/usr/local/lib/python3.8/dist-packages/torch/cuda/__init__.py:52: UserWarning: CUDA initialization: Found no NVIDIA driver on your system. Please check that you have an NVIDIA GPU and installed a driver from http://www.nvidia.com/Download/index.aspx (Triggered internally at /pytorch/c10/cuda/CUDAFunctions.cpp:100.) return torch._C._cuda_getDeviceCount() > 0
When using multiple GPUs, you will most probably want to fit using distributed training. See examples/distrib.py for a complete example. To use distributed training, there are only two required steps:
with learn.distrib_ctx():before your
- Run your training script with
python -m fastai.launch scriptname.py ...args...
fastai.launch you can add
--gpus 0,1 for instance, to use only using GPUs 1 and 2.
If you're using
untar_data, or may be downloading or uncompressing data or models as part of your script, you should wrap that code with
rank0_first, which forces that step to occur first just once on the master process, prior to the remaining processes running it in parallel. E.g. instead of:
path = untar_data(URLs.IMAGEWOOF_320)
...you instead use:
path = rank0_first(untar_data, URLs.IMAGEWOOF_320)
See below for details on the full API and underlying helper functions, if needed -- however, note that you will not need anything except the above unless you need to change how the distributed training is implemented.
dl = TfmdDL(list(range(50)), bs=12, num_workers=2) for i in range(4): dl1 = DistributedDL(dl, i, 4) test_eq(list(dl1), (torch.arange(i*13, i*13+12)%50,torch.tensor([i*13+12])%50))
distrib_ctx prepares a learner to train in distributed data parallel mode. It assumes these environment variables have all been setup properly, such as those launched by
python -m fastai.launch.
with learn.distrib_ctx(): learn.fit(.....)
It attaches a
DistributedTrainer callback and
DistributedDL data loader to the learner, then executes
learn.fit(.....). Upon exiting the context, it removes the
DistributedDL, and destroys any locally created distributed process group. The process is still attached to the GPU though.
f() in rank-0 process first, then in parallel on the rest, in distributed training mode. In single process, non-distributed training mode,
f() is called only once as expected.
One application of
rank0_first() is to make fresh downloads via
untar_data safe in distributed training scripts launched by
python -m fastai.launch <script>:
path = untar_data(URLs.IMDB)
path = rank0_first(lambda: untar_data(URLs.IMDB))
Some learner factory methods may use
untar_data to download pretrained models:
learn = text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy)
learn = rank0_first(lambda: text_classifier_learner(dls, AWD_LSTM, drop_mult=0.5, metrics=accuracy))
Otherwise, multiple processes will download at the same time and corrupt the data.