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CITATION.cff
- family-names: Kaiser given-names: Lukasz - family-names: Kudlur given-names: Manjunath - family-names: Levenberg given-names: Josh - family-names: Mané given-names: Dan - family-names: Schuster given-names: Mike - family-names: Monga given-names: Rajat - family-names: Moore given-names: Sherry - family-names: Murray given-names: Derek - family-names: Olah
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tensorflow/c/checkpoint_reader.h
namespace tensorflow { namespace checkpoint { class TensorSliceReader; // A wrapper around BundleReader (for V2 checkpoints) and // checkpoint::TensorSliceReader (for V1), that is more easily SWIG wrapped for // other languages. // // The class currently only interacts with single-slice (i.e., non-partitioned) // variables. class CheckpointReader { public:
C - Registered: Tue Apr 30 12:39:09 GMT 2024 - Last Modified: Thu Oct 12 08:49:52 GMT 2023 - 3.1K bytes - Viewed (0) -
ci/official/containers/linux_arm64/Dockerfile
RUN dpkg -i /patchelf/patchelf_0.14.3-1_arm64.deb # All lines past this point are reset when $CACHEBUSTER is set. We need this # for Python specifically because we install some nightly packages which are # likely to change daily. ARG CACHEBUSTER=0 RUN echo $CACHEBUSTER # Setup build and environment COPY devel.usertools /usertools COPY devel.bashrc /root/.bashrc COPY ld.so.conf /dt10/etc/ # Setup JAX Python environment.
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.github/bot_config.yml
* It has an added advantage since you can you easily switch to different hardware accelerators (cpu, gpu, tpu) as per the task. * All you need is a good internet connection and you are all set. * Try to build TF from sources by changing CPU optimization flags.
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.zenodo.json
"license": "Apache-2.0", "title": "TensorFlow", "upload_type": "software", "creators": [ { "name": "TensorFlow Developers" } ],
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tensorflow/c/eager/c_api_remote_test_util.cc
} if (remote) { TFE_OpSetDevice(matmul, task1_name, status); ASSERT_EQ(TF_GetCode(status), TF_OK) << TF_Message(status); } else if (!async) { // Set the local device to CPU to easily validate mirroring string cpu_device_name; ASSERT_TRUE(GetDeviceName(ctx, &cpu_device_name, "CPU")); TFE_OpSetDevice(matmul, cpu_device_name.c_str(), status);
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SECURITY.md
vulnerabilities. ## Security properties of execution modes TensorFlow has several execution modes, with Eager-mode being the default in v2. Eager mode lets users write imperative-style statements that can be easily inspected and debugged and it is intended to be used during the development phase. As part of the differences that make Eager mode easier to debug, the [shape inference
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tensorflow/c/eager/abstract_context.h
virtual void Release() = 0; // Creates an operation builder and ties it to this context. // The returned object can be used for setting operation's attributes, // adding inputs and finally executing (immediately or lazily as in tracing) // it in this context. virtual AbstractOperation* CreateOperation() = 0; // Registers a function with this context, after this the function is
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