Can we install tensorflow==0.11.0rc0 version in colab , as one of the pre-trained model code I use is coded in this version
You can install any version of TensorFlow in google collab.
However, there are specific versions that are available, so you may want to pick from those options . version 0.11.0rc0 is not currently available.
!pip install tensorflow==1.1.0rc0. #install a tensorflow version
import tensorflow as tf # import tensorflow
print(tf.__version__). # print tensorflow version
Here is the list of available versions as of now.
0.12.1, 1.0.0, 1.0.1, 1.1.0rc0, 1.1.0rc1, 1.1.0rc2, 1.1.0, 1.2.0rc0, 1.2.0rc1, 1.2.0rc2, 1.2.0, 1.2.1, 1.3.0rc0, 1.3.0rc1, 1.3.0rc2, 1.3.0, 1.4.0rc0, 1.4.0rc1, 1.4.0, 1.4.1, 1.5.0rc0, 1.5.0rc1, 1.5.0, 1.5.1, 1.6.0rc0, 1.6.0rc1, 1.6.0, 1.7.0rc0, 1.7.0rc1, 1.7.0, 1.7.1, 1.8.0rc0, 1.8.0rc1, 1.8.0, 1.9.0rc0, 1.9.0rc1, 1.9.0rc2, 1.9.0, 1.10.0rc0, 1.10.0rc1, 1.10.0, 1.10.1, 1.11.0rc0, 1.11.0rc1, 1.11.0rc2, 1.11.0, 1.12.0rc0, 1.12.0rc1, 1.12.0rc2, 1.12.0, 1.12.2, 1.12.3, 1.13.0rc0, 1.13.0rc1, 1.13.0rc2, 1.13.1, 1.13.2, 1.14.0rc0, 1.14.0rc1, 1.14.0, 1.15.0rc0, 1.15.0rc1, 1.15.0rc2, 1.15.0rc3, 1.15.0, 1.15.2, 1.15.3, 2.0.0a0, 2.0.0b0, 2.0.0b1, 2.0.0rc0, 2.0.0rc1, 2.0.0rc2, 2.0.0, 2.0.1, 2.0.2, 2.1.0rc0, 2.1.0rc1, 2.1.0rc2, 2.1.0, 2.1.1, 2.2.0rc0, 2.2.0rc1, 2.2.0rc2, 2.2.0rc3, 2.2.0rc4, 2.2.0, 2.3.0rc0, 2.3.0rc1, 2.3.0rc2, 2.3.0
Related
I have python 3.10.6 installed.
i am following the TFOD course
i installed TensorFlow 2.11
while training the mode the gpu was not being used and therefore the process was very slow.
then i installed CUDA 11.2 and Cudnn 8.1
but when i installed tensorflow-gpu 2.11.0 it says
ERROR: Could not find a version that satisfies the requirement tensorflow-gpu==2.11 (from versions: 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)
ERROR: No matching distribution found for tensorflow-gpu==2.11
alter
I tried uninstalling TensorFlow 2.11 and installing 2.10,
and installed tensorflow2.10.0.
Did not update cuda and cudnn as same versions were required
I was hoping that it would run on GPU this time. but it did not
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 $(brew --repository homebrew/core) checkout master
Now verify the installation of the brew command:
which brew
The command should output /usr/local/bin/brew, which is the expected path.
In Colab notebook, I did:
!pip install pandas==1.4.1
but returned:
ERROR: Could not find a version that satisfies the requirement pandas==1.4.1 (from versions: 0.1, 0.2, 0.3.0, 0.4.0, 0.4.1, 0.4.2, 0.4.3, 0.5.0, 0.6.0, 0.6.1, 0.7.0, 0.7.1, 0.7.2, 0.7.3, 0.8.0, 0.8.1, 0.9.0, 0.9.1, 0.10.0, 0.10.1, 0.11.0, 0.12.0, 0.13.0, 0.13.1, 0.14.0, 0.14.1, 0.15.0, 0.15.1, 0.15.2, 0.16.0, 0.16.1, 0.16.2, 0.17.0, 0.17.1, 0.18.0, 0.18.1, 0.19.0, 0.19.1, 0.19.2, 0.20.0, 0.20.1, 0.20.2, 0.20.3, 0.21.0, 0.21.1, 0.22.0, 0.23.0, 0.23.1, 0.23.2, 0.23.3, 0.23.4, 0.24.0, 0.24.1, 0.24.2, 0.25.0, 0.25.1, 0.25.2, 0.25.3, 1.0.0, 1.0.1, 1.0.2, 1.0.3, 1.0.4, 1.0.5, 1.1.0, 1.1.1, 1.1.2, 1.1.3, 1.1.4, 1.1.5, 1.2.0, 1.2.1, 1.2.2, 1.2.3, 1.2.4, 1.2.5, 1.3.0, 1.3.1, 1.3.2, 1.3.3, 1.3.4, 1.3.5)
ERROR: No matching distribution found for pandas==1.4.1
Any idea how to upgrade to pandas==1.4.1 in colab?
pandas 1.4+ requires Python >= 3.8. From the list of available versions I can guess you use Python 3.7 or lower.
Upgrade Python or use lower version of pandas. Just pip install pandas should find compatible version.
While following Cloud TPU profiling guide and Bert FineTuning, I got error when creating Profile data.
Scalars and Graphs in TensorBoards are working well.
Is there anything I'm missing?
Configurations
Zone: us-central1-a(Both for Storage and TPU)
TPU Type: v3-8
TPU software version: tpu-vm-tf-2.7.0
TPU Architecture: TPU VM
Error log
Using CLI
(Run when training is process)
> capture_tpu_profile --tpu=bert-tpu --logdir=${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 trying to use keras-tcn:
https://github.com/philipperemy/keras-tcn
But it seems that there is some conflict.
Installing it is downgrading keras from 2.4.3 to 2.3.1. But keras 2.3.1 seems to need tensorflow 2.1.0.
Yet by trying to install tensorflow:
pip install tensorflow == 2.1.0, I do have this error message:
ERROR: Could not find a version that satisfies the requirement tensorflow==2.1.0 (from versions: 2.2.0rc1, 2.2.0rc2, 2.2.0rc3, 2.2.0rc4, 2.2.0, 2.2.1, 2.3.0rc0, 2.3.0rc1, 2.3.0rc2, 2.3.0, 2.3.1, 2.4.0rc0, 2.4.0rc1, 2.4.0rc2, 2.4.0rc3)
ERROR: No matching distribution found for tensorflow==2.1.0
Does anyone have some solutions for installing it ?
Here are some infos that might be useful
pip : 20.2.4
python : 3.8.5
Downgrade your python to 3.7 and install tensorflow 2.1