- Sort Score
- Num 10 results
- Language All
Results 1 - 10 of 14 for lsum (0.02 seconds)
-
go.sum
Klaus Post <******@****.***> 1759093161 +0200
Created: Sun Dec 28 19:28:13 GMT 2025 - Last Modified: Sun Sep 28 20:59:21 GMT 2025 - 79.8K bytes - Click Count (0) -
docs/metrics/prometheus/grafana/replication/minio-replication-node.json
"targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "sum by (server) (minio_node_replication_average_active_workers{job=\"$scrape_jobs\"})", "interval": "1m", "intervalFactor": 2, "legendFormat": "{{server}}", "refId": "A" }
Created: Sun Dec 28 19:28:13 GMT 2025 - Last Modified: Mon Aug 04 01:46:49 GMT 2025 - 57.5K bytes - Click Count (0) -
docs/metrics/prometheus/grafana/replication/minio-replication-cluster.json
"targets": [ { "datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "sum by (server) (minio_cluster_replication_received_bytes{job=\"$scrape_jobs\"})", "interval": "1m", "intervalFactor": 2, "legendFormat": "{{server}}", "refId": "A" }
Created: Sun Dec 28 19:28:13 GMT 2025 - Last Modified: Mon Aug 04 01:46:49 GMT 2025 - 71.2K bytes - Click Count (0) -
docs/metrics/prometheus/grafana/minio-dashboard.json
"datasource": { "type": "prometheus", "uid": "${DS_PROMETHEUS}" }, "exemplar": true, "expr": "topk(1, sum(minio_cluster_capacity_usable_total_bytes{job=~\"$scrape_jobs\"}) by (instance)) - topk(1, sum(minio_cluster_capacity_usable_free_bytes{job=~\"$scrape_jobs\"}) by (instance))", "format": "time_series", "instant": false, "interval": "1m",
Created: Sun Dec 28 19:28:13 GMT 2025 - Last Modified: Mon Aug 04 01:46:49 GMT 2025 - 93.1K bytes - Click Count (0) -
tensorflow/c/c_api_function_test.cc
Created: Tue Dec 30 12:39:10 GMT 2025 - Last Modified: Mon Nov 17 00:00:38 GMT 2025 - 63.6K bytes - Click Count (1) -
cmd/object-api-listobjects_test.go
{testBuckets[0], "unique/folder/1.txt", "content", nil, false}, {testBuckets[1], "unique/folder/1.txt", "content", nil, true}, } for _, object := range testObjects { md5Bytes := md5.Sum([]byte(object.content)) _, err = obj.PutObject(context.Background(), object.parentBucket, object.name, mustGetPutObjReader(t, bytes.NewBufferString(object.content),Created: Sun Dec 28 19:28:13 GMT 2025 - Last Modified: Fri Oct 10 18:57:03 GMT 2025 - 76.1K bytes - Click Count (0) -
cmd/xl-storage.go
return int64(n), err } if _, err = h.Write(buffer); err != nil { return 0, err } if _, err = io.Copy(h, file); err != nil { return 0, err } if !bytes.Equal(h.Sum(nil), verifier.sum) { return 0, errFileCorrupt } return int64(len(buffer)), nil } func (s *xlStorage) openFileDirect(path string, mode int) (f *os.File, err error) {
Created: Sun Dec 28 19:28:13 GMT 2025 - Last Modified: Sun Sep 28 20:59:21 GMT 2025 - 91.7K bytes - Click Count (0) -
cmd/admin-handlers-users_test.go
} buf, err = madmin.EncryptData(secretKey, buf) if err != nil { c.Fatalf("unexpected encryption err: %v", err) } req.ContentLength = int64(len(buf)) sum := sha256.Sum256(buf) req.Header.Set("X-Amz-Content-Sha256", hex.EncodeToString(sum[:])) req.Body = io.NopCloser(bytes.NewReader(buf)) req = signer.SignV4(*req, accessKey, secretKey, "", "") // 3.1 Execute the request.
Created: Sun Dec 28 19:28:13 GMT 2025 - Last Modified: Wed Oct 15 17:00:45 GMT 2025 - 50.6K bytes - Click Count (0) -
cmd/xl-storage_test.go
h.Write([]byte{0}) } buffer := make([]byte, test.length) n, err := xlStorage.ReadFile(t.Context(), volume, test.file, int64(test.offset), buffer, NewBitrotVerifier(test.algorithm, h.Sum(nil))) switch { case err == nil && test.expError != nil: t.Errorf("Test %d: Expected error %v but got none.", i, test.expError) case err == nil && n != int64(test.length):
Created: Sun Dec 28 19:28:13 GMT 2025 - Last Modified: Fri Aug 29 02:39:48 GMT 2025 - 66K bytes - Click Count (0) -
tensorflow/c/c_api.h
// called after a successful TF_NewWhile() call. TF_CAPI_EXPORT extern void TF_AbortWhile(const TF_WhileParams* params); // Adds operations to compute the partial derivatives of sum of `y`s w.r.t `x`s, // i.e., d(y_1 + y_2 + ...)/dx_1, d(y_1 + y_2 + ...)/dx_2... // // `dx` are used as initial gradients (which represent the symbolic partial // derivatives of some loss function `L` w.r.t. `y`).
Created: Tue Dec 30 12:39:10 GMT 2025 - Last Modified: Thu Oct 26 21:08:15 GMT 2023 - 82.3K bytes - Click Count (0)