device;"
"\n 2. No.1 or No.2 GPU or both of them are occupied now"
"\n 3. Wrong installation of NVIDIA-NCCL2, please follow instruction on https://github.com/NVIDIA/nccl-tests "
"\n to test your NCCL, or reinstall it following https://docs.nvidia.com/deeplearning/sdk/nccl-install-guide/index.html"
)
print(
"Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now"
)
print("\n Original Error is: {}".format(e))
print(
"Your Paddle Fluid is installed successfully ONLY for SINGLE GPU or CPU! "
"\n Let's start deep Learning with Paddle Fluid now")

4
python/paddle/fluid/tests/unittests/CMakeLists.txt
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@ -116,6 +116,7 @@ list(REMOVE_ITEM TEST_OPS test_imperative_mnist)
list(REMOVE_ITEM TEST_OPS test_ir_memory_optimize_transformer)
list(REMOVE_ITEM TEST_OPS test_layers)
list(REMOVE_ITEM TEST_OPS test_imperative_ocr_attention_model)
list(REMOVE_ITEM TEST_OPS test_install_check)
# Some ops need to check results when gc is enabled
# Currently, only ops that register NoNeedBufferVarsInference need to do this test
@ -172,6 +173,9 @@ py_test_modules(test_imperative_mnist_sorted_gradient MODULES test_imperative_mn
py_test_modules(test_imperative_se_resnext MODULES test_imperative_se_resnext ENVS
FLAGS_cudnn_deterministic=1 SERIAL)
set_tests_properties(test_imperative_se_resnext PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE")
py_test_modules(test_install_check MODULES test_install_check ENVS
FLAGS_cudnn_deterministic=1 SERIAL)
set_tests_properties(test_install_check PROPERTIES LABELS "RUN_TYPE=DIST")
if(WITH_DISTRIBUTE)
py_test_modules(test_dist_train MODULES test_dist_train)

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