When I ran the Stable Baselines3 RL Colab Notebooks, an error occurred.
stable_baselines_getting_started.ipynb
record_video('CartPole-v1', model, video_length=500, prefix='ppo-cartpole')
GLException Traceback (most recent call last)
<ipython-input-30-e858dbb4fbff> in <module>
----> 1 record_video('CartPole-v1', model, video_length=500, prefix='ppo-cartpole')
18 frames
/usr/local/lib/python3.8/dist-packages/pyglet/gl/lib.py in errcheck(result, func, arguments)
100 context = gl.current_context
101 if not context:
--> 102 raise GLException('No GL context; create a Window first')
103 if not context._gl_begin:
104 error = gl.glGetError()
GLException: No GL context; create a Window first
Could anyone tell me how to solve this problem?
Related
I am currently running a market basket analysis on my dataset.
When I run my association_rules I get an error.
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)
rules.head()
ValueError Traceback (most recent call last)
<ipython-input-47-60252dc62442> in <module>
----> 1 rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)
2 rules.head()
3 frames
/usr/local/lib/python3.8/dist-packages/numpy/lib/function_base.py in _get_ufunc_and_otypes(self, func, args)
2195 args = [asarray(arg) for arg in args]
2196 if builtins.any(arg.size == 0 for arg in args):
-> 2197 raise ValueError('cannot call `vectorize` on size 0 inputs '
2198 'unless `otypes` is set')
2199
ValueError: cannot call `vectorize` on size 0 inputs unless `otypes` is set
In my dataset their is alot of 0's, I am currently looking into this to see if it is effecting my results.
When i am trying sample code from Hugging face i get below error.
the code can be found from https://huggingface.co/facebook/tts_transformer-en-ljspeech
Code:
from fairseq.checkpoint_utils import load_model_ensemble_and_task_from_hf_hub
from fairseq.models.text_to_speech.hub_interface import TTSHubInterface
import IPython.display as ipd
models, cfg, task = load_model_ensemble_and_task_from_hf_hub(
"facebook/fastspeech2-en-ljspeech",
arg_overrides={"vocoder": "hifigan", "fp16": False}
)
model = models[0]
TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
generator = task.build_generator(model, cfg)
text = "Hello, this is a test run."
sample = TTSHubInterface.get_model_input(task, text)
wav, rate = TTSHubInterface.get_prediction(task, model, generator, sample)
ipd.Audio(wav, rate=rate)
Error:
TypeError Traceback (most recent call last)
Input In [1], in <module>
10 model = models[0]
11 TTSHubInterface.update_cfg_with_data_cfg(cfg, task.data_cfg)
---> 12 generator = task.build_generator(model, cfg)
14 text = "Hello, this is a test run."
16 sample = TTSHubInterface.get_model_input(task, text)
File ~/office/virtual_environments/eye_for_bliend/Images/fairseq/fairseq/tasks/text_to_speech.py:151, in TextToSpeechTask.build_generator(self, models, cfg, vocoder, **unused)
149 if vocoder is None:
150 vocoder = self.build_default_vocoder()
--> 151 model = models[0]
152 if getattr(model, "NON_AUTOREGRESSIVE", False):
153 return NonAutoregressiveSpeechGenerator(model, vocoder, self.data_cfg)
TypeError: 'TTSTransformerModel' object is not subscriptable
What worked for me was to put the model in a list where you build the generator on line 12.
generator = task.build_generator([model], cfg)
I'm using KeyBERT on Google Colab to extract keywords from the text.
from keybert import KeyBERT
model = KeyBERT('distilbert-base-nli-mean-tokens')
text_keywords = model.extract_keywords(my_long_text)
But I get the following error:
OSError: Model name 'distilbert-base-nli-mean-token' was not found in model name list (distilbert-base-uncased, distilbert-base-uncased-distilled-squad). We assumed 'distilbert-base-nli-mean-token' was a path or url to a configuration file named config.json or a directory containing such a file but couldn't find any such file at this path or url.
Any idea how to fix this?
Thanks
Exception when trying to download http://sbert.net/models/distilbert-base-nli-mean-token.zip. Response 404
SentenceTransformer-Model http://sbert.net/models/distilbert-base-nli-mean-token.zip not found. Try to create it from scratch
Try to create Transformer Model distilbert-base-nli-mean-token with mean pooling
---------------------------------------------------------------------------
HTTPError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/sentence_transformers/SentenceTransformer.py in __init__(self, model_name_or_path, modules, device)
78 zip_save_path = os.path.join(model_path_tmp, 'model.zip')
---> 79 http_get(model_url, zip_save_path)
80 with ZipFile(zip_save_path, 'r') as zip:
11 frames
/usr/local/lib/python3.7/dist-packages/sentence_transformers/util.py in http_get(url, path)
241 print("Exception when trying to download {}. Response {}".format(url, req.status_code), file=sys.stderr)
--> 242 req.raise_for_status()
243 return
/usr/local/lib/python3.7/dist-packages/requests/models.py in raise_for_status(self)
940 if http_error_msg:
--> 941 raise HTTPError(http_error_msg, response=self)
942
HTTPError: 404 Client Error: Not Found for url: https://public.ukp.informatik.tu-darmstadt.de/reimers/sentence-transformers/v0.2/distilbert-base-nli-mean-token.zip
During handling of the above exception, another exception occurred:
OSError Traceback (most recent call last)
/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
133 that will be used by default in the :obj:`generate` method of the model. In order to get the tokens of the
--> 134 words that should not appear in the generated text, use :obj:`tokenizer.encode(bad_word,
135 add_prefix_space=True)`.
/usr/local/lib/python3.7/dist-packages/transformers/file_utils.py in cached_path(url_or_filename, cache_dir, force_download, proxies)
181 except importlib_metadata.PackageNotFoundError:
--> 182 _timm_available = False
183
OSError: file distilbert-base-nli-mean-token not found
During handling of the above exception, another exception occurred:
OSError Traceback (most recent call last)
<ipython-input-59-d0fa7b6b7cd1> in <module>()
1 doc = full_text
----> 2 model = KeyBERT('distilbert-base-nli-mean-token')
/usr/local/lib/python3.7/dist-packages/keybert/model.py in __init__(self, model)
46 * https://www.sbert.net/docs/pretrained_models.html
47 """
---> 48 self.model = select_backend(model)
49
50 def extract_keywords(self,
/usr/local/lib/python3.7/dist-packages/keybert/backend/_utils.py in select_backend(embedding_model)
40 # Create a Sentence Transformer model based on a string
41 if isinstance(embedding_model, str):
---> 42 return SentenceTransformerBackend(embedding_model)
43
44 return SentenceTransformerBackend("xlm-r-bert-base-nli-stsb-mean-tokens")
/usr/local/lib/python3.7/dist-packages/keybert/backend/_sentencetransformers.py in __init__(self, embedding_model)
33 self.embedding_model = embedding_model
34 elif isinstance(embedding_model, str):
---> 35 self.embedding_model = SentenceTransformer(embedding_model)
36 else:
37 raise ValueError("Please select a correct SentenceTransformers model: \n"
/usr/local/lib/python3.7/dist-packages/sentence_transformers/SentenceTransformer.py in __init__(self, model_name_or_path, modules, device)
93 save_model_to = model_path
94 model_path = None
---> 95 transformer_model = Transformer(model_name_or_path)
96 pooling_model = Pooling(transformer_model.get_word_embedding_dimension())
97 modules = [transformer_model, pooling_model]
/usr/local/lib/python3.7/dist-packages/sentence_transformers/models/Transformer.py in __init__(self, model_name_or_path, max_seq_length, model_args, cache_dir, tokenizer_args, do_lower_case)
25 self.do_lower_case = do_lower_case
26
---> 27 config = AutoConfig.from_pretrained(model_name_or_path, **model_args, cache_dir=cache_dir)
28 self.auto_model = AutoModel.from_pretrained(model_name_or_path, config=config, cache_dir=cache_dir)
29 self.tokenizer = AutoTokenizer.from_pretrained(model_name_or_path, cache_dir=cache_dir, **tokenizer_args)
/usr/local/lib/python3.7/dist-packages/transformers/configuration_auto.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
/usr/local/lib/python3.7/dist-packages/transformers/configuration_utils.py in from_pretrained(cls, pretrained_model_name_or_path, **kwargs)
144 after the :obj:`decoder_start_token_id`. Useful for multilingual models like :doc:`mBART
145 <../model_doc/mbart>` where the first generated token needs to be the target language token.
--> 146 - **forced_eos_token_id** (:obj:`int`, `optional`) -- The id of the token to force as the last generated token
147 when :obj:`max_length` is reached.
148 - **remove_invalid_values** (:obj:`bool`, `optional`) -- Whether to remove possible `nan` and `inf` outputs of
OSError: Model name 'distilbert-base-nli-mean-token' was not found in model name list (distilbert-base-uncased, distilbert-base-uncased-distilled-squad). We assumed 'distilbert-base-nli-mean-token' was a path or url to a configuration file named config.json or a directory containing such a file but couldn't find any such file at this path or url.
I couldn't reproduce this issue with the code you've provided but from the provided error message I believe you're just missing an 's' in the model name so just make sure that the model name is as follows:
distilbert-base-nli-mean-tokens
and not
distilbert-base-nli-mean-token
Also refer to this link for all models available for use.
Code:
gm['MA50'] = gm['Open'].rolling(50).mean()
gm['Ma200'] = gm['Open'].rolling(200).mean()
gm('Open','MA50','MA200').plot(label='gm',figsize=(16,8))
I have been having the following error despite every way to change it and still getting the same error.
Error
--------------------------------------------------------------------------
TypeError Traceback (most recent call last)
<ipython-input-35-062557c07834> in <module>
1 gm['MA50'] = gm['Open'].rolling(50).mean()
2 gm['Ma200'] = gm['Open'].rolling(200).mean()
----> 3 gm('Open','MA50','MA200').plot(label='gm',figsize=(16,8))
TypeError: 'DataFrame' object is not callable
Would need some advice to overcoming this error?
Here is a snippet of my code taken directly from the tf.contrib.learn tutorial on tensorflow.org:
# Load Data Sets
training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
filename = IRIS_TRAINING,
target_dtype = np.int,
features_dtype = np.float32)
Here is the error message:
AttributeError Traceback (most recent call last)
<ipython-input-14-7122d1244c55> in <module>()
11
12 # Load Data Sets
---> 13 training_set = tf.contrib.learn.datasets.base.load_csv_with_header(
14 filename = IRIS_TRAINING,
15 target_dtype = np.int,
AttributeError: 'module' object has no attribute 'learn'
Clearly the module has the attribute learn since tensorflow has a section on learning tf.contrib.learn. What am I doing wrong? All guidance is appreciated.