Version I used:
python 3.6.5
mxnet 1.5.0
cuda 9.2 (I also installed cuda 11.4 and cudnn 8.2.4 because I checked cmd and my NVIDIA used it)
cudnn 7.6.5
window10 64bit
Question:
I used mxnet and gluoncv for image segmentation and gpu problem occured consistently.
I install and uninstall almost every cuda versions(and cudnns) but it didn't help.
plus, I'm little confused that should I use mxnet-cu92 or something else?
when I first installed cuda 11.4, I installed mxnet-cu101(mxnet-cu112 didn't work for me)
but I found cu92 is for using gpu so I installed it again with cuda9.2.
and still not working
here is my code
ctx = mx.gpu(0)
model = gluoncv.model_zoo.get_model('fcn_resnet50_ade', pretrained=True, ctx=ctx) #deeplab_resnet101_ade #fcn_resnet50_ade
total_df = pd.DataFrame(columns=ADE20KSegmentation.CLASSES)
start = time.time()
Moly = []
Fences = {}
for i in range(len(image_file)):
if i%100==0:
print(i)
print(time.time()-start)
start = time.time()
img = mx.image.imread(image_file[i])
image = test_transform(mx.img.imresize(img, 1200, 1200), ctx)
output_array = model.predict(image)
predict_index = mx.nd.argmax(output_array,1).asnumpy()
holy = find_fence(predict_index)
Moly.append(holy)
flat = predict_index.flatten()
output_dict = {}
for index, cls in enumerate(ADE20KSegmentation.CLASSES):
num_pixel = len(np.where(flat==index)[0])
output_dict[cls] = round(num_pixel/1440000, 4)
total_df = total_df.append(output_dict, ignore_index=True)
for names, holy in zip(image_names, Moly):
Fences[names] = holy
and I got "MXNetError: C:\Jenkins\workspace\mxnet-tag\mxnet\src\ndarray\ndarray.cc:1285: GPU is not enabled" this error on
model = gluoncv.model_zoo.get_model('fcn_resnet50_ade', pretrained=True, ctx=ctx)
this code.
what should I do now...?
Related
I recently use mlflow 1.29.0 to track my model training, I use BERT for text embedding which need to import tensorflow_text to register op before training, here is an example:
import tensorflow_hub as hub
import tensorflow_text as text
def create_model():
text_input = tf.keras.layers.Input(shape = (),dtype = tf.string, name = 'text_input')
preprocessed_text = preprocess_model(text_input)
encoder_text = encoder_model(preprocessed_text)['pooled_output']
text_output = tf.keras.layers.Dropout(0.1,name = 'dropout1')(encoder_text)
text_output = tf.keras.layers.Dense(units = 400, activation = tf.keras.activations.sigmoid, name = 'text_dense1')(text_output)
text_output = tf.keras.layers.Dropout(0.1,name = 'dropout2')(text_output)
final_output = tf.keras.layers.Dense(units = 1, activation = tf.keras.activations.sigmoid,name = 'output')(text_output)
model = tf.keras.Model(inputs = [text_input],outputs = [final_output])
return model
if __name__ == '__main__':
preprocess_path = 'https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3'
encoder_path = 'https://tfhub.dev/tensorflow/bert_en_uncased_L-24_H-1024_A-16/4'
preprocess_model = hub.KerasLayer(preprocess_path)
encoder_model = hub.KerasLayer(encoder_path)
with mlflow.start_run() as run:
model = create_model()
model.fit(...)
mlflow.keras.log_model(keras_model = model,...)
mlflow.end_run()
The code run successfully, and the mlflow ui showed everything, however, when I start to deploy the data on my local machine with the following command
mlflow sagemaker build-and-push-container
mlflow sagemaker run-local -m runs:/XXXXX/XXXX -p 4999
it showed the following error:
FileNotFoundError: Op type not registered 'CaseFoldUTF8' in binary running on mighty. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) tf.contrib.resampler should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.
I think it's because the tensorflow_text need to be import before running the model. (based on mlflow, the conda.yaml contain tensorflow-text==2.3.0)
I've meet this error several times when I train the model, I put 'import tensorflow_text as text' at top and the problem was fixed.
However I'm not quite sure how to do that when I deploy the model locally, can anyone help me with that? thank you!
I tried other command like mlflow models serve -m runs:/XXXXX/XXXX -p 4999, and the error is still there.
I'm trying to parallelize the training step of my model with tensorflow ParameterServerStrategy. I work with GCP AI Platform to create the cluster and launch the task.
As my dataset is huge, I use the bigquery tensorflow connector included in tensorflow-io.
My script is inspired by the documentation of tensorflow bigquery reader and the documentation of tensorflow ParameterServerStrategy
Locally my script works well but when I launch it with AI Platform I get the following error :
{"created":"#1633444428.903993309","description":"Error received from peer ipv4:10.46.92.135:2222","file":"external/com_github_grpc_grpc/src/core/lib/surface/call.cc","file_line":1056,"grpc_message":"Op type not registered \'IO>BigQueryClient\' in binary running on gke-cml-1005-141531--n1-standard-16-2-644bc3f8-7h8p. Make sure the Op and Kernel are registered in the binary running in this process. Note that if you are loading a saved graph which used ops from tf.contrib, accessing (e.g.) `tf.contrib.resampler` should be done before importing the graph, as contrib ops are lazily registered when the module is first accessed.","grpc_status":5}
The scripts works with fake data on AI platform and works locally with bigquery connector.
I imagine that the compilation of the model including the bigquery connector and its calls on other devices creates the bug but I don't know how to fix it.
I read this error happens when devices don't have same tensorflow versions so I checked tensorflow and tensorflow-io version on each device.
tensorflow : 2.5.0
tensorflow-io : 0.19.1
I created a similar example which reproduce the bug on AI platform
import os
from tensorflow_io.bigquery import BigQueryClient
from tensorflow_io.bigquery import BigQueryReadSession
import tensorflow as tf
import multiprocessing
import portpicker
from tensorflow.keras.layers.experimental import preprocessing
from google.cloud import bigquery
from tensorflow.python.framework import dtypes
import numpy as np
import pandas as pd
client = bigquery.Client()
PROJECT_ID = <your_project>
DATASET_ID = 'tmp'
TABLE_ID = 'bq_tf_io'
BATCH_SIZE = 32
# Bigquery requirements
def init_bq_table():
table = '%s.%s.%s' %(PROJECT_ID, DATASET_ID, TABLE_ID)
# Create toy_data
def create_toy_data(N):
x = np.random.random(size = N)
y = 0.2 + x + np.random.normal(loc=0, scale = 0.3, size = N)
return x, y
x, y =create_toy_data(1000)
df = pd.DataFrame(data = {'x': x, 'y': y})
job_config = bigquery.LoadJobConfig(write_disposition="WRITE_TRUNCATE",)
job = client.load_table_from_dataframe( df, table, job_config=job_config )
job.result()
# Create initial data
#init_bq_table()
CSV_SCHEMA = [
bigquery.SchemaField("x", "FLOAT64"),
bigquery.SchemaField("y", "FLOAT64"),
]
def transform_row(row_dict):
# Trim all string tensors
dataset_x = row_dict
dataset_x['constant'] = tf.cast(1, tf.float64)
# Extract feature column
dataset_y = dataset_x.pop('y')
#Export as tensor
dataset_x = tf.stack([dataset_x[column] for column in dataset_x], axis=-1)
return (dataset_x, dataset_y)
def read_bigquery(table_name):
tensorflow_io_bigquery_client = BigQueryClient()
read_session = tensorflow_io_bigquery_client.read_session(
"projects/" + PROJECT_ID,
PROJECT_ID, TABLE_ID, DATASET_ID,
list(field.name for field in CSV_SCHEMA),
list(dtypes.double if field.field_type == 'FLOAT64'
else dtypes.string for field in CSV_SCHEMA),
requested_streams=2)
dataset = read_session.parallel_read_rows()
return dataset
def get_data():
dataset = read_bigquery(TABLE_ID)
dataset = dataset.map(transform_row, num_parallel_calls=4)
dataset = dataset.batch(BATCH_SIZE).prefetch(2)
return dataset
cluster_resolver = tf.distribute.cluster_resolver.TFConfigClusterResolver()
# parameter server and worker just wait jobs from the coordinator (chief)
if cluster_resolver.task_type in ("worker"):
worker_config = tf.compat.v1.ConfigProto()
server = tf.distribute.Server(
cluster_resolver.cluster_spec(),
job_name=cluster_resolver.task_type,
task_index=cluster_resolver.task_id,
config=worker_config,
protocol="grpc")
server.join()
elif cluster_resolver.task_type in ("ps"):
server = tf.distribute.Server(
cluster_resolver.cluster_spec(),
job_name=cluster_resolver.task_type,
task_index=cluster_resolver.task_id,
protocol="grpc")
server.join()
elif cluster_resolver.task_type == 'chief':
strategy = tf.distribute.experimental.ParameterServerStrategy(cluster_resolver=cluster_resolver)
if cluster_resolver.task_type == 'chief':
learning_rate = 0.01
with strategy.scope():
# model
model_input = tf.keras.layers.Input(
shape=(2,), dtype=tf.float64)
layer_1 = tf.keras.layers.Dense( 8, activation='relu')(model_input)
dense_output = tf.keras.layers.Dense(1)(layer_1)
model = tf.keras.Model(model_input, dense_output)
#optimizer
optimizer=tf.keras.optimizers.SGD(learning_rate=learning_rate)
accuracy = tf.keras.metrics.MeanSquaredError()
#tf.function
def distributed_train_step(iterator):
def train_step(x_batch_train, y_batch_train):
with tf.GradientTape() as tape:
y_predict = model(x_batch_train, training=True)
loss_value = tf.keras.losses.MeanSquaredError(reduction=tf.keras.losses.Reduction.NONE)(y_batch_train, y_predict)
grads = tape.gradient(loss_value, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
accuracy.update_state(y_batch_train, y_predict)
return loss_value
x_batch_train, y_batch_train = next(iterator)
return strategy.run(train_step, args=(x_batch_train, y_batch_train))
coordinator = tf.distribute.experimental.coordinator.ClusterCoordinator(strategy)
#test
def dataset_fn(_):
def create_toy_data(N):
x = np.random.random(size = N)
y = 0.2 + x + np.random.normal(loc=0, scale = 0.3, size = N)
return np.c_[x,y]
def toy_transform_row(row):
dataset_x = tf.stack([row[0], tf.cast(1, tf.float64)], axis=-1)
dataset_y = row[1]
return dataset_x, dataset_y
N = 1000
data =create_toy_data(N)
dataset = tf.data.Dataset.from_tensor_slices(data)
dataset = dataset.map(toy_transform_row, num_parallel_calls=4)
dataset = dataset.batch(BATCH_SIZE)
dataset = dataset.prefetch(2)
return dataset
#tf.function
def per_worker_dataset_fn():
return strategy.distribute_datasets_from_function(lambda x : get_data()) # <-- Not working with AI platform
#return strategy.distribute_datasets_from_function(dataset_fn) # <-- Working with AI platform
per_worker_dataset = coordinator.create_per_worker_dataset(per_worker_dataset_fn)
# Train model
for epoch in range(5):
per_worker_iterator = iter(per_worker_dataset)
accuracy.reset_states()
for step in range(5):
coordinator.schedule(distributed_train_step, args=(per_worker_iterator,))
coordinator.join()
print ("Finished epoch %d, accuracy is %f." % (epoch, accuracy.result().numpy()))
When I create the dataset with per_worker_dataset_fn() I can use the bigquery connector (bugging) or create the dataset in live (working).
AI Platform Cluster configuration :
runtimeVersion: "2.5"
pythonVersion: "3.7"
Did someone get this issue ? Bigquery connector worked pretty well with MirroredStrategy on AI Platform. Tell me if I should report the issue somewhere else.
I think this is due to lazy loading of libtensorflow_io.so.
https://github.com/tensorflow/io/commit/85d018ee59ceccfae06914ec2a2f6d6583775ff7
Can you try adding something like this to your code:
import tensorflow_io
tensorflow_io.experimental.oss()
As far as I understand this happens because when you submit your training job to Cloud AI training, it is using a stock TensorFlow 2.5 environment that doesn't have tensorflow-io package installed. Therefore it is complaining that it doesn't know about 'IO>BigQueryClient' op defined in tensorflow-io package.
Instead you can submit your training job to be using a custom container:
https://cloud.google.com/ai-platform/training/docs/custom-containers-training
You don't need to write a new Docker file, you can use
gcr.io/deeplearning-platform-release/tf-cpu.2-5
or
gcr.io/deeplearning-platform-release/tf-gpu.2-5 (if your training job needs GPU) that has the right version of tensorflow-io installed.
You can read more about these containers here:
https://cloud.google.com/tensorflow-enterprise/docs/use-with-deep-learning-containers
Here is my old example showing how to run a distributed training on Cloud AI using BigQueryReader: https://github.com/vlasenkoalexey/criteo/blob/master/scripts/train-cloud.sh
It is no longer maintained, but should give you a general idea how it should look like.
excuse my english.
I've been trying to handle Estimators API of tensorflow (v2.x), but when i'm trying to convert a model from tf.estimators to tflite with this code :
import tensorflow as tf
import numpy as np
feature_name = "features"
feature_columns = [tf.feature_column.numeric_column(feature_name, shape=[2])]
classifier = tf.estimator.LinearClassifier(
feature_columns=feature_columns,
n_classes=2,
model_dir="Z:\\tests\\iris")
feature_spec = {'features': tf.io.FixedLenFeature(shape=[2], dtype=np.float32)}
serving_fn = tf.estimator.export.build_parsing_serving_input_receiver_fn(feature_spec)
classifier.export_saved_model(export_dir_base='Z:\\tests\\iris\\', serving_input_receiver_fn=serving_fn)
saved_model_obj = tf.saved_model.load("Z:\\tests\\iris\\1613055608")
concrete_func = saved_model_obj.signatures['serving_default']
converter = tf.lite.TFLiteConverter.from_concrete_functions([concrete_func])
print(saved_model_obj.signatures.keys())
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.experimental_new_converter = True
tflite_model = converter.convert()
with open('Z:\\tests\\model.tflite_estimators', 'wb') as f:
f.write(tflite_model)
I got the following error :
ConverterError: C:\Users\\.....\tensorflow\python\saved_model\load.py:909:0: error: 'tf.ParseExampleV2' op is neither a custom op nor a flex op
C:\Users\\.....\tensorflow\python\saved_model\load.py:859:0: note: called from
P:\\.....\sanstitre3.py:19:0: note: called from
C:\Users\\.....\AppData\Local\Continuum\anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py:465:0: note: called from
C:\Users\\.....\AppData\Local\Continuum\anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py:578:0: note: called from
<ipython-input-115-f30bf3b642d5>:1:0: note: called from
C:\Users\\.....\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3343:0: note: called from
C:\Users\\.....\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3263:0: note: called from
C:\Users\\.....\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\interactiveshell.py:3072:0: note: called from
C:\Users\\.....\AppData\Local\Continuum\anaconda3\lib\site-packages\IPython\core\async_helpers.py:68:0: note: called from
<unknown>:0: error: failed while converting: 'main': Ops that can be supported by the flex runtime (enabled via setting the -emit-select-tf-ops flag):
tf.ParseExampleV2 {dense_shapes = [#tf.shape<2>], device = "", num_sparse = 0 : i64, result_segment_sizes = dense<[0, 0, 0, 1, 0, 0]> : vector<6xi32>}
Some guy on the internet already proposed to add those 2 lines under converter.experimental_new_converter = True :
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS]
It compiles without errors, just warnings, but when i put the TFLite model on my STM32, it gaves me the error TOOL ERROR/ Unknown layer type FlexParseExampleV2, stopping.
Can someone help me on this ?
Have a nice day
TensorFlow Lite Micro doesn't support Flex delegate, so Selece TF ops can't be run on MCUs. You can try restructuring your model with (for example) keras sequential API instead to make it converted only with TFLite ops.
context: https://github.com/tensorflow/tensorflow/issues/34350#issuecomment-579027135
I have trained gradient boosted classifier with TF exampled code
https://www.tensorflow.org/tutorials/estimators/boosted_trees_model_understanding
but,
TF estimator gradient boosted classifier suddenly stopped while training
I think it takes several steps at begging , than suddenly stopped without any exception print
how can i get any reason why python crash
it 's hard to get the reason why it stopped
res:
lib : TF-gpu 1.13.1
cuda : 10.0
cudnn : 7.5
logs :
2019-04-15 16:40:26.175889: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1433] Found device 0
with properties: name: GeForce GTX 1060 6GB major: 6 minor: 1
memoryClockRate(GHz): 1.7845 pciBusID: 0000:07:00.0 totalMemory:
6.00GiB freeMemory: 4.97GiB 2019-04-15 16:40:26.182620: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1512] Adding visible
gpu devices: 0 2019-04-15 16:40:26.832040: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:984] Device
interconnect StreamExecutor with strength 1 edge matrix: 2019-04-15
16:40:26.835620: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0
2019-04-15 16:40:26.836840: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1003] 0: N
2019-04-15 16:40:26.838276: I
tensorflow/core/common_runtime/gpu/gpu_device.cc:1115] Created
TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with
4716 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1060
6GB, pci bus id: 0000:07:00.0, compute capability: 6.1)
WARNING:tensorflow:From
D:\python\lib\site-packages\tensorflow\python\training\saver.py:1266:
checkpoint_exists (from
tensorflow.python.training.checkpoint_management) is deprecated and
will be removed in a future version. Instructions for updating: Use
standard file APIs to check for files with this prefix.
WARNING:tensorflow:From
D:\python\lib\site-packages\tensorflow\python\training\saver.py:1070:
get_checkpoint_mtimes (from
tensorflow.python.training.checkpoint_management) is deprecated and
will be removed in a future version. Instructions for updating: Use
standard file utilities to get mtimes. WARNING:tensorflow:Issue
encountered when serializing resources. Type is unsupported, or the
types of the items don't match field type in CollectionDef. Note this
is a warning and probably safe to ignore. '_Resource' object has no
attribute 'name' WARNING:tensorflow:Issue encountered when serializing
resources. Type is unsupported, or the types of the items don't match
field type in CollectionDef. Note this is a warning and probably safe
to ignore. '_Resource' object has no attribute 'name'
D:\py> (just finished on training)
trn = pd.read_csv('data/santander-customer-transaction-prediction/train.csv')
tst = pd.read_csv('data/santander-customer-transaction-prediction/test.csv')
#trn = upsample(trn[trn.target==0], trn[trn.target==1])
# trn = downsample(trn[trn.target==0], trn[trn.target==1])
features = trn.columns.values[2:202]
target_name = trn.columns.values[1]
train=trn[features]
target=trn[target_name]
NUM_EXAMPLES = len (target)
print (NUM_EXAMPLES)
feat1 = train.corrwith(target).sort_values().head(20).index
feat2 = train.corrwith(target).sort_values().tail(20).index
featonly = feat1.append(feat2)
feat = featonly.append(pd.Index(['target']))
train_origin, tt = train_test_split(trn, test_size=0.2)
train = train_origin[featonly]
target = train_origin[target_name]
test = tst[featonly]
target_name_tst = tst.columns.values[1]
target_tst=tst[target_name_tst]
val_origin=tt
val_train = tt[featonly]
val_target = tt[target_name]
# Training and evaluation input functions.
train_input_fn = make_input_fn(train, target)
val_input_fn = make_input_fn(val_train, val_target)
ttt=tf.estimator.inputs.pandas_input_fn(x=test,num_epochs=1,shuffle=False)
del train,target,val_train,train_origin,trn,tst
fc = tf.feature_column
feature_columns = []
for feature_name in featonly:
feature_columns.append(fc.numeric_column(feature_name,dtype=tf.float32))
#feature_columns
#5
#tf.logging.set_verbosity(tf.logging.INFO)
#logging_hook = tf.train.LoggingTensorHook({"loss" : loss, "accuracy" : accuracy}, every_n_iter=10)
params = {
'n_trees': 50,
'max_depth': 3,
'n_batches_per_layer': 1,
# You must enable center_bias = True to get DFCs. This will force the model to
# make an initial prediction before using any features (e.g. use the mean of
# the training labels for regression or log odds for classification when
# using cross entropy loss).
'center_bias': True
}
# config = tf.estimator.RunConfig().replace(keep_checkpoint_max = 1,
# log_step_count_steps=20, save_checkpoints_steps=20)
est = tf.estimator.BoostedTreesClassifier(feature_columns, **params,model_dir='d:\py/model/')
est.train(train_input_fn, max_steps=50)
-------------------------------------------stopped
metrics = est.evaluate(input_fn=val_input_fn,steps=1)
results = est.predict(input_fn=ttt )
result_list = list(results)
classi = list(map(lambda x : x['classes'][0].decode("utf-8"), result_list))
num = list(range(0,len(classi)))
numi = list(map(lambda x : 'test_' + str(x),num))
#df1 = pd.DataFrame(columns=('ID_code','target'))
df_result = pd.DataFrame({'ID_code' : numi, 'target' : classi})
df_result.to_csv('result/submission03.csv',index=False)
def make_input_fn(X, y, n_epochs=None, shuffle=True):
def input_fn():
NUM_EXAMPLES = len(y)
dataset = tf.data.Dataset.from_tensor_slices((dict(X), y))
# dataset = tf.data.Dataset.from_tensor_slices((X.to_dict(orient='list'), y))
#if shuffle:
# dataset = dataset.shuffle(NUM_EXAMPLES)
# For training, cycle thru dataset as many times as need (n_epochs=None).
dataset = (dataset.repeat(n_epochs).batch(NUM_EXAMPLES))
return dataset
return input_fn
evaluation result should be shown
I think the problem is caused by GPU memory overflow.
You can try to modify the value of 'n_batches_per_layer' to some bigger value according to you GPU memory size.
I worked with a 6G GPU, the value is 16.
Recently I want to try some open source solvers instead of CPLEX. I found that PICOS + zibopt may be a good choice. However, I can merely find instruction on how to make zibopt work with python under windows properly. I downloaded the windows libraries (.dll file) of scip, and I try to install python-zibopt according to the command "python setup.py install". The error " blockmemshell/memory.h no such file" always popped out. I felt that it is because my compiler, which is VS120COMNTOOL, doecn't find the scip solver. Is there any chance that I can make scip work under windows now?
Did you have a look at the current python interface of SCIP 3.1.0? It uses the library from the SCIP Optimization Suite so you don't have to link another LP solver to SCIP.
On Windows, please try this modified setup.py file:
import sys, os, readline, glob, platform
from distutils.core import setup
from distutils.extension import Extension
from Cython.Distutils import build_ext
from Cython.Build import cythonize
BASEDIR = os.path.dirname(os.path.abspath(__file__))
BASEDIR = os.path.dirname(BASEDIR)
BASEDIR = os.path.dirname(BASEDIR)
INCLUDEDIR = os.path.join(BASEDIR,'src')
BASEDIR = os.path.dirname(BASEDIR)
#identify compiler version
prefix = "MSC v."
i = sys.version.find(prefix)
if i == -1:
raise Exception('cannot determine compiler version')
i = i + len(prefix)
s, rest = sys.version[i:].split(" ", 1)
majorVersion = int(s[:-2]) - 6
minorVersion = int(s[2:3]) / 10.0
if platform.architecture()[0].find('64')>=0:
LIBDIR = os.path.join(BASEDIR,'vc'+str(majorVersion),'scip_spx','x64','Release')
else:
LIBDIR = os.path.join(BASEDIR,'vc'+str(majorVersion),'scip_spx','Release')
print('BASEDIR='+ BASEDIR)
print('INCLUDEDIR='+ INCLUDEDIR)
print('LIBDIR='+ LIBDIR)
def complete(text, state):
return (glob.glob(text+'*')+[None])[state]
readline.set_completer_delims(' \t\n;')
readline.parse_and_bind("tab: complete")
readline.set_completer(complete)
libscipopt = 'lib/libscipopt.so'
includescip = 'include/scip'
ext_modules = []
ext_modules += [Extension('pyscipopt.scip', [os.path.join('pyscipopt', 'scip.pyx')],
#extra_compile_args=['-g', '-O0', '-UNDEBUG'],
include_dirs=[INCLUDEDIR],
library_dirs=[LIBDIR],
#runtime_library_dirs=[os.path.abspath('lib')],
libraries=['spx', 'scip_spx'])]
#libraries=['scipopt', 'readline', 'z', 'gmp', 'ncurses', 'm'])]
setup(
name = 'pyscipopt',
version = '0.1',
description = 'wrapper for SCIP in Python',
author = 'Zuse Institute Berlin',
author_email = 'scip#zib.de',
license = 'MIT',
cmdclass = {'build_ext' : build_ext},
ext_modules = ext_modules,
packages=['pyscipopt']
)