I m running locally and I m trying to install pytorch but du

Question

I m running locally and I m trying to install pytorch but during the installation in the enironments the container is killed at the downloading stage Did anyone get pytorch installed into an environment before

Jacopo · Answer

Hi Max thanks for reporting the issue I installed pytorch on multiple occasions so this is new to me I wonder if this might be related to GPU pass through

Rick Lamers · Answer

The installation phase shouldn t be affected by Docker GPU pass through Did you check on disk availability PyTorch takes quite some space in extracted form post install

Rick Lamers · Answer

Thanks for reporting the issue by the way slightly smiling face

Jacopo · Answer

Could you try to build the environment again run `docker exec celery worker cat celery builds log` and report the result here

Max Zwiessele · Answer

```Given no hashes to check 21 links for project torch discarding no candidates Using version 1 10 1 newest of versions 0 4 1 0 4 1 post2 1 0 0 1 0 1 1 0 1 post2 1 1 0 1 2 0 1 3 0 1 3 1 1 4 0 1 5 0 1 5 1 1 6 0 1 7 0 1 7 1 1 8 0 1 8 1 1 9 0 1 9 1 1 10 0 1 10 1 Collecting torch Created temporary directory tmp pip unpack jja34bo0 Looking up in the cache No cache entry available 2022 01 10 10 25 26 217 INFO ForkPoolWorker 6 output Starting new HTTPS connection 1 2022 01 10 10 25 26 242 INFO ForkPoolWorker 6 output GET packages 20 8a c1e970cf64a1fa105bc5064b353ecabe77974b69029a80d04580fee38d5f torch 1 10 1 cp37 cp37m manylinux1 x86 64 whl HTTP 1 1 200 881907340 Downloading torch 1 10 1 cp37 cp37m manylinux1 x86 64 whl 881 9 MB 2022 01 10 10 26 19 096 INFO MainProcess missed heartbeat from celery worker interactive 2022 01 10 10 26 37 643 INFO ForkPoolWorker 6 output Removing intermediate container 028e028af174 2022 01 10 10 26 37 679 INFO ForkPoolWorker 6 output gt Running in c48f2f9ada62 2022 01 10 10 26 37 890 INFO ForkPoolWorker 6 output Successfully built a088f19263c3 2022 01 10 10 26 37 903 INFO ForkPoolWorker 6 output Successfully tagged orchest env 90caa39b 32b5 44a3 96ef 4f066e303ccc 381d92b7 3b3f 4384 b778 16a7fbccbeac latest 2022 01 10 10 26 37 918 INFO ForkPoolWorker 6 task done status SUCCESS 2022 01 10 10 26 37 920 INFO ForkPoolWorker 6 Killed child pid 135 2022 01 10 10 26 38 152 INFO ForkPoolWorker 6 Task app core tasks build environment 42f9dbd6 7751 4440 8157 4f27eaada108 succeeded in 105 47356860000218s SUCCESS ```

Jacopo · Answer

What status is the environment build panel reporting

Max Zwiessele · Answer

```Build status SUCCESS Build requested Jan 10 2022 10 24 AM Build finished Jan 10 2022 10 26 AM```

Max Zwiessele · Answer

It might just be I need to give the worker more memory

Jacopo · Answer

How much memory does your worker have currently I would say that would be a good first thing to try we are brainstorming a bit to understand what could have happened

Rick Lamers · Answer

How did you infer that it did not in fact successfully built the environment Where is it breaking so to speak

Max Zwiessele · Answer

I m running a `jupyterlab` notebook and try to run `import torch` which says it s not available

Jacopo · Answer

Assuming the right environment is set for the step could you try restarting the kernel and or the orchest session

Rick Lamers · Answer

Only a kernel restart should be sufficient

Rick Lamers · Answer

Just to make sure you have a single environment in the project in which you re testing

Max Zwiessele · Answer

Your very first suggestion worked flawlessly +1 More memory and it worked like a charm Should have thought of that

Jacopo · Answer

Happy to hear it worked Max FYI we are working on improving resource tracking and management

juanlu · Answer

``` Downloading torch 1 10 1 cp37 cp37m manylinux1 x86 64 whl 881 9 MB ``` grimacing <https github com pypa pip issues 2984 issuecomment 789708327> pip uses ~2 7 GB of RAM to install that particular wheel

Yannick · Answer

< Jacopo> Could we catch the error code and display a help message in Orchest to help debug this issue so we catch `137` exit code

Jacopo · Answer

That should be possible depends on what kind of scheduling we are looking for thinking of the multi node migration