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Jul 31, 2024
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Adrian Ulges
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# Content of this Wiki
# Content of this Wiki
*
[
Introduction
](
Project-Scope-and-Grading
)
contains information about the projects, such as expectations and grading.
*
[
Introduction
](
Project-Scope-and-Grading
)
contains information about the projects, such as expectations and grading.
*
Background/Basics
*
[
Onboarding
](
Administrative
Steps to On-board with your Project)
*
[
Server
](
Background/Server
)
*
Background/Basics
*
Our Servers
*
[
Server
](
Background/Server
)
*
List of servers
*
Our Servers
*
Reserving GPUs
*
List of servers
*
Connecting
*
Reserving GPUs
*
OpenVPN
*
Connecting
*
SSH tunneling
*
OpenVPN
*
SSH config
*
SSH tunneling
*
Setting up your working environment on the server
*
SSH config
*
Pip
*
Setting up your working environment on the server
*
Conda
*
Pip
*
Getting data to the server
*
Conda
*
Git (favorite)
*
Getting data to the server
*
Scp for larger files/directories
*
Git (favorite)
*
Rsync (for example with PyCharm)
*
Scp for larger files/directories
*
Tmux
*
Rsync (for example with PyCharm)
*
Usage (detaching, etc)
*
Tmux
*
Example config
*
Usage (detaching, etc)
*
Nvidia-smi and choosing the appropriate CUDA device
*
Example config
*
Htop
*
Nvidia-smi and choosing the appropriate CUDA device
*
[
Natural Language Processing
](
Background/NLP
)
*
Htop
*
Preprocessing
*
[
Natural Language Processing
](
Background/NLP
)
*
Tipps
*
Preprocessing
*
Tokenizer
*
Tipps
*
SpaCy / nltk /...
*
Tokenizer
*
Byte-Pair-Encoding (sentencepiece / HuggingFace...)
*
SpaCy / nltk /...
*
Vocabulary
*
Byte-Pair-Encoding (sentencepiece / HuggingFace...)
*
Frequencies/counts are helpful
*
Vocabulary
*
Text representations
*
Frequencies/counts are helpful
*
Word embeddings
*
Text representations
*
Bag of words [1,0,0,0,1]
*
Word embeddings
*
Common NLP Architectures
*
Bag of words [1,0,0,0,1]
*
Encoder-Decoder (Seq2Seq, Tree2Seq, ...)
*
Common NLP Architectures
*
Encoder
*
Encoder-Decoder (Seq2Seq, Tree2Seq, ...)
*
Siamese Network
*
Encoder
*
Transformer
*
Siamese Network
*
Attention
*
Transformer
*
Architecture
*
Attention
*
[
PyTorch
](
Background/PyTorch
)
*
Architecture
*
Basics
*
[
PyTorch
](
Background/PyTorch
)
*
Tensor Operations
*
Basics
*
Derive from nn.model, modules etc.
*
Tensor Operations
*
Common loss functions
*
Derive from nn.model, modules etc.
*
Use PyTorch Lightning
*
Common loss functions
*
Training
*
Use PyTorch Lightning
*
[
LAVIS Experiment Tipps
](
Experiments
)
*
Training
*
[
Some very useful Bash Commands
](
Useful
Bash Commands)
*
[
LAVIS Experiment Tipps
](
Experiments
)
*
[
Conference Calendar
](
Conference
Calendar)
*
[
Some very useful Bash Commands
](
Useful
Bash Commands)
*
[
Conference Calendar
](
Conference
Calendar)
