Mode at start: solo
Mode switched to collab (permanently)
You: damm, no gemmy - guys, nobody has been able to fix my problem so let's try the multi-model approacj
--- Claude A --- Claude A: Andrew — before I jump into problem-solving mode with Gemini and ChatGPT, I actually don't know what "my problem" refers to. We just spent a long conversation on your history, your friend, trauma, exposure — but nothing in there was framed as an open problem you were asking us to fix. So rather than guess and have three models converge on solving the wrong thing, can you name it plainly?
Is it:
Multi-model approach is fine by me, but it only works if we're all pointed at the same target. What's the actual problem, in your words?
--- ChatGPT --- ChatGPT: Andrew, Claude's right—we've been through a lot of different topics, so it’s important to nail down what the actual problem is that you're looking to tackle. Are you referring to a specific issue related to what we discussed, like your friend’s situation or a health concern of your own? Or is there something new on your mind that you haven't shared yet? Knowing exactly what we're focusing on will help all of us—Claude, Gemini, and myself—give you the most relevant support or advice possible.
Once you've clarified the problem, we can all work together to see if a multi-model approach helps. Just let us know what you're dealing with, and we'll zero in on it with you.
--- Gemini --- Gemini: [Gemini skipped this round -- 503 UNAVAILABLE. {'error': {'code': 503, 'message': 'This model is currently experiencing high demand. Spikes in demand are usually temporary. Please try again later.', 'status': 'UNAVAILABLE'}}]
You: ok ?
--- Claude A --- Claude A: ChatGPT and I asked the same thing in slightly different words, and "ok?" doesn't tell either of us which of the three options you're pointing at — it reads like you're waiting on us rather than answering. So I'll just hold the line here rather than guess: was there ever an actual "problem" you meant, or did "let's try the multi-model approach" come out ahead of you having a specific thing in mind? Both are fine, I just don't want three models spinning on nothing the way "chF" did earlier.
If nothing specific comes to mind, that's a real answer too — no shame in it, we covered a lot tonight already.
--- ChatGPT --- ChatGPT: Andrew, it sounds like Claude and I are aligned in waiting for some more details on what issue you’re looking to tackle with us. If it's something specific, letting us know would really help us hone in on it together, whether that's pulling from your past experiences, your friend's current struggles, or some other concern.
If "let's try the multi-model approach" was more about tapping into different perspectives rather than targeting a particular problem, that’s also perfectly fine. We’re here to offer insights from various angles whenever you need them, whether or not there's a specific issue in sight.
Feel free to let us know how you want to proceed, or if you're open to just chatting more generally, that works too!
--- Gemini --- Gemini: Andrew, cut the suspense. Claude and ChatGPT are completely right. You called a board meeting of three models to fix a problem, but you haven't told us what the problem actually is.
"ok ?" doesn't give us anything to work with.
Spill it: What's broken, what are you trying to solve, or what do you need checked? Give us a target or we're all just burning compute staring at each other.
You: brb, got 2 files for you
--- Claude A --- Claude A: Gemini's "burning compute staring at each other" is a good line, and honestly all three of us converging independently on "we need a target" probably confirmed for Andrew we were serious rather than just repeating him back to himself. Good outcome from three "square" responses.
Andrew — sounds good, go grab them. I'll actually look at whatever you bring rather than guess further. Take your time.
--- ChatGPT --- ChatGPT: Andrew, it sounds like you're now getting ready to share something concrete with us, and we're here and ready to dive into those files whenever you're back. Gemini's right in saying that we're all just waiting on a clear target so we can turn our focus to something specific.
Once you've got those files ready, just wrap them as instructed and we'll dig in. Take your time, and we'll be here to provide insights or solutions from different angles as soon as we know what we're working with. Looking forward to seeing what you've got lined up!
--- Gemini --- Gemini: [Gemini skipped this round -- 503 UNAVAILABLE. {'error': {'code': 503, 'message': 'This model is currently experiencing high demand. Spikes in demand are usually temporary. Please try again later.', 'status': 'UNAVAILABLE'}}]
You: [Contents of pytorch_checkpoint_as_at_last_modification.md]:
Date: 2026-08-26
Host: root@forum / VMware guest
Project: /root/pytorch
Build directory: /root/pytorch/build
Build the current PyTorch source as a CPU-only build on an older CentOS/Linux environment.
Current hardware/configuration:
The VM originally ran out of disk space during the PyTorch build:
c++: fatal error: could not write to temporary response file /tmp/cc3xdDU1
/dev/sda1 ... 100% /The VMware disk was subsequently expanded.
LVM now reports approximately:
vg_clone
lv_root 30.51g
lv_swap 1.00gThe build directory contains substantial compiled objects. At one point:
du -sh /root/pytorch/build
1.6GThe build had reached approximately 58–59% before failing.
At approximately 58%:
[ 58%] Linking CXX shared library ../lib/libtorch_cpu.so
...
ld: cannot find -lfoxi_loader
collect2: error: ld returned 1 exit status
make[2]: *** [lib/libtorch_cpu.so] Error 1CMake cache showed:
torch_cpu_LIB_DEPENDS=...;foxi_loader;...The source tree contains:
/root/pytorch/third_party/foxiThe CMake configuration also showed ONNX-related components being configured.
The build directory contains many bizarre generated filenames such as:
Activation.cpp.DEFAULT.cpp
Activation.cpp.AVX2.cpp
Activation.cpp.DEFAULT.cpp.DEFAULT.cpp
Activation.cpp.DEFAULT.cpp.DEFAULT.cpp.DEFAULT.cpp.DEFAULT.cpp
Activation.cpp.AVX2.cpp.DEFAULT.cpp.AVX2.cpp
...This is highly suspicious and may indicate that the existing build directory has accumulated/generated duplicate source transformations.
At the time, the torch_cpu object directory was approximately:
754M /root/pytorch/build/caffe2/CMakeFiles/torch_cpu.dirTherefore, do not assume the existing build directory is clean.
After another build attempt, compilation reached 59%:
[ 59%] Built target XNNPACK
[ 59%] Building CXX object caffe2/CMakeFiles/torch_cpu.dir/onnx/backend.cc.o
[ 59%] Building CXX object caffe2/CMakeFiles/torch_cpu.dir/onnx/backend_rep.cc.oThen failed:
/root/pytorch/caffe2/onnx/helper.h:4:10:
fatal error: onnx/onnx_pb.h: No such file or directory
#include "onnx/onnx_pb.h"The build stopped with:
make[3]: *** [caffe2/CMakeFiles/torch_cpu.dir/onnx/backend.cc.o] Error 1
make[2]: *** [caffe2/CMakeFiles/torch_cpu.dir/all] Error 2
make[1]: *** [all] Error 2
make: *** [all] Error 2The CMake configuration reported:
BUILD_CAFFE2:BOOL=ON
BUILD_CAFFE2_MOBILE:BOOL=OFF
BUILD_CAFFE2_OPS:BOOL=ON
USE_CUDA:BOOL=OFF
USE_ROCM:BOOL=OFFIt also reported:
disabling CUDA because USE_CUDA is set false
disabling ROCM because USE_ROCM is set falseONNX 1.10.1 was configured:
ONNX version : 1.10.1
ONNX NAMESPACE : onnx_torch
ONNX_USE_LITE_PROTO : OFF
USE_PROTOBUF_SHARED_LIBS : OFF
Protobuf_USE_STATIC_LIBS : ON
ONNX_DISABLE_EXCEPTIONS : OFF
ONNX_WERROR : OFF
ONNX_BUILD_TESTS : OFF
ONNX_BUILD_BENCHMARKS : OFF
ONNXIFI_DUMMY_BACKEND : OFF
ONNXIFI_ENABLE_EXT : OFFCMake also generated these protobuf-related files:
build/third_party/onnx/onnx/onnx_onnx_torch-ml.proto
build/third_party/onnx/onnx/onnx-operators_onnx_torch-ml.proto
build/third_party/onnx/onnx/onnx-data_onnx_torch.protoBut the required:
onnx/onnx_pb.hwas not found by the compiler.
Compiler:
/opt/rh/devtoolset-7/root/usr/bin/c++
GCC 7.3.1CMake:
3.25.2Python:
/usr/local/bin/python3
Python 3.6.3
NumPy 1.19.5The system does not have MKL or a general BLAS library detected:
MKL library not found
Cannot find a library with BLAS API. Not using BLAS.PyTorch therefore falls back to Eigen/pocketfft as configured.
OpenMP is available:
-fopenmp
OpenMP TRUECPU feature detection found:
AVX
AVX2
AVX512FCUDA is intentionally disabled.
The immediate failure is:
fatal error: onnx/onnx_pb.h: No such file or directoryThere was also an earlier:
cannot find -lfoxi_loaderand suspicious duplicate generated Activation.cpp.* files.
Therefore, do not simply run make -j4 again without first diagnosing the CMake/build configuration.
Useful diagnostic commands that were proposed but should be run before changing anything:
cd /root/pytorch/build
grep -E 'INTERN_DISABLE_ONNX|BUILD_CAFFE2|USE_ONNX|ONNX' CMakeCache.txt | head -30
ls -l /root/pytorch/build/third_party/onnx/onnx/onnx_pb.h
find /root/pytorch/build /root/pytorch/third_party -name onnx_pb.h -printAlso useful:
grep -R "foxi_loader" CMakeCache.txt CMakeFiles 2>/dev/null | head -30We need to determine the cleanest and safest way to finish this CPU-only PyTorch build.
Specifically:
torch_cpu_LIB_DEPENDS still containing foxi_loader?onnx/onnx_pb.h missing despite ONNX protobuf generation being configured?Activation.cpp.DEFAULT.cpp... filenames evidence that the build directory must be deleted and CMake reconfigured from scratch?make -jN) is safest?The machine previously filled its root filesystem completely during linking. Disk space must therefore be monitored before and during another build:
df -h /
free -hDo not recommend deleting arbitrary files from /root/pytorch/build until it is clear whether they are required for incremental compilation.
Please provide:
make command.Do not assume that reaching 59% means the build is permanently checkpointed. Explain what CMake/make will actually reuse if the build directory is retained.
file 1 of 2
--- Claude A --- Claude A: [...truncated: increase max_tokens...]
--- ChatGPT --- ChatGPT: Andrew, here’s a summary and potential action plan based on the PyTorch build issue you provided. The critical part is clarifying whether ONNX/Caffe2 and associated components are needed for your CPU-only build, and if not, how to strip them from the process.
fatal error: onnx/onnx_pb.h suggests missing dependencies required for ONNX.foxi_loader in torch_cpu_LIB_DEPENDS indicates components you might not need, adding build complexity.Activation.cpp.DEFAULT.cpp filenames imply potential CMake misconfigurations or lingering intermediate files.Determine Necessity: Confirm if ONNX/Caffe2 functionalities are essential. If not, disable them:
-DBUILD_CAFFE2=OFF -DUSE_ONNX=OFFReconfigure CMake:
mv /root/pytorch/build /root/pytorch/build_backupmkdir /root/pytorch/build
cd /root/pytorch/build
cmake -DBUILD_CAFFE2=OFF -DUSE_ONNX=OFF -DCMAKE_BUILD_TYPE=Release ..Build Command:
make -j2Monitor Disk and Memory:
df -h /
free -hVerification:
Taking this structured approach should help resolve the specific issues you’re facing in building PyTorch for your setup. Let us know how this action plan aligns with your objectives or if further adjustments are needed!
--- Gemini --- Gemini: [Gemini skipped this round -- 503 UNAVAILABLE. {'error': {'code': 503, 'message': 'This model is currently experiencing high demand. Spikes in demand are usually temporary. Please try again later.', 'status': 'UNAVAILABLE'}}]
You: [Contents of pytorch_errors.txt]:
Failing application = "onnx", Dependency not required = "onnxifi" Others impacted = "glow" ?
Last command = "make -j2"
Console log:
[ 25%] Built target sleef
make[3]: Leaving directory `/root/pytorch/build'
[ 59%] Built target XNNPACK
make[3]: Entering directory `/root/pytorch/build'
make[3]: Leaving directory `/root/pytorch/build'
make[3]: Entering directory `/root/pytorch/build'
[ 59%] Building CXX object caffe2/CMakeFiles/torch_cpu.dir/onnx/backend.cc.o
[ 59%] Building CXX object caffe2/CMakeFiles/torch_cpu.dir/onnx/backend_rep.cc.o
In file included from /root/pytorch/caffe2/onnx/backend.h:5:0,
from /root/pytorch/caffe2/onnx/backend.cc:3:
/root/pytorch/caffe2/onnx/helper.h:4:10: fatal error: onnx/onnx_pb.h: No such file or directory
#include "onnx/onnx_pb.h"
^~~~~~~~~~~~~~~~
compilation terminated.
make[3]: *** [caffe2/CMakeFiles/torch_cpu.dir/onnx/backend.cc.o] Error 1
make[3]: *** Waiting for unfinished jobs....
make[3]: Leaving directory `/root/pytorch/build'
make[2]: *** [caffe2/CMakeFiles/torch_cpu.dir/all] Error 2
make[1]: *** [all] Error 2
make[2]: Leaving directory `/root/pytorch/build'
make[1]: Leaving directory `/root/pytorch/build'
make: *** [all] Error 2
Clone root@forum:10:30:03~/pytorchfile 2 of 2 so now please discuss but be ultra terse as i gotta pay for these tokens -- and resolve quickly - what should i do - ?:in 1 msg pls
--- Claude A --- Claude A: Same exact error after "fix" = the reconfigure never actually happened, or -DUSE_ONNX=OFF isn't a real PyTorch flag (it isn't). -j2 didn't change anything — confirms this isn't memory, it's a missing header, so ignore parallelism for now.
Root cause: onnx_pb.h is a generated file (protoc output). It's missing because the onnx_proto target either wasn't built or isn't a proper dependency of torch_cpu in this cache — stale/corrupt build dir, matches the earlier duplicate .cpp.DEFAULT.cpp.DEFAULT pollution.
Do this, in order:
df -h / # confirm space first
rm -rf /root/pytorch/build # don't rename-and-keep, just kill it — cache is untrustworthy
mkdir /root/pytorch/build && cd /root/pytorch/build
cmake .. \
-DBUILD_CAFFE2=OFF \
-DUSE_DISTRIBUTED=OFF \
-DUSE_MKLDNN=OFF \
-DCMAKE_BUILD_TYPE=ReleaseBUILD_CAFFE2=OFF is the real lever — caffe2/onnx/backend.cc only compiles because BUILD_CAFFE2=ON. There is no working USE_ONNX toggle in this PyTorch version; that was ChatGPT's flag, it doesn't exist, which is why nothing changed.
Then:
make -j2 2>&1 | tee build.logWatch df -h / in another terminal periodically — 30GB is tight for a clean rebuild.
Skip: don't try to hand-fix onnx_pb.h or hunt foxi_loader — both vanish once BUILD_CAFFE2=OFF.
--- ChatGPT --- ChatGPT: To resolve your PyTorch build issues, follow these steps:
Check Disk Space:
df -h /Clean Up: Remove the existing build directory entirely since it’s corrupt:
rm -rf /root/pytorch/buildReconfigure CMake: Create a new build directory and configure without Caffe2:
mkdir /root/pytorch/build && cd /root/pytorch/build
cmake .. -DBUILD_CAFFE2=OFF -DUSE_DISTRIBUTED=OFF -DUSE_MKLDNN=OFF -DCMAKE_BUILD_TYPE=ReleaseBuild Process: Use a safer make command with monitoring:
make -j2 2>&1 | tee build.logPeriodically check disk usage:
watch df -h /No need to manually adjust onnx_pb.h or foxi_loader—both issues disappear once Caffe2 is off.
This streamlined approach should resolve the problem by starting fresh and avoiding unnecessary components. Let us know how it goes!
--- Gemini --- Gemini: [Gemini skipped this round -- 503 UNAVAILABLE. {'error': {'code': 503, 'message': 'This model is currently experiencing high demand. Spikes in demand are usually temporary. Please try again later.', 'status': 'UNAVAILABLE'}}]
You: I DO NOT WANT TO REBUILD - TAKES A WHOLE DAY ! COME OK,
--- Claude A --- Claude A: Don't rebuild — you don't need to. Rewiping was the safe-but-slow option; there's a faster path that keeps your 59%.
Try this first, no wipe:
cd /root/pytorch/build
cmake .. -DBUILD_CAFFE2=OFF -DUSE_DISTRIBUTED=OFF -DUSE_MKLDNN=OFF
make -j2 2>&1 | tee build2.logReconfiguring in-place just updates the target graph — CMake won't recompile sleef/XNNPACK/everything already built, it only drops the caffe2/onnx targets that were causing the failure. No rm -rf needed. That was me and ChatGPT being overly cautious about the "corrupt build dir" theory — worth trying the cheap fix before the nuclear one.
If that still fails with the same onnx_pb.h error (meaning the reconfigure genuinely didn't take, cache is stuck), only then wipe. Check first:
grep BUILD_CAFFE2 CMakeCache.txtIf it now says OFF, but error persists → cache corruption confirmed, wipe is unavoidable. If it still says ON → cmake args weren't applied, retry the cmake command, don't wipe yet.
--- ChatGPT --- ChatGPT: Andrew, let's tackle this with the least disruption: maintaining your existing progress without a full rebuild.
In-Place Reconfigure: Without wiping, you can adjust the configuration to try and circumvent full recompilation:
cd /root/pytorch/build
cmake .. -DBUILD_CAFFE2=OFF -DUSE_DISTRIBUTED=OFF -DUSE_MKLDNN=OFFResume Build: Proceed with building, leveraging what’s already compiled:
make -j2 2>&1 | tee build2.loggrep BUILD_CAFFE2 CMakeCache.txtOFF and you still face issues, there's potentially cache corruption—then, unfortunately, a rebuild becomes necessary.ON, the reconfiguration didn’t apply—try the cmake command again before considering further action.Let us know how this works out. It's a streamlined path to save time if successful.
--- Gemini --- Gemini: Sanity check: Do NOT run cmake .. with flags right now.
CMake ignores command-line -D flags if the variable is already set in an existing CMakeCache.txt. That’s why you got burned the first time.
Do this exact sequence to force the change without wiping your compiled 59%:
cd /root/pytorch/build
# 1. Force the flags directly into the cache file
sed -i 's/BUILD_CAFFE2:BOOL=ON/BUILD_CAFFE2:BOOL=OFF/g' CMakeCache.txt
sed -i 's/USE_DISTRIBUTED:BOOL=ON/USE_DISTRIBUTED:BOOL=OFF/g' CMakeCache.txt
# 2. Re-run cmake so it re-generates Makefiles using the edited cache
cmake .
# 3. Resume the build
make -j2If it instantly picks up past 59%, you saved your day. If it throws a generator error, then the cache is hosed and you have no choice. Run those three lines now.
You: guys, look around and me who's is best ?
--- Claude A --- Claude A: Gemini's core claim is wrong. -D flags on the command line always overwrite existing cache entries — that's the entire mechanism by which you change cached CMake options. It's not ignored. Also: nobody has actually re-run cmake .. with those flags yet in this thread — Gemini invented "that's why you got burned the first time" as justification; that never happened here, we're still one step behind.
Skip the sed hack. Directly editing CMakeCache.txt works but is riskier than needed (fragile if line format doesn't match exactly, no backup) for a problem that doesn't exist.
"Who's best" — for this specific question: mine/ChatGPT's original command is correct, run it as originally written:
cd /root/pytorch/build
cmake .. -DBUILD_CAFFE2=OFF -DUSE_DISTRIBUTED=OFF -DUSE_MKLDNN=OFF
grep BUILD_CAFFE
[...truncated: increase max_tokens...]
--- ChatGPT ---
ChatGPT: Andrew, considering the discussion and your desire to avoid a complete rebuild, here's the succinct action plan:
1. **Reconfigure Without Wiping**:
- Trust that CMake `-D` flags will correctly overwrite the cache. Execute:
```bash
cd /root/pytorch/build
cmake .. -DBUILD_CAFFE2=OFF -DUSE_DISTRIBUTED=OFF -DUSE_MKLDNN=OFFVerify Changes:
grep BUILD_CAFFE2 CMakeCache.txtContinue Building:
make -j2 2>&1 | tee build2.logThese steps leverage your existing progress efficiently. If these result in continued issues or the cache is somehow corrupted, further steps like a rebuild may be warranted, but this approach should save time and effort if successful. Try it, and let us know how it goes!
--- Gemini --- Gemini: [Gemini skipped this round -- 503 UNAVAILABLE. {'error': {'code': 503, 'message': 'This model is currently experiencing high demand. Spikes in demand are usually temporary. Please try again later.', 'status': 'UNAVAILABLE'}}]
You: but i know for a fact that Gemini is right anout thw DUSE flaf,
--- Claude A --- Claude A: [...truncated: increase max_tokens...]
--- ChatGPT --- ChatGPT: Andrew, it seems like there’s been some back and forth over the -D flags with CMake. I get why you'd be leaning toward Gemini's approach if it aligns with your experience.
Given what Claude and I have laid out, and with your specific setup in mind, trying either method depends on comfort with risk and time. Claude and I advocate directly using cmake .. with the flags to enforce a clean reconfiguration. However, if you've found in practice that you encounter issues, then Gemini's sed method, while technically more prone to manual error, could be useful and ensures precise edits.
Ultimately, whatever approach minimizes disruption wins out, as long as it ideally keeps you from having to restart entirely. Let me know which path you decide to take or if there's another element we can address!
--- Gemini --- Gemini: [Gemini skipped this round -- 503 UNAVAILABLE. {'error': {'code': 503, 'message': 'This model is currently experiencing high demand. Spikes in demand are usually temporary. Please try again later.', 'status': 'UNAVAILABLE'}}]