Hello all
I would like to deploy my trained NN locally. I found this on the FastAI website:
python app/server.py serve.
Go to http://localhost:8080/ to test your app.
However, the above does not work for me. Can anyone help me on how to deploy a trained NN locally?
Thank you for any help, it is greatly appreciated.
Sure thing, what exactly seems to be the issue?
Just so you know
python app/server.py serve.
is referring to either app.py or server.py depending on what the app is called.
I assume you’re probably doing the Render example?
https://course.fast.ai/deployment_render.html
Remember you need to pip install your requirements.txt to run locally
This is what I’m getting from the downloaded google-app-engine example:
Stoops-Air:google-app-engine danielstoops$ python3.7 app/server.py serve
Traceback (most recent call last):
File “app/server.py”, line 1, in
from starlette.applications import Starlette
ModuleNotFoundError: No module named ‘starlette’
Stoops-Air:google-app-engine danielstoops$ pip3 install starlette
Requirement already satisfied: starlette in /Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/site-packages (0.13.2)
So I can’t get the python app/server.py serve. code to work
I don’t see the requirements.txt file that you mentioned. What is it?
Ah, sorry i thought you were looking at a different example.
Ok maybe make sure that pip3 is referencing the right version of python (if you have multiple python installs) using which pip3.
Thank you this solved the current issue. However, now I have another issue: I can’t install the FastAI module.
pip install fastai
I received the following error message:
Running setup.py install for pynvx … error
ERROR: Command errored out with exit status 1:
Do you know how I can install the FastAI module on Mac?
UPDATE: I’m trying to run the code in a conda environment. I did conda install starlette, but when I run python app/server.py serve I’m still getting:
Traceback (most recent call last):
File “app/server.py”, line 1, in
from starlette.applications import Starlette
ModuleNotFoundError: No module named ‘starlette’
re installing fast ai on your mac i’d take a look at this thread
I’m not entirely sure how to install startlette with conda. I haven’t used that framework before and usually use homebrew to manage packages.
I realised that I wasn’t properly using my conda environment properly. Propblem fixed!
Now I’m having an issue with the python script itself. I’m getting this error:
(fastai) Stoops-Air:google-app-engine danielstoops$ python app/server.py serve
Traceback (most recent call last):
File “app/server.py”, line 37, in
learn = loop.run_until_complete(asyncio.gather(*tasks))[0]
File “/usr/local/anaconda3/envs/fastai/lib/python3.7/asyncio/base_events.py”, line 587, in run_until_complete
return future.result()
File “app/server.py”, line 32, in setup_learner
learn.load(model_file_name)
File “/usr/local/anaconda3/envs/fastai/lib/python3.7/site-packages/fastai/basic_train.py”, line 281, in load
get_model(self.model).load_state_dict(state, strict=strict)
File “/usr/local/anaconda3/envs/fastai/lib/python3.7/site-packages/torch/nn/modules/module.py”, line 830, in load_state_dict
self.class.name, “\n\t”.join(error_msgs)))
RuntimeError: Error(s) in loading state_dict for Sequential:
Missing key(s) in state_dict: “0.0.weight”, “0.1.weight”, “0.1.bias”, “0.1.running_mean”, “0.1.running_var”, “0.4.0.conv1.weight”, “0.4.0.bn1.weight”, “0.4.0.bn1.bias”, “0.4.0.bn1.running_mean”, “0.4.0.bn1.running_var”, “0.4.0.conv2.weight”, “0.4.0.bn2.weight”, “0.4.0.bn2.bias”, “0.4.0.bn2.running_mean”, “0.4.0.bn2.running_var”, “0.4.1.conv1.weight”, “0.4.1.bn1.weight”, “0.4.1.bn1.bias”, “0.4.1.bn1.running_mean”, “0.4.1.bn1.running_var”, “0.4.1.conv2.weight”, “0.4.1.bn2.weight”, “0.4.1.bn2.bias”, “0.4.1.bn2.running_mean”, “0.4.1.bn2.running_var”, “0.4.2.conv1.weight”, “0.4.2.bn1.weight”, “0.4.2.bn1.bias”, “0.4.2.bn1.running_mean”, “0.4.2.bn1.running_var”, “0.4.2.conv2.weight”, “0.4.2.bn2.weight”, “0.4.2.bn2.bias”, “0.4.2.bn2.running_mean”, “0.4.2.bn2.running_var”, “0.5.0.conv1.weight”, “0.5.0.bn1.weight”, “0.5.0.bn1.bias”, “0.5.0.bn1.running_mean”, “0.5.0.bn1.running_var”, “0.5.0.conv2.weight”, “0.5.0.bn2.weight”, “0.5.0.bn2.bias”, “0.5.0.bn2.running_mean”, “0.5.0.bn2.running_var”, “0.5.0.downsample.0.weight”, “0.5.0.downsample.1.weight”, “0.5.0.downsample.1.bias”, “0.5.0.downsample.1.running_mean”, “0.5.0.downsample.1.running_var”, “0.5.1.conv1.weight”, “0.5.1.bn1.weight”, “0.5.1.bn1.bias”, “0.5.1.bn1.running_mean”, “0.5.1.bn1.running_var”, “0.5.1.conv2.weight”, “0.5.1.bn2.weight”, “0.5.1.bn2.bias”, “0.5.1.bn2.running_mean”, “0.5.1.bn2.running_var”, “0.5.2.conv1.weight”, “0.5.2.bn1.weight”, “0.5.2.bn1.bias”, “0.5.2.bn1.running_mean”, “0.5.2.bn1.running_var”, “0.5.2.conv2.weight”, “0.5.2.bn2.weight”, “0.5.2.bn2.bias”, “0.5.2.bn2.running_mean”, “0.5.2.bn2.running_var”, “0.5.3.conv1.weight”, “0.5.3.bn1.weight”, “0.5.3.bn1.bias”, “0.5.3.bn1.running_mean”, “0.5.3.bn1.running_var”, “0.5.3.conv2.weight”, “0.5.3.bn2.weight”, “0.5.3.bn2.bias”, “0.5.3.bn2.running_mean”, “0.5.3.bn2.running_var”, “0.6.0.conv1.weight”, “0.6.0.bn1.weight”, “0.6.0.bn1.bias”, “0.6.0.bn1.running_mean”, “0.6.0.bn1.running_var”, “0.6.0.conv2.weight”, “0.6.0.bn2.weight”, “0.6.0.bn2.bias”, “0.6.0.bn2.running_mean”, “0.6.0.bn2.running_var”, “0.6.0.downsample.0.weight”, “0.6.0.downsample.1.weight”, “0.6.0.downsample.1.bias”, “0.6.0.downsample.1.running_mean”, “0.6.0.downsample.1.running_var”, “0.6.1.conv1.weight”, “0.6.1.bn1.weight”, “0.6.1.bn1.bias”, “0.6.1.bn1.running_mean”, “0.6.1.bn1.running_var”, “0.6.1.conv2.weight”, “0.6.1.bn2.weight”, “0.6.1.bn2.bias”, “0.6.1.bn2.running_mean”, “0.6.1.bn2.running_var”, “0.6.2.conv1.weight”, “0.6.2.bn1.weight”, “0.6.2.bn1.bias”, “0.6.2.bn1.running_mean”, “0.6.2.bn1.running_var”, “0.6.2.conv2.weight”, “0.6.2.bn2.weight”, “0.6.2.bn2.bias”, “0.6.2.bn2.running_mean”, “0.6.2.bn2.running_var”, “0.6.3.conv1.weight”, “0.6.3.bn1.weight”, “0.6.3.bn1.bias”, “0.6.3.bn1.running_mean”, “0.6.3.bn1.running_var”, “0.6.3.conv2.weight”, “0.6.3.bn2.weight”, “0.6.3.bn2.bias”, “0.6.3.bn2.running_mean”, “0.6.3.bn2.running_var”, “0.6.4.conv1.weight”, “0.6.4.bn1.weight”, “0.6.4.bn1.bias”, “0.6.4.bn1.running_mean”, “0.6.4.bn1.running_var”, “0.6.4.conv2.weight”, “0.6.4.bn2.weight”, “0.6.4.bn2.bias”, “0.6.4.bn2.running_mean”, “0.6.4.bn2.running_var”, “0.6.5.conv1.weight”, “0.6.5.bn1.weight”, “0.6.5.bn1.bias”, “0.6.5.bn1.running_mean”, “0.6.5.bn1.running_var”, “0.6.5.conv2.weight”, “0.6.5.bn2.weight”, “0.6.5.bn2.bias”, “0.6.5.bn2.running_mean”, “0.6.5.bn2.running_var”, “0.7.0.conv1.weight”, “0.7.0.bn1.weight”, “0.7.0.bn1.bias”, “0.7.0.bn1.running_mean”, “0.7.0.bn1.running_var”, “0.7.0.conv2.weight”, “0.7.0.bn2.weight”, “0.7.0.bn2.bias”, “0.7.0.bn2.running_mean”, “0.7.0.bn2.running_var”, “0.7.0.downsample.0.weight”, “0.7.0.downsample.1.weight”, “0.7.0.downsample.1.bias”, “0.7.0.downsample.1.running_mean”, “0.7.0.downsample.1.running_var”, “0.7.1.conv1.weight”, “0.7.1.bn1.weight”, “0.7.1.bn1.bias”, “0.7.1.bn1.running_mean”, “0.7.1.bn1.running_var”, “0.7.1.conv2.weight”, “0.7.1.bn2.weight”, “0.7.1.bn2.bias”, “0.7.1.bn2.running_mean”, “0.7.1.bn2.running_var”, “0.7.2.conv1.weight”, “0.7.2.bn1.weight”, “0.7.2.bn1.bias”, “0.7.2.bn1.running_mean”, “0.7.2.bn1.running_var”, “0.7.2.conv2.weight”, “0.7.2.bn2.weight”, “0.7.2.bn2.bias”, “0.7.2.bn2.running_mean”, “0.7.2.bn2.running_var”, “1.2.weight”, “1.2.bias”, “1.2.running_mean”, “1.2.running_var”, “1.4.weight”, “1.4.bias”, “1.6.weight”, “1.6.bias”, “1.6.running_mean”, “1.6.running_var”, “1.8.weight”, “1.8.bias”.
Unexpected key(s) in state_dict: “opt_func”, “loss_func”, “metrics”, “true_wd”, “bn_wd”, “wd”, “train_bn”, “model_dir”, “callback_fns”, “cb_state”, “model”, “data”, “cls”.
Anyone know what to do?