I'm running some code that works when there is GPU. But I'm trying to figure out how to run it locally with CPU. Here's the error:
2022-07-06 17:58:39,042 - INFO - allennlp.common.plugins - Plugin allennlp_models available
Traceback (most recent call last):
File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/bin/allennlp", line 8, in <module>
sys.exit(run())
File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/lib/python3.7/site-packages/allennlp/__main__.py", line 34, in run
main(prog="allennlp")
File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/lib/python3.7/site-packages/allennlp/commands/__init__.py", line 118, in main
args.func(args)
File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/lib/python3.7/site-packages/allennlp/commands/predict.py", line 205, in _predict
predictor = _get_predictor(args)
File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/lib/python3.7/site-packages/allennlp/commands/predict.py", line 105, in _get_predictor
check_for_gpu(args.cuda_device)
File "/Users/xiaoqingwan/opt/miniconda3/envs/absa/lib/python3.7/site-packages/allennlp/common/checks.py", line 131, in check_for_gpu
" 'trainer.cuda_device=-1' in the json config file." + torch_gpu_error
allennlp.common.checks.ConfigurationError: **Experiment specified a GPU but none is available; if you want to run on CPU use the override 'trainer.cuda_device=-1' in the json config file.**
module 'torch.cuda' has no attribute '_check_driver'
Could you give me some guidance on what to do? Where is the config file and what is it called?
Here's the code (originally from: https://colab.research.google.com/drive/1F9zW_nVkwfwIVXTOA_juFDrlPz5TLjpK?usp=sharing):
# Use pretrained SpanModel weights for prediction
import sys
sys.path.append("aste")
from pathlib import Path
from data_utils import Data, Sentence, SplitEnum
from wrapper import SpanModel
def predict_sentence(text: str, model: SpanModel) -> Sentence:
path_in = "temp_in.txt"
path_out = "temp_out.txt"
sent = Sentence(tokens=text.split(), triples=[], pos=[], is_labeled=False, weight=1, id=1)
data = Data(root=Path(), data_split=SplitEnum.test, sentences=[sent])
data.save_to_path(path_in)
model.predict(path_in, path_out)
data = Data.load_from_full_path(path_out)
return data.sentences[0]
text = "Did not enjoy the new Windows 8 and touchscreen functions ."
model = SpanModel(save_dir="pretrained_14lap", random_seed=0)
sent = predict_sentence(text, model)
Try using something like:
device = torch.device("cpu")
model = SpanModel(save_dir="pretrained_14lap", random_seed=0)
model.to(device)
The config file is inside of the model.tar.gz in the pretrained_14lap directory (it is always named config.json). It also contains the param "cuda_device": 0, which may be causing your problem.
I am trying to gather historical prices/data from Deribit using Pycharm and Spyder but I keep getting errors. I used the code below from the following website:
https://www.codearmo.com/python-tutorial/crypto-algo-trading-historical-data1
If anyone has a suggested fix that would be a huge help. I am relatively new to coding.
Thanks.
import asyncio
import websockets
import json
import pandas as pd
import datetime as dt
async def call_api(msg):
async with websockets.connect('wss://test.deribit.com/ws/api/v2') as websocket:
await websocket.send(msg)
while websocket.open:
response = await websocket.recv()
return response
def async_loop(api, message):
return asyncio.get_event_loop().run_until_complete(api(message))
def retrieve_historic_data(start, end, instrument, timeframe):
msg = \
{
"jsonrpc": "2.0",
"id": 833,
"method": "public/get_tradingview_chart_data",
"params": {
"instrument_name": instrument,
"start_timestamp": start,
"end_timestamp": end,
"resolution": timeframe
}
}
resp = async_loop(call_api, json.dumps(msg))
return resp
def json_to_dataframe(json_resp):
res = json.loads(json_resp)
df = pd.DataFrame(res['result'])
df['ticks'] = df.ticks / 1000
df['timestamp'] = [dt.datetime.fromtimestamp(date) for date in df.ticks]
return df
if __name__ == '__main__':
start = 1554373800000
end = 1554376800000
instrument = "BTC-PERPETUAL"
timeframe = '1'
json_resp = retrieve_historic_data(start, end, instrument, timeframe)
df = json_to_dataframe(json_resp)
print(df.head())
Console Message:
/Users/macbookair/PycharmProjects/untitled/venv/bin/python /Users/macbookair/PycharmProjects/Deribit01/Deribit_Options_01.py
Traceback (most recent call last):
File "/Users/macbookair/PycharmProjects/Deribit01/Deribit_Options_01.py", line 2, in <module>
import websockets
File "/Users/macbookair/PycharmProjects/untitled/venv/lib/python3.8/site-packages/websockets/__init__.py", line 4, in <module>
from .client import * # noqa
File "/Users/macbookair/PycharmProjects/untitled/venv/lib/python3.8/site-packages/websockets/client.py", line 20, in <module>
asyncio.get_event_loop().run_until_complete(call_api(json.dumps(msg)))
File "/Library/Frameworks/Python.framework/Versions/3.8/lib/python3.8/asyncio/base_events.py", line 616, in run_until_complete
return future.result()
File "/Users/macbookair/PycharmProjects/untitled/venv/lib/python3.8/site-packages/websockets/client.py", line 13, in call_api
async with websockets.connect('wss://test.deribit.com/ws/api/v2') as websocket:
AttributeError: partially initialized module 'websockets' has no attribute 'connect' (most likely due to a circular import)
Process finished with exit code 1
I'm trying to call my TensorFlow model which is deployed on a cloud foundry server with an Python 2.7 API using TensorFlow Serving and gRPC. The model expects a 200 dim vector as input, which I hardcoded at the moment. The connection Variables are stored in a virtualenv and checked twice.
The code:
import os
from grpc.beta import implementations
import tensorflow as tf
from tensorflow_serving.apis import predict_pb2
from tensorflow_serving.apis import prediction_service_pb2
from grpc._cython import cygrpc
MODEL_NAME = str(os.getenv('MODEL_NAME', ''))
MODEL_SERVER_HOST = str(os.getenv('MODEL_SERVER_HOST', ''))
MODEL_SERVER_PORT = int(os.getenv('MODEL_SERVER_PORT', ''))
ROOT_CERT = str(os.getenv('ROOT_CERT', '')).replace('\\n', '\n')
def metadata_transformer(metadata):
additions = []
token = 'Bearer <my access token>'
additions.append(('authorization', token))
return tuple(metadata) + tuple(additions)
credentials = implementations.ssl_channel_credentials(root_certificates=ROOT_CERT)
channel = implementations.secure_channel(MODEL_SERVER_HOST, MODEL_SERVER_PORT, credentials)
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel, metadata_transformer=metadata_transformer)
import numpy as np
data = np.matrix([0.06222425773739815, 0.08211926370859146, -0.060986146330833435, 0.13920938968658447, 0.10515272617340088, -0.06220443174242973, -0.05927170068025589, -0.054189786314964294, -0.0986655130982399, 0.013334010727703571, -0.05667420104146004, 0.059366412460803986, -0.03483295068144798, -0.05382293462753296, 0.02721281163394451, -0.1428503543138504, 0.029297124594449997, 0.07006879895925522, 0.06501731276512146, 0.028620243072509766, 0.07128454744815826, 0.029960375279188156, 0.0710490494966507, -0.04619687795639038, -0.03106304071843624, -0.04266272485256195, 0.004348727408796549, 0.03099834732711315, 0.09248803555965424, -0.036939311772584915, 0.00017547572497278452, 0.03521900251507759, 0.10932505130767822, -0.019729139283299446, 0.12315405160188675, 0.10092845559120178, -0.12633951008319855, -0.022320391610264778, 0.0870826318860054, -0.06696301698684692, -0.016253307461738586, -0.0413096621632576, -0.040929097682237625, 0.09338817000389099, -0.08800378441810608, 0.015543102286756039, 0.018787918612360954, 0.07351260632276535, 0.038140904158353806, 0.019255049526691437, 0.0875692293047905, -0.07542476058006287, -0.04116508364677429, 0.04507743567228317, -0.06986603885889053, -0.24688798189163208, -0.035459864884614944, 0.06200174242258072, -0.06932217627763748, 0.06320516765117645, -0.023999478667974472, -0.04712359234690666, 0.03672196343541145, -0.02999514900147915, 0.04105519875884056, 0.08891177922487259, 0.15175248682498932, -0.0021488466300070286, 0.04398706927895546, -0.04429445043206215, 0.04708605632185936, 0.043234940618276596, -0.043555982410907745, 0.017381751909852028, 0.048889972269535065, -0.016929129138588905, 0.01731136068701744, -0.04694319888949394, 0.20381565392017365, 0.009074307978153229, 0.004490611143410206, -0.08525945991277695, -0.03385556861758232, 0.017475442960858345, -0.040392760187387466, 0.14970248937606812, 0.042721331119537354, -0.1257765144109726, -0.07097769528627396, -0.10943038016557693, 0.015442096628248692, -0.06519876420497894, -0.07588690519332886, -0.07620779424905777, 0.04572996124625206, -0.058589719235897064, -0.04492143541574478, -0.01922304928302765, -0.008066931739449501, 0.04317406192421913, 0.020763304084539413, -0.025430725887417793, 0.04271349683403969, 0.07393930852413177, 0.0020402593072503805, 0.0783640518784523, 0.047386448830366135, 0.010610940866172314, 0.022059153765439987, 0.034980181604623795, -0.006882485933601856, -0.08911270648241043, -0.001243607490323484, -0.06307544559240341, -0.01352659147232771, -0.24622271955013275, 0.07930449396371841, 0.03659113869071007, -0.05077377334237099, 0.08726480603218079, -0.09274136275053024, -0.05766649544239044, -0.12269984930753708, 0.056026071310043335, -0.0048304214142262936, -0.05568183213472366, -0.08890420943498611, -0.02911136858165264, -0.0944124087691307, 0.0011820291401818395, -0.08908636122941971, -0.008728212676942348, -0.014545259065926075, -0.008866528049111366, 0.02728298306465149, -0.020994992926716805, 0.031155599281191826, 0.036098793148994446, 0.06911332905292511, -0.06691643595695496, -0.00014896543871145695, -0.007080242037773132, 0.0031992685981094837, 0.043563224375247955, 0.02550852671265602, -0.015397937037050724, 0.06041031703352928, -0.08981014788150787, -0.10881254076957703, 0.03226703032851219, -0.02039985917508602, -0.05354547128081322, -0.026514282450079918, 0.09616094827651978, -0.04160488396883011, -0.06793050467967987, -0.17060619592666626, -0.08044841140508652, 0.042605575174093246, 0.08186516910791397, 0.026051705703139305, 0.1254323273897171, 0.09807661175727844, 0.04692094400525093, 0.05536479875445366, 0.004592049401253462, 0.01953544095158577, -0.02827763929963112, 0.11051501333713531, -0.05077047273516655, -0.09987067431211472, 0.025186538696289062, -0.24119670689105988, -0.054666098207235336, 0.03561021387577057, -0.006030901800841093, 0.14740994572639465, 0.09515859931707382, 0.0628485381603241, 0.020558597519993782, -0.04458167776465416, -0.04740617796778679, 0.024550801143050194, -0.09533495455980301, 0.057229768484830856, -0.08855120837688446, 0.027864644303917885, -0.07248448580503464, 0.0647491067647934, 0.09660986065864563, 0.038834456354379654, -0.030274877324700356, -0.024261653423309326, 0.05457066744565964, -0.00860705878585577, 0.04901411384344101, 0.017157232388854027, -0.02722001262009144, 0.012187148444354534, 0.05596058815717697])
request = predict_pb2.PredictRequest()
request.model_spec.name = MODEL_NAME
request.model_spec.signature_name = 'ticketCatFeature2'
request.inputs['input'].CopyFrom(
tf.contrib.util.make_tensor_proto(data, shape=[200]))
print stub.Classify(request, 10)
I'm getting following error message when running the app:
Traceback (most recent call last):
File "app.py", line 36, in
print stub.Classify(request, 10)
File "/home/vagrant/Desktop/Masterarbeit/appDir/venv/local/lib/python2.7/site-packages/grpc/beta/_client_adaptations.py", line 309, in call
self._request_serializer, self._response_deserializer)
File "/home/vagrant/Desktop/Masterarbeit/appDir/venv/local/lib/python2.7/site-packages/grpc/beta/_client_adaptations.py", line 195, in _blocking_unary_unary
raise _abortion_error(rpc_error_call)
grpc.framework.interfaces.face.face.AbortionError: AbortionError(code=StatusCode.INTERNAL, details="Did not read entire message")
Log of grpc Debug: https://ufile.io/owk76
Omxplayer crashes after pushbuttons
I like to change movies with pushbuttons and this is code that I have so far but Omxplayer crashes after few pushbutton are push!
I am new to raspberry pi and python been looking for a fix but cannot find any. Any help is welcome.
The error I get is:
Traceback (most recent call last):
File "mygpio.py", line 34, in <module>
player.load(vida)
File "build/bdist.linux-armv7l/egg/omxplayer/player.py", line 162, in load
File "build/bdist.linux-armv7l/egg/omxplayer/player.py", line 88, in _load_source
File "build/bdist.linux-armv7l/egg/omxplayer/player.py", line 134, in _setup_dbus_connection
SystemError: DBus cannot connect to the OMXPlayer process
#!/usr/bin/env python2
import os.path
from time import sleep
import subprocess
import os
from omxplayer import OMXPlayer
vida = '/home/pi/Videos/testvids/6.mov'
vidb = '/home/pi/Videos/testvids/3.mov'
vidc = '/home/pi/Videos/testvids/t2.mp4'
default = '/home/pi/Videos/testvids/t1.mp4'
import RPi.GPIO as GPIO
#set up GPIO using BCM numbering
GPIO.setmode(GPIO.BCM)
#All Gpio's as input and pull up
GPIO.setup(2, GPIO.IN, pull_up_down = GPIO.PUD_UP)
GPIO.setup(3, GPIO.IN, pull_up_down = GPIO.PUD_UP)
GPIO.setup(4, GPIO.IN, pull_up_down = GPIO.PUD_UP)
player = OMXPlayer(default,args=['--no-osd','--blank'],)
while True:
if GPIO.input(2) ==0:
player.load(vida)
print("gpio 2")
player.play()
#sleep(5)
if (GPIO.input(3) == 0):
player.load(vidb)
print("gpio 3")
player.play()
# sleep(5)
if (GPIO.input(4) == 0):
player.load(vidc)
print("gpio 4")
player.play()
#sleep(5)
GPIO.cleanup()
looks like this was a bug in the wrapper
https://github.com/willprice/python-omxplayer-wrapper/issues/85