From 118d37068bb0113e328b43cc985d7951a30569ef Mon Sep 17 00:00:00 2001 From: William Falcon Date: Wed, 26 Jun 2019 19:15:27 -0500 Subject: [PATCH] Deployed 1de54e5 with MkDocs version: 1.0.4 --- index.html | 34 ++++++++++++++++++++++++++++++---- search/search_index.json | 2 +- sitemap.xml.gz | Bin 190 -> 190 bytes 3 files changed, 31 insertions(+), 5 deletions(-) diff --git a/index.html b/index.html index 2e089857..19928f21 100644 --- a/index.html +++ b/index.html @@ -59,12 +59,16 @@
  • Quick start
  • +
  • Quick start examples
  • +
  • Distributed training
  • -
  • Mixed precision training
  • +
  • Checkpointing
  • Computing cluster (SLURM)
  • +
  • Common training use cases
  • + @@ -109,6 +113,11 @@

    PYTORCH-LIGHTNING DOCUMENTATION

    Quick start
    +
    Quick start examples
    + -
    Mixed precision training
    +
    Checkpointing
    Computing cluster (SLURM)
    +
    Common training use cases
    + @@ -171,5 +197,5 @@ diff --git a/search/search_index.json b/search/search_index.json index c281c7a4..077ab1a1 100644 --- a/search/search_index.json +++ b/search/search_index.json @@ -1 +1 @@ -{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"PYTORCH-LIGHTNING DOCUMENTATION Quick start CPU example Single GPU example Multi-gpu example SLURM cluster example Distributed training Single-gpu Multi-gpu Multi-node Mixed precision training 16-bit mixed precision Computing cluster (SLURM) Automatic checkpointing Automatic saving, loading Walltime auto-resubmit","title":"PYTORCH-LIGHTNING DOCUMENTATION"},{"location":"#pytorch-lightning-documentation","text":"","title":"PYTORCH-LIGHTNING DOCUMENTATION"},{"location":"#quick-start","text":"CPU example Single GPU example Multi-gpu example SLURM cluster example","title":"Quick start"},{"location":"#distributed-training","text":"Single-gpu Multi-gpu Multi-node","title":"Distributed training"},{"location":"#mixed-precision-training","text":"16-bit mixed precision","title":"Mixed precision training"},{"location":"#computing-cluster-slurm","text":"Automatic checkpointing Automatic saving, loading Walltime auto-resubmit","title":"Computing cluster (SLURM)"}]} \ No newline at end of file +{"config":{"lang":["en"],"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"PYTORCH-LIGHTNING DOCUMENTATION Quick start Define a lightning model Set up the trainer Quick start examples CPU example Single GPU example Multi-gpu example SLURM cluster example Distributed training Single-gpu Multi-gpu Multi-node Checkpointing Model saving Model loading Computing cluster (SLURM) Automatic checkpointing Automatic saving, loading Walltime auto-resubmit Common training use cases 16-bit mixed precision Accumulate gradients Check val many times during 1 training epoch Check GPU usage Check validation every n epochs Check which gradients are nan Inspect gradient norms Learning rate annealing Make model overfit on subset of data Min, max epochs Multiple optimizers (like GANs) Run a sanity check of model val and tng step Set how much of the tng, val, test sets to check (1-100%)","title":"PYTORCH-LIGHTNING DOCUMENTATION"},{"location":"#pytorch-lightning-documentation","text":"","title":"PYTORCH-LIGHTNING DOCUMENTATION"},{"location":"#quick-start","text":"Define a lightning model Set up the trainer","title":"Quick start"},{"location":"#quick-start-examples","text":"CPU example Single GPU example Multi-gpu example SLURM cluster example","title":"Quick start examples"},{"location":"#distributed-training","text":"Single-gpu Multi-gpu Multi-node","title":"Distributed training"},{"location":"#checkpointing","text":"Model saving Model loading","title":"Checkpointing"},{"location":"#computing-cluster-slurm","text":"Automatic checkpointing Automatic saving, loading Walltime auto-resubmit","title":"Computing cluster (SLURM)"},{"location":"#common-training-use-cases","text":"16-bit mixed precision Accumulate gradients Check val many times during 1 training epoch Check GPU usage Check validation every n epochs Check which gradients are nan Inspect gradient norms Learning rate annealing Make model overfit on subset of data Min, max epochs Multiple optimizers (like GANs) Run a sanity check of model val and tng step Set how much of the tng, val, test sets to check (1-100%)","title":"Common training use cases"}]} \ No newline at end of file diff --git a/sitemap.xml.gz b/sitemap.xml.gz index eccb8ade9572e48080e7b569a514884424e97b22..27b57bf421e098ac619fc32f17b8380cb146c778 100644 GIT binary patch delta 15 WcmdnTxQ~%tzMF$%K9|Tu_U!;7g9J