<jupyter_start><jupyter_text>Description-- We need a Google Cloud Platform project with billing enabled.We first have to enable the `Cloud AI Platform Models API` and the `Compute Engine API`.We then create an AI Platform Notebooks instance, then we select the latest `PyTorch` instance type (without/with ?????To determine?????? GPUs).Once the instance has been created, we select `Open JupyterLab`:After setting up the environment, we run the folling commands :<jupyter_code>!gsutil version -l
GCP_PROJECT = 'My First Project'
BUCKET_URL = 'gs://storage_bucket_speech'
!gsutil mb $BUCKET_URL<jupyter_output>Creating gs://storage_bucket_speech/...
ServiceException: 409 A Cloud Storage bucket named 'storage_bucket_speech' already exists. Try another name. Bucket names must be globally unique across all Google Cloud projects, including those outside of your organization.<jupyter_text>The bucket already exists because it was already created  Store the data on Google Storage ---<jupyter_code>train_path = '../feature-extraction/train/'
test_path  = '../feature-extraction/test/'
valid_path = '../feature-extraction/valid/'
!gsutil -m -q cp -r $train_path $BUCKET_URL
!gsutil -m -q cp -r $test_path $BUCKET_URL
!gsutil -m -q cp -r $valid_path $BUCKET_URL<jupyter_output><empty_output><jupyter_text>Setup for local training :---<jupyter_code>REGION = "us-west1-b"
SERVICE_ACCOUNT = "<EMAIL>"
content_name = "pt-img-cls-gpu-cust"
! ls trainer
! cat trainer/requirements.txt
! pip3 install -r trainer/requirements.txt
%run trainer/task.py<jupyter_output><empty_output>
{'fraction_non_alphanumeric': 0.10395408163265306, 'fraction_numerical': 0.0031887755102040817, 'mean_word_length': 5.45679012345679, 'pattern_counts': {'":': 0, '<': 13, '<?xml version=': 0, '>': 13, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
{'chain_length': '3', 'dir': 'jupyter-structured-clean-dedup', 'id': '47158', 'license': 'MIT', 'n_tokens_mistral': 492, 'n_tokens_neox': 482, 'n_words': 166, 'path': 'gcp/gcp-training-job.ipynb', 'repo_name': 'younesslanda/Speech-Emotion-Recognition', 'size': '1611'}