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Paddle/python/paddle/fluid/install_check.py

164 lines
6.5 KiB

# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import paddle
from .framework import Program, program_guard, unique_name, cuda_places, cpu_places
from .param_attr import ParamAttr
from .initializer import Constant
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
__all__ = ['run_check']
class SimpleLayer(Layer):
def __init__(self, input_size):
super(SimpleLayer, self).__init__()
self._linear1 = nn.Linear(
input_size,
3,
param_attr=ParamAttr(initializer=Constant(value=0.1)))
def forward(self, inputs):
x = self._linear1(inputs)
x = layers.reduce_sum(x)
return x
def run_check():
"""To check whether install is successful
This func should not be called only if you need to verify installation
Examples:
.. code-block:: python
import paddle.fluid as fluid
fluid.install_check.run_check()
# If installed successfully, output may be
# Running Verify Fluid Program ...
# W0805 04:24:59.496919 35357 device_context.cc:268] Please NOTE: device: 0, CUDA Capability: 70, Driver API Version: 10.2, Runtime API Version: 10.1
# W0805 04:24:59.505594 35357 device_context.cc:276] device: 0, cuDNN Version: 7.6.
# Your Paddle Fluid works well on SINGLE GPU or CPU.
# Your Paddle Fluid works well on MUTIPLE GPU or CPU.
# Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now
"""
paddle.enable_static()
print("Running Verify Fluid Program ... ")
device_list = []
if core.is_compiled_with_cuda():
try:
core.get_cuda_device_count()
except Exception as e:
logging.warning(
"You are using GPU version Paddle Fluid, But Your CUDA Device is not set properly"
"\n Original Error is {}".format(e))
return 0
device_list = cuda_places()
else:
device_list = [core.CPUPlace(), core.CPUPlace()]
np_inp_single = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
inp = []
for i in range(len(device_list)):
inp.append(np_inp_single)
np_inp_muti = np.array(inp)
np_inp_muti = np_inp_muti.reshape(len(device_list), 2, 2)
def test_parallerl_exe():
train_prog = Program()
startup_prog = Program()
scope = core.Scope()
with executor.scope_guard(scope):
with program_guard(train_prog, startup_prog):
with unique_name.guard():
build_strategy = compiler.BuildStrategy()
build_strategy.enable_inplace = True
inp = layers.data(name="inp", shape=[2, 2])
simple_layer = SimpleLayer(input_size=2)
out = simple_layer(inp)
exe = executor.Executor(
core.CUDAPlace(0) if core.is_compiled_with_cuda() and
(core.get_cuda_device_count() > 0) else 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=device_list)
exe.run(startup_prog)
exe.run(compiled_prog,
feed={inp.name: np_inp_muti},
fetch_list=[loss.name])
def test_simple_exe():
train_prog = Program()
startup_prog = Program()
scope = core.Scope()
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(input_size=2)
out0 = simple_layer0(inp0)
param_grads = backward.append_backward(
out0,
parameter_list=[simple_layer0._linear1.weight.name])[0]
exe0 = executor.Executor(
core.CUDAPlace(0) if core.is_compiled_with_cuda() and
(core.get_cuda_device_count() > 0) else core.CPUPlace())
exe0.run(startup_prog)
exe0.run(feed={inp0.name: np_inp_single},
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 or 0 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("\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")
paddle.disable_static()