I'm following a beginner's TensorFlow tutorial and trying out classification. There are a bunch of GPU errors. I have cuda tools installed as well as my latest GPU drivers. Here is the output:
2021-01-13 15:42:24.186914: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2021-01-13 15:42:24.187065: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine. [NumericColumn(key='SepalLength', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='SepalWidth', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='PetalLength', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None), NumericColumn(key='PetalWidth', shape=(1,), default_value=None, dtype=tf.float32, normalizer_fn=None)] 2021-01-13 15:42:26.282013: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll 2021-01-13 15:42:26.302224: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1760] Found device 0 with properties: pciBusID: 0000:0e:00.0 name: GeForce GTX 1080 computeCapability: 6.1 coreClock: 1.86GHz coreCount: 20 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 298.32GiB/s 2021-01-13 15:42:26.302958: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudart64_110.dll'; dlerror: cudart64_110.dll not found 2021-01-13 15:42:26.303513: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublas64_11.dll'; dlerror: cublas64_11.dll not found 2021-01-13 15:42:26.304062: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cublasLt64_11.dll'; dlerror: cublasLt64_11.dll not found starting training 2021-01-13 15:42:26.307161: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll 2021-01-13 15:42:26.308219: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll 2021-01-13 15:42:26.312354: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll 2021-01-13 15:42:26.312941: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cusparse64_11.dll'; dlerror: cusparse64_11.dll not found 2021-01-13 15:42:26.313499: W tensorflow/stream_executor/platform/default/dso_loader.cc:60] Could not load dynamic library 'cudnn64_8.dll'; dlerror: cudnn64_8.dll not found 2021-01-13 15:42:26.313623: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1793] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... 2021-01-13 15:42:26.314323: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags. 2021-01-13 15:42:26.315481: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1300] Device interconnect StreamExecutor with strength 1 edge matrix: 2021-01-13 15:42:26.315604: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306]
WARNING:tensorflow:Using temporary folder as model directory: C:\Users\levig\AppData\Local\Temp\tmpbmbc3as1 WARNING:tensorflow:From C:\Users\levig\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\training\training_util.py:235: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. WARNING:tensorflow:From C:\Users\levig\AppData\Local\Programs\Python\Python38\lib\site-packages\tensorflow\python\keras\optimizer_v2\adagrad.py:82: calling Constant.init (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor 2021-01-13 15:42:27.410575: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1760] Found device 0 with properties: pciBusID: 0000:0e:00.0 name: GeForce GTX 1080 computeCapability: 6.1 coreClock: 1.86GHz coreCount: 20 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 298.32GiB/s 2021-01-13 15:42:27.410786: W tensorflow/core/common_runtime/gpu/gpu_device.cc:1793] Cannot dlopen some GPU libraries. Please make sure the missing libraries mentioned above are installed properly if you would like to use GPU. Follow the guide at https://www.tensorflow.org/install/gpu for how to download and setup the required libraries for your platform. Skipping registering GPU devices... 2021-01-13 15:42:27.474456: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1300] Device interconnect StreamExecutor with strength 1 edge matrix: 2021-01-13 15:42:27.474571: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1306] 0 2021-01-13 15:42:27.474637: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1319] 0: N 2021-01-13 15:42:27.482654: I tensorflow/compiler/mlir/mlir_graph_optimization_pass.cc:258] None of the MLIR optimization passes are enabled (registered 0 passes)
Here is my code:
from __future__ import absolute_import, division, print_function, unicode_literals
import tensorflow as tf
import pandas as pd
CSV_COLUMN_NAMES = ['SepalLength', 'SepalWidth', 'PetalLength', 'PetalWidth', 'Species']
SPECIES = ['Setosa', 'Versicolor', 'Virginica']
train_path = tf.keras.utils.get_file(
"iris_training.csv", "https://storage.googleapis.com/download.tensorflow.org/data/iris_training.csv")
test_path = tf.keras.utils.get_file(
"iris_test.csv", "https://storage.googleapis.com/download.tensorflow.org/data/iris_test.csv")
train = pd.read_csv(train_path, names=CSV_COLUMN_NAMES, header=0)
test = pd.read_csv(test_path, names=CSV_COLUMN_NAMES, header=0)
# Here we use keras (a module inside of TensorFlow) to grab our datasets and read them into a pandas dataframe
train_y = train.pop('Species')
test_y = test.pop('Species')
train.head() # the species column is now gone
def input_fn(features, labels, training=True, batch_size=256):
# Convert the inputs to a Dataset.
dataset = tf.data.Dataset.from_tensor_slices((dict(features), labels))
# Shuffle and repeat if you are in training mode.
if training:
dataset = dataset.shuffle(1000).repeat()
return dataset.batch(batch_size)
# Feature columns describe how to use the input.
my_feature_columns = []
for key in train.keys():
my_feature_columns.append(tf.feature_column.numeric_column(key=key))
# Build a DNN with 2 hidden layers with 30 and 10 hidden nodes each.
classifier = tf.estimator.DNNClassifier(
feature_columns=my_feature_columns,
# Two hidden layers of 30 and 10 nodes respectively.
hidden_units=[30, 10],
# The model must choose between 3 classes.
n_classes=3)
print("starting training")
classifier.train(
input_fn=lambda: input_fn(train, train_y, training=True),
steps=5000)
