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ci/official/README.md
- Nightly jobs (Run nightly on the `nightly` branch) - Uses `wheel.sh`, `libtensorflow.sh`, `code_check_full.sh` - Continuous jobs (Run on every GitHub commit) - Uses `pycpp.sh` - Presubmit jobs (Run on every GitHub PR) - Uses `pycpp.sh`, `code_check_changed_files.sh` These "env" files match up with an environment matrix that roughly covers: - Different Python versions
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tensorflow/c/experimental/next_pluggable_device/c_api.h
typedef struct TF_VariableInfo TF_VariableInfo; // Returns a `TF_Device` pointer, which actually points to a C++ `Device`. // Currently we only allow `NextPluggableDevice` to be casted as `TF_Device`, // but in theory every this is a C API for every kind of device. TF_CAPI_EXPORT extern TF_Device* TF_GetDevice(TF_OpKernelContext* ctx); // -------------------------- Resource ---------------------------------------
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CONTRIBUTING.md
### Typical Pull Request Workflow - **1. New PR** - As a contributor, you submit a New PR on GitHub. - We inspect every incoming PR and add certain labels to the PR such as `size:`, `comp:` etc. At this stage we check if the PR is valid and meets certain quality requirements. For example, we check if the CLA is signed, PR has
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ci/official/utilities/code_check_full.bats
https://github.com/tensorflow/tensorflow/blob/master/tensorflow/tools/tf_sig_build_dockerfiles/devel.usertools/code_check_full.bats Here are the affected tests: EOF while read dep; do echo "For dependency $dep:" # For every missing dependency, find the tests which directly depend on # it, and print that list for debugging. Not really clear if this is # helpful since the only examples I've seen are enormous.
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ci/official/utilities/setup.sh
# variables in it, OR may be left empty if the user has already exported the # relevant variables in their environment. Because of 'set -o allexport' above # (which is equivalent to "set -a"), every variable in the file is exported # for other files to use. # # Separately, if TFCI is set *and* there are also additional TFCI_ variables # set in the shell environment, those variables will be restored after the
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ci/official/envs/ci_default
# ============================================================================== # Note: this file gets sourced in utilities/setup.sh, which has "set -u" # (error on undefined variables). This ensures that (a) every TFCI variable # has an explicit default value, and (b) no script can accidentally use a # variable that doesn't exist. Please keep this list in alphabetical order. # # Find usage in scripts with e.g.: # cd ci/official
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ci/official/containers/linux_arm64/devel.usertools/aarch64.bazelrc
# This bazelrc can build a CPU-supporting TF package. # Convenient cache configurations # Use a cache directory mounted to /tf/cache. Very useful! build:sigbuild_local_cache --disk_cache=/tf/cache # Use the public-access TF DevInfra cache (read only) build:sigbuild_remote_cache --remote_cache="https://storage.googleapis.com/tensorflow-devinfra-bazel-cache/manylinux2014" --remote_upload_local_results=false
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tensorflow/c/eager/c_api_experimental.h
#ifdef __cplusplus extern "C" { #endif // Resets `op_to_reset` with `op_or_function_name` and `raw_device_name`. This // is for performance optimization by reusing an exiting unused op rather than // creating a new op every time. If `raw_device_name` is `NULL` or empty, it // does not set the device name. If it's not `NULL`, then it attempts to parse // and set the device name. It's effectively `TFE_OpSetDevice`, but it is faster
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tensorflow/c/eager/parallel_device/parallel_device_lib.cc
: status_(TF_NewStatus()), // If the context's default exector is set to async, re-using that in // each thread would cause collectives to deadlock. For consistency we // create a new sync executor for every thread. // // TODO(allenl): We should have an async API that works with the // parallel device. device_(device), executor_(
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tensorflow/c/c_api.cc
} else { return ToOperation(iter->second); } } TF_Operation* TF_GraphNextOperation(TF_Graph* graph, size_t* pos) { if (*pos == 0) { // Advance past the first sentinel nodes in every graph (the source & sink). *pos += 2; } else { // Advance to the next node. *pos += 1; } mutex_lock l(graph->mu);
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