Before all manipulation, i created new virtual environment, and watch pip list, his have two modules:
pip 21.2.3
setuptools 57.4.0
Why, after this command:
pip install selenium
I watch this list modules?:
async-generator 1.10
attrs 21.4.0
certifi 2022.6.15
cffi 1.15.0
cryptography 37.0.2
h11 0.13.0
idna 3.3
outcome 1.2.0
pip 21.2.3
pycparser 2.21
pyOpenSSL 22.0.0
PySocks 1.7.1
selenium 4.2.0
setuptools 57.4.0
sniffio 1.2.0
sortedcontainers 2.4.0
trio 0.21.0
trio-websocket 0.9.2
urllib3 1.26.9
wsproto 1.1.0
I'ts normal? If True, please, send link to this info
Related
I am using a MacBook Pro M1 and try to install the package dmol-book, which has tensorflow-dependency, via pip3 install dmol-book==1.3.2 .
It throws the following error:
ERROR: Ignored the following versions that require a different python version: 1.0.1 Requires-Python >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3,!=3.4.*,<3.9; 1.1.0 Requires-Python >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,<3.9; 1.1.1 Requires-Python >=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,<3.9
ERROR: Could not find a version that satisfies the requirement tensorflow>=2.7 (from dmol-book) (from versions: none)
ERROR: No matching distribution found for tensorflow>=2.7
I have installed tensorflow via
pip3 install tensorflow-macos
pip3 install tensorflow-metal
I can use tensorflow within python3:
>>> import tensorflow
>>> tensorflow.__version__
'2.11.0'
>>> tensorflow.__file__
'/Users/username/.pyenv/versions/3.10.7/envs/venv/lib/python3.10/site-packages/tensorflow/__init__.py'
but pip3 list does not list tensorflow, only
tensorboard 2.11.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.8.1
tensorboardX 2.5.1
tensorflow-estimator 2.11.0
tensorflow-macos 2.11.0
tensorflow-metal 0.7.0
tensorstore 0.1.28
tensorflow also shows up as a directory in the original virtual environment directory, that I created:
/Users/username/pathtovirtualenv/venv/lib/python3.10/site-packages/tensorflow
Can someone help me to resolve this issue?
Thanks in advance!
I think its because it does not find a version that satisfies the requirement as mentioned.
Colab does not allow to downgrade TensorFlow and says that only versions 2 is available.
here is code and output:
!pip install tensorflow-gpu==1.15.2
import tensorflow as tf
print(tf.__version__)
output:
Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/
ERROR: Could not find a version that satisfies the requirement tensorflow-gpu==1.15.2 (from versions: 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.0, 2.3.1, 2.3.2, 2.3.3, 2.3.4, 2.4.0, 2.4.1, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0rc0, 2.7.0rc1, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0rc0, 2.8.0rc1, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0rc0, 2.9.0rc1, 2.9.0rc2, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0rc0, 2.10.0rc1, 2.10.0rc2, 2.10.0rc3, 2.10.0, 2.10.1, 2.11.0rc0, 2.11.0rc1, 2.11.0rc2, 2.11.0)
ERROR: No matching distribution found for tensorflow-gpu==1.15.2
2.9.2
I tried this code by creating some new projects in Colab but I got no new results
To downgrade tensorflow in google colab you must downgrade the version of python because python3.8 is not compatible with tensorflow1.x. So, Downgrade the version of python to python3.7 and install tensorflow1.x. This will work.
1.At first, Install python==3.7 version.
!sudo apt-get update -y
!sudo apt-get install python3.7
#change alternatives
!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.8 1
!sudo update-alternatives --install /usr/bin/python3 python3 /usr/bin/python3.7 2
#check python version
!python --version
The above code will install python3.7 version, but it is not mapped to colab kernel, so we must map newly installed python.
# install pip for python==3.7
!sudo apt-get install python3.7-distutils
!wget https://bootstrap.pypa.io/get-pip.py
!python get-pip.py
# install colab dependencies
!python -m pip install ipython ipython_genutils ipykernel jupyter_console prompt_toolkit httplib2 astor
# link to the old google package
!ln -s /usr/local/lib/python3.8/dist-packages/google \
/usr/local/lib/python3.7/dist-packages/google
Let's check version of python again, it will be python==3.7.16:
!python --version
Now, it's time to install tensorflow 1.x.
!pip install tensorflow==1.x
I hope that it will help to fix your issue.Thank you!
I have been finished install Tensorflow env step by step from "https://developer.apple.com/metal/tensorflow-plugin/"
Tf is working!numpy is working! scipy is working!
but when i import sklearn package, have an error message like this:
ImportError: dlopen(/Users/mecilmeng/miniforge3/envs/tf/lib/python3.9/site-packages/scipy/spatial/qhull.cpython-39-darwin.so, 0x0002): Library not loaded: #rpath/liblapack.3.dylib
Referenced from: /Users/mecilmeng/miniforge3/envs/tf/lib/python3.9/site-packages/scipy/spatial/qhull.cpython-39-darwin.so
Reason: tried: '/Users/mecilmeng/miniforge3/envs/tf/lib/liblapack.3.dylib' (no such file), '/Users/mecilmeng/miniforge3/envs/tf/lib/liblapack.3.dylib' (no such file), '/Users/mecilmeng/miniforge3/envs/tf/lib/python3.9/site-packages/scipy/spatial/../../../../liblapack.3.dylib' (no such file), '/Users/mecilmeng/miniforge3/envs/tf/lib/liblapack.3.dylib' (no such file), '/Users/mecilmeng/miniforge3/envs/tf/lib/liblapack.3.dylib' (no such file), '/Users/mecilmeng/miniforge3/envs/tf/lib/python3.9/site-packages/scipy/spatial/../../../../liblapack.3.dylib' (no such file), '/Users/mecilmeng/miniforge3/envs/tf/bin/../lib/liblapack.3.dylib' (no such file), '/Users/mecilmeng/miniforge3/envs/tf/bin/../lib/liblapack.3.dylib' (no such file), '/usr/local/lib/liblapack.3.dylib' (no such file), '/usr/lib/liblapack.3.dylib' (no such file)
How to fix it?
pip list
Package Version
------------------------ -------------------
absl-py 0.10.0
aiohttp 3.8.1
aiosignal 1.2.0
anyio 3.5.0
appnope 0.1.2
argon2-cffi 20.1.0
astunparse 1.6.3
async-generator 1.10
async-timeout 4.0.1
attrs 21.4.0
Babel 2.9.1
backcall 0.2.0
beniget 0.3.0
bleach 4.1.0
blinker 1.4
Bottleneck 1.3.2
brotlipy 0.7.0
cached-property 1.5.2
cachetools 4.2.2
certifi 2021.10.8
cffi 1.15.0
charset-normalizer 2.0.4
click 8.0.3
cryptography 3.4.7
cycler 0.11.0
Cython 0.29.28
debugpy 1.5.1
decorator 5.1.1
defusedxml 0.7.1
dill 0.3.4
entrypoints 0.3
flatbuffers 2.0
fonttools 4.25.0
frozenlist 1.2.0
gast 0.4.0
google-auth 1.33.0
google-auth-oauthlib 0.4.1
google-pasta 0.2.0
googleapis-common-protos 1.54.0
grpcio 1.42.0
h5py 3.1.0
idna 3.3
importlib-metadata 4.8.2
ipykernel 6.4.1
ipython 7.31.1
ipython-genutils 0.2.0
jedi 0.18.1
Jinja2 3.0.2
joblib 1.1.0
json5 0.9.6
jsonschema 3.2.0
jupyter-client 7.1.2
jupyter-core 4.9.1
jupyter-server 1.13.5
jupyterlab 3.2.1
jupyterlab-pygments 0.1.2
jupyterlab-server 2.10.3
keras 2.8.0
Keras-Preprocessing 1.1.2
kiwisolver 1.3.1
libclang 13.0.0
Markdown 3.3.4
MarkupSafe 2.0.1
matplotlib 3.5.0
matplotlib-inline 0.1.2
mistune 0.8.4
multidict 5.2.0
munkres 1.1.4
nbclassic 0.2.6
nbclient 0.5.3
nbconvert 6.3.0
nbformat 5.1.3
nest-asyncio 1.5.1
networkx 2.6.3
notebook 6.4.6
numexpr 2.8.1
numpy 1.22.2
oauthlib 3.1.1
opencv-python 4.5.5.62
opt-einsum 3.3.0
packaging 21.3
pandas 1.3.5
pandocfilters 1.5.0
parso 0.8.3
pexpect 4.8.0
pickleshare 0.7.5
Pillow 9.0.1
pip 21.2.4
ply 3.11
prometheus-client 0.13.1
promise 2.3
prompt-toolkit 3.0.20
protobuf 3.19.1
ptyprocess 0.7.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pybind11 2.9.1
pycparser 2.21
Pygments 2.11.2
PyJWT 2.1.0
pyOpenSSL 21.0.0
pyparsing 3.0.4
pyrsistent 0.18.0
PySocks 1.7.1
python-dateutil 2.8.2
pythran 0.9.11
pytz 2021.3
pyzmq 22.3.0
requests 2.27.1
requests-oauthlib 1.3.0
rsa 4.7.2
scikit-learn 1.0.2
scipy 1.7.1
Send2Trash 1.8.0
setuptools 58.0.4
six 1.15.0
sklearn 0.0
sniffio 1.2.0
tensorboard 2.8.0
tensorboard-data-server 0.6.1
tensorboard-plugin-wit 1.6.0
tensorflow-datasets 4.5.2
tensorflow-macos 2.8.0
tensorflow-metadata 1.6.0
tensorflow-metal 0.3.0
termcolor 1.1.0
terminado 0.13.1
testpath 0.5.0
tf-estimator-nightly 2.8.0.dev2021122109
threadpoolctl 2.2.0
tornado 6.1
tqdm 4.62.3
traitlets 5.1.1
typing-extensions 3.7.4.3
urllib3 1.26.8
wcwidth 0.2.5
webencodings 0.5.1
websocket-client 0.58.0
Werkzeug 2.0.2
wheel 0.35.1
wrapt 1.12.1
yarl 1.6.3
zipp 3.7.0
You can install using Rosetta2 Mode.
To work in Rosetta Mode:
If Rosetta 2 is not installed by default in your M1 Mac, then open the pre-installed Terminal app and run the following command:
/usr/sbin/softwareupdate --install-rosetta --agree-to-license
Rosetta allows us to use apps built for Mac with intel chip.
Several CLI tools do not have native versions built for the new M1 architecture.
Enabling them on your native M1 Mac terminal can be frustrating.
Follow these steps to enable Rosetta:
Select the app(Terminal) in the Finder.
Right click on the app(Terminal) and select Get Info.
In General, check the Open using Rosetta check-box.
Close the Terminal Info.
Now when you quit the terminal and open it again.
If you haven't installed Rosetta yet, then it would prompt you to install it.
If the popup shows up, then click on Install button, then enter your user name and password to allow installation to proceed.
Close the Terminal and open again.
Now we have a special terminal that can install tools with Rosetta translation.
To verify that you are using a Rosetta terminal, run the following command and it should output i386:
arch
The native terminal without Rosetta would output arm64 for the above command.
Moving forward, all commands we ask you to execute should be done in Rosetta enabled terminal.
Uninstall arm64 brew
If you have installed brew in the past from the native terminal, it is likely that you have an arm64 build of brew. Having two different builds of brew can cause major problems as the packages with different builds will not be compatible with each other.
To avoid this problem you need to uninstall your current installation of arm64 brew.
You can check which build you have by running the following command:
which brew
If your installation of brew is the Intel build, then the command should output /usr/local/bin/brew. If that is the case you can skip installing brew and just update your current installation by running brew update.
If your output is /opt/homebrew then your installation of brew is the arm64 build.
You need to uninstall the arm64 build of brew by running the following command from the native terminal:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/uninstall.sh)"
Install Intel brew
Install Homebrew, which is the package manager:
/bin/bash -c "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/HEAD/install.sh)"
Once done, run the below command to ensure that we make use of the HEAD revision:
git -C $(brew --repository homebrew/core) checkout master
Now verify the installation of the brew command:
which brew
The command should output /usr/local/bin/brew, which is the expected path.
While following Cloud TPU profiling guide and Bert FineTuning, I got error when creating Profile data.
Scalars and Graphs in TensorBoards are working well.
Is there anything I'm missing?
Configurations
Zone: us-central1-a(Both for Storage and TPU)
TPU Type: v3-8
TPU software version: tpu-vm-tf-2.7.0
TPU Architecture: TPU VM
Error log
Using CLI
(Run when training is process)
> capture_tpu_profile --tpu=bert-tpu --logdir=${MODEL_DIR} --duration_ms=3000
2022-01-20 06:34:29.301737: I tensorflow/core/tpu/tpu_initializer_helper.cc:68] libtpu.so already in used by another process. Not attempting to load libtpu.so in this process.
2022-01-20 06:34:29.301787: I tensorflow/core/tpu/tpu_api_dlsym_initializer.cc:116] Libtpu path is: libtpu.so
WARNING: Logging before InitGoogle() is written to STDERR
I0120 06:34:29.324573 67944 tpu_initializer_helper.cc:68] libtpu.so already in used by another process. Not attempting to load libtpu.so in this process.
2022-01-20 06:34:29.336671: I tensorflow/core/tpu/tpu_initializer_helper.cc:68] libtpu.so already in used by another process. Not attempting to load libtpu.so in this process.
2022-01-20 06:34:31.607899: I tensorflow/stream_executor/tpu/tpu_platform_interface.cc:77] No TPU platform registered. Waiting 1 second and trying again... (4 tries left)
2022-01-20 06:34:32.608170: I tensorflow/stream_executor/tpu/tpu_platform_interface.cc:77] No TPU platform registered. Waiting 1 second and trying again... (3 tries left)
2022-01-20 06:34:33.608461: I tensorflow/stream_executor/tpu/tpu_platform_interface.cc:77] No TPU platform registered. Waiting 1 second and trying again... (2 tries left)
2022-01-20 06:34:34.608757: I tensorflow/stream_executor/tpu/tpu_platform_interface.cc:77] No TPU platform registered. Waiting 1 second and trying again... (1 tries left)
2022-01-20 06:34:35.609050: I tensorflow/stream_executor/tpu/tpu_platform_interface.cc:74] No TPU platform found.
TensorFlow version 2.7.0 detected
Welcome to the Cloud TPU Profiler v2.4.0
I0120 06:34:35.628104 140127504198720 discovery.py:280] URL being requested: GET https://www.googleapis.com/discovery/v1/apis/tpu/v1/rest
I0120 06:34:35.709828 140127504198720 discovery.py:911] URL being requested: GET https://tpu.googleapis.com/v1/projects/elsa-270714/locations/us-central1-a/nodes/bert-tpu?alt=json
I0120 06:34:35.710047 140127504198720 transport.py:157] Attempting refresh to obtain initial access_token
I0120 06:34:35.710207 140127504198720 client.py:777] Refreshing access_token
I0120 06:34:35.806093 140127504198720 discovery.py:280] URL being requested: GET https://www.googleapis.com/discovery/v1/apis/tpu/v1/rest
I0120 06:34:35.838788 140127504198720 discovery.py:911] URL being requested: GET https://tpu.googleapis.com/v1/projects/elsa-270714/locations/us-central1-a/nodes/bert-tpu?alt=json
I0120 06:34:35.838929 140127504198720 transport.py:157] Attempting refresh to obtain initial access_token
I0120 06:34:35.839013 140127504198720 client.py:777] Refreshing access_token
Starting to trace for 3000 ms. Remaining attempt(s): 2
No trace event is collected. Automatically retrying.
Starting to trace for 3000 ms. Remaining attempt(s): 1
No trace event is collected. Automatically retrying.
Starting to trace for 3000 ms. Remaining attempt(s): 0
No trace event is collected after 3 attempt(s). Perhaps, you want to try again (with more attempts?).
Tip: increase number of attempts with --num_tracing_attempts.
Using TensorBoard
(TPU name: bert-tpu)
Packages
> pip3 list
Package Version
--------------------------------- --------------------
absl-py 0.12.0
anyio 3.5.0
argon2-cffi 21.3.0
argon2-cffi-bindings 21.2.0
asttokens 2.0.5
astunparse 1.6.3
attrs 19.3.0
Automat 0.8.0
Babel 2.9.1
backcall 0.2.0
backports.entry-points-selectable 1.1.1
black 21.12b0
bleach 4.1.0
blinker 1.4
cachetools 4.2.4
certifi 2021.10.8
cffi 1.15.0
chardet 3.0.4
charset-normalizer 2.0.7
click 8.0.3
cloud-init 21.4
cloud-tpu-client 0.10
cloud-tpu-profiler 2.4.0
colorama 0.4.3
command-not-found 0.3
configobj 5.0.6
constantly 15.1.0
cryptography 2.8
cycler 0.11.0
Cython 0.29.24
dbus-python 1.2.16
debugpy 1.5.1
decorator 5.1.1
defusedxml 0.7.1
dill 0.3.4
distlib 0.3.3
distro 1.4.0
distro-info 0.23ubuntu1
dm-tree 0.1.6
entrypoints 0.3
executing 0.8.2
filelock 3.4.0
flatbuffers 2.0
fonttools 4.28.5
future 0.18.2
gast 0.4.0
gin-config 0.5.0
google-api-core 1.31.4
google-api-python-client 1.8.0
google-auth 1.35.0
google-auth-httplib2 0.1.0
google-auth-oauthlib 0.4.6
google-pasta 0.2.0
googleapis-common-protos 1.53.0
grpcio 1.42.0
gviz-api 1.10.0
h5py 3.6.0
httplib2 0.20.2
hyperlink 19.0.0
idna 3.3
importlib-metadata 4.8.2
importlib-resources 5.4.0
incremental 16.10.1
intel-openmp 2021.4.0
ipykernel 6.7.0
ipython 8.0.0
ipython-genutils 0.2.0
jax 0.2.25
jaxlib 0.1.74
jedi 0.18.1
Jinja2 2.10.1
joblib 1.1.0
json5 0.9.6
jsonpatch 1.22
jsonpointer 2.0
jsonschema 3.2.0
jupyter-client 7.1.1
jupyter-core 4.9.1
jupyter-server 1.13.3
jupyterlab 3.2.8
jupyterlab-pygments 0.1.2
jupyterlab-server 2.10.3
kaggle 1.5.12
keras 2.7.0
Keras-Applications 1.0.8
Keras-Preprocessing 1.1.2
keyring 18.0.1
kiwisolver 1.3.2
language-selector 0.1
launchpadlib 1.10.13
lazr.restfulclient 0.14.2
lazr.uri 1.0.3
libclang 12.0.0
Markdown 3.3.6
MarkupSafe 1.1.0
matplotlib 3.5.1
matplotlib-inline 0.1.3
mistune 0.8.4
mkl 2021.4.0
mkl-include 2021.4.0
mock 4.0.3
more-itertools 4.2.0
mypy-extensions 0.4.3
nbclassic 0.3.5
nbclient 0.5.10
nbconvert 6.4.0
nbformat 5.1.3
nest-asyncio 1.5.4
netifaces 0.10.4
notebook 6.4.7
numpy 1.18.5
oauth2client 4.1.3
oauthlib 3.1.0
opencv-python-headless 4.5.5.62
opt-einsum 3.3.0
packaging 21.3
pandas 1.3.5
pandocfilters 1.5.0
parso 0.8.3
pathspec 0.9.0
pexpect 4.6.0
pickleshare 0.7.5
Pillow 8.4.0
pip 21.3.1
platformdirs 2.4.0
portalocker 2.3.2
prometheus-client 0.12.0
promise 2.3
prompt-toolkit 3.0.24
protobuf 3.19.1
psutil 5.9.0
ptyprocess 0.7.0
pure-eval 0.2.1
py-cpuinfo 8.0.0
pyasn1 0.4.8
pyasn1-modules 0.2.8
pycocotools 2.0.4
pycparser 2.21
Pygments 2.11.2
PyGObject 3.36.0
PyHamcrest 1.9.0
PyJWT 1.7.1
pymacaroons 0.13.0
PyNaCl 1.3.0
pyOpenSSL 19.0.0
pyparsing 3.0.6
pyrsistent 0.15.5
pyserial 3.4
python-apt 2.0.0+ubuntu0.20.4.6
python-dateutil 2.8.2
python-debian 0.1.36ubuntu1
python-slugify 5.0.2
pytz 2021.3
PyYAML 5.4.1
pyzmq 22.3.0
regex 2022.1.18
requests 2.26.0
requests-oauthlib 1.3.0
requests-unixsocket 0.2.0
rsa 4.7.2
sacrebleu 2.0.0
scikit-learn 1.0.2
scipy 1.7.2
SecretStorage 2.3.1
Send2Trash 1.8.0
sentencepiece 0.1.96
seqeval 1.2.2
service-identity 18.1.0
setuptools 59.2.0
simplejson 3.16.0
six 1.16.0
sniffio 1.2.0
sos 4.1
ssh-import-id 5.10
stack-data 0.1.4
systemd-python 234
tabulate 0.8.9
tbb 2021.4.0
tensorboard 2.7.0
tensorboard-data-server 0.6.1
tensorboard-plugin-profile 2.5.0
tensorboard-plugin-wit 1.8.0
tensorflow 2.7.0
tensorflow-addons 0.15.0
tensorflow-datasets 4.4.0
tensorflow-estimator 2.7.0
tensorflow-hub 0.12.0
tensorflow-io-gcs-filesystem 0.22.0
tensorflow-metadata 1.5.0
tensorflow-model-optimization 0.7.0
tensorflow-text 2.7.0rc1
termcolor 1.1.0
terminado 0.12.1
testpath 0.5.0
text-unidecode 1.3
tf-slim 1.1.0
threadpoolctl 3.0.0
tomli 1.2.3
torch 1.11.0a0+git4635f57
torch-xla 1.11+73a3937
torchvision 0.12.0a0+59baae9
tornado 6.1
tqdm 4.62.3
traitlets 5.1.1
Twisted 18.9.0
typeguard 2.13.3
typing_extensions 4.0.0
ubuntu-advantage-tools 27.4
ufw 0.36
unattended-upgrades 0.1
uritemplate 3.0.1
urllib3 1.26.7
virtualenv 20.10.0
wadllib 1.3.3
wcwidth 0.2.5
webencodings 0.5.1
websocket-client 1.2.3
Werkzeug 2.0.2
wheel 0.34.2
wrapt 1.13.3
zipp 3.7.0
zope.interface 4.7.1
Unfortunately capture_tpu_profile doesn't work with TPU VM.
If you're using TF2/Keras, one very accessible way is to use the TensorBoard Callback and set profile_batch=1 for instance. This would work for v3-8 but unfortunately wouldn't work for >v3-8.
Alternatively, you can use tf.profiler.experimental.start(...) and tf.profiler.experimental.stop() which is what the TensorBoard callback uses under the hood.
If you're using >v3-8 (for instance v3-32) you can use tf.profiler.experimental.client.trace() where service_addr is accessible from TPUClusterResolver's get_master() function.
I'm going over these example from google-cloud Coursera courses, and although they worked till a few weeks ago, I can't install tf.transform or apache_beam on Datalab anymore.
https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/feateng/tftransform.ipynb
https://github.com/GoogleCloudPlatform/training-data-analyst/blob/master/courses/machine_learning/deepdive/06_structured/4_preproc_tft.ipynb
When installing tensorflow_transform I get the following errors:
%bash
pip install --upgrade --force tensorflow_transform==0.6.0
twisted 18.7.0 requires PyHamcrest>=1.9.0, which is not installed.
datalab 1.1.3 has requirement six==1.10.0, but you'll have six 1.11.0 which is incompatible.
gapic-google-cloud-pubsub-v1 0.15.4 has requirement oauth2client<4.0dev,>=2.0.0, but you'll have oauth2client 4.1.2 which is incompatible.
proto-google-cloud-pubsub-v1 0.15.4 has requirement oauth2client<4.0dev,>=2.0.0, but you'll have oauth2client 4.1.2 which is incompatible.
apache-airflow 1.9.0 has requirement bleach==2.1.2, but you'll have bleach 1.5.0 which is incompatible.
apache-airflow 1.9.0 has requirement funcsigs==1.0.0, but you'll have funcsigs 1.0.2 which is incompatible.
google-cloud-monitoring 0.28.0 has requirement google-cloud-core<0.29dev,>=0.28.0, but you'll have google-cloud-core 0.25.0 which is incompatible.
proto-google-cloud-datastore-v1 0.90.4 has requirement oauth2client<4.0dev,>=2.0.0, but you'll have oauth2client 4.1.2 which is incompatible.
pandas-gbq 0.3.0 has requirement google-cloud-bigquery>=0.28.0, but you'll have google-cloud-bigquery 0.25.0 which is incompatible.
googledatastore 7.0.1 has requirement httplib2<0.10,>=0.9.1, but you'll have httplib2 0.11.3 which is incompatible.
googledatastore 7.0.1 has requirement oauth2client<4.0.0,>=2.0.1, but you'll have oauth2client 4.1.2 which is incompatible.
Cannot uninstall 'dill'. It is a distutils installed project and thus we cannot accurately determine which files belong to it which would lead to only a partial uninstall.
The tensorflow version on my Datalab instance was 1.4.
I had to add this one line of code to update tensorflow to 1.10.1
%bash
pip install --upgrade --force-reinstall pip==10.0.1
pip install tensorflow==1.10.1
pip install tensorflow_transform
my environment:
apache-airflow==1.9.0
apache-beam==2.6.0
tensorflow==1.10.1
tensorflow-metadata==0.9.0
tensorflow-tensorboard==0.4.0rc3
tensorflow-transform==0.8.0
The current version of Datalab uses TensorFlow 1.8, so please change the notebook cell in question to:
%bash
pip uninstall -y google-cloud-dataflow
pip install --upgrade --force tensorflow_transform==0.8.0 apache-beam[gcp]
I've updated and checked in the two notebooks linked above.
Another problem might be that you are using Python 2. Datalab by default now uses Python 3 and your pip install (above) happens in Python 3 even if the kernel is Python 2 because %%bash opens up a new shell in which the conda activate of Python 2 has not happened.
To make sure the pip install happens in Python 2, change your pip install of apache-beam[gcp] as follows:
%%bash
source activate py2env
conda install -y dill pytz # do this for all the distutils complaints
pip uninstall -y google-cloud-dataflow
pip install --upgrade --force tensorflow_transform==0.8.0 apache-beam[gcp]