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definitelynotmcarillidefinitelynotmcarilli
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Comprehensive imagenet example for the new API
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‎apex/amp/frontend.py‎

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@@ -170,15 +170,15 @@ def check_params_fp32(model):
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if param.type() != "torch.cuda.FloatTensor":
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print("Warning: Found param {} with type {}, expected torch.cuda.FloatTensor.\n"
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"When using amp.initialize, you do not need to call .half() on your model\n"
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"before passing it, no matter what optimization level you choose.",
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name, param.type())
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"before passing it, no matter what optimization level you choose.".format(
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name, param.type()))
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for name, param in model.named_buffers():
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if param.type() != "torch.cuda.FloatTensor":
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print("Warning: Found buffer {} with type {}, expected torch.cuda.FloatTensor.\n"
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"When using amp.initialize, you do not need to call .half() on your model\n"
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"before passing it, no matter what optimization level you choose.",
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name, param.type())
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"before passing it, no matter what optimization level you choose.".format(
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name, param.type()))
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# allow user to directly pass Properties struct as well?

‎examples/FP16_Optimizer_simple/distributed_apex_legacy_launcher/README.md‎

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‎examples/FP16_Optimizer_simple/distributed_apex_legacy_launcher/distributed_data_parallel.py‎

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‎examples/FP16_Optimizer_simple/distributed_apex_legacy_launcher/run.sh‎

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‎examples/README.md‎

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## Contents:
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This directory contains examples illustrating Apex mixed precision and distributed tools.
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**distributed**: Walkthrough of apex distributed data parallel utilities.
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**FP16_Optimizer_simple**: Simple examples demonstrating various use cases of `FP16_Optimizer` to automatically manage master parameters and static or dynamic loss scaling.
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**imagenet**: Example based on [https://github.com/pytorch/examples/tree/master/imagenet](https://github.com/pytorch/examples/tree/master/imagenet) showing the use of `FP16_Optimizer`, as well as manual management of master parameters and loss scaling for illustration/comparison.
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**word_language_model**: Example based on [https://github.com/pytorch/examples/tree/master/word_language_model](https://github.com/pytorch/examples/tree/master/word_language_model) showing the use of `FP16_Optimizer`, as well as manual management of master parameters and loss scaling for illustration/comparison.
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**docker**: Example of a minimal Dockerfile that installs Apex on top of an existing container.
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**Note for users of the pre-unification API**:
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`deprecated_api` contains examples illustrating the old (pre-unified) APIs. These APIs will be removed soon, and users are strongly encouraged to switch. The separate mixed precision tools called `Amp` and `FP16_Optimizer` in the old API are exposed via different flags/optimization levels in the new API.

examples/FP16_Optimizer_simple/README.md renamed to examples/deprecated_api/FP16_Optimizer_simple/README.md

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examples/FP16_Optimizer_simple/closure.py renamed to examples/deprecated_api/FP16_Optimizer_simple/closure.py

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examples/FP16_Optimizer_simple/distributed_apex/README.md renamed to examples/deprecated_api/FP16_Optimizer_simple/distributed_apex/README.md

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examples/FP16_Optimizer_simple/distributed_apex/distributed_data_parallel.py renamed to examples/deprecated_api/FP16_Optimizer_simple/distributed_apex/distributed_data_parallel.py

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examples/FP16_Optimizer_simple/distributed_apex/run.sh renamed to examples/deprecated_api/FP16_Optimizer_simple/distributed_apex/run.sh

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