Skip to content

Cherry pick install check #18326

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Merged
merged 2 commits into from
Jun 25, 2019
Merged

Cherry pick install check #18326

Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
f3a5c3b
test=release/1.5, add mutigpu install check
JiabinYang Jun 25, 2019
145ade7
test=develop, refine code to use cuda_devices
JiabinYang Jun 25, 2019
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
123 changes: 99 additions & 24 deletions python/paddle/fluid/install_check.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,15 +12,18 @@
# See the License for the specific language governing permissions and
# limitations under the License.

from .framework import Program, program_guard, unique_name, default_startup_program
import os
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']
Expand All @@ -45,25 +48,97 @@ 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])

print(
"Your Paddle Fluid is installed successfully! Let's start deep Learning with Paddle Fluid now!"
)

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
build_strategy.memory_optimize = True
inp = layers.data(name="inp", shape=[2, 2])
simple_layer = SimpleLayer("simple_layer")
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("simple_layer")
out0 = simple_layer0(inp0)
param_grads = backward.append_backward(
out0, parameter_list=[simple_layer0._fc1._w.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")