Resource Limit Overages

Greetings, I have deployed a Pytorch model on Streamlit Sharing (which is absolutely amazing by the way!!!) to

Full repository at:

It works for a bit, but then I’m running into time-outs (i.e. “[manager] Error checking Streamlit healthz: Get “http://localhost:8501/healthz”: dial tcp connect: connection refused” in the Streamlit console) or resource limit overages. I have done some profiling on my local Linux box and it looks like it uses just over 1GB of ram with 1 user, and just over 1.3GB of ram with 2 users. CPU is greedy and looks like it will choose to max out all cores.

I’m using the GPT-2 small model so my model is the smallest it can be using off the shelf GPT-2. I’m finetuning the DistilGPT model right now to see if that will work, but in the meantime I wanted to see if there were any other tricks I could incorporate to reduce resource consumption.

My question is, are there any recommended solutions to remedy this, or is it just a current limitation of the free Streamlit Sharing option?

Here is how I’m loading & caching the model, could this be improved? I don’t believe it’s loading multiple times, but perhaps?

    @st.cache(allow_output_mutation=True, ttl=120000, max_entries=1)
    def load_aitextgen():
        return aitextgen(model="bigjoedata/rockbot")

My log is attached inline below, I don’t believe I could remove any Streamlit dependencies? Perhaps there is a lighter version of Pytorch you recommend? I believe “torch==1.7.1” in my requirements.txt is loading the CPU version, but maybe not?

The log shows a GPU error, but I believe it doesn’t affect anything since it still works after defaulting back to CPU.

I don’t know your infrastructure, but If RAM is the issue, perhaps there’s a way to enable compression in the VM? Perhaps this is already enabled? I suspect this wouldn’t help since it’s probably a CPU bound issue.

Thanks in advance!

My log (relevant parts):

    [manager] Starting up for owner: 'bigjoedata', repository: 'rockbot', branch: 'main', main module: 'src/'
    [manager] Cloning repository ...
    [manager] Cloning into 'rockbot'...

    [manager] Cloned repository!
    [manager] Processing dependencies ...
    Requirement already satisfied: altair==4.1.0 in /usr/local/lib/python3.7/site-packages (from -r requirements.txt (line 4)) (4.1.0)
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    Successfully installed GitPython-3.1.12 Markdown-3.3.3 Pillow-8.1.0 PyYAML-5.3.1 Werkzeug-1.0.1 absl-py-0.11.0 aiohttp-3.7.3 aitextgen-0.3.0 async-timeout-3.0.1 chardet-3.0.4 filelock-3.0.12 fire-0.3.1 fsspec-0.8.5 future-0.18.2 google-auth-1.24.0 google-auth-oauthlib-0.4.2 grpcio-1.34.0 joblib-1.0.0 multidict-5.1.0 numpy-1.19.5 oauthlib-3.1.0 prompt-toolkit-3.0.9 pyasn1-0.4.8 pyasn1-modules-0.2.8 pytorch-lightning-1.1.3 regex-2020.11.13 requests-oauthlib-1.3.0 rsa-4.6 sacremoses-0.0.43 tensorboard-2.4.0 tensorboard-plugin-wit-1.7.0 termcolor-1.1.0 terminado-0.9.2 tokenizers-0.9.4 torch-1.7.1 tqdm-4.55.1 transformers-4.1.1 yarl-1.6.3
    [manager] Processed dependencies!
    /home/appuser/.local/lib/python3.7/site-packages/torch/cuda/ UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from (Triggered internally at  /pytorch/c10/cuda/CUDAFunctions.cpp:100.)
      return torch._C._cuda_getDeviceCount() > 0
    2021-01-07 18:09:56.334 Downloading bigjoedata/rockbot model to /aitextgen.

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    2021-01-07 18:10:32.262 Using the tokenizer for bigjoedata/rockbot.
    Model name 'bigjoedata/rockbot' not found in model shortcut name list (gpt2, gpt2-medium, gpt2-large, gpt2-xl, distilgpt2). Assuming 'bigjoedata/rockbot' is a path, a model identifier, or url to a directory containing tokenizer files.

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    loading file from cache at aitextgen/d0691afbae042a3bd4c8865ec801754a2a2f953819015b858639dfc70894b013.a552555fdda56a1c7c9a285bccfd44ac8e4b9e26c8c9b307831b3ea3ac782b45
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    [manager] Error checking Streamlit healthz: Get "http://localhost:8501/healthz": dial tcp connect: connection refused
    [manager] Error checking Streamlit healthz: Get "http://localhost:8501/healthz": dial tcp connect: connection refused
    [manager] Error checking Streamlit healthz: Get "http://localhost:8501/healthz": dial tcp connect: connection refused
    [manager] Error checking Streamlit healthz: Get "http://localhost:8501/healthz": dial tcp connect: connection refused
    [manager] Error checking Streamlit healthz: Get "http://localhost:8501/healthz": dial tcp connect: connection refused
    [manager] Error checking Streamlit healthz: Get "http://localhost:8501/healthz": dial tcp connect: connection refused
    [manager] Error checking Streamlit healthz: Get "http://localhost:8501/healthz": dial tcp connect: connection refused
    [manager] Error checking Streamlit healthz: Get "http://localhost:8501/healthz": dial tcp connect: connection refused
    [manager] Error checking Streamlit healthz: Get "http://localhost:8501/healthz": dial tcp connect: connection refused
    [manager] Error checking Streamlit healthz: Get "http://localhost:8501/healthz": dial tcp connect: connection refused
    [manager] Streamlit server consistently failed status checks
    [manager] Please fix the errors, push an update to the git repo, or reboot the app.

Hi @bigjoedata, glad you’re finding Streamlit sharing to be a useful service!

I think your instincts are correct about pytorch being the culprit. I don’t have any firm evidence, but I feel like most of the resource issues that are being reported seem to have this use case in mind. Is it possible to do a CPU-only version of Torch? I know you are saying that the app does fallback to CPU, but I wonder if there is a CPU-only version, it could be lighter on RAM usage without loading all the GPU code, exception handling, etc.

Internally, we are deeply analyzing the performance characteristics of the currently deployed apps, so that we can figure out how to provide more resources to the community without dramatically increasing our costs. Part of that will be creating paid tiers, but hopefully we can also find a way to set users resources in such a way that apps that obviously don’t need many resources can be scaled down, apps like yours can be scaled up, and yet maintain the same relative overall costs to us.


@randyzwitch Yes indeed, I really like how the sharing is set up, it’s a pretty great experience and incredibly polished! I really like the automatic update upon push and console to see what’s going on during spin-up and installation.

I did some more tinkering under the hood and found a few things:

  • Retraining with a DistilGPT-2 time cut generation times by about 1/3 but the output quality dropped notably so I stuck with the original GPT-2 124M model.

  • Changing length of text massively impacts generation time so I have set the default lower

  • Pytorch was partially a culprit, but optimizing only helps a bit. For reference for any others, I forced the smaller pytorch version by changing my requirements.txt from
    --find-links torch==1.7.1+cpu

I pushed out a new commit and it seems to run stably, though I did notice it crashed at the time of another commit; one guess is that something is off in the caching, but I’ll keep an eye on it.

Thanks again!

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