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Alagris 2021-05-23 17:00:43 +02:00
parent 3e0649786b
commit 099bfb8540
4 changed files with 111 additions and 38 deletions
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2
Dockerfile
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@ -5,7 +5,7 @@ ENV PYTHONIOENCODING=utf-8
# Instalujemy niezbędne zależności. Zwróć uwagę na flagę "-y" (assume yes)
RUN apt update && apt install -y python3 python3-pip git locales
RUN pip3 install requests python-Levenshtein tqdm sacred pymongo
RUN pip3 install requests python-Levenshtein tqdm sacred pymongo mlflow
RUN pip3 install torch==1.8.1+cpu torchvision==0.9.1+cpu torchaudio==0.8.1 -f https://download.pytorch.org/whl/torch_stable.html
RUN sed -i '/en_US.UTF-8/s/^# //g' /etc/locale.gen && locale-gen en_US.UTF-8

24
MLProject Normal file
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@ -0,0 +1,24 @@
name: cnn
conda_env: conda_env.yaml
# Can have a docker_env instead of a conda_env, e.g.
# docker_env:
# image: mlflow-docker-example
entry_points:
main:
parameters:
data_file: path
regularization: {type: float, default: 0.1}
batch_size: {type: int, default: 32}
learning_rate: {type: float, default: 0.001}
epochs: {type: int, default: 2}
command: "python train_model.py with 'batch_size={batch_size}' 'learning_rate=${learning_rate}' 'epochs=${epochs}'"
validate:
parameters:
data_file: path
regularization: {type: float, default: 0.1}
batch_size: {type: int, default: 32}
learning_rate: {type: float, default: 0.001}
epochs: {type: int, default: 2}
command: "python train_model.py with 'batch_size={batch_size}' 'learning_rate=${learning_rate}' 'epochs=${epochs}'"

16
conda.yaml Normal file
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@ -0,0 +1,16 @@
name: cnn
channels:
- defaults
dependencies:
- python=3.6
- pip
- pip:
- mlflow==1.17.0
- requests==2.25.1
- tqdm==4.59.0
- pymongo==3.11.3
- torch==1.8.1+cpu
- torchvision==0.9.1+cpu
- torchaudio==0.8.1
- python-Levenshtein-0.12.2
- sacred-0.8.2

107
train_model.py
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@ -19,6 +19,16 @@ from tqdm import tqdm
from Levenshtein import distance as levenshtein_distance
from sacred import Experiment
import traceback
from mlflow import log_metric, log_param, log_artifacts
import mlflow
import logging
logging.basicConfig(level=logging.WARN)
logger = logging.getLogger(__name__)
mlflow.set_tracking_uri("http://172.17.0.1:5000")
mlflow.set_experiment("s434749")
ex = Experiment("CNN")
ex.observers.append(FileStorageObserver('sacred_file_observer'))
@ -56,6 +66,20 @@ def dist(a: [str], b: [str]):
return torch.tensor([levenshtein_distance(a[i], b[i]) for i in range(len(a))], dtype=torch.float, device=device)
def encode(batch: [(torch.tensor, str)], in_alphabet, max_len):
batch_text = torch.zeros((len(batch), len(in_alphabet), max_len))
batch_phonemes = list(map(lambda x: x[1], batch))
for i, (sample, _) in enumerate(batch):
for chr_pos, index in enumerate(sample):
batch_text[i, index, chr_pos] = 1
return batch_text, batch_phonemes
def encode_str(batch: [(str, str)], in_alphabet, max_len):
batch = [(torch.tensor([in_alphabet[letter] for letter in in_str], dtype=torch.int), out_str) for in_str, out_str in batch]
return encode(batch)
def train_model(model, learning_rate, in_alphabet, max_len, data, epochs, batch_size):
optimizer = optim.Adam(filter(lambda x: x.requires_grad, model.parameters()),
lr=learning_rate)
@ -65,12 +89,7 @@ def train_model(model, learning_rate, in_alphabet, max_len, data, epochs, batch_
outer_bar.set_description("Epochs")
def collate(batch: [(torch.tensor, str)]):
batch_text = torch.zeros((len(batch), len(in_alphabet), max_len))
batch_phonemes = list(map(lambda x: x[1], batch))
for i, (sample, _) in enumerate(batch):
for chr_pos, index in enumerate(sample):
batch_text[i, index, chr_pos] = 1
return batch_text, batch_phonemes
return encode(batch, in_alphabet, max_len)
data_loader = DataLoader(dataset=data, drop_last=True,
batch_size=3 * batch_size,
@ -106,6 +125,7 @@ def train_model(model, learning_rate, in_alphabet, max_len, data, epochs, batch_
total_loss += loss_scalar
inner_bar.set_description("loss %.2f" % loss_scalar)
ex.log_scalar("avg_loss", total_loss / len(data) * 3)
log_metric("avg_loss", total_loss / len(data) * 3)
# print()
# print("Total epoch loss:", total_loss)
# print("Total epoch avg loss:", total_loss / TOTAL_TRAINING_OUT_LEN)
@ -126,12 +146,7 @@ def evaluate_monte_carlo(model, repeats, data, batch_size, in_alphabet, max_len)
outer_bar.set_description("Epochs")
def collate(batch: [(torch.tensor, str)]):
batch_text = torch.zeros((len(batch), len(in_alphabet), max_len))
batch_phonemes = list(map(lambda x: x[1], batch))
for i, (sample, _) in enumerate(batch):
for chr_pos, index in enumerate(sample):
batch_text[i, index, chr_pos] = 1
return batch_text, batch_phonemes
return encode(batch, in_alphabet, max_len)
for _ in range(repeats):
data_loader = DataLoader(dataset=data, drop_last=True,
@ -177,38 +192,56 @@ def cfg():
'u', 'v', 'w', 'x', 'y', 'z']
def signature(model,in_alphabet,max_len):
mock_x = [('abc', 'xyz'), ('hey', 'man')]
mock_text, _ = encode_str(mock_x, in_alphabet, max_len)
mock_y = model(mock_text)
return mlflow.models.signature.infer_signature(mock_x, mock_y)
@ex.automain
def run(kernel_size, hidden_layers, data_file, epochs, teacher_forcing_probability, learning_rate, batch_size, max_len,
total_out_len, model_file, out_lookup, in_lookup, mode):
in_alphabet = {letter: idx for idx, letter in enumerate(in_lookup)}
with mlflow.start_run():
log_param("kernel_size", kernel_size)
log_param("hidden_layers", hidden_layers)
log_param("data_file", data_file)
log_param("epochs", epochs)
log_param("learning_rate", learning_rate)
log_param("batch_size", batch_size)
log_param("max_len", max_len)
in_alphabet = {letter: idx for idx, letter in enumerate(in_lookup)}
out_alphabet = {letter: idx for idx, letter in enumerate(out_lookup)}
out_alphabet = {letter: idx for idx, letter in enumerate(out_lookup)}
data: [(torch.tensor, torch.tensor)] = []
data: [(torch.tensor, torch.tensor)] = []
texts: [str] = []
texts: [str] = []
with open(data_file) as f:
for line in f:
text, phonemes = line.split("\t")
texts.append(text)
assert len(text) <= max_len, text
text = torch.tensor([in_alphabet[letter] for letter in text], dtype=torch.int)
data.append((text, phonemes))
with open(data_file) as f:
for line in f:
text, phonemes = line.split("\t")
texts.append(text)
assert len(text) <= max_len, text
text = torch.tensor([in_alphabet[letter] for letter in text], dtype=torch.int)
data.append((text, phonemes))
cnn = CNN(kernel_size=kernel_size, hidden_layers=hidden_layers, channels=max_len, embedding_size=max_len,
in_alphabet=in_alphabet, max_len=max_len).to(device)
if os.path.isfile(model_file):
cnn.load_state_dict(torch.load(model_file, map_location=torch.device('cpu')))
else:
if mode == 'train':
train_model(cnn, learning_rate, in_alphabet, max_len, data, epochs, batch_size)
torch.save(cnn.state_dict(), model_file)
ex.add_artifact(model_file)
cnn = CNN(kernel_size=kernel_size, hidden_layers=hidden_layers, channels=max_len, embedding_size=max_len,
in_alphabet=in_alphabet, max_len=max_len).to(device)
if os.path.isfile(model_file):
cnn.load_state_dict(torch.load(model_file, map_location=torch.device('cpu')))
else:
print(model_file + " missing!")
exit(2)
if mode == 'train':
train_model(cnn, learning_rate, in_alphabet, max_len, data, epochs, batch_size)
torch.save(cnn.state_dict(), model_file)
ex.add_artifact(model_file)
if mode == 'eval':
cnn.eval()
evaluate_monte_carlo(cnn, 1, data, batch_size, in_alphabet, max_len)
mlflow.pytorch.log_model(cnn, "cnn-model", registered_model_name="PhoneticEdDistEmbeddings",
signature=signature(cnn,in_alphabet, max_len))
log_artifacts(model_file)
else:
print(model_file + " missing!")
exit(2)
if mode == 'eval':
cnn.eval()
evaluate_monte_carlo(cnn, 1, data, batch_size, in_alphabet, max_len)