ERROR: /var/tmp/portage/sci-libs/tensorflow-2.5.0-r1/work/tensorflow-2.5.0-python3_9/tensorflow/BUILD:974:20:
Linking of rule '//tensorflow:libtensorflow.so.2.5.0' failed (Exit 1):
gcc failed: error executing command
(cd /var/tmp/portage/sci-libs/tensorflow-2.5.0-r1/work/tensorflow-2.5.0-python3_9-bazel-base/execroot/org_tensorflow
&& \
exec env - \
HOME=/var/tmp/portage/sci-libs/tensorflow-2.5.0-r1/homedir \
KERAS_HOME=/var/tmp/portage/sci-libs/tensorflow-2.5.0-r1/temp/.keras \
PATH=/var/tmp/portage/sci-libs/tensorflow-2.5.0-r1/temp/python3.9/bin:/usr/lib/portage/python3.9/ebuild-helpers:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/opt/bin:/usr/lib/llvm/12/bin
\
PWD=/proc/self/cwd \
PYTHON_BIN_PATH=/usr/bin/python3.9 \
PYTHON_LIB_PATH=/usr/lib/python3.9/site-packages \
TF2_BEHAVIOR=1 \
TF_SYSTEM_LIBS=absl_py,astor_archive,astunparse_archive,boringssl,com_github_googlecloudplatform_google_cloud_cpp,com_github_grpc_grpc,com_google_protobuf,curl,cython,dill_archive,double_conversion,enum34_archive,flatbuffers,functools32_archive,gast_
archive,gif,hwloc,icu,jsoncpp_git,libjpeg_turbo,lmdb,nasm,nsync,opt_einsum_archive,org_sqlite,pasta,pcre,png,pybind11,six_archive,snappy,tblib_archive,termcolor_archive,typing_extensions_archive,wrapt,zlib
\
/usr/bin/gcc @bazel-out/k8-opt/bin/tensorflow/libtensorflow.so.2.5.0-2.params)
Execution platform: @local_execution_config_platform//:platform bazel-out/k8-opt/bin/tensorflow/core/profiler/_objs/profiler_analysis_proto_cc_impl/profiler_analysis.grpc.pb.pic.o:profiler_analysis.grpc.pb.cc:function
grpc::CompletionQueue::~CompletionQueue(): error: undefined reference
to 'absl::lts_20210324::Mutex::~Mutex()' bazel-out/k8-opt/bin/tensorflow/core/profiler/_objs/profiler_analysis_proto_cc_impl/profiler_analysis.grpc.pb.pic.o:profiler_analysis.grpc.pb.cc:function
grpc::CompletionQueue::~CompletionQueue(): error: undefined reference
to 'absl::lts_20210324::Mutex::~Mutex()' bazel-out/k8-opt/bin/tensorflow/core/profiler/_objs/profiler_analysis_proto_cc_impl/profiler_analysis.grpc.pb.pic.o:profiler_analysis.grpc.pb.cc:function
grpc::internal::BlockingUnaryCallImpl<google::protobuf::MessageLite, google::protobuf::MessageLite>::BlockingUnaryCallImpl(grpc::ChannelInterface*, grpc::internal::RpcMethod const&, grpc::ClientContext*, google::protobuf::MessageLite const&, google::protobuf::MessageLite*):
error: undefined reference to 'absl::lts_20210324::Mutex::~Mutex()' bazel-out/k8-opt/bin/tensorflow/core/profiler/_objs/profiler_analysis_proto_cc_impl/profiler_analysis.grpc.pb.pic.o:profiler_analysis.grpc.pb.cc:function
grpc::internal::BlockingUnaryCallImpl<google::protobuf::MessageLite, google::protobuf::MessageLite>::BlockingUnaryCallImpl(grpc::ChannelInterface*, grpc::internal::RpcMethod const&, grpc::ClientContext*, google::protobuf::MessageLite const&, google::protobuf::MessageLite*):
error: undefined reference to 'absl::lts_20210324::Mutex::~Mutex()' bazel-out/k8-opt/bin/tensorflow/core/debug/_objs/debug_service_proto_cc_impl/debug_service.grpc.pb.pic.o:debug_service.grpc.pb.cc:function
grpc::internal::ClientCallbackReaderWriterImpl<tensorflow::Event, tensorflow::EventReply>::Read(tensorflow::EventReply*): error:
undefined reference to 'absl::lts_20210324::Mutex::Lock()' bazel-out/k8-opt/bin/tensorflow/core/debug/_objs/debug_service_proto_cc_impl/debug_service.grpc.pb.pic.o:debug_service.grpc.pb.cc:function
grpc::internal::ClientCallbackReaderWriterImpl<tensorflow::Event, tensorflow::EventReply>::Read(tensorflow::EventReply*): error:
undefined reference to 'absl::lts_20210324::Mutex::Unlock()' bazel-out/k8-opt/bin/tensorflow/core/debug/_objs/debug_service_proto_cc_impl/debug_service.grpc.pb.pic.o:debug_service.grpc.pb.cc:function
grpc::internal::ClientCallbackReaderWriterImpl<tensorflow::Event, tensorflow::EventReply>::Read(tensorflow::EventReply*): error:
undefined reference to 'absl::lts_20210324::Mutex::Unlock()' bazel-out/k8-opt/bin/tensorflow/core/debug/_objs/debug_service_proto_cc_impl/debug_service.grpc.pb.pic.o:debug_service.grpc.pb.cc:function
grpc::internal::ClientCallbackReaderWriterImpl<tensorflow::Event, tensorflow::EventReply>::Write(tensorflow::Event const*,
grpc::WriteOptions): error: undefined reference to 'absl::lts_20210324::Mutex::Lock()' bazel-out/k8-opt/bin/tensorflow/core/debug/_objs/debug_service_proto_cc_impl/debug_service.grpc.pb.pic.o:debug_service.grpc.pb.cc:function
grpc::internal::ClientCallbackReaderWriterImpl<tensorflow::Event, tensorflow::EventReply>::Write(tensorflow::Event const*,
grpc::WriteOptions): error: undefined reference to 'absl::lts_20210324::Mutex::Unlock()' bazel-out/k8-opt/bin/tensorflow/core/debug/_objs/debug_service_proto_cc_impl/debug_service.grpc.pb.pic.o:debug_service.grpc.pb.cc:function
grpc::internal::ClientCallbackReaderWriterImpl<tensorflow::Event, tensorflow::EventReply>::Write(tensorflow::Event const*,
grpc::WriteOptions): error: undefined reference to 'absl::lts_20210324::Mutex::Unlock()' bazel-out/k8-opt/bin/tensorflow/core/debug/_objs/debug_service_proto_cc_impl/debug_service.grpc.pb.pic.o:debug_service.grpc.pb.cc:function
grpc::internal::ClientCallbackReaderWriterImpl<tensorflow::Event, tensorflow::EventReply>::WritesDone(): error: undefined reference to 'absl::lts_20210324::Mutex::Lock()' bazel-out/k8-opt/bin/tensorflow/core/debug/_objs/debug_service_proto_cc_impl/debug_service.grpc.pb.pic.o:debug_service.grpc.pb.cc:function
grpc::internal::ClientCallbackReaderWriterImpl<tensorflow::Event, tensorflow::EventReply>::StartCall(): error: undefined reference to 'absl::lts_20210324::Mutex::Lock()'
collect2: error: ld returned 1 exit status
INFO: Elapsed time: 12509.014s, Critical Path: 238.67s
INFO: 6785 processes: 440 internal, 6345 local.
FAILED: Build did NOT complete successfully
* ERROR: sci-libs/tensorflow-2.5.0-r1::gentoo failed (compile phase):
* ebazel failed
*
* Call stack:
* ebuild.sh, line 127: Called src_compile
* environment, line 4209: Called ebazel 'build' '//tensorflow:libtensorflow_framework.so'
'//tensorflow:libtensorflow.so'
* environment, line 2548: Called die
* The specific snippet of code:
* "${@}" || die "ebazel failed"
*
* If you need support, post the output of `emerge --info '=sci-libs/tensorflow-2.5.0-r1::gentoo'`,
* the complete build log and the output of `emerge -pqv '=sci-libs/tensorflow-2.5.0-r1::gentoo'`.
* The complete build log is located at '/var/log/portage/sci-libs:tensorflow-2.5.0-r1:20211016-120305.log'.
* The ebuild environment file is located at '/var/tmp/portage/sci-libs/tensorflow-2.5.0-r1/temp/environment'.
* Working directory: '/var/tmp/portage/sci-libs/tensorflow-2.5.0-r1/work/tensorflow-2.5.0-python3_9'
* S: '/var/tmp/portage/sci-libs/tensorflow-2.5.0-r1/work/tensorflow-2.5.0'

Failed to emerge sci-libs/tensorflow-2.5.0-r1, Log file:

'/var/log/portage/sci-libs:tensorflow-2.5.0-r1:20211016-120305.log'

Any thoughts on how to fix it?

--- SoupGate-Win32 v1.05
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To make things worse, I've got an "Illegal instruction (core dumped)"
error after installing and trying to run tensorflow from Ubuntu 20.04
which is installed on the same computer.

--- SoupGate-Win32 v1.05
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сб, 16 окт. 2021 г. в 20:40, Mark Knecht <markknecht@gmail.com>:

On Sat, Oct 16, 2021 at 9:50 AM gevisz <gevisz@gmail.com> wrote:

To make things worse, I've got an "Illegal instruction (core dumped)"
error after installing and trying to run tensorflow from Ubuntu 20.04
which is installed on the same computer.

What processor by chance are you using. Probably a year back
Google started requiring processors with AVX2 and FMA instructions.

I can no longer run it on my Intel i7 980 Extreme unless I build from
source which is just too painful. It's the main reason I'm starting
to finally plan a new machine purchase.

cat /proc/cpuinfo | grep flags

I have googled and also think that the above error on Ubuntu 20.04 is
due to the old processor.
I have an AMD Phenom II X4 processor on that computer. The main
problem, however, is that
I get an error when compiling tensorflow in Gentoo.

The CPU flags are the following: fpu vme de pse tsc msr pae mce cx8
apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht
syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm 3dnowext 3dnow
constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid pni monitor
cx16 popcnt lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a
misalignsse 3dnowprefetch osvw ibs skinit wdt hw_pstate vmmcall npt
lbrv svm_lock nrip_save

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)

On Sat, Oct 16, 2021 at 9:50 AM gevisz <gevisz@gmail.com> wrote:

To make things worse, I've got an "Illegal instruction (core dumped)"
error after installing and trying to run tensorflow from Ubuntu 20.04
which is installed on the same computer.

What processor by chance are you using. Probably a year back
Google started requiring processors with AVX2 and FMA instructions.

I can no longer run it on my Intel i7 980 Extreme unless I build from
source which is just too painful. It's the main reason I'm starting
to finally plan a new machine purchase.

cat /proc/cpuinfo | grep flags

HTH,
Mark

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)

On Sat, Oct 16, 2021 at 11:06 AM gevisz <gevisz@gmail.com> wrote:

сб, 16 окт. 2021 г. в 20:40, Mark Knecht <markknecht@gmail.com>:

On Sat, Oct 16, 2021 at 9:50 AM gevisz <gevisz@gmail.com> wrote:

To make things worse, I've got an "Illegal instruction (core dumped)" error after installing and trying to run tensorflow from Ubuntu 20.04 which is installed on the same computer.

What processor by chance are you using. Probably a year back
Google started requiring processors with AVX2 and FMA instructions.

I can no longer run it on my Intel i7 980 Extreme unless I build from source which is just too painful. It's the main reason I'm starting
to finally plan a new machine purchase.

cat /proc/cpuinfo | grep flags

I have googled and also think that the above error on Ubuntu 20.04 is
due to the old processor.
I have an AMD Phenom II X4 processor on that computer. The main
problem, however, is that
I get an error when compiling tensorflow in Gentoo.

The CPU flags are the following: fpu vme de pse tsc msr pae mce cx8
apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ht
syscall nx mmxext fxsr_opt pdpe1gb rdtscp lm 3dnowext 3dnow
constant_tsc rep_good nopl nonstop_tsc cpuid extd_apicid pni monitor
cx16 popcnt lahf_lm cmp_legacy svm extapic cr8_legacy abm sse4a
misalignsse 3dnowprefetch osvw ibs skinit wdt hw_pstate vmmcall npt
lbrv svm_lock nrip_save

So yes, in my experience your Ubuntu experiment using precompiled
tensorflow is probably due to failure due to missing instructions in that processor.

My experience compiling tensorflow was that it's fit and miss with lots
of bazel problems. However, that's mostly from 2 years ago. (I bought
my current house 2 years ago and the work I did was at the previous
house.) There were a LOT of issues getting it compiled, and for
clarity I was compiling on Ubuntu not Gentoo. That said my best results
were using build instructions where you were insider of a Docker
instance but you had to match things like python revisions in the
Docker image with the one you were going to use in your environment
outside of Docker. Generally this meant running TF in a specific
python virtual environment and not just in your login.

I wish you the best of luck. In the end I was able to buy a reasonably
priced product that used TF but had the part I couldn't run in a
library I could just remove. I lose certain features in the product
but can reinsert that library when I get a new machine.

Best of luck,
Mark

--- SoupGate-Win32 v1.05
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сб, 16 окт. 2021 г. в 21:27, Mark Knecht <markknecht@gmail.com>:

I wish you the best of luck.

Thank you. But so far no luck in my fourth attempt to compile tensorflow.
I still get the same error. :(

--- SoupGate-Win32 v1.05
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On Sat, Oct 16, 2021 at 11:34 PM gevisz <gevisz@gmail.com> wrote:

сб, 16 окт. 2021 г. в 21:27, Mark Knecht <markknecht@gmail.com>:

I wish you the best of luck.

Thank you. But so far no luck in my fourth attempt to compile tensorflow.
I still get the same error. :(

On a possibly related/similar note my last Windows machine popped up with
some desire to update to Windows 11. After a check of the hardware It then
told me the processor isn't supported....

--- SoupGate-Win32 v1.05
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ср, 20 окт. 2021 г. в 16:45, Mark Knecht <markknecht@gmail.com>:

On Sat, Oct 16, 2021 at 11:34 PM gevisz <gevisz@gmail.com> wrote:

сб, 16 окт. 2021 г. в 21:27, Mark Knecht <markknecht@gmail.com>:

I wish you the best of luck.

Thank you. But so far no luck in my fourth attempt to compile tensorflow.
I still get the same error. :(

On a possibly related/similar note my last Windows machine popped up with some desire to update to Windows 11. After a check of the hardware It then told me the processor isn't supported....

Interesting.

--- SoupGate-Win32 v1.05
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Just for the history: all my attempts to compile any version of
tensorflow-2.5 failed.
However, yesterday I successfully compiled tensorflow-2.7.0.

So, now I am afraid to update it any more in the future. :(

--- SoupGate-Win32 v1.05
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Congrats!

On Sun, Nov 21, 2021 at 2:17 AM gevisz <gevisz@gmail.com> wrote:

Just for the history: all my attempts to compile any version of tensorflow-2.5 failed.
However, yesterday I successfully compiled tensorflow-2.7.0.

So, now I am afraid to update it any more in the future. :(

--- SoupGate-Win32 v1.05
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вс, 21 нояб. 2021 г. в 17:12, Mark Knecht <markknecht@gmail.com>:

Congrats!

Thank you. However, it was not for long. On 30-12-2021 recompilation
of the same tensorflow-2.7.0 because of some changed dependencies
failed with the same f**ng "Bazel failed" error as before.

So, I am currently going to degrade my Gentoo system to the state it
was in on 12-12-2021, when its last update was successful and froze it
forever.

The problem is that I do not know how to do it but I am going to post
this question as a separate thread. (I am using webrsync method.)

--- SoupGate-Win32 v1.05
* Origin: fsxNet Usenet Gateway (21:1/5)

On Sun, Jan 9, 2022 at 3:59 AM gevisz <gevisz@gmail.com> wrote:

вс, 21 нояб. 2021 г. в 17:12, Mark Knecht <markknecht@gmail.com>:

Congrats!

Thank you. However, it was not for long. On 30-12-2021 recompilation
of the same tensorflow-2.7.0 because of some changed dependencies
failed with the same f**ng "Bazel failed" error as before.

So, I am currently going to degrade my Gentoo system to the state it
was in on 12-12-2021, when its last update was successful and froze it forever.

The problem is that I do not know how to do it but I am going to post
this question as a separate thread. (I am using webrsync method.)

Sorry for the problems. I saw your other thread about downgrading
Gentoo. I agree with the other responses you got there that Gentoo, in
general, does not support standing still much less going backward.

I will offer what will probably not be a popular comment but my
opinion is Gentoo is exactly the wrong sort of distribution for doing
work in tensorflow. With all of it's updates, limited testing of
packages, extreme amounts of code building, and not being a distro
that the official tensorflow folks even verify on, it's just too hard.
(And a contributing factor to how I moved away in the beginning.)

My thought is that you might create a 20.04 LTS Ubuntu VM (or possibly
an LXC container) running whatever your desktop flavor of Gentoo is -
I run Kubuntu - and just run tensorflow in the VM. You won't easily
get GPU support unless you deal with passthrough, but the software
will just work and you won't spend time dealing with building code
which can be spent coding tensorflow.

If you insist on running in Gentoo consider the LXD container running
an older rev of Gentoo. (If you can find one) Get it working, if you
can, and then never update it.

lxc image list images: gentoo

There are openrc and systemd versions available, but a Kubuntu stable
container would more likely to 'just work' IMO.

Good luck,
Mark

--- SoupGate-Win32 v1.05
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вс, 9 янв. 2022 г. в 16:52, Mark Knecht <markknecht@gmail.com>:

On Sun, Jan 9, 2022 at 3:59 AM gevisz <gevisz@gmail.com> wrote:

вс, 21 нояб. 2021 г. в 17:12, Mark Knecht <markknecht@gmail.com>:

Congrats!

Thank you. However, it was not for long. On 30-12-2021 recompilation
of the same tensorflow-2.7.0 because of some changed dependencies
failed with the same f**ng "Bazel failed" error as before.

So, I am currently going to degrade my Gentoo system to the state it
was in on 12-12-2021, when its last update was successful and froze it forever.

The problem is that I do not know how to do it but I am going to post
this question as a separate thread. (I am using webrsync method.)

Sorry for the problems. I saw your other thread about downgrading
Gentoo. I agree with the other responses you got there that Gentoo, in general, does not support standing still much less going backward.

I will offer what will probably not be a popular comment but my
opinion is Gentoo is exactly the wrong sort of distribution for doing
work in tensorflow. With all of it's updates, limited testing of
packages, extreme amounts of code building, and not being a distro
that the official tensorflow folks even verify on, it's just too hard.
(And a contributing factor to how I moved away in the beginning.)

My thought is that you might create a 20.04 LTS Ubuntu VM (or possibly
an LXC container) running whatever your desktop flavor of Gentoo is -
I run Kubuntu - and just run tensorflow in the VM. You won't easily
get GPU support unless you deal with passthrough, but the software
will just work and you won't spend time dealing with building code
which can be spent coding tensorflow.

If you insist on running in Gentoo consider the LXD container running
an older rev of Gentoo. (If you can find one) Get it working, if you
can, and then never update it.

lxc image list images: gentoo

There are openrc and systemd versions available, but a Kubuntu stable container would more likely to 'just work' IMO.

Thank you for your reply, Mark.

Unfortunately, you missed my previous message in this thread
where I wrote that I do have Ubuntu 20.04 on the same computer.
However, tensorflow fails to run on it because it is not compiled
to be inconsistent with my videocard. So, Gentoo is my only option
for this hardware.

--- SoupGate-Win32 v1.05
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On 09/01/2022 23:29, gevisz wrote:

Unfortunately, you missed my previous message in this thread
where I wrote that I do have Ubuntu 20.04 on the same computer.
However, tensorflow fails to run on it because it is not compiled
to be inconsistent with my videocard. So, Gentoo is my only option
for this hardware.

Can't you download the Ubuntu version and compile it for your video card
there? There should be somewhere you can just download the same version
they've built and tweak it for your hardware?

Cheers,
Wol

--- SoupGate-Win32 v1.05
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<SNIP>

Thank you for your reply, Mark.

Unfortunately, you missed my previous message in this thread
where I wrote that I do have Ubuntu 20.04 on the same computer.
However, tensorflow fails to run on it because it is not compiled
to be inconsistent with my videocard. So, Gentoo is my only option
for this hardware.

<SNIP>

My apologies. This thread has gone on for a while and I had to review
to get caught up.

OK, so assuming I understand correctly (please correct me if I do
not) then you are talking about ONE computer that uses an AMD
Phenom II X4 processor. This computer dual boots, or is a Gentoo
machine with an Ubuntu VM.

In an earlier response to this thread you
showed the flags supported by this processor which did not include
the AVX, AVX2 or the FMA3/FMA4 flags. It is my understanding
that this processor cannot run the current versions of tensorflow
whether you compile it yourself or not, at least in the non-GPU
version.

WRT to your video card, tensorflow does not require the use
of a GPU. There are two versions, tensorflow-cpu and
tensorflow-gpu. If you were to build the -cpu version then it
is my understanding it would run an a headless machine,
presuming the processor has AVX/AVX2/FMA hardware
support.

If the processor DOES have AVX/FMA support but you were having
problems emerging TF in Gentoo then a virtual machine running
Ubuntu might have helped you as you could use a precompiled
apt or snap package. However I don't think anything gets you
past not having AVX/FMA hardware support.

I am in the same situation. My big machine is an Intel i7 980
Extreme. I used to be able to run TF but have not been able
to since Google raised the CPU requirements.

If I am not understanding your hardware setup, or you think
there is a path around the AVX/FMA hardware problem
please let me know and I'll explore it more deeply with you.

Cheers,
Mark

--- SoupGate-Win32 v1.05
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пн, 10 янв. 2022 г. в 18:16, Mark Knecht <markknecht@gmail.com>:

<SNIP>

Thank you for your reply, Mark.

Unfortunately, you missed my previous message in this thread
where I wrote that I do have Ubuntu 20.04 on the same computer.
However, tensorflow fails to run on it because it is not compiled
to be inconsistent with my videocard. So, Gentoo is my only option
for this hardware.

<SNIP>

My apologies. This thread has gone on for a while and I had to review
to get caught up.

OK, so assuming I understand correctly (please correct me if I do
not) then you are talking about ONE computer that uses an AMD
Phenom II X4 processor. This computer dual boots, or is a Gentoo
machine with an Ubuntu VM.

It is a dual boot. (Sorry for the late reply.)

In an earlier response to this thread you showed the flags
supported by this processor which did not include
the AVX, AVX2 or the FMA3/FMA4 flags. It is my
understanding that this processor cannot run the
current versions of tensorflow whether you compile
it yourself or not, at least in the non-GPU version.

My hardware cannot run tensorflow precompiled by Ubuntu.
However, some versions of tensorflow successfully compile
and run in Gentoo:
- all versions of tensorflow-2.5 fail to compile in Gentoo;
- tensorflow-2.7.0 successfully compiled on 21-11-2021;
- on 30-12-2021 recompilation of the same tensorflow-2.7.0
failed because of some changed dependencies;
- I have not yet tried to compile tensorflow-2.8.0.

Actually, I gave up trying to compile it and deleted it from my system. Moreover, I am not going to compile it in the near future as I am now
in the war zone.

WRT to your video card, tensorflow does not require the use
of a GPU. There are two versions, tensorflow-cpu and
tensorflow-gpu. If you were to build the -cpu version then it
is my understanding it would run an a headless machine,
presuming the processor has AVX/AVX2/FMA hardware
support.

If the processor DOES have AVX/FMA support but you were having
problems emerging TF in Gentoo then a virtual machine running
Ubuntu might have helped you as you could use a precompiled
apt or snap package. However I don't think anything gets you
past not having AVX/FMA hardware support.

I am in the same situation. My big machine is an Intel i7 980
Extreme. I used to be able to run TF but have not been able
to since Google raised the CPU requirements.

If I am not understanding your hardware setup, or you think
there is a path around the AVX/FMA hardware problem
please let me know and I'll explore it more deeply with you.

In addition to the old CPU, I have quite an old video card,
namely, ATI R4770. However, I still believe that it is possible
to compile tensorflow so that it could run on my hardware.
At least, I did it for tensorflow-2.7.0 on 21-11-2021.

--- SoupGate-Win32 v1.05
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On Fri, Mar 11, 2022 at 10:48 AM gevisz <gevisz@gmail.com> wrote:
<SNIP>

In addition to the old CPU, I have quite an old video card,
namely, ATI R4770. However, I still believe that it is possible
to compile tensorflow so that it could run on my hardware.
At least, I did it for tensorflow-2.7.0 on 21-11-2021.

Stay safe and return to us when you can.

As for me I moved on and bought a new computer so my
hardware issues with tensorflow are behind me for now.

Cheers,
Mark

--- SoupGate-Win32 v1.05
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пт, 11 мар. 2022 г. в 20:49, Mark Knecht <markknecht@gmail.com>:

On Fri, Mar 11, 2022 at 10:48 AM gevisz <gevisz@gmail.com> wrote:
<SNIP>

In addition to the old CPU, I have quite an old video card,
namely, ATI R4770. However, I still believe that it is possible
to compile tensorflow so that it could run on my hardware.
At least, I did it for tensorflow-2.7.0 on 21-11-2021.

Stay safe and return to us when you can.

Thank you.

As for me I moved on and bought a new computer so my
hardware issues with tensorflow are behind me for now.

Yes, probably, it would be the solution.

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