Latest navi open source refresh
latest code change including the global thread pool Closes twitter/the-algorithm#452 Closes twitter/the-algorithm#505
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@ -31,6 +31,11 @@ In navi/navi, you can run the following commands:
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- `scripts/run_onnx.sh` for [Onnx](https://onnx.ai/)
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Do note that you need to create a models directory and create some versions, preferably using epoch time, e.g., `1679693908377`.
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so the models structure looks like:
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models/
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-web_click
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- 1809000
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- 1809010
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## Build
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You can adapt the above scripts to build using Cargo.
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@ -3,7 +3,6 @@ name = "dr_transform"
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version = "0.1.0"
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edition = "2021"
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# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
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[dependencies]
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serde = { version = "1.0", features = ["derive"] }
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serde_json = "1.0"
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@ -12,7 +11,6 @@ bpr_thrift = { path = "../thrift_bpr_adapter/thrift/"}
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segdense = { path = "../segdense/"}
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thrift = "0.17.0"
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ndarray = "0.15"
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ort = {git ="https://github.com/pykeio/ort.git", tag="v1.14.2"}
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base64 = "0.20.0"
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npyz = "0.7.2"
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log = "0.4.17"
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@ -21,6 +19,11 @@ prometheus = "0.13.1"
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once_cell = "1.17.0"
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rand = "0.8.5"
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itertools = "0.10.5"
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anyhow = "1.0.70"
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[target.'cfg(not(target_os="linux"))'.dependencies]
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ort = {git ="https://github.com/pykeio/ort.git", features=["profiling"], tag="v1.14.6"}
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[target.'cfg(target_os="linux")'.dependencies]
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ort = {git ="https://github.com/pykeio/ort.git", features=["profiling", "tensorrt", "cuda", "copy-dylibs"], tag="v1.14.6"}
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[dev-dependencies]
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criterion = "0.3.0"
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@ -3,3 +3,4 @@ pub mod converter;
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#[cfg(test)]
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mod test;
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pub mod util;
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pub extern crate ort;
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@ -1,8 +1,7 @@
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[package]
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name = "navi"
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version = "2.0.42"
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version = "2.0.45"
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edition = "2021"
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# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
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[[bin]]
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name = "navi"
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@ -16,12 +15,19 @@ required-features=["torch"]
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name = "navi_onnx"
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path = "src/bin/navi_onnx.rs"
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required-features=["onnx"]
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[[bin]]
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name = "navi_onnx_test"
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path = "src/bin/bin_tests/navi_onnx_test.rs"
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[[bin]]
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name = "navi_torch_test"
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path = "src/bin/bin_tests/navi_torch_test.rs"
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required-features=["torch"]
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[features]
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default=[]
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navi_console=[]
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torch=["tch"]
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onnx=["ort"]
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onnx=[]
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tf=["tensorflow"]
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[dependencies]
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itertools = "0.10.5"
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@ -47,6 +53,7 @@ parking_lot = "0.12.1"
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rand = "0.8.5"
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rand_pcg = "0.3.1"
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random = "0.12.2"
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x509-parser = "0.15.0"
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sha256 = "1.0.3"
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tonic = { version = "0.6.2", features=['compression', 'tls'] }
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tokio = { version = "1.17.0", features = ["macros", "rt-multi-thread", "fs", "process"] }
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@ -55,16 +62,12 @@ npyz = "0.7.3"
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base64 = "0.21.0"
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histogram = "0.6.9"
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tch = {version = "0.10.3", optional = true}
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tensorflow = { version = "0.20.0", optional = true }
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tensorflow = { version = "0.18.0", optional = true }
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once_cell = {version = "1.17.1"}
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ndarray = "0.15"
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serde = "1.0.154"
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serde_json = "1.0.94"
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dr_transform = { path = "../dr_transform"}
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[target.'cfg(not(target_os="linux"))'.dependencies]
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ort = {git ="https://github.com/pykeio/ort.git", features=["profiling"], optional = true, tag="v1.14.2"}
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[target.'cfg(target_os="linux")'.dependencies]
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ort = {git ="https://github.com/pykeio/ort.git", features=["profiling", "tensorrt", "cuda", "copy-dylibs"], optional = true, tag="v1.14.2"}
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[build-dependencies]
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tonic-build = {version = "0.6.2", features=['prost', "compression"] }
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[profile.release]
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@ -74,3 +77,5 @@ ndarray-rand = "0.14.0"
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tokio-test = "*"
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assert_cmd = "2.0"
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criterion = "0.4.0"
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@ -1,10 +1,9 @@
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#!/bin/sh
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#RUST_LOG=debug LD_LIBRARY_PATH=so/onnx/lib target/release/navi_onnx --port 30 --num-worker-threads 8 --intra-op-parallelism 8 --inter-op-parallelism 8 \
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RUST_LOG=info LD_LIBRARY_PATH=so/onnx/lib cargo run --bin navi_onnx --features onnx -- \
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--port 30 --num-worker-threads 8 --intra-op-parallelism 8 --inter-op-parallelism 8 \
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--port 8030 --num-worker-threads 8 \
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--model-check-interval-secs 30 \
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--model-dir models/int8 \
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--output caligrated_probabilities \
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--input "" \
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--modelsync-cli "echo" \
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--onnx-ep-options use_arena=true
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--onnx-ep-options use_arena=true \
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--model-dir models/prod_home --output caligrated_probabilities --input "" --intra-op-parallelism 8 --inter-op-parallelism 8 --max-batch-size 1 --batch-time-out-millis 1 \
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--model-dir models/prod_home1 --output caligrated_probabilities --input "" --intra-op-parallelism 8 --inter-op-parallelism 8 --max-batch-size 1 --batch-time-out-millis 1 \
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@ -1,11 +1,24 @@
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use anyhow::Result;
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use log::info;
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use navi::cli_args::{ARGS, MODEL_SPECS};
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use navi::onnx_model::onnx::OnnxModel;
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use navi::{bootstrap, metrics};
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fn main() -> Result<()> {
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env_logger::init();
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assert_eq!(MODEL_SPECS.len(), ARGS.inter_op_parallelism.len());
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info!("global: {:?}", ARGS.onnx_global_thread_pool_options);
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let assert_session_params = if ARGS.onnx_global_thread_pool_options.is_empty() {
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// std::env::set_var("OMP_NUM_THREADS", "1");
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info!("now we use per session thread pool");
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MODEL_SPECS.len()
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}
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else {
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info!("now we use global thread pool");
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0
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};
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assert_eq!(assert_session_params, ARGS.inter_op_parallelism.len());
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assert_eq!(assert_session_params, ARGS.inter_op_parallelism.len());
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metrics::register_custom_metrics();
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bootstrap::bootstrap(OnnxModel::new)
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}
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@ -207,6 +207,9 @@ impl<T: Model> PredictionService for PredictService<T> {
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PredictResult::DropDueToOverload => Err(Status::resource_exhausted("")),
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PredictResult::ModelNotFound(idx) => {
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Err(Status::not_found(format!("model index {}", idx)))
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},
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PredictResult::ModelNotReady(idx) => {
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Err(Status::unavailable(format!("model index {}", idx)))
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}
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PredictResult::ModelVersionNotFound(idx, version) => Err(
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Status::not_found(format!("model index:{}, version {}", idx, version)),
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@ -87,13 +87,11 @@ pub struct Args {
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pub intra_op_parallelism: Vec<String>,
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#[clap(
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long,
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default_value = "14",
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help = "number of threads to parallelize computations of the graph"
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)]
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pub inter_op_parallelism: Vec<String>,
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#[clap(
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long,
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default_value = "serving_default",
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help = "signature of a serving. only TF"
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)]
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pub serving_sig: Vec<String>,
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@ -107,6 +105,8 @@ pub struct Args {
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help = "max warmup records to use. warmup only implemented for TF"
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)]
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pub max_warmup_records: usize,
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#[clap(long, value_parser = Args::parse_key_val::<String, String>, value_delimiter=',')]
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pub onnx_global_thread_pool_options: Vec<(String, String)>,
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#[clap(
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long,
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default_value = "true",
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@ -146,6 +146,7 @@ pub enum PredictResult {
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Ok(Vec<TensorScores>, i64),
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DropDueToOverload,
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ModelNotFound(usize),
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ModelNotReady(usize),
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ModelVersionNotFound(usize, i64),
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}
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@ -13,21 +13,22 @@ pub mod onnx {
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use dr_transform::converter::{BatchPredictionRequestToTorchTensorConverter, Converter};
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use itertools::Itertools;
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use log::{debug, info};
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use ort::environment::Environment;
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use ort::session::Session;
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use ort::tensor::InputTensor;
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use ort::{ExecutionProvider, GraphOptimizationLevel, SessionBuilder};
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use dr_transform::ort::environment::Environment;
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use dr_transform::ort::session::Session;
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use dr_transform::ort::tensor::InputTensor;
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use dr_transform::ort::{ExecutionProvider, GraphOptimizationLevel, SessionBuilder};
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use dr_transform::ort::LoggingLevel;
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use serde_json::Value;
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use std::fmt::{Debug, Display};
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use std::sync::Arc;
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use std::{fmt, fs};
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use tokio::time::Instant;
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lazy_static! {
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pub static ref ENVIRONMENT: Arc<Environment> = Arc::new(
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Environment::builder()
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.with_name("onnx home")
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.with_log_level(ort::LoggingLevel::Error)
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.with_log_level(LoggingLevel::Error)
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.with_global_thread_pool(ARGS.onnx_global_thread_pool_options.clone())
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.build()
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.unwrap()
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);
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let meta_info = format!("{}/{}/{}", ARGS.model_dir[idx], version, META_INFO);
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let mut builder = SessionBuilder::new(&ENVIRONMENT)?
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.with_optimization_level(GraphOptimizationLevel::Level3)?
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.with_parallel_execution(ARGS.onnx_use_parallel_mode == "true")?
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.with_parallel_execution(ARGS.onnx_use_parallel_mode == "true")?;
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if ARGS.onnx_global_thread_pool_options.is_empty() {
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builder = builder
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.with_inter_threads(
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utils::get_config_or(
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model_config,
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&ARGS.intra_op_parallelism[idx],
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)
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.parse()?,
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)?
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)?;
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}
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else {
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builder = builder.with_disable_per_session_threads()?;
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}
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builder = builder
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.with_memory_pattern(ARGS.onnx_use_memory_pattern == "true")?
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.with_execution_providers(&OnnxModel::ep_choices())?;
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match &ARGS.profiling {
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@ -1,7 +1,7 @@
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use anyhow::{anyhow, Result};
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use arrayvec::ArrayVec;
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use itertools::Itertools;
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use log::{error, info, warn};
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use log::{error, info};
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use std::fmt::{Debug, Display};
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use std::string::String;
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use std::sync::Arc;
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//initialize the latest version array
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let mut cur_versions = vec!["".to_owned(); MODEL_SPECS.len()];
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loop {
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let config = utils::read_config(&meta_file).unwrap_or_else(|e| {
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warn!("config file {} not found due to: {}", meta_file, e);
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Value::Null
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});
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info!("***polling for models***"); //nice deliminter
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info!("config:{}", config);
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if let Some(ref cli) = ARGS.modelsync_cli {
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if let Err(e) = call_external_modelsync(cli, &cur_versions).await {
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error!("model sync cli running error:{}", e)
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}
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}
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let config = utils::read_config(&meta_file).unwrap_or_else(|e| {
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info!("config file {} not found due to: {}", meta_file, e);
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Value::Null
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});
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info!("config:{}", config);
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for (idx, cur_version) in cur_versions.iter_mut().enumerate() {
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let model_dir = &ARGS.model_dir[idx];
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PredictService::scan_load_latest_model_from_model_dir(
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let no_more_msg = match msg {
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Ok(PredictMessage::Predict(model_spec_at, version, val, resp, ts)) => {
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if let Some(model_predictors) = all_model_predictors.get_mut(model_spec_at) {
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if model_predictors.is_empty() {
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resp.send(PredictResult::ModelNotReady(model_spec_at))
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.unwrap_or_else(|e| error!("cannot send back model not ready error: {:?}", e));
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}
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else {
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match version {
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None => model_predictors[0].push(val, resp, ts),
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Some(the_version) => match model_predictors
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@ -246,9 +251,10 @@ impl<T: Model> PredictService<T> {
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Some(predictor) => predictor.push(val, resp, ts),
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},
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}
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}
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} else {
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resp.send(PredictResult::ModelNotFound(model_spec_at))
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.unwrap_or_else(|e| error!("cannot send back model error: {:?}", e))
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.unwrap_or_else(|e| error!("cannot send back model not found error: {:?}", e))
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}
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MPSC_CHANNEL_SIZE.dec();
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false
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@ -3,9 +3,9 @@ name = "segdense"
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version = "0.1.0"
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edition = "2021"
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# See more keys and their definitions at https://doc.rust-lang.org/cargo/reference/manifest.html
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[dependencies]
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env_logger = "0.10.0"
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serde = { version = "1.0.104", features = ["derive"] }
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serde_json = "1.0.48"
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log = "0.4.17"
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