The Google Colab output is being truncated. I've looked through the settings and I didn't see a limitation there. What is the best option to solve the problem?
I had the same problem and managed it by writing the output on a file on drive:
from google.colab import drive
drive.mount('/content/drive')
import os
os.chdir("/content/drive/")
with open('/content/drive/output.txt','w') as out:
out.write(' abcd \n')
I have the same issue currently, I found this link on medium, check the part "How do I use Colab for long training times/runs?"
So basically according to this article you need to store checkpoints on your drive and by using callbacks from Keras, you will be able to run it nonstop.
from keras.callbacks import *
filepath = "/content/gdrive/My Drive/MyCNN/epochs:{epoch:03d}-val_acc:{val_acc:.3f}.hdf5"
checkpoint = ModelCheckpoint(filepath, monitor='val_acc', verbose=1, save_best_only=True, mode='max')
callbacks_list = [checkpoint]
Other solution to solve this problem is according to my researches, you should put this code to your console but make sure that you save your progress to drive, because it will be terminated in 12 hours.
function ClickConnect() {
console.log("Working");
document
.querySelector('#top-toolbar > colab-connect-button')
.shadowRoot.querySelector('#connect')
.click()
}
setInterval(ClickConnect, 60000)
Related
I have this code which based on t5 notebook (https://colab.research.google.com/github/google-research/text-to-text-transfer-transformer/blob/master/notebooks/t5-trivia.ipynb)
FINETUNE_STEPS = 3000##param {type: "integer"}
model.finetune(
mixture_or_task_name="text_diacritization_short",
pretrained_model_dir=PRETRAINED_DIR,
finetune_steps=FINETUNE_STEPS
)
my code was working fine in 8 Augustus then something happened resulting of this error.
these two lines appeared when my model worked so i don't think they are the problem.
INFO:root:system_path_file_exists:gs://my_bucket/my_file/models/small/operative_config.gin
ERROR:root:Path not found: gs://my_bucket/my_file/models/small/operative_config.gin
Rest of the error.
From /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/training_util.py:399: 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:absl:Using an uncached FunctionDataset for training is not recommended since it often results in insufficient shuffling on restarts, resulting in overfitting. It is highly recommended that you cache this task before training with it or use a data source that supports lower-level shuffling (e.g., FileDataSource).
SimdMeshImpl ignoring devices ['', '', '', '', '', '', '', '']
Using default tf glorot_uniform_initializer for variable encoder/block_000/layer_000/SelfAttention/relative_attention_bias The initialzer will guess the input and output dimensions based on dimension order.
Using default tf glorot_uniform_initializer for variable decoder/block_000/layer_000/SelfAttention/relative_attention_bias The initialzer will guess the input and output dimensions based on dimension order.
From /usr/local/lib/python3.7/dist-packages/tensorflow/python/training/saver.py:1161: 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.
From /usr/local/lib/python3.7/dist-packages/tensorflow_estimator/python/estimator/tpu/tpu_estimator.py:758: Variable.load (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version.
Instructions for updating:
Prefer Variable.assign which has equivalent behavior in 2.X.
I changed the google cloud account and the Colab notebook to completely new gmail account, I think the problem is that something got updated in google Colab regarding connecting to Google Cloud TPUs.
Also, I can connect to my bucket normally using this code.
BASE_DIR = "gs://my_bucket/my_file" ##param { type: "string" }
if not BASE_DIR or BASE_DIR == "gs://":
raise ValueError("You must enter a BASE_DIR.")
DATA_DIR = os.path.join(BASE_DIR, "data")
FINETUNE_MODELS_DIR = os.path.join(BASE_DIR, "models")
ON_CLOUD = True
if ON_CLOUD:
print("Setting up GCS access...")
import tensorflow_gcs_config
from google.colab import auth
# Set credentials for GCS reading/writing from Colab and TPU.
TPU_TOPOLOGY = "v2-8"
try:
tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # TPU detection
TPU_ADDRESS = tpu.get_master()
print('Running on TPU:', TPU_ADDRESS)
except ValueError:
raise BaseException('ERROR: Not connected to a TPU runtime; please see the previous cell in this notebook for instructions!')
auth.authenticate_user()
tf.enable_eager_execution()
tf.config.experimental_connect_to_host(TPU_ADDRESS)
tensorflow_gcs_config.configure_gcs_from_colab_auth()
tf.disable_v2_behavior()
# Improve logging.
from contextlib import contextmanager
import logging as py_logging
if ON_CLOUD:
tf.get_logger().propagate = False
py_logging.root.setLevel('INFO')
#contextmanager
def tf_verbosity_level(level):
og_level = tf.logging.get_verbosity()
tf.logging.set_verbosity(level)
yield
tf.logging.set_verbosity(og_level)
it would be great if someone can help me I have been looking in the issue for a week and found nothing, is there any changes to how Google Colab works that I am not aware of.
Thanks in advance.
I am trying to work with the quite recently published tensorflow_dataset API to train a Keras model on the Open Images Dataset. The dataset is about 570 GB in size. I downloaded the data with the following code:
import tensorflow_datasets as tfds
import tensorflow as tf
open_images_dataset = tfds.image.OpenImagesV4()
open_images_dataset.download_and_prepare(download_dir="/notebooks/dataset/")
After the download was complete, the connection to my jupyter notebook somehow interrupted but the extraction seemed to be finished as well, at least all downloaded files had a counterpart in the "extracted" folder. However, I am not able to access the downloaded data now:
tfds.load(name="open_images_v4", data_dir="/notebooks/open_images_dataset/extracted/", download=False)
This only gives the following error:
AssertionError: Dataset open_images_v4: could not find data in /notebooks/open_images_dataset/extracted/. Please make sure to call dataset_builder.download_and_prepare(), or pass download=True to tfds.load() before trying to access the tf.data.Dataset object.
When I call the function download_and_prepare() it only downloads the whole dataset again.
Am I missing something here?
Edit:
After the download the folder under "extracted" has 18 .tar.gz files.
This is with tensorflow-datasets 1.0.1 and tensorflow 2.0.
The folder hierarchy should be like this:
/notebooks/open_images_dataset/extracted/open_images_v4/0.1.0
All the datasets have a version. Then the data could be loaded like this.
ds = tf.load('open_images_v4', data_dir='/notebooks/open_images_dataset/extracted', download=False)
I didn't have open_images_v4 data. I put cifar10 data into a folder named open_images_v4 to check what folder structure tensorflow_datasets was expecting.
The solution to this was to also use the "data_dir" parameter when initializing the dataset:
builder = tfds.image.OpenImagesV4(data_dir="/raid/openimages/dataset")
builder.download_and_prepare(download_dir="/raid/openimages/dataset")
This way the dataset is donwloaded and extracted in the same directory. Before, it was (for me unnoticeably) extracting to the default directory, which is under /home/.../. That's what caused the error, as there wasn't enough space left under my home directory.
After the extraction, the folder structure is exactly as Manoj-Mohan described.
Above solution haven't worked for me.
builder = tfds.builder(name='folder_name', data_dir=data_dir)
builder.download_and_prepare(download_dir="/home/...")
ds = builder.as_dataset()
I am trying to do this tutorial for a machine learning class I am taking in college.
www.tensorflow.org/tutorials/keras/basic_classification
When it executes the lines
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
it is taking forever to download the data. At the rate it is downloading, it is going to take a few days or weeks to download all of it. I am using a MacBook. My classmate is also using a MacBook and when he downloads the data it only takes a few seconds. Please help.
In my case the download was giving me an error. By digging into the error I was able to find the file in which the base URL was declared, which in my case was:
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow_core/python/keras/datasets/fashion_mnist.py
At line 44 I have commented out the line:
# base = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
And declared a different base URL, which I had found looking into the documentation of the original dataset:
base = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'
The download started immediately and gave no errors. Hope this helps.
I am trying to download data from Fashion MNIST, but it produces an error. Originally, it was downloading and working properly, but I had to terminate it because I had to turn off my computer. Once I opened the file up again, it gives me an error. I'm not sure what the problem is, but is it because I already downloaded some parts of the data once, and keras doesn't recognize that? I am using Jupyter notebook in a conda environment
Here is the link to the image:
https://i.stack.imgur.com/wLGDm.png
You have missed adding tf. to the line
fashion_mnist = keras.datasets.fashion_mnist
The below code works perfectly for me. Importing the fashion_mnist dataset has been outlined in tensorflow documention here.
Change your code to:
import tensorflow as tf
fashion_mnist = tf.keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
or, use the better way to do it below. This avoids creating an extra variable fashion_mnist:
import tensorflow as tf
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.fashion_mnist.load_data()
I am using tensorflow 1.9.0, keras 2.2.2 and python 3.6.6 on Windows 10 x64 OS.
I know my pc well, I can't download anything larger than 2.7 MB (in terminal), due to WinError 8.
So I manually downloaded all packs from storage.google (since some packs are 25 MB).
Check the packs:
then I paste all packs to \datasets\fashion-mnist
The next time u run your code, it should be fixed.
Note : If u have VScode then just CTRL and click the link, then you can download it easily.
I had an error regarding the cURL connection, and by looking into the error message I was able to track the file where the URL was declared. In my case it was:
/Library/Frameworks/Python.framework/Versions/3.6/lib/python3.6/site-packages/tensorflow_core/python/keras/datasets/fashion_mnist.py
At line 44 I have commented out the line:
# base = 'https://storage.googleapis.com/tensorflow/tf-keras-datasets/'
And declared a different base URL, which I had found looking into the documentation of the original dataset:
base = 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/'
The download started immediately and gave no errors. Hope this helps.
This is because for some reason you have an incomplete download for the MNIST dataset.
You will have to manually delete the downloaded folder which usually resides in ~/.keras/datasets or any path specified by you relative to this path, in your case MNIST_data.
Go to : C:\Users\Username.keras\datasets
and then Delete the Dataset that you want to redownload or has the error
You should be good to go!
You can also manually add print for the path from which it is taking dataset ..
Ex: print(paths) in file fashion_mnist.py
with gzip.open(paths[3], 'rb') as imgpath:
print(paths) #debug print in fashion_mnist.py
x_test = np.frombuffer(
imgpath.read(), np.uint8, offset=16).reshape(len(y_test), 28, 28)
& from this path, remove the files & this will start to download fresh data ..
Change The base address with 'http://fashion-mnist.s3-website.eu-central-1.amazonaws.com/' as described previously. It works for me.
I was getting error of Downloading data from https://storage.googleapis.com/tensorflow/tf-keras-datasets/mnist.npz
Traceback (most recent call last):
File "C:\Users\AsadA\AppData\Local\Programs\Python\Python38\lib\site-packages\numpy\lib\npyio.py", line 448, in load
return pickle.load(fid, **pickle_kwargs)
EOFError: Ran out of input
During handling of the above exception, another exception occurred:
Traceback (most recent call last):
File "C:\Users\AsadA\AppData\Local\Programs\Python\Python38\lib\site-packages\numpy\lib\npyio.py", line 450, in load
raise IOError(
OSError: Failed to interpret file 'C:\\Users\\AsadA\\.keras\\datasets\\mnist.npz' as a pickle"**
GO TO FILE C:\Users\AsadA\AppData\Local\Programs\Python\Python38\Lib\site-packages\tensorflow\python\keras\datasets (In my Case) and follow the instructions:
Google recently announced the Clould ML, https://cloud.google.com/ml/ and it's very useful. However, one limitation is that the input/out of a Tensorflow program should support gs://.
If we use all tensorflow APIS to read/write files, it should OK, since these APIs support gs://.
However, if we use native file IO APIs such as open, it does not work, because they don't understand gs://
For example:
with open(vocab_file, 'wb') as f:
cPickle.dump(self.words, f)
This code won't work in Google Cloud ML.
However, modifying all native file IO APIs to tensorflow APIs or Google Storage Python APIs is really tedious. Is there any simple way to do this? Any wrappers to support google storage systems, gs:// on top of the native file IO?
As suggested here Pickled scipy sparse matrix as input data?, perhaps we can use file_io.read_file_to_string('gs://...'), but still this requrements significant code modifcation.
Do it like this:
from tensorflow.python.lib.io import file_io
with file_io.FileIO('gs://.....', mode='w+') as f:
cPickle.dump(self.words, f)
Or you can read pickle file in like this:
file_stream = file_io.FileIO(train_file, mode='r')
x_train, y_train, x_test, y_test = pickle.load(file_stream)
One solution is to copy all of the data to local disk when the program starts up. You can do that using gsutil inside the Python script that gets run, something like:
vocab_file = 'vocab.pickled'
subprocess.check_call(['gsutil', '-m' , 'cp', '-r',
os.path.join('gs://path/to/', vocab_file), '/tmp'])
with open(os.path.join('/tmp', vocab_file), 'wb') as f:
cPickle.dump(self.words, f)
And if you have any outputs, you can write them to local disk and gsutil rsync them. (But, be careful to handle restarts correctly, because you may be put on a different machine).
The other solution is to monkey patch open (Note: untested):
import __builtin__
# NB: not all modes are compatible; should handle more carefully.
# Probably should be reported on
# https://github.com/tensorflow/tensorflow/issues/4357
def new_open(name, mode='r', buffering=-1):
return file_io.FileIO(name, mode)
__builtin__.open = new_open
Just be sure to do that before any module actually tries to read from GCS.
apache_beam has the gcsio module which can be used to return a standard Python file object to read/write GCS objects. You can use this object with any method that works with Python file objects. For example
def open_local_or_gcs(path, mode):
"""Opens the given path."""
if path.startswith('gs://'):
try:
return gcsio.GcsIO().open(path, mode)
except Exception as e: # pylint: disable=broad-except
# Currently we retry exactly once, to work around flaky gcs calls.
logging.error('Retrying after exception reading gcs file: %s', e)
time.sleep(10)
return gcsio.GcsIO().open(path, mode)
else:
return open(path, mode)
with open_local_or_gcs(vocab_file, 'wb') as f:
cPickle.dump(self.words, f)