We have not released anything for python3.9.

Release process is as follows:

1. Ensure all of our dependencies support python3.9 (not yet there)
2. Ensure our code works with all these dependencies on all supported versions of python (at the moment, at least gast needs additional support work).
3. Release nightly version supporting the new python version
4. Release a full release.

As we are already doing the 2.4 release, python3.9 support for TF will come in 2.5 release at the earliest.

@ravikyram, @amahendrakar, @rmothukuru let's deduplicate to this issue all py39 requests/issues. I'll monitor this one and provide updates as we start adding support.

@mihaimaruseac,
Sure

I may be wrong, but I believe this is the case:

python3.8 -m pipdeptree -p tensorflow | grep "^  -" | sed "s/  - //" | sed "s/ \[.*]//" | xargs python3.9 -m pip install

which is essentially

python3.9 -m pip install absl-py astunparse flatbuffers gast google-pasta grpcio h5py keras-preprocessing numpy opt-einsum protobuf six tensorboard tensorflow-estimator termcolor typing-extensions wheel wrapt

works fine for me. Am I missing anything?

(from #40840 (comment))

The main issue is that we also have C++ code and we need the needed dependencies to also upgrade for their C++ code. By this time, likely most are solved, so 2.5 release will have py3.9 support.

We should get py3.9 in nightly soon.

So, all of our depdencies have a release for py3.9 but our setup.py upper bounds to versions which are below that release. We will need to increase these bounds while making sure we don't break existing usecases/tests. Will slowly happen over December.

So, all of our depdencies have a release for py3.9 but our setup.py upper bounds to versions which are below that release. We will need to increase these bounds while making sure we don't break existing usecases/tests. Will slowly happen over December.

@mihaimaruseac Any update? (Quite concerned here as we did an upgrade to 3.9 for some various reasons such Python's core enhancements but totally zapped out the TensorFlow's upper bound T_T)

Oh, and why limiting on upper bound nightly releases if they are for testing?
I tried pip install tf-nightly 2.5.0.dev20201211 but it ended up as the stable version:

ERROR: Could not find a version that satisfies the requirement tf-nightly
ERROR: No matching distribution found for tf-nightly

I've built tf 2.4 for python 3.9.1 on windows 10 and ubuntu 18.04 and they work fine, no problem at all

I've built tf 2.4 for python 3.9.1 on windows 10 and ubuntu 18.04 and they work fine, no problem at all

Curious to know how you did that since you cannot use it because of this “stupid” dependency check. Oh you mean you built it from source? If that’s so, would you mind sharing the commands/options you used?

thanks!

@willemavjc I can share my build from source setup. Win 10 and works with python 3.9.1, TF 2.4.
Python 3.9 installed in c:\python39\ dir.

# Copy https://github.com/bazelbuild/bazel/releases/download/3.7.2/bazel-3.7.2-windows-x86_64.exe to somewhere in PATH and rename to bazel.exe 
# Make sure you have Visual Studio 2019 Build Tools installed (located under C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC)
# Make sure cudnn-8.0.5.39 is unpacked into C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0 respective dirs.
# Ensure your env vars contain something like this:
# CUDA_PATH=C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.0
# CUDA_TOOLKIT_PATH=%CUDA_PATH%
# PATH=%CUDA_PATH%\bin;%PATH%
# PATH=%CUDA_PATH%\extras\CUPTI\libx64;%PATH%

# Using PowerShell 7.1.0
git clone https://github.com/tensorflow/tensorflow.git tf24build
cd tf24build
git checkout v2.4.0
\python39\python -m venv .venv
.\.venv\Scripts\Activate.ps1
pip install -U pip

# Now, depending on how fresh is your Windows installation, you need either numpy==1.19.3 or or 1.19.4.
# See https://github.com/numpy/numpy/issues/16744 for details
pip install six numpy==1.19.4 wheel keras_applications keras_preprocessing --no-deps

$Env:BAZEL_VC = "C:\Program Files (x86)\Microsoft Visual Studio\2019\BuildTools\VC"
# Adjust the Compute Capabilities to match your target GPU!
$Env:TF_CUDA_COMPUTE_CAPABILITIES = "7.5"
$Env:CC_OPT_FLAGS = "/arch:AVX2"
$Env:TF_NEED_CUDA = "1"

# Check the prompts but should be just enter for everything
.\configure.cmd

bazel build --config=opt --define=no_tensorflow_py_deps=true //tensorflow/tools/pip_package:build_pip_package

# Generates tensorflow_2_4_py39.whl
bazel-bin\tensorflow\tools\pip_package\build_pip_package tensorflow_2_4_py39

# Create a new venv and install the new wheel, test it's health with
# python -c "import tensorflow as tf;print(tf.reduce_sum(tf.random.normal([1000, 1000])));print(tf.config.list_physical_devices('GPU'))" 

Sorry but AFAIK protobuf does not support Python 3.9 properly with their Linux wheels. See protocolbuffers/protobuf#7978.

Ah, I think I also left out a few bits here...
Because grpcio version that tensorflow depends on is not having the python 3.9 wheels (but newer version has), my setup to install the wheels is twofold:

# Requirements from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/pip_package/setup.py
pip install "grpcio==1.34.0" "absl-py~=0.10" "astunparse~=1.6.3" "flatbuffers~=1.12.0" "google_pasta~=0.2" "h5py~=3.1.0" "keras_preprocessing~=1.1.2" "numpy~=1.19.2" "opt_einsum~=3.3.0" "protobuf>=3.9.2" "six~=1.15.0" "termcolor~=1.1.0" "typing_extensions~=3.7.4" "wheel~=0.35" "wrapt~=1.12.1" "gast==0.3.3"

pip install --no-deps C:\tmp\tensorflow-2.4.0-cp39-cp39-win_amd64.whl

This makes it easier to also manage the protobuf installation separately. At least for Windows there is an unofficial Python 3.9 protobuf wheel at https://www.lfd.uci.edu/~gohlke/pythonlibs/#protobuf (not the latest but good enough, 3.12.3, ).

protobuf and gast are current blockers to have 100% tests passing

protobuf and gast are current blockers to have 100% tests passing

gast 0.4.0 seems to support Python 3.9. Haven't tested it yet though.

@willemavjc
only problem blocked me was grpcio 1.32.0, which I ignored using --no-dependency option

I've built tensorflow from source of course

cuda 11.2
cudnn 8.0.5
branch - r2.4

protobuf and gast are current blockers to have 100% tests passing

gast 0.4.0 seems to support Python 3.9. Haven't tested it yet though.

It does, but I think @mdanatg has a change to update and there are a few things that need to change to make all tests pass.