# install
pip install marearts-anpr
# code
..
# Ask license is here: https://study.marearts.com/p/anpr-lpr-solution.html
# Live Test is here: https://live.marearts.com
Computer Vision & Machine Learning Research Laboratory
# install
pip install marearts-anpr
# code
..
# Ask license is here: https://study.marearts.com/p/anpr-lpr-solution.html
# Live Test is here: https://live.marearts.com
Audio → Spectrogram → Mel-spectrogram → MFCC
.
..
code..
..
That's it.
but install this
pip install yt-dlp
Thank you!!!
toy model
.
configuration
..
in_proj
(ColwiseParallel):out_proj
(RowwiseParallel):Key Corrections and Clarifications:
This corrected diagram and explanation more accurately represent the sequence parallelism process as described in the original comment. It shows how the input is gathered, processed in parallel, and then the output is scattered, allowing for efficient parallel processing of the entire sequence across GPUs.
FSDP and TP are complementary parallelism techniques:
When combined:
Textual Representation:
GPU 1 GPU 2 GPU 3 GPU 4 +--------+ +--------+ +--------+ +--------+ | L1 P1 | | L1 P2 | | L2 P1 | | L2 P2 | | TP1 | | TP2 | | TP1 | | TP2 | +--------+ +--------+ +--------+ +--------+ | | | | +------------+ +------------+ Layer 1 Layer 2 L1, L2: Layers 1 and 2 P1, P2: Parameter shards (FSDP) TP1, TP2: Tensor Parallel splits
Let's use a simplified example with just 2 data points and walk through the process with actual numbers. This will help illustrate how gradients are calculated and accumulated for a batch.
Let's assume we have a very simple model with one parameter w
, currently set to 1.0. Our loss function is the square error, and we're using basic gradient descent with a learning rate of 0.1.
Data points:
Batch size = 2 (both data points in one batch)
Step 1: Forward pass
Step 2: Calculate losses
Step 3: Backward pass (calculate gradients)
Step 4: Accumulate gradients
Step 5: Update weight (once for the batch)
So, after processing this batch of 2 data points:
This process would then repeat for the next batch. In this case, we've processed all our data, so this completes one epoch.