I'm trying to measure the available space on each of my GPUs using torch.cuda module. However it is returning me the following error.
module 'torch.cuda' has no attribute 'memory_summary'
My code is below
if torch.cuda.is_available():
for i in range(torch.cuda.device_count()):
print(torch.cuda.get_device_name(i))
a = torch.cuda.memory_summary(torch.device('cuda:{}'.format(i)))
print(a)
Similarly memory_stats, mem_get_info and memory_reserved all are failing.
torch.cuda.memory_summary is introduced in Pytorch 1.4.0. So if your torch install is older than that you won't be able to use it.
Related
I am using someone else code (Code Link) which is implemented in Tensorflow1. I want to run this code into Tensorflow2 However I am getting this error:
mnist = tf.contrib.learn.datasets.load_dataset("mnist")
AttributeError: module 'tensorflow' has no attribute 'contrib'
I upgraded this code by using this instruction:
!tf_upgrade_v2 \
--infile /research/dept8/gds/anafees/MyTest.py \
--outfile /research/dept8/gds/anafees/MyTest2.py
Most things are updated, however generated report showed:
168:21: ERROR: Using member tf.contrib.distribute.MirroredStrategy in deprecated module tf.contrib. tf.contrib.distribute.MirroredStrategy cannot be converted automatically. tf.contrib will not be distributed with TensorFlow 2.0, please consider an alternative in non-contrib TensorFlow, a community-maintained repository such as tensorflow/addons, or fork the required code.
I search google; however, I could not find any suitable solution. I do not want to move back to Tensorflow1. Is there any alternate solution? Can anyone help?
Few libraries are deprecated in Tensorflow 2.x such tf.contrib, To make the code compatible with Tf 2 needs some changes in library.
Replace
tf.contrib.learn.datasets.load_dataset("mnist")
with
tf.keras.datasets.mnist.load_data()
And Replace
tf.contrib.distribute.MirroredStrategy
with
tf.distribute.MirroredStrategy(devices=None, cross_device_ops=None)
I am playing around with TensorFlow, and I am trying to export a Keras Model as a TensorFlow Model. And I ran into the above-mentioned error. I am following the "Build Deep Learning Applications with Keras 2.0" from Lynda (https://www.linkedin.com/learning/building-deep-learning-applications-with-keras-2-0/exporting-google-cloud-compatible-models?u=42751868)
While trying to build a tensor flow model, I came across this error, thrown at line 66 where the add meta graphs and variables function is defined.
line 66, in build_tensor_info
raise RuntimeError("build_tensor_info is not supported in Eager mode.")
RuntimeError: build_tensor_info is not supported in Eager mode.
...model_builder.add_meta_graph_and_variables(
K.get_session(),
tags=[tf.compat.v1.saved_model.tag_constants.SERVING],
signature_def_map={
tf.compat.v1.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: signature_def
}
)
...
Any thoughts folks?
Is because you are using tensorflow v2. You have to use the tensorflow v2 compatibility and disable eager mode.
Be careful with the tensorflow imports that you use, for example if you use tensorflow_core, be sure that you are using all the dependencies from "tensorflow".
You have to add before your code:
import tensorflow as tf
if tf.executing_eagerly():
tf.compat.v1.disable_eager_execution()
Ran into that exact same problem when compiling that code from LinkedIn Learning for exporting the Keras model that was written using TensorFlow 1.x API. After replacing everything with the equivalent tf.compat.v1 functions, such as changing
model_builder = tf.saved_model.builder.SavedModelBuilder("exported_model")
to
model_builder = tf.compat.v1.saved_model.Builder("exported_model")
and disables eager execution as suggested above by Cristian Zumelzu, the code was able to run fine with the expected warnings about the deprecated functions.
accuracy = tf.streaming_accuracy (y_pred,y_true,name='acc')
recall = tf.streaming_recall (y_pred,y_true,name='acc')
precision = tf.streaming_precision(y_pred,y_true,name='acc')
confusion = tf.confuson_matrix(Labels, y_pred,num_classes=10,dtype=tf.float32,name='conf')
For the above code, I have received the same error in past few days.
Isn't the syntax same as it is in the API documentation for tensorflow?
try to use this instead (in a fresh python file, I would suggest create a /tmp/temp.py and run that)
from tensorflow.contrib.metrics import streaming_accuracy
and if this doesn't work then
either there is an installation problem (In which case reinstall)
or you are importing the wrong tensorflow module.
While trying to run my Keras code on GPU (CUDA installed), I am not able to execute the following statement, as has been suggested on many online references.
set THEANO_FLAGS="mode=FAST_RUN,device=gpu,floatX=float32" & python theanogpu_example.py
I am getting the following error.
ValueError: Invalid value ("FAST_RUN,device=gpu,floatX=float32") for configurati
on variable "mode". Valid options are ('Mode', 'DebugMode', 'FAST_RUN', 'NanGuar
dMode', 'FAST_COMPILE', 'DEBUG_MODE')
I have tried the other mode suggested as well from inside the code.
import theano
theano.config.device = 'gpu'
theano.config.floatX = 'float32'
I get the following error.
Exception: Can't change the value of this config parameter after initialization!
Apart from knowing how to make it run, I would also take this opportunity to ask a simpler question. How to know in Windows what is my device i.e. whether 'gpu' or 'gpu1' or 'gpu0'? I have tried all 3 for my case but it hasn't yielded result.
Any suggestions will be appreciated.
The best way is using THEANO_FLAGS before run code, because the config variables cannot be changed after importing Theano, try this:
import os
os.environ['THEANO_FLAGS'] = "device=cuda,force_device=True,floatX=float32"
import theano
I have some experience with writing machine learning programs in python, but I'm new to TensorFlow and am checking it out. My dev environment is a lubuntu 14.04 64-bit virtual machine. I've created a python 3.5 conda environment from miniconda and installed TensorFlow 0.12 and its dependencies. I began trying to run some example code from TensorFlow's tutorials and encountered this warning when calling fit() in the boston.py example for input functions: source.
WARNING:tensorflow:Rank of input Tensor (1) should be the same as
output_rank (2) for column. Will attempt to expand dims. It is highly
recommended that you resize your input, as this behavior may change.
After some searching in Google, I found other people encountered this same warning:
https://github.com/tensorflow/tensorflow/issues/6184
https://github.com/tensorflow/tensorflow/issues/5098
Tensorflow - Boston Housing Data Tutorial Errors
However, they also experienced errors which prevent code execution from completing. In my case, the code executes with the above warning. Unfortunately, I couldn't find a single answer in those links regarding what caused the warning and how to fix the warning. They all focused on the error. How does one remove the warning? Or is the warning safe to ignore?
Cheers!
Extra info, I also see the following warnings when running the aforementioned boston.py example.
WARNING:tensorflow:*******************************************************
WARNING:tensorflow:TensorFlow's V1 checkpoint format has been
deprecated. WARNING:tensorflow:Consider switching to the more
efficient V2 format: WARNING:tensorflow:
'tf.train.Saver(write_version=tf.train.SaverDef.V2)'
WARNING:tensorflow:now on by default.
WARNING:tensorflow:*******************************************************
and
WARNING:tensorflow:From
/home/kade/miniconda3/envs/tensorflow/lib/python3.5/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py:1053
in predict.: calling BaseEstimator.predict (from
tensorflow.contrib.learn.python.learn.estimators.estimator) with x is
deprecated and will be removed after 2016-12-01. Instructions for
updating: Estimator is decoupled from Scikit Learn interface by moving
into separate class SKCompat. Arguments x, y and batch_size are only
available in the SKCompat class, Estimator will only accept input_fn.
Example conversion: est = Estimator(...) -> est =
SKCompat(Estimator(...))
UPDATE (2016-12-22):
I've tracked the warning to this file:
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/contrib/layers/python/layers/feature_column_ops.py
and this code block:
except NotImplementedError:
with variable_scope.variable_scope(
None,
default_name=column.name,
values=columns_to_tensors.values()):
tensor = column._to_dense_tensor(transformed_tensor)
tensor = fc._reshape_real_valued_tensor(tensor, 2, column.name)
variable = [
contrib_variables.model_variable(
name='weight',
shape=[tensor.get_shape()[1], num_outputs],
initializer=init_ops.zeros_initializer(),
trainable=trainable,
collections=weight_collections)
]
predictions = math_ops.matmul(tensor, variable[0], name='matmul')
Note the line: tensor = fc._reshape_real_valued_tensor(tensor, 2, column.name)
The method signature is: _reshape_real_valued_tensor(input_tensor, output_rank, column_name=None)
The value 2 is hardcoded as the value of output_rank, but the boston.py example is passing in an input_tensor of rank 1. I will continue to investigate.
If you specify the shape of your tensor explicitly:
tf.constant(df[k].values, shape=[df[k].size, 1])
the warning should go away.
After I specify the shape of the tensor explicitly.
continuous_cols = {k: tf.constant(df[k].values, shape=[df[k].size, 1]) for k in CONTINUOUS_COLUMNS}
It works!