google-colaboratory

问题 Google colab brings TPUs in the Runtime Accelerator. I found an example, How to use TPU in Official Tensorflow github. But the example not worked on google-colaboratory. It stuck on following line: tf.contrib.tpu.keras_to_tpu_model(model, strategy=strategy) When I print available devices on colab it return [] for TPU accelerator. Does anyone knows how to use TPU on colab? 回答1: Here's a Colab-specific TPU example: https://colab.research.google.com/github/tensorflow/tpu/blob/master/tools/colab

问题 I have been stuck on how to download a pandas DataFrame into my local disk (or onto Google Drive) after creating it in Google Colab. I have tried converting it to bytes, a string (using pd.to_string), a dict (pd.to_dict), but nothing seems to work. Additionally, I've looked at using the drive.CreateFile method as explained in the intro Colab Code and as specified here: How to download file created in Colaboratory workspace?, but I'm not sure how to apply this to pandas. Any help is

问题 print ('Files in Drive:') !ls drive/AI Files in Drive: database.sqlite Reviews.csv Untitled0.ipynb fine_food_reviews.ipynb Titanic.csv When I run the above code in Google Colab, clearly my sqlite file is present in my drive. But whenever I run some query on this file, it says # using the SQLite Table to read data. con = sqlite3.connect('database.sqlite') #filtering only positive and negative reviews i.e. # not taking into consideration those reviews with Score=3 filtered_data = pd.read_sql

问题 My final goal is to find a way to 3d visualize all layers during the training something like tensorSpace but real time during the training phase . I am using tensorflow and google colab. I managed to get each output like a tensor by creating a custom callback and creating this method def on_train_batch_end(self, batch, logs=None): for i in range(len(model_inception.layers)): get_layer_output = K.function(inputs = self.model.layers[i].input, outputs = self.model.layers[i].output) print('\n

问题 I've been using Google Colab with the GPU backend. On December when I used it, the disk size for the GPU backend was more than 300 GB. Now running df -h on the virtual machine shows this: Filesystem Size Used Avail Use% Mounted on overlay 69G 33G 33G 50% / tmpfs 64M 0 64M 0% /dev tmpfs 6.4G 0 6.4G 0% /sys/fs/cgroup /dev/sda1 75G 37G 39G 49% /opt/bin tmpfs 6.4G 12K 6.4G 1% /var/colab shm 5.9G 4.0K 5.9G 1% /dev/shm tmpfs 6.4G 0 6.4G 0% /proc/acpi tmpfs 6.4G 0 6.4G 0% /proc/scsi tmpfs 6.4G 0 6

问题 I have the following google colab code: code : !wget https://bin.equinox.io/c/4VmDzA7iaHb/ngrok-stable-linux-amd64.zip !unzip ngrok-stable-linux-amd64.zip LOG_DIR = './log' get_ipython().system_raw( 'tensorboard --logdir {} --host 0.0.0.0 --port 6006 &' .format(LOG_DIR) ) get_ipython().system_raw('./ngrok http 6006 &') ! curl -s http://localhost:4040/api/tunnels | python3 -c \ "import sys, json; print(json.load(sys.stdin)['tunnels'][0]['public_url'])" output: https://6a112ff8.ngrok.io My

问题 Currently google colaboratory uses tensorflow 1.4.1. I want to upgrade it to 1.5.0 version. Each time when i executed !pip install --upgrade tensorflow command, notebook instance succesfully upgrades the tensorflow version to 1.5.0. But after upgrade operation tensorflow instance only supports "CPU". When i have executed this command it shows nothing : from tensorflow.python.client import device_lib device_lib.list_local_devices() Should there be another way for upgrading tensorflow ? such as

问题 I am using text analytics library "Spacy". I've installed spacy on Google Colab notebook without any issue. But for using it I need to download "en" model. Generally, that command should look like this: python -m spacy download en I tried few ways but I am not able to get it to install on the notebook. Looking for help. Cheers 回答1: If you have a Python interpreter but not a teriminal, you could try: import spacy.cli spacy.cli.download("en_core_web_sm") More manual alternatives can be found

问题 I am using pip3 install tensorflow==1.8.0 , but it doesn't have GPU support. So I am using pip3 install tensorflow-gpu==1.8.0 , but it still raises an exception libcudart.so.VERSION No such file. Should I use colab to install tensorflow from source? After pip3 list : tensorboard 1.10.0 tensorflow 1.10.0 tensorflow-hub 0.1.1 回答1: You can downgrade Tensorflow to a previous version without GPU support on Google Colab. I ran: !pip install tensorflow==1.12.0 import tensorflow as tf print(tf._

问题 I am working on a Dataset of 70gb Earlier using df -BG command I was being shown Filesystem 1G-blocks Used Available Use% Mounted on overlay 359G 6G 335G 2% / tmpfs 7G 0G 7G 0% /dev tmpfs 7G 0G 7G 0% /sys/fs/cgroup /dev/root 2G 1G 1G 44% /opt/bin tmpfs 7G 1G 7G 4% /usr/lib64-nvidia /dev/sda1 365G 8G 358G 3% /etc/hosts shm 1G 0G 1G 0% /dev/shm tmpfs 7G 0G 7G 0% /sys/firmware Suddenly Now it has changed to Filesystem 1G-blocks Used Available Use% Mounted on overlay 40G 5G 33G 14% / tmpfs 7G 0G