MLflow models
Search LoggedModels
POST
/
api
/
2.0
/
mlflow
/
logged-models
/
search
Search LoggedModels
curl --request POST \
--url https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search \
--header 'Content-Type: application/json' \
--data '
{
"experiment_ids": [
"<string>"
],
"filter": "params.alpha < 0.3 AND metrics.accuracy > 0.9",
"datasets": [
{
"name": "<string>",
"digest": "<string>",
"source_type": "<string>",
"source": "<string>",
"schema": "<string>",
"profile": "<string>"
}
],
"max_results": 10,
"order_by": [
{
"field_name": "metrics.accuracy",
"ascending": true,
"dataset_name": "validation_set",
"dataset_digest": "<string>"
}
],
"page_token": "<string>"
}
'import requests
url = "https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search"
payload = {
"experiment_ids": ["<string>"],
"filter": "params.alpha < 0.3 AND metrics.accuracy > 0.9",
"datasets": [
{
"name": "<string>",
"digest": "<string>",
"source_type": "<string>",
"source": "<string>",
"schema": "<string>",
"profile": "<string>"
}
],
"max_results": 10,
"order_by": [
{
"field_name": "metrics.accuracy",
"ascending": True,
"dataset_name": "validation_set",
"dataset_digest": "<string>"
}
],
"page_token": "<string>"
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
experiment_ids: ['<string>'],
filter: 'params.alpha < 0.3 AND metrics.accuracy > 0.9',
datasets: [
{
name: '<string>',
digest: '<string>',
source_type: '<string>',
source: '<string>',
schema: '<string>',
profile: '<string>'
}
],
max_results: 10,
order_by: [
{
field_name: 'metrics.accuracy',
ascending: true,
dataset_name: 'validation_set',
dataset_digest: '<string>'
}
],
page_token: '<string>'
})
};
fetch('https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'experiment_ids' => [
'<string>'
],
'filter' => 'params.alpha < 0.3 AND metrics.accuracy > 0.9',
'datasets' => [
[
'name' => '<string>',
'digest' => '<string>',
'source_type' => '<string>',
'source' => '<string>',
'schema' => '<string>',
'profile' => '<string>'
]
],
'max_results' => 10,
'order_by' => [
[
'field_name' => 'metrics.accuracy',
'ascending' => true,
'dataset_name' => 'validation_set',
'dataset_digest' => '<string>'
]
],
'page_token' => '<string>'
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search"
payload := strings.NewReader("{\n \"experiment_ids\": [\n \"<string>\"\n ],\n \"filter\": \"params.alpha < 0.3 AND metrics.accuracy > 0.9\",\n \"datasets\": [\n {\n \"name\": \"<string>\",\n \"digest\": \"<string>\",\n \"source_type\": \"<string>\",\n \"source\": \"<string>\",\n \"schema\": \"<string>\",\n \"profile\": \"<string>\"\n }\n ],\n \"max_results\": 10,\n \"order_by\": [\n {\n \"field_name\": \"metrics.accuracy\",\n \"ascending\": true,\n \"dataset_name\": \"validation_set\",\n \"dataset_digest\": \"<string>\"\n }\n ],\n \"page_token\": \"<string>\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search")
.header("Content-Type", "application/json")
.body("{\n \"experiment_ids\": [\n \"<string>\"\n ],\n \"filter\": \"params.alpha < 0.3 AND metrics.accuracy > 0.9\",\n \"datasets\": [\n {\n \"name\": \"<string>\",\n \"digest\": \"<string>\",\n \"source_type\": \"<string>\",\n \"source\": \"<string>\",\n \"schema\": \"<string>\",\n \"profile\": \"<string>\"\n }\n ],\n \"max_results\": 10,\n \"order_by\": [\n {\n \"field_name\": \"metrics.accuracy\",\n \"ascending\": true,\n \"dataset_name\": \"validation_set\",\n \"dataset_digest\": \"<string>\"\n }\n ],\n \"page_token\": \"<string>\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n \"experiment_ids\": [\n \"<string>\"\n ],\n \"filter\": \"params.alpha < 0.3 AND metrics.accuracy > 0.9\",\n \"datasets\": [\n {\n \"name\": \"<string>\",\n \"digest\": \"<string>\",\n \"source_type\": \"<string>\",\n \"source\": \"<string>\",\n \"schema\": \"<string>\",\n \"profile\": \"<string>\"\n }\n ],\n \"max_results\": 10,\n \"order_by\": [\n {\n \"field_name\": \"metrics.accuracy\",\n \"ascending\": true,\n \"dataset_name\": \"validation_set\",\n \"dataset_digest\": \"<string>\"\n }\n ],\n \"page_token\": \"<string>\"\n}"
response = http.request(request)
puts response.read_body{
"models": [
{
"info": {
"model_id": "logged_model_12345",
"experiment_id": "exp_001",
"name": "my_awesome_model",
"creation_timestamp_ms": 1678886400000,
"last_updated_timestamp_ms": 1678887000000,
"artifact_uri": "s3://my-bucket/models/my_awesome_model",
"status": "LOGGED_MODEL_STATUS_UNSPECIFIED",
"creator_id": 12345,
"model_type": "Classifier",
"source_run_id": "run_abcd1234efgh5678",
"status_message": "Model successfully logged.",
"tags": [
{
"key": "<string>",
"value": "<string>"
}
],
"registrations": [
{
"name": "<string>",
"version": "<string>"
}
]
},
"data": {
"params": [
{
"key": "<string>",
"value": "<string>"
}
],
"metrics": [
{
"key": "<string>",
"step": "0",
"timestamp": 123,
"value": 123
}
]
}
}
],
"next_page_token": "<string>"
}Body
application/json
Request body for searching logged models.
IDs of the Experiments in which to search for Logged Models.
A filter expression over Logged Model info and data that allows returning a subset of
Logged Models. The syntax is a subset of SQL that supports ANDing together binary operations
Example: params.alpha < 0.3 AND metrics.accuracy > 0.9.
Example:
"params.alpha < 0.3 AND metrics.accuracy > 0.9"
List of datasets on which to apply the metrics filter clauses.
For example, a filter with metrics.accuracy > 0.9 and dataset info with name "test_dataset"
means we will return all logged models with accuracy > 0.9 on the test_dataset.
Metric values from ANY dataset matching the criteria are considered.
If no datasets are specified, then metrics across all datasets are considered in the filter.
Show child attributes
Show child attributes
Maximum number of Logged Models to return. Max threshold is 50.
Example:
10
List of columns for ordering the results, with additional fields for sorting criteria.
Show child attributes
Show child attributes
Token indicating the page of Logged Models to fetch.
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⌘I
Search LoggedModels
curl --request POST \
--url https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search \
--header 'Content-Type: application/json' \
--data '
{
"experiment_ids": [
"<string>"
],
"filter": "params.alpha < 0.3 AND metrics.accuracy > 0.9",
"datasets": [
{
"name": "<string>",
"digest": "<string>",
"source_type": "<string>",
"source": "<string>",
"schema": "<string>",
"profile": "<string>"
}
],
"max_results": 10,
"order_by": [
{
"field_name": "metrics.accuracy",
"ascending": true,
"dataset_name": "validation_set",
"dataset_digest": "<string>"
}
],
"page_token": "<string>"
}
'import requests
url = "https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search"
payload = {
"experiment_ids": ["<string>"],
"filter": "params.alpha < 0.3 AND metrics.accuracy > 0.9",
"datasets": [
{
"name": "<string>",
"digest": "<string>",
"source_type": "<string>",
"source": "<string>",
"schema": "<string>",
"profile": "<string>"
}
],
"max_results": 10,
"order_by": [
{
"field_name": "metrics.accuracy",
"ascending": True,
"dataset_name": "validation_set",
"dataset_digest": "<string>"
}
],
"page_token": "<string>"
}
headers = {"Content-Type": "application/json"}
response = requests.post(url, json=payload, headers=headers)
print(response.text)const options = {
method: 'POST',
headers: {'Content-Type': 'application/json'},
body: JSON.stringify({
experiment_ids: ['<string>'],
filter: 'params.alpha < 0.3 AND metrics.accuracy > 0.9',
datasets: [
{
name: '<string>',
digest: '<string>',
source_type: '<string>',
source: '<string>',
schema: '<string>',
profile: '<string>'
}
],
max_results: 10,
order_by: [
{
field_name: 'metrics.accuracy',
ascending: true,
dataset_name: 'validation_set',
dataset_digest: '<string>'
}
],
page_token: '<string>'
})
};
fetch('https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search', options)
.then(res => res.json())
.then(res => console.log(res))
.catch(err => console.error(err));<?php
$curl = curl_init();
curl_setopt_array($curl, [
CURLOPT_URL => "https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search",
CURLOPT_RETURNTRANSFER => true,
CURLOPT_ENCODING => "",
CURLOPT_MAXREDIRS => 10,
CURLOPT_TIMEOUT => 30,
CURLOPT_HTTP_VERSION => CURL_HTTP_VERSION_1_1,
CURLOPT_CUSTOMREQUEST => "POST",
CURLOPT_POSTFIELDS => json_encode([
'experiment_ids' => [
'<string>'
],
'filter' => 'params.alpha < 0.3 AND metrics.accuracy > 0.9',
'datasets' => [
[
'name' => '<string>',
'digest' => '<string>',
'source_type' => '<string>',
'source' => '<string>',
'schema' => '<string>',
'profile' => '<string>'
]
],
'max_results' => 10,
'order_by' => [
[
'field_name' => 'metrics.accuracy',
'ascending' => true,
'dataset_name' => 'validation_set',
'dataset_digest' => '<string>'
]
],
'page_token' => '<string>'
]),
CURLOPT_HTTPHEADER => [
"Content-Type: application/json"
],
]);
$response = curl_exec($curl);
$err = curl_error($curl);
curl_close($curl);
if ($err) {
echo "cURL Error #:" . $err;
} else {
echo $response;
}package main
import (
"fmt"
"strings"
"net/http"
"io"
)
func main() {
url := "https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search"
payload := strings.NewReader("{\n \"experiment_ids\": [\n \"<string>\"\n ],\n \"filter\": \"params.alpha < 0.3 AND metrics.accuracy > 0.9\",\n \"datasets\": [\n {\n \"name\": \"<string>\",\n \"digest\": \"<string>\",\n \"source_type\": \"<string>\",\n \"source\": \"<string>\",\n \"schema\": \"<string>\",\n \"profile\": \"<string>\"\n }\n ],\n \"max_results\": 10,\n \"order_by\": [\n {\n \"field_name\": \"metrics.accuracy\",\n \"ascending\": true,\n \"dataset_name\": \"validation_set\",\n \"dataset_digest\": \"<string>\"\n }\n ],\n \"page_token\": \"<string>\"\n}")
req, _ := http.NewRequest("POST", url, payload)
req.Header.Add("Content-Type", "application/json")
res, _ := http.DefaultClient.Do(req)
defer res.Body.Close()
body, _ := io.ReadAll(res.Body)
fmt.Println(string(body))
}HttpResponse<String> response = Unirest.post("https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search")
.header("Content-Type", "application/json")
.body("{\n \"experiment_ids\": [\n \"<string>\"\n ],\n \"filter\": \"params.alpha < 0.3 AND metrics.accuracy > 0.9\",\n \"datasets\": [\n {\n \"name\": \"<string>\",\n \"digest\": \"<string>\",\n \"source_type\": \"<string>\",\n \"source\": \"<string>\",\n \"schema\": \"<string>\",\n \"profile\": \"<string>\"\n }\n ],\n \"max_results\": 10,\n \"order_by\": [\n {\n \"field_name\": \"metrics.accuracy\",\n \"ascending\": true,\n \"dataset_name\": \"validation_set\",\n \"dataset_digest\": \"<string>\"\n }\n ],\n \"page_token\": \"<string>\"\n}")
.asString();require 'uri'
require 'net/http'
url = URI("https://mycluster.domino.tech/api/2.0/mlflow/logged-models/search")
http = Net::HTTP.new(url.host, url.port)
http.use_ssl = true
request = Net::HTTP::Post.new(url)
request["Content-Type"] = 'application/json'
request.body = "{\n \"experiment_ids\": [\n \"<string>\"\n ],\n \"filter\": \"params.alpha < 0.3 AND metrics.accuracy > 0.9\",\n \"datasets\": [\n {\n \"name\": \"<string>\",\n \"digest\": \"<string>\",\n \"source_type\": \"<string>\",\n \"source\": \"<string>\",\n \"schema\": \"<string>\",\n \"profile\": \"<string>\"\n }\n ],\n \"max_results\": 10,\n \"order_by\": [\n {\n \"field_name\": \"metrics.accuracy\",\n \"ascending\": true,\n \"dataset_name\": \"validation_set\",\n \"dataset_digest\": \"<string>\"\n }\n ],\n \"page_token\": \"<string>\"\n}"
response = http.request(request)
puts response.read_body{
"models": [
{
"info": {
"model_id": "logged_model_12345",
"experiment_id": "exp_001",
"name": "my_awesome_model",
"creation_timestamp_ms": 1678886400000,
"last_updated_timestamp_ms": 1678887000000,
"artifact_uri": "s3://my-bucket/models/my_awesome_model",
"status": "LOGGED_MODEL_STATUS_UNSPECIFIED",
"creator_id": 12345,
"model_type": "Classifier",
"source_run_id": "run_abcd1234efgh5678",
"status_message": "Model successfully logged.",
"tags": [
{
"key": "<string>",
"value": "<string>"
}
],
"registrations": [
{
"name": "<string>",
"version": "<string>"
}
]
},
"data": {
"params": [
{
"key": "<string>",
"value": "<string>"
}
],
"metrics": [
{
"key": "<string>",
"step": "0",
"timestamp": 123,
"value": 123
}
]
}
}
],
"next_page_token": "<string>"
}