# profiler **Repository Path**: mirrors_tensorflow/profiler ## Basic Information - **Project Name**: profiler - **Description**: A profiling and performance analysis tool for machine learning - **Primary Language**: Unknown - **License**: Apache-2.0 - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-08-22 - **Last Updated**: 2026-10-03 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README

XProf (+ Tensorboard Profiler Plugin)

An open, scalable, and extensible profiler for the modern ML stack.

About ✧ Installation ✧ Usage ✧ Resources ✧ Citing

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## About XProf offers a number of tools to analyse and visualize the performance of your model across multiple devices. Some of the tools include:

Overview Page

A high-level overview of the performance of your model. This is an aggregated overview for your host and all devices. It includes:
  • Performance summary and breakdown of step times.
  • A graph of individual step times.
  • High level details of the run environment.

Trace Viewer

Displays a timeline of the execution of your model that shows:
  • The duration of each op.
  • Which part of the system (host or device) executed an op.
  • The communication between devices.

Memory Profile

Monitors the memory usage of your model.

Graph Viewer

A visualization of the graph structure of HLOs of your model.
To learn more about the various XProf tools, check out the [XProf documentation](https://openxla.org/xprof) >[!TIP] > New to profiling? Come and check out this [Colab > Demo](https://docs.jaxstack.ai/en/latest/JAX_for_LLM_pretraining.html). ## Installation To get the most recent release version of XProf, install it via pip: ``` $ pip install xprof ``` > [!NOTE] > For Python 3.12+ users, if you encounter `ModuleNotFoundError: No module > named 'pkg_resources'`, install an older version of setuptools: > ``` > pip install "setuptools<70" > ``` **Alternative installation options:**
Installation with Tensorboard ``` $ pip install xprof tensorboard ```
Google Cloud If you use Google Cloud to run your workloads, we recommend the [xprofiler tool](https://github.com/AI-Hypercomputer/cloud-diagnostics-xprof).It provides a streamlined profile collection and viewing experience using VMs running XProf.
Nightly Releases Every night, a nightly version of the package is released under the name of `xprof-nightly`. This package contains the latest changes made by the XProf developers. To install the nightly version of profiler: ``` $ pip uninstall xprof tensorboard-plugin-profile $ pip install xprof-nightly ```
Build from Source If the pip packages don't work, you can build XProf from source using Bazel. **1. Set up Bazel** Bazel is the build system used for XProf. Bazelisk is a wrapper for Bazel that simplifies Bazel version management. Download the appropriate `.deb` package for your system from the [Bazelisk releases page](https://github.com/bazelbuild/bazelisk/releases) and install the downloaded package: ``` sudo apt install ~/Downloads/bazelisk-amd64.deb ``` **2. Obtain the Repository** Clone the XProf GitHub repository to your local machine: ``` git clone https://github.com/openxla/xprof.git cd xprof ``` **3. Build the Project** Build the pip Package: Use Bazel to build the XProf pip package: ``` bazel run --config=public_cache plugin:build_pip_package ``` Navigate to the Bazel Output Directory and install: ``` cd /tmp/profile-pip pip install . ``` **Building on macOS (Apple Silicon)** On macOS the native profiler extension must be built explicitly, pinning the SDK and minimum OS version. Building without these flags fails with errors such as an `xcrun` SDK `macosx10.11` that cannot be located and undefined symbols (`_PyModule_Create2`). The following invocation is verified on Apple Silicon: ``` bazel build --config=macos --config=public_cache --macos_sdk_version=$(xcrun --show-sdk-version) --macos_minimum_os=13.0 //xprof/pywrap:profiler_plugin_c_api.so ```
## Usage > [!IMPORTANT] > XProf requires access to the Internet to load the [Google Chart > library](https://developers.google.com/chart/interactive/docs/basic_load_libs#basic-library-loading). > Some charts and tables may be missing if you run XProf entirely offline on > your local machine, behind a corporate firewall, or in a datacenter. ### Standalone If you have profile data in a directory (e.g., `profiler/demo`), you can view it by running: ``` $ xprof --logdir=profiler/demo --port=6006 ``` Or with the optional command name: ``` $ xprof server --logdir=profiler/demo --port=6006 ``` ### With TensorBoard If you have TensorBoard installed, you can run: ``` $ tensorboard --logdir=profiler/demo ``` If you are behind a corporate firewall, you may need to include the `--bind_all` tensorboard flag. Go to `localhost:6006/#profile` of your browser, you should now see the demo overview page show up. Congratulations\! You're now ready to capture a profile. ### Command-Line Arguments When launching the XProf server from the command line, you can use the following arguments: |Command |Shorthand |Default |Description | |---------------------------------------|------------------|---------------------|-------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------| |`--logdir ` |`-l ` | |The directory containing XProf profile data (files ending in .xplane.pb). If provided, XProf will load and display profiles from this directory. If omitted, XProf will start without loading any profiles.1 | |`--port ` |`-p ` |`8791` |The port for the XProf web server. | |`--grpc_port ` |`-g ` |`50051` |The port for the gRPC server used for distributed processing. This must be different from --port. | |`--worker_service_address `|`-w `|`0.0.0.0:`|A comma-separated list of worker addresses (e.g., host1:50051,host2:50051) for distributed processing. | |`--hide_capture_profile_button` |`-h` |N/A |If set, hides the 'Capture Profile' button in the UI. | |`--enable_tab_name_label` |`-e` |`False` |If set, shows a per-tab name label in the UI. | |`--src_prefix ` |`-s ` | |A prefix prepended to source file paths, used for source-code linking in the UI. | |`--max_concurrent_worker_requests ` |`-m ` |`1` |The maximum number of concurrent requests a worker's gRPC server will process, used in distributed profiling. | 1 You can dynamically load profiles using `session_path` or `run_path` URL parameters, as described in the [Log Directory Structure](#log-directory-structure) section. ### Log Directory Structure When using XProf, profile data must be placed in a specific directory structure. XProf expects `.xplane.pb` files to be in the following path: ``` /plugins/profile// ``` * ``: This is the root directory that you supply to `tensorboard --logdir`. * `plugins/profile/`: This is a required subdirectory. * `/`: Each subdirectory inside `plugins/profile/` represents a single profiling session. The name of this directory will appear in the TensorBoard UI dropdown to select the session. **Example:** If your log directory is structured like this: ``` /path/to/your/log_dir/ └── plugins/ └── profile/ ├── my_experiment_run_1/ │ └── host0.xplane.pb └── benchmark_20251107/ └── host1.xplane.pb ``` You would launch TensorBoard with: ```bash tensorboard --logdir /path/to/your/log_dir/ ``` The runs `my_experiment_run_1` and `benchmark_20251107` will be available in the "Sessions" tab of the UI. You can also dynamically load sessions from a GCS bucket or local filesystem by passing URL parameters when loading XProf in your browser. This method works whether or not you provided a `logdir` at startup and is useful for viewing profiles from various locations without restarting XProf. For example, if you start XProf with no log directory: ```bash xprof server ``` You can load sessions using the following URL parameters. Assume you have profile data stored on GCS or locally, structured like this: ``` gs://your-bucket/profile_runs/ ├── my_experiment_run_1/ │ ├── host0.xplane.pb │ └── host1.xplane.pb └── benchmark_20251107/ └── host0.xplane.pb ``` There are two URL parameters you can use: * **`session_path`**: Use this to load a *single* session directly. The path should point to a directory containing `.xplane.pb` files for one session. * GCS Example: `http://localhost:8791/?session_path=gs://your-bucket/profile_runs/my_experiment_run_1` * Local Path Example: `http://localhost:8791/?session_path=/path/to/profile_runs/my_experiment_run_1` * Result: XProf will load the `my_experiment_run_1` session, and you will see its data in the UI. * **`run_path`**: Use this to point to a directory that contains *multiple* session directories. * GCS Example: `http://localhost:8791/?run_path=gs://your-bucket/profile_runs/` * Local Path Example: `http://localhost:8791/?run_path=/path/to/profile_runs/` * Result: XProf will list all session directories found under `run_path` (i.e., `my_experiment_run_1` and `benchmark_20251107`) in the "Sessions" dropdown in the UI, allowing you to switch between them. **Loading Precedence** If multiple sources are provided, XProf uses the following order of precedence to determine which profiles to load: 1. **`session_path`** URL parameter 2. **`run_path`** URL parameter 3. **`logdir`** command-line argument ### Distributed Profiling > [!WARNING] > Currently, distributed processing only benefits the following tools: > `overview_page`, `framework_op_stats`, `input_pipeline`, and `pod_viewer`. XProf supports distributed profile processing by using an aggregator that distributes work to multiple XProf workers. This is useful for processing large profiles or handling multiple users. > [!NOTE] > The ports used in these examples (`6006` for the aggregator HTTP > server, `9999` for the worker HTTP server, and `50051` > for the worker gRPC server) are suggestions and can be customized. **Worker Node** Each worker node should run XProf with a gRPC port exposed so it can receive processing requests. You should also hide the capture button as workers are not meant to be interacted with directly. ``` $ xprof server --grpc_port=50051 --port=9999 --hide_capture_profile_button ``` **Aggregator Node** The aggregator node runs XProf with the `--worker_service_address` flag pointing to all available workers. Users will interact with aggregator node's UI. ``` $ xprof server --worker_service_address=:50051,:50051 --port=6006 --logdir=profiler/demo ``` Replace `, ` with the addresses of your worker machines. Requests sent to the aggregator on port 6006 will be distributed among the workers for processing. For deploying a distributed XProf setup in a Kubernetes environment, see [Kubernetes Deployment Guide](docs/kubernetes_deployment.md). ## Resources * [JAX Profiling Guide](https://jax.readthedocs.io/en/latest/profiling.html#xprof-tensorboard-profiling) * [PyTorch/XLA Profiling Guide](https://cloud.google.com/tpu/docs/pytorch-xla-performance-profiling-tpu-vm) * [TensorFlow Profiling Guide](https://tensorflow.org/guide/profiler) * [Cloud TPU Profiling Guide](https://cloud.google.com/tpu/docs/cloud-tpu-tools) * [Colab Tutorial](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras) * [Tensorflow Colab](https://www.tensorflow.org/tensorboard/tensorboard_profiling_keras) ## Citing XProf To cite XProf, please use the following BibTeX entry for the MLSys 2026 paper: ``` @inproceedings{1076558, title = {XProf: An Open, Scalable and Extensible Profiling System for the Modern ML Stack}, author = {Robert Hundt and Naveen Kumar and Jose Baiocchi Paredes and Scott Goodson and Clive Verghese and Prasanna Rengasamy and Kelvin Le and Jiya Zhang and Charles Alaras and Yin Zhang and Kan Cai and Jiten Thakkar and Sai Ganesh Bandiatmakuri and Yogesh SY and Ani Udipi and Vikas Aggarwal}, year = {2026}, booktitle = {Ninth Conference on Machine Learning and Systems} } ```