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atong01 / conditional-flow-matching

TorchCFM: a Conditional Flow Matching library
https://arxiv.org/abs/2302.00482
MIT License
1.25k stars 101 forks source link

Bump pytorch-lightning from 1.8.3 to 2.2.0 #108

Open dependabot[bot] opened 9 months ago

dependabot[bot] commented 9 months ago

Bumps pytorch-lightning from 1.8.3 to 2.2.0.

Release notes

Sourced from pytorch-lightning's releases.

Lightning 2.2

Lightning AI is excited to announce the release of Lightning 2.2 :zap:

Did you know? The Lightning philosophy extends beyond a boilerplate-free deep learning framework: We've been hard at work bringing you Lightning Studio. Code together, prototype, train, deploy, host AI web apps. All from your browser, with zero setup.

While our previous release was packed with many big new features, this time around we're rolling out mainly improvements based on feedback from the community. And of course, as the name implies, this release fully supports the latest PyTorch 2.2 :tada:

Highlights

Monitoring Throughput

Lightning now has built-in utilities to measure throughput metrics such as batches/sec, samples/sec and Model FLOP Utilization (MFU) (#18848).

Trainer:

For the Trainer, this comes in form of a ThroughputMonitor callback. In order to track samples/sec, you need to provide a function to tell the monitor how to extract the batch dimension from your input. Furthermore, if you want to track MFU, you can provide a sample forward pass and the ThroughputMonitor will automatically estimate the utilization based on the hardware you are running on:

import lightning as L
from lightning.pytorch.callbacks import ThroughputMonitor
from lightning.fabric.utilities.throughput import measure_flops

class MyModel(LightningModule):
def setup(self, stage):
with torch.device("meta"):
model = MyModel()


    def sample_forward():
        batch = torch.randn(..., device="meta")
        return model(batch)

    self.flops_per_batch = measure_flops(model, sample_forward, loss_fn=torch.Tensor.sum)



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codecov[bot] commented 9 months ago

Codecov Report

All modified and coverable lines are covered by tests :white_check_mark:

Comparison is base (bfdfb86) 35.92% compared to head (09dd62b) 45.26%.

Additional details and impacted files ```diff @@ Coverage Diff @@ ## main #108 +/- ## ========================================== + Coverage 35.92% 45.26% +9.34% ========================================== Files 67 12 -55 Lines 7399 1131 -6268 ========================================== - Hits 2658 512 -2146 + Misses 4741 619 -4122 ```

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