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public_repos/dl-fundamentals/unit06-dl-tips/exercises/solutions | public_repos/dl-fundamentals/unit06-dl-tips/exercises/solutions/2_adam-with-weight-decay/exercise-2-solution.ipynb | %load_ext watermark
%watermark -p torch,lightningimport lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModule, PyTorchMLPnum_epochs = 100class LightningModel(L.LightningModule):
def __init__(self... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/exercises/solutions | public_repos/dl-fundamentals/unit06-dl-tips/exercises/solutions/1_cosine-with-warmup/exercise-1-solution.ipynb | %load_ext watermark
%watermark -p torch,lightning,pl_bolts --condaimport lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModule, PyTorchMLPnum_epochs = 100import matplotlib.pyplot as plt
model = torc... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/exercises | public_repos/dl-fundamentals/unit06-dl-tips/exercises/1_cosine-with-warmup/exercise-1.ipynb | %load_ext watermark
%watermark -p torch,lightning,pl_bolts --condaimport lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from lightning.pytorch.loggers import CSVLogger
from shared_utilities import CustomDataModule, PyTorchMLPnum_epochs = 100import matplotlib.pyplot as plt
model = torc... | 0 |
public_repos/dl-fundamentals/unit06-dl-tips/exercises | public_repos/dl-fundamentals/unit06-dl-tips/exercises/1_cosine-with-warmup/README.md | # Exercise 1: Learning Rate Warmup
This exercise asks you to experiment with learning rate warmup during cosine annealing.
Learning rate warmup is a technique that involves gradually increasing the learning rate from a small value to a larger target value over a certain number of iterations or epochs. Learning rate... | 0 |
public_repos/dl-fundamentals/unit10-after-training | public_repos/dl-fundamentals/unit10-after-training/10.1-confidence-intervals/10.1-ci.ipynb | # pip install transformers# pip install datasets# pip install lightning%load_ext watermark
%watermark --conda -p torch,transformers,datasets,lightning# pip install datasets
import os.path as op
from datasets import load_dataset
import lightning as L
from lightning.pytorch.loggers import CSVLogger
from lightning.pyto... | 0 |
public_repos/dl-fundamentals/unit10-after-training/exercises | public_repos/dl-fundamentals/unit10-after-training/exercises/exercise_1-fabric/exercise_1.md | ## # Exercise 1: Using Fabric
It's been a while since we used pure PyTorch code in Unit 4. If you are interested in using Fabric, a good exercise is to convert the existing PyTorch code into Fabric -- it only requires changing a handful of lines.
For this, you can use [this template](exercise_1-template.ipynb) based... | 0 |
public_repos/dl-fundamentals/unit10-after-training/exercises | public_repos/dl-fundamentals/unit10-after-training/exercises/exercise_1-fabric/exercise_1-template.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !pip install watermark# !pip install lightning%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets.MNIST(... | 0 |
public_repos/dl-fundamentals/unit10-after-training/exercises/solution | public_repos/dl-fundamentals/unit10-after-training/exercises/solution/exercise_1-fabric/exercise_1-solution.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !conda install watermark%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torch,lightningfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets.MNIST(
root=".... | 0 |
public_repos/dl-fundamentals/unit10-after-training/exercises/solution | public_repos/dl-fundamentals/unit10-after-training/exercises/solution/exercise_2-ci/exercise_2-solution.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !pip install watermark# !pip install lightning%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets.MNIST(... | 0 |
public_repos/dl-fundamentals/unit10-after-training/exercises | public_repos/dl-fundamentals/unit10-after-training/exercises/exercise_2-ci/exercise_2.md | ## # Exercise 2: Confidence Intervals
If you want to practice using the confidence interval method introduced in this lecture, tru to compute a confidence interval for the MNIST classifier from Unit 4 using [this template](exercise_2-template.ipynb).
Hint: In this case, since it's a relatively large test set, the co... | 0 |
public_repos/dl-fundamentals/unit10-after-training/exercises | public_repos/dl-fundamentals/unit10-after-training/exercises/exercise_2-ci/exercise_2-template.ipynb | # !conda install numpy pandas matplotlib --yes# !pip install torch torchvision torchaudio# !pip install watermark# !pip install lightning%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torchfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets.MNIST(... | 0 |
public_repos/dl-fundamentals/unit10-after-training | public_repos/dl-fundamentals/unit10-after-training/10.2-fabric/pytorch-llm.py |
import os
import os.path as op
import time
from datasets import load_dataset
import torch
from torch.utils.data import DataLoader
import torchmetrics
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from watermark import watermark
from local_dataset_utilities import ... | 0 |
public_repos/dl-fundamentals/unit10-after-training | public_repos/dl-fundamentals/unit10-after-training/10.2-fabric/local_dataset_utilities.py | import os
import sys
import tarfile
import time
import numpy as np
import pandas as pd
from packaging import version
from torch.utils.data import Dataset
from tqdm import tqdm
import urllib
def reporthook(count, block_size, total_size):
global start_time
if count == 0:
start_time = time.time()
... | 0 |
public_repos/dl-fundamentals/unit10-after-training | public_repos/dl-fundamentals/unit10-after-training/10.2-fabric/fabric-llm.py |
import os
import os.path as op
import time
from datasets import load_dataset
from lightning import Fabric
import torch
from torch.utils.data import DataLoader
import torchmetrics
from transformers import AutoTokenizer
from transformers import AutoModelForSequenceClassification
from watermark import watermark
from lo... | 0 |
public_repos/dl-fundamentals/unit09-performance | public_repos/dl-fundamentals/unit09-performance/9.5-batchsize-finder/batchsize.ipynb | # pip install transformers# pip install datasets# pip install lightning%load_ext watermark
%watermark --conda -p torch,transformers,datasets,lightning# pip install datasets
import os.path as op
from datasets import load_dataset
import lightning as L
from lightning.pytorch.loggers import CSVLogger
from lightning.pyto... | 0 |
public_repos/dl-fundamentals/unit09-performance | public_repos/dl-fundamentals/unit09-performance/9.4-compile/compile.py | import os
import os.path as op
import time
import lightning as L
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torchmetrics
from datasets import load_dataset
from lightning.pytorch.callbacks import ModelCheckpoint
from lightning.pytorch.loggers import CSVLogger
from local_dataset_utilities i... | 0 |
public_repos/dl-fundamentals/unit09-performance/9.1-mixed-precision | public_repos/dl-fundamentals/unit09-performance/9.1-mixed-precision/part2-distilbert-example/2_distilbert-finetuning-mixed-fp16.ipynb | # pip install transformers# pip install datasets# pip install lightning%load_ext watermark
%watermark --conda -p torch,transformers,datasets,lightning# pip install datasets
import os.path as op
from datasets import load_dataset
import lightning as L
from lightning.pytorch.loggers import CSVLogger
from lightning.pyto... | 0 |
public_repos/dl-fundamentals/unit09-performance/9.1-mixed-precision | public_repos/dl-fundamentals/unit09-performance/9.1-mixed-precision/part2-distilbert-example/1_distilbert-finetuning-whole.ipynb | # pip install transformers# pip install datasets# pip install lightning%load_ext watermark
%watermark --conda -p torch,transformers,datasets,lightning# pip install datasets
import os.path as op
from datasets import load_dataset
import lightning as L
from lightning.pytorch.loggers import CSVLogger
from lightning.pyto... | 0 |
public_repos/dl-fundamentals/unit09-performance/9.1-mixed-precision | public_repos/dl-fundamentals/unit09-performance/9.1-mixed-precision/part2-distilbert-example/local_dataset_utilities.py | import os
import sys
import tarfile
import time
import numpy as np
import pandas as pd
from packaging import version
from torch.utils.data import Dataset
from tqdm import tqdm
import urllib
def reporthook(count, block_size, total_size):
global start_time
if count == 0:
start_time = time.time()
... | 0 |
public_repos/dl-fundamentals/unit09-performance/9.1-mixed-precision | public_repos/dl-fundamentals/unit09-performance/9.1-mixed-precision/part2-distilbert-example/3_distilbert-finetuning-mixed-bfloat16.ipynb | # pip install transformers# pip install datasets# pip install lightning%load_ext watermark
%watermark --conda -p torch,transformers,datasets,lightning# pip install datasets
import os.path as op
from datasets import load_dataset
import lightning as L
from lightning.pytorch.loggers import CSVLogger
from lightning.pyto... | 0 |
public_repos/dl-fundamentals/unit09-performance | public_repos/dl-fundamentals/unit09-performance/9.3-multi-gpu/notes.txt | - torch : 1.13.1
- lightning : 2.0.0.dev0
- transformers: 4.26.1
- A100
### lightning-trainer.py, 1 gpu
trainer = L.Trainer(
max_epochs=3,
callbacks=callbacks,
precision="16",
accelerator="gpu",
devices=[1],
logger=logger,
log_every_n_steps=10,
... | 0 |
public_repos/dl-fundamentals/unit09-performance | public_repos/dl-fundamentals/unit09-performance/9.3-multi-gpu/local_dataset_utilities.py | import os
import sys
import tarfile
import time
import numpy as np
import pandas as pd
from packaging import version
from torch.utils.data import Dataset
from tqdm import tqdm
import urllib
def reporthook(count, block_size, total_size):
global start_time
if count == 0:
start_time = time.time()
... | 0 |
public_repos/dl-fundamentals/unit09-performance | public_repos/dl-fundamentals/unit09-performance/9.3-multi-gpu/finetune-multigpu.py | import os
import os.path as op
import time
import lightning as L
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torchmetrics
from datasets import load_dataset
from lightning.pytorch.callbacks import ModelCheckpoint
from lightning.pytorch.loggers import CSVLogger
from local_dataset_utilities im... | 0 |
public_repos/dl-fundamentals/unit09-performance | public_repos/dl-fundamentals/unit09-performance/exercises/exercise-1.md | ## Exercise 1: Evaluating Mixed-Precision Performance
I recommend trying out the different mixed-precision choices on your GPU (if you have one):
- regular float32 (`"32-true"`)
- regular float16 (`"16-true"`)
- regular float64 (`"64-true"`)
- mixed precision with float16 (`"16-mixed"`)
- mixed-precision with bf... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.4-reproducibility/shared_utilities.py | import torch
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchvision import datasets, transforms
class PyTorchMLP(torch.nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
self.all_layers = torch.nn.Sequential(
... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.4-reproducibility/5.4-part3-deterministic-lightning.py | # Unit 5.4. Making Code Reproducible
# Part 3. Using the Deterministic Setting in Lightning
import lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from shared_utilities import PyTorchMLP, get_dataset_loaders
from watermark import watermark
class LightningModel(L.LightningModule):
... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.7-evaluating/5.7-part-2-2-evaluate.ipynb | %load_ext watermark
%watermark -p torch,lightning,torchmetrics --condaimport lightning as L
import torch
from shared_utilities import PyTorchMLP, LightningModel, MNISTDataModulepytorch_model = PyTorchMLP(num_features=784, num_classes=10)
lightning_model = LightningModel.load_from_checkpoint(
checkpoint_path="model... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.7-evaluating/shared_utilities.py | import lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchvision import datasets, transforms
class PyTorchMLP(torch.nn.Module):
def __init__(self, num_features, num_classes):
su... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.7-evaluating/shared_plotting.py | import matplotlib.pyplot as plt
import numpy as np
import torch
def show_failures(
model,
data_loader,
unnormalizer=None,
class_dict=None,
nrows=3,
ncols=5,
figsize=None,
):
failure_features = []
failure_pred_labels = []
failure_true_labels = []
for batch_idx, (features, ... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.7-evaluating/5.7-part-2-1-train.ipynb | %load_ext watermark
%watermark -p torch,lightning --condaimport lightning as L
import torch
from shared_utilities import PyTorchMLP, LightningModel, MNISTDataModulefrom lightning.pytorch.loggers import CSVLoggertorch.manual_seed(123)
dm = MNISTDataModule()
pytorch_model = PyTorchMLP(num_features=784, num_classes=10)
... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.3-torchmetrics/5.3-part3-torchmetrics-testset.py | # Unit 5.3. Computing Metrics Efficiently with TorchMetrics
# Part 3. Evaluating the Results
import lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from shared_utilities import PyTorchMLP, get_dataset_loaders
from watermark import watermark
class LightningModel(L.LightningModule):
... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.3-torchmetrics/shared_utilities.py | import torch
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchvision import datasets, transforms
class PyTorchMLP(torch.nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
self.all_layers = torch.nn.Sequential(
... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.3-torchmetrics/5.2-part3-lightning-mlp.py | import lightning as L
import torch
import torch.nn.functional as F
from shared_utilities import PyTorchMLP, compute_accuracy, get_dataset_loaders
from watermark import watermark
# LightningModule that receives a PyTorch model as input
class LightningModel(L.LightningModule):
def __init__(self, model, learning_rat... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.3-torchmetrics/5.3-part2-lightning-torchmetrics.py | # Unit 5.3. Computing Metrics Efficiently with TorchMetrics
# Part 2. A Lightning Module with TorchMetrics
import lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from shared_utilities import PyTorchMLP, get_dataset_loaders
from watermark import watermark
class LightningModel(L.Lightni... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.6-logging/5.6-part2-logging-tensorboard.py | # Unit 5.6. The Benefits of Logging Your Model Training
# Part 2. Logging with TensorBoard
import lightning as L
import torch
from shared_utilities import LightningModel, MNISTDataModule, PyTorchMLP
from watermark import watermark
if __name__ == "__main__":
print(watermark(packages="torch,lightning", python=True... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.6-logging/shared_utilities.py | import lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchvision import datasets, transforms
class PyTorchMLP(torch.nn.Module):
def __init__(self, num_features, num_classes):
su... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.6-logging/5.6-part3-logging-csv.ipynb | %load_ext watermark
%watermark -p torch,lightning --condaimport lightning as L
import torch
from shared_utilities import PyTorchMLP, LightningModel, MNISTDataModulefrom lightning.pytorch.loggers import CSVLogger ### Newtorch.manual_seed(123)
dm = MNISTDataModule()
pytorch_model = PyTorchMLP(num_features=784, num_clas... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.5-datamodules/5.5-part2-datamodules.py | # Unit 5.5. Organizing Your Data Loaders with Data Modules
import lightning as L
import torch
from shared_utilities import LightningModel, MNISTDataModule, PyTorchMLP
from watermark import watermark
if __name__ == "__main__":
print(watermark(packages="torch,lightning", python=True))
print("Torch CUDA availab... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.5-datamodules/shared_utilities.py | import lightning as L
import torch
import torch.nn.functional as F
import torchmetrics
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchvision import datasets, transforms
class PyTorchMLP(torch.nn.Module):
def __init__(self, num_features, num_classes):
su... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.2-mlp-lightning/5.2-part1-preamble.ipynb | # !conda install jupyterlab numpy pandas matplotlib watermark --yes# !pip install torch torchvision torchaudio# !pip install lightning%load_ext watermark
%watermark -v -p numpy,pandas,matplotlib,torch,lightningfrom torch.utils.data import DataLoader
from torchvision import datasets, transforms
train_dataset = datasets... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.2-mlp-lightning/shared_utilities.py | import torch
from torch.utils.data import DataLoader
from torch.utils.data.dataset import random_split
from torchvision import datasets, transforms
class PyTorchMLP(torch.nn.Module):
def __init__(self, num_features, num_classes):
super().__init__()
self.all_layers = torch.nn.Sequential(
... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.2-mlp-lightning/5.2-part3-lightning-mlp.py | # Unit 5.2. Training a Multilayer Perceptron in PyTorch & Lightning
# Part 3. Training a Multilayer Perceptron in PyTorch using Lightning
import lightning as L
import torch
import torch.nn.functional as F
from shared_utilities import PyTorchMLP, compute_accuracy, get_dataset_loaders
from watermark import watermark
#... | 0 |
public_repos/dl-fundamentals/unit05-lightning | public_repos/dl-fundamentals/unit05-lightning/5.2-mlp-lightning/5.2-part2-plain-pytorch-mlp.py | # Unit 5.2. Training a Multilayer Perceptron in PyTorch & Lightning
# Part 2. Training a Multilayer Perceptron in pure PyTorch
import torch
import torch.nn.functional as F
from shared_utilities import PyTorchMLP, compute_accuracy, get_dataset_loaders
from watermark import watermark
def compute_total_loss(model, data... | 0 |
public_repos/dl-fundamentals/unit05-lightning/exercises | public_repos/dl-fundamentals/unit05-lightning/exercises/2_custom-callback/shared_utilities.py | import lightning as L
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import torch
import torch.nn.functional as F
import torchmetrics
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.dataset import random_split
from torchvision import datasets, transforms
... | 0 |
public_repos/dl-fundamentals/unit05-lightning/exercises | public_repos/dl-fundamentals/unit05-lightning/exercises/2_custom-callback/training.py | # Unit 5.5. Organizing Your Data Loaders with Data Modules
import lightning as L
from lightning.pytorch.loggers import CSVLogger
import matplotlib.pyplot as plt
import pandas as pd
import torch
from shared_utilities import LightningModel, MNISTDataModule, PyTorchMLP
from watermark import watermark
if __name__ == "__m... | 0 |
public_repos/dl-fundamentals/unit05-lightning/exercises | public_repos/dl-fundamentals/unit05-lightning/exercises/2_custom-callback/README.md | # Exercise 2 - A Custom Plugin for Tracking Training and Validation Accuracy Difference
In this exercise, modify the existing MNIST classifier such that it tracks the difference between the training set and validation set accuracy after each epoch:
<img src="example-output-1.png" alt="example-output-1" style="zo... | 0 |
public_repos/dl-fundamentals/unit05-lightning/exercises | public_repos/dl-fundamentals/unit05-lightning/exercises/1_lightning-regression/shared_utilities.py | import lightning as L
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import torch
import torch.nn.functional as F
import torchmetrics
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.dataset import random_split
from torchvision import datasets, transforms
... | 0 |
public_repos/dl-fundamentals/unit05-lightning/exercises | public_repos/dl-fundamentals/unit05-lightning/exercises/1_lightning-regression/training.py | # Unit 5.5. Organizing Your Data Loaders with Data Modules
import lightning as L
import torch
from shared_utilities import LightningModel, MNISTDataModule, PyTorchMLP
from watermark import watermark
if __name__ == "__main__":
print(watermark(packages="torch,lightning", python=True))
print("Torch CUDA availab... | 0 |
public_repos/dl-fundamentals/unit05-lightning/exercises | public_repos/dl-fundamentals/unit05-lightning/exercises/1_lightning-regression/README.md | # Exercise 1 - Changing the Classifier to a Regression Model
Remember the regression model we trained in Unit 4.5? To get some hands-on practice with PyTorch and the `LightningModule` class, we are going to convert the MNIST classifier we used in this unit (Unit 5) and convert it to a regression model.
For this, w... | 0 |
public_repos/dl-fundamentals/unit05-lightning/exercises/solutions | public_repos/dl-fundamentals/unit05-lightning/exercises/solutions/2_custom-callback/shared_utilities.py | import lightning as L
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import torch
import torch.nn.functional as F
import torchmetrics
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.dataset import random_split
from torchvision import datasets, transforms
... | 0 |
public_repos/dl-fundamentals/unit05-lightning/exercises/solutions | public_repos/dl-fundamentals/unit05-lightning/exercises/solutions/2_custom-callback/training.py | import lightning as L
from lightning.pytorch.loggers import CSVLogger
from lightning.pytorch.callbacks import Callback
import torch
from shared_utilities import LightningModel, MNISTDataModule, PyTorchMLP
from watermark import watermark
train_val_diff = []
class CustomCallback(Callback):
def on_validation_epoch_... | 0 |
public_repos/dl-fundamentals/unit05-lightning/exercises/solutions | public_repos/dl-fundamentals/unit05-lightning/exercises/solutions/1_lightning-regression/shared_utilities.py | import os
import lightning as L
import numpy as np
import pandas as pd
from sklearn.preprocessing import StandardScaler
import torch
import torch.nn.functional as F
import torchmetrics
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.dataset import random_split
from torchvision import datasets, tr... | 0 |
public_repos/dl-fundamentals/unit05-lightning/exercises/solutions | public_repos/dl-fundamentals/unit05-lightning/exercises/solutions/1_lightning-regression/training.py | # Unit 5.5. Organizing Your Data Loaders with Data Modules
import lightning as L
from lightning.pytorch.loggers import CSVLogger
import matplotlib.pyplot as plt
import pandas as pd
import torch
from shared_utilities import LightningModel, AmesHousingDataModule, PyTorchMLP
from watermark import watermark
if __name__ =... | 0 |
public_repos/dl-fundamentals/unit08-large-language-models | public_repos/dl-fundamentals/unit08-large-language-models/8.7-distilbert-finetuning/part3_distilbert-finetuning-whole.ipynb | # pip install transformers# pip install datasets# pip install lightning%load_ext watermark
%watermark --conda -p torch,transformers,datasets,lightning# pip install datasets
import os.path as op
from datasets import load_dataset
import lightning as L
from lightning.pytorch.loggers import CSVLogger
from lightning.pyto... | 0 |
public_repos/dl-fundamentals/unit08-large-language-models | public_repos/dl-fundamentals/unit08-large-language-models/8.7-distilbert-finetuning/local_dataset_utilities.py | import os
import sys
import tarfile
import time
import numpy as np
import pandas as pd
from packaging import version
from torch.utils.data import Dataset
from tqdm import tqdm
import urllib
def reporthook(count, block_size, total_size):
global start_time
if count == 0:
start_time = time.time()
... | 0 |
public_repos/dl-fundamentals/unit08-large-language-models | public_repos/dl-fundamentals/unit08-large-language-models/8.7-distilbert-finetuning/part2_distilbert-finetune-last-layers.ipynb | # pip install transformers# pip install datasets# pip install lightning%load_ext watermark
%watermark --conda -p torch,transformers,datasets,lightning# pip install datasets
import os.path as op
from datasets import load_dataset
import lightning as L
from lightning.pytorch.loggers import CSVLogger
from lightning.pyto... | 0 |
public_repos/dl-fundamentals/unit08-large-language-models | public_repos/dl-fundamentals/unit08-large-language-models/8.2-bag-of-words/local_dataset_utilities.py | import os
import sys
import tarfile
import time
import numpy as np
import pandas as pd
from packaging import version
from torch.utils.data import Dataset
from tqdm import tqdm
import urllib
def reporthook(count, block_size, total_size):
global start_time
if count == 0:
start_time = time.time()
... | 0 |
public_repos/dl-fundamentals/unit08-large-language-models | public_repos/dl-fundamentals/unit08-large-language-models/8.2-bag-of-words/8.2-part2-bag-of-words-classifier.ipynb | %load_ext watermark
%watermark -p torch,lightning,pandas,scikit-learn --conda# pip install datasets
import os.path as op
import lightning as L
from lightning.pytorch.loggers import CSVLogger
from lightning.pytorch.callbacks import ModelCheckpoint
import numpy as np
import pandas as pd
import torch
from sklearn.feat... | 0 |
public_repos/dl-fundamentals/unit08-large-language-models | public_repos/dl-fundamentals/unit08-large-language-models/8.2-bag-of-words/8.2-part1-bag-of-words-data.ipynb | %load_ext watermark
%watermark -p torch,lightning,pandas --conda# pip install datasets
import os.path as op
import numpy as np
import pandas as pd
from local_dataset_utilities import download_dataset, load_dataset_into_to_dataframe, partition_dataset
from local_dataset_utilities import IMDBDatasetdownload_dataset()
... | 0 |
public_repos/dl-fundamentals/unit08-large-language-models | public_repos/dl-fundamentals/unit08-large-language-models/8.2-bag-of-words/local_utilities.py | import lightning as L
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import torch
import torch.nn.functional as F
import torchmetrics
class LightningModel(L.LightningModule):
def __init__(self, model, learning_rate):
super().__init__()
self.learning_rate = learning_rate
... | 0 |
public_repos/dl-fundamentals/unit08-large-language-models/exercises | public_repos/dl-fundamentals/unit08-large-language-models/exercises/solution/local_dataset_utilities.py | import os
import sys
import tarfile
import time
import numpy as np
import pandas as pd
from packaging import version
from torch.utils.data import Dataset
from tqdm import tqdm
import urllib
def reporthook(count, block_size, total_size):
global start_time
if count == 0:
start_time = time.time()
... | 0 |
public_repos/dl-fundamentals/unit08-large-language-models/exercises | public_repos/dl-fundamentals/unit08-large-language-models/exercises/solution/solution.ipynb | # pip install transformers# pip install datasets# pip install lightning%load_ext watermark
%watermark --conda -p torch,transformers,datasets,lightning# pip install datasets
import os.path as op
from datasets import load_dataset
import lightning as L
from lightning.pytorch.loggers import CSVLogger
from lightning.pyto... | 0 |
public_repos/dl-fundamentals/unit08-large-language-models/exercises | public_repos/dl-fundamentals/unit08-large-language-models/exercises/exercise-1-other-llms/README.md | # Exercise 1: Trying Other LLMs
In Unit 8.7, we finetuned a DistilBERT model and got ~86% accuracy when finetuning only the last layers. In fact, there are many other LLMs that can even get you better performance. For this exercise, experiment with different LLMs for text classification from [here](https://huggingfa... | 0 |
public_repos | public_repos/lit-gpt/requirements-all.txt | -r requirements.txt
bitsandbytes==0.41.0 # quantization
scipy # required by bitsandbytes
sentencepiece # pythia, falcon, redpajama
tokenizers # llama-based models
datasets # quantize/gptq.py
zstandard # scripts/prepare_redpajama.py, scripts/prepare_starcoder.py
pan... | 0 |
public_repos | public_repos/lit-gpt/setup.py | import os
from setuptools import find_packages, setup
_PATH_ROOT = os.path.dirname(__file__)
with open(os.path.join(_PATH_ROOT, "README.md"), encoding="utf-8") as fo:
readme = fo.read()
setup(
name="lit-gpt",
version="0.1.0",
description="Open source large language model implementation",
author=... | 0 |
public_repos | public_repos/lit-gpt/requirements.txt | torch>=2.1.0
lightning @ git+https://github.com/Lightning-AI/lightning@4e72dcc8db6a0cbe94042ddbc310340556e8fee7
jsonargparse[signatures] # CLI
| 0 |
public_repos | public_repos/lit-gpt/README.md | <div align="center">
<img src="https://pl-public-data.s3.amazonaws.com/assets_lightning/LitStableLM_Badge.png" alt="Lit-GPT" width="128"/>
# ⚡ Lit-GPT
<!--
<p align="center">
<a href="https://www.lightning.ai/">Lightning.ai</a> •
<a href="https://lightning.ai/docs/pytorch/stable/">PyTorch Lightning</a> •
<a hre... | 0 |
public_repos | public_repos/lit-gpt/LICENSE | Apache License
Version 2.0, January 2004
http://www.apache.org/licenses/
TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
1. Definitions.
"License" shall mean the terms and conditions for use, reproduction,
... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/pretrain/tinyllama.py | """
This script is adapted from TinyLlama:
https://github.com/jzhang38/TinyLlama/blob/main/pretrain/tinyllama.py
"""
import math
import sys
import time
from functools import partial
from pathlib import Path
from typing import Tuple, Union
import lightning as L
import torch
import torch.nn as nn
from lightning.fabric.l... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/pretrain/redpajama.py | import glob
import math
import sys
import time
from pathlib import Path
from typing import Optional, Tuple, Union
import lightning as L
import torch
from lightning.fabric.loggers import CSVLogger
from lightning.fabric.strategies import FSDPStrategy
from lightning.fabric.utilities import ThroughputMonitor, measure_flop... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/pretrain/openwebtext.py | import math
import sys
import time
from pathlib import Path
from typing import Optional, Tuple, Union
import lightning as L
import numpy as np
import torch
from lightning.fabric.loggers import CSVLogger
from lightning.fabric.strategies import FSDPStrategy
from lightning.fabric.utilities import ThroughputMonitor, measu... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/pretrain/openwebtext_trainer.py | import math
import sys
import time
from pathlib import Path
from typing import Any, Optional
import lightning as L
import numpy as np
import torch
from lightning.fabric.utilities import measure_flops
from lightning.pytorch.callbacks import ModelCheckpoint, ThroughputMonitor
from lightning.pytorch.loggers import CSVLog... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/eval/lm_eval_harness.py | import json
import sys
from pathlib import Path
from typing import List, Literal, Optional
import lightning as L
import torch
from lightning.fabric.plugins import BitsandbytesPrecision
from lm_eval import base, evaluator, tasks
from lm_eval.base import BaseLM
# support running without installing as a package
wd = Pat... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/chat/base.py | import re
import sys
import time
from pathlib import Path
from typing import Iterator, List, Literal, Optional, Tuple
import lightning as L
import torch
from lightning.fabric.plugins import BitsandbytesPrecision
# support running without installing as a package
wd = Path(__file__).parent.parent.resolve()
sys.path.app... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/notebooks/falcon-inference.ipynb | # clone Lit-GPT
!git clone https://github.com/Lightning-AI/lit-gpt
%cd lit-gpt/# install the dependencies
!pip install huggingface_hub tokenizers sentencepiece -r requirements.txt -q# download the weights
!python scripts/download.py --repo_id tiiuae/falcon-7b
!python scripts/convert_hf_checkpoint.py --checkpoint_dir ch... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/quantize/gptq.py | # This adapts GPTQ's quantization process: https://github.com/IST-DASLab/gptq/
# E. Frantar et al GPTQ: Accurate Post-training Compression for GPT, arXiv:2210.17323
# portions copyright by the authors licensed under the Apache License 2.0
import gc
import math
import sys
import time
from pathlib import Path
from typing... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_openllama.md | ## Download [OpenLLaMA](https://github.com/openlm-research/open_llama) weights
OpenLLaMA is a permissively licensed open source reproduction of [Meta AI’s LLaMA](https://github.com/facebookresearch/llama)
7B and 13B checkpoints trained on the [RedPajama dataset](https://github.com/togethercomputer/RedPajama-Data).
The... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_freewilly_2.md |
## Download [FreeWilly 2](https://stability.ai/blog/freewilly-large-instruction-fine-tuned-models) weights
Stability AI announced FreeWilly inspired by the methodology pioneered by Microsoft in its paper: "Orca: Progressive Learning from Complex Explanation Traces of GPT-4”.
FreeWilly2 leverages the Llama 2 70B found... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_llama_2.md | ## Download [Llama 2](https://ai.meta.com/llama) weights
Meta developed and publicly released the Llama 2 family of large language models (LLMs), a collection of pretrained and
fine-tuned generative text models ranging in scale from 7 billion to 70 billion parameters. Its fine-tuned LLMs,
called Llama-2-Chat, are opti... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/finetune_full.md | # Finetuning the whole model
If you are interested in parameter-efficient finetuning, check out [finetune_adapter.md](finetune_adapter.md). In contrast to parameter-efficient finetuning, this "full" approach finetunes all model parameters, which is substantially more expensive. It may only be recommended as a baseline... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_redpajama_incite.md | ## Download [RedPajama-INCITE](https://www.together.xyz/blog/redpajama-models-v1) weights
Togethercomputer's RedPajama-INCITE family of models were trained over the [RedPajama v1](https://www.together.xyz/blog/redpajama) dataset, with the same architecture as the popular [Pythia](download_pythia.md) model suite. Weigh... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/evaluation.md | # LLM Evaluation
## Using lm-evaluation-harness
You can evaluate Lit-GPT using [EleutherAI's lm-eval](https://github.com/EleutherAI/lm-evaluation-harness/tree/master) framework with a large number of different evaluation tasks.
You need to install the `lm-eval` framework first:
```bash
pip install https://g... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_code_llama.md | ## Download [Code Llama](https://ai.meta.com/blog/code-llama-large-language-model-coding/) weights
Meta developed and publicly released the Code Llama family of large language models (LLMs) on top of Llama 2.
Code Llama models come in three sizes: 7B, 13B, and 34B parameter models. Furthermore, there are three model ... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_stablelm.md | ## Download [StableLM](https://github.com/Stability-AI/StableLM) weights
StableLM is a family of generative language models trained by StabilityAI, trained on a dataset derived from [The Pile](https://pile.eleuther.ai/) but 3x larger, for a total of 1.5 trillion tokens. Weights are released under the [CC-BY-SA license... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/finetune_lora.md | # Finetuning with LoRA / QLoRA
[Low-rank adaption (LoRA)](https://arxiv.org/abs/2106.09685) is a technique to approximate the update to the linear layers in a LLM with a low-rank matrix factorization. This significantly reduces the number of trainable parameters and speeds up training with little impact on the final p... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/inference.md | # Inference
We demonstrate how to run inference (next token prediction) with the GPT base model in the [`generate.py`](generate.py) script:
```bash
python generate/base.py --prompt "Hello, my name is" --checkpoint_dir checkpoints/stabilityai/stablelm-base-alpha-3b
```
Output:
```text
Hello, my name is Levi Durrer, ... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/pretrain_openwebtext.md | # Pretrain Llama 2 on OpenWebText
This tutorial will walk you through setting up the OpenWebText dataset and launching the pretraining script.
## What's OpenWebText
[OpenWebText](https://github.com/jcpeterson/openwebtext) is an open-source reproduction of OpenAI's unreleased WebText training dataset, which was origi... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/resource-tables.md | # Resource Tables
- Last updated: 10/20/2023
- Lit-GPT version: commit 8641822
- Hardware: NVIDIA A100-SXM4-40GB
- OS: Ubuntu 22.04.3 LTS (x86_64)
- Nvidia driver version: 525.125.06
- Relevant libraries
- CMake 3.26.4
- Libc glibc-2.35
- PyTorch 2.1.0+cu121
- Lightning 2.1.0.rc0
- Bitsandbytes 0.41.1
Thi... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_mistral.md | ## Download [Mistral](https://mistral.ai) weights
[Mistral 7B](https://mistral.ai/news/announcing-mistral-7b) is Apache 2.0 licensed and can be used without restrictions. It:
* Outperforms Llama 2 13B on all benchmarks
* Outperforms Llama 1 34B on many benchmarks
* Approaches CodeLlama 7B performance on code, while r... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/pretrain_redpajama.md | # Pretrain Llama 2 on RedPajama
This tutorial will walk you through setting up the RedPajama dataset and launching the pretraining script.
## What's RedPajama
[RedPajama](https://github.com/togethercomputer/RedPajama-Data) is an open-source reproduction of the original LLaMA training dataset.
It contains a total of... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_pythia.md | ## Download [Pythia](https://github.com/EleutherAI/pythia) weights
EleutherAI's project Pythia combines interpretability analysis and scaling laws to understand how knowledge develops and evolves during training in autoregressive transformers. Weights are released under the [Apache 2.0 license](https://www.apache.org/... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/prepare_dataset.md | # Preparing Datasets
Below is a table of all datasets that are currently supported in Lit-GPT:
| Name | Task | Size | Reference Repo | Paper / Blog ... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/neurips_challenge_quickstart.md | # NeurIPS 2023 LLM Efficiency Challenge Quickstart Guide
The [NeurIPS 2023 Efficiency Challenge](https://llm-efficiency-challenge.github.io/) is a competition focused on training **1 LLM for 24 hours on 1 GPU** – the team with the best LLM gets to present their results at NeurIPS 2023.
This quick start guide is a s... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_longchat.md | ## Download [LongChat](https://lmsys.org/blog/2023-06-29-longchat) weights
LongChat is an open-source family of chatbots based on LLaMA featuring an extended context length up to 16K tokens.
The technique used to extend the context length is described in [this blogpost](https://kaiokendev.github.io/context).
To see a... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/pretrain_tinyllama.md | # Pretrain TinyLlama
This tutorial will walk you through pretraining [TinyLlama](https://github.com/jzhang38/TinyLlama/).
## What's TinyLlama?
[TinyLlama](https://github.com/jzhang38/TinyLlama/) is architecturally the same as Meta AI's LLama 2, but only has 1.1B parameters and is instead trained on multiple epochs o... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/quantize.md | # Quantize the model
This document provides different strategies for quantizing the various models available in Lit-GPT to reduce GPU memory usage, which is useful for running larger models on certain GPU hardware.
**All the examples below were run on an A100 40GB GPU with CUDA 12.1.**
> [!NOTE]
> Quantization also ... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_phi15.md | ## Download [phi-1.5](https://arxiv.org/abs/2309.05463) weights
A team at Microsoft Research has made available Phi 1.5, which is a 1.3 billion parameter model optimized for common sense reasoning in natural language, showing performance on par with models 5x its size, especially in grade-school mathematics and basic ... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_falcon.md | ## Download [Falcon](https://falconllm.tii.ae) weights
UAE's Technology Innovation Institute has open-sourced Falcon LLM.
It is trained on [RefinedWeb](https://huggingface.co/datasets/tiiuae/falcon-refinedweb) enhanced with curated corpora
Weights are released under the [Apache 2.0 license](https://www.apache.org/lic... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/finetune_adapter.md | # Finetuning with Adapter
Adapter, first introduced for the LLaMA model as [LLaMA-Adapter](https://arxiv.org/abs/2303.16199), is a form of prefix-tuning that prepends a learnable adaption-prompt to the inputs of the attention blocks in an LLM. In total, there are only ~500k parameters to update during finetuning in St... | 0 |
public_repos/lit-gpt | public_repos/lit-gpt/tutorials/download_tinyllama.md | ## Download TinyLlama weights
[TinyLlama 1.1B](https://github.com/jzhang38/TinyLlama/) is Apache 2.0 licensed and can be used without restrictions.
It is still in development and at the time of writing this, checkpoints for the model trained up to 1T tokens are available.
The target is to train it for ~3 epochs on 3T ... | 0 |
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